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The metastasis of cancer cells from the primary tumor site to other organs in the body, notably the lungs, bones, brain, and liver, is what causes breast cancer to ultimately be fatal. Brain metastases occur in as many as 30% of patients with advanced breast cancer, and the 1-year survival rate of these patients is around 20%. Many researchers have focused on brain metastasis, but due to its complexities, many aspects of this process are still relatively unclear. To develop and test novel therapies for this fatal condition, pre-clinical models are required that can mimic the biological processes involved in breast cancer brain metastasis (BCBM). The application of many breakthroughs in the area of tissue engineering has resulted in the development of scaffold or matrix-based culture methods that more accurately imitate the original extracellular matrix (ECM) of metastatic tumors. Furthermore, specific cell lines are now being used to create three-dimensional (3D) cultures that can be used to model metastasis. These 3D cultures satisfy the requirement for in vitro methodologies that allow for a more accurate investigation of the molecular pathways as well as a more in-depth examination of the effects of the medication being tested. 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H, He G, Yan S, Chen C, Song L, Rosol TJ, et al. Triple-negative breast cancer: is there a treatment on the horizon? Oncotarget. 2017;8(1):1913.",{"doi":297},"10.18632\u002Foncotarget.12284",{"id":23,"text":299,"url":23,"identifiers":300},"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.",{"doi":301},"10.3322\u002Fcaac.21492",{"id":23,"text":303,"url":23,"identifiers":304},"Vaez A, Abbasi M, Shabani L, Azizipour E, Shafiee M, Zare MA, Rahbar O, Azari A, Amani AM, Golchin A. A Bright Horizon of Intelligent Targeted-cancer Therapy: Nanoparticles Against Breast Cancer Stem Cells. Curr Stem Cell Res Ther. 2023;18(6):787-99. https:\u002F\u002Fdoi.org\u002F10.2174\u002F1574888X17666221004105330.",{"doi":305},"10.2174\u002F1574888X17666221004105330",{"id":23,"text":307,"url":23,"identifiers":308},"Alkabban FM, Ferguson T. Breast Cancer. 2022 Sep 26. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023. PMID: 29493913.",{},{"id":23,"text":310,"url":23,"identifiers":311},"Sun Y-S, Zhao Z, Yang Z-N, Xu F, Lu H-J, Zhu Z-Y, et al. Risk factors and preventions of breast cancer. Int J Biol Sci. 2017;13(11):1387.",{"doi":312},"10.7150\u002Fijbs.21635",{"id":23,"text":314,"url":23,"identifiers":315},"Waks AG, Winer EP. Breast cancer treatment: a review. JAMA. 2019;321(3):288–300.",{"doi":316},"10.1001\u002Fjama.2018.19323",{"id":23,"text":318,"url":23,"identifiers":319},"Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, et al. Breast cancer. Nat Rev Dis Prim. 2019;5(1):66.",{"doi":320},"10.1038\u002Fs41572-019-0111-2",{"id":23,"text":322,"url":23,"identifiers":323},"Isakoff SJ. Triple negative breast cancer: role of specific chemotherapy agents. Cancer J. 2010;16(1):53.",{"doi":324},"10.1097\u002FPPO.0b013e3181d24ff7",{"id":23,"text":326,"url":23,"identifiers":327},"Rivera E, Gomez H. Chemotherapy resistance in metastatic breast cancer: the evolving role of ixabepilone. Breast Cancer Res. 2010;12 Suppl 2:S2. BioMed Central.",{"doi":328},"10.1186\u002Fbcr2573",{"id":23,"text":330,"url":23,"identifiers":331},"Davuluri G, Schiemann WP, Plow EF, Sossey-Alaoui K. Loss of WAVE3 sensitizes triple-negative breast cancers to chemotherapeutics by inhibiting the STAT-HIF-1α-mediated angiogenesis. Jak-Stat. 2014;3(4):e1009276.",{"doi":332},"10.1080\u002F21623996.2015.1009276",{"id":23,"text":334,"url":23,"identifiers":335},"Makki J. Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin Med Insights Pathol. 2015;8:S31563.",{"doi":336},"10.4137\u002FCPath.S31563",{"id":23,"text":338,"url":23,"identifiers":339},"Arpino G, Bardou VJ, Clark GM, Elledge RM. Infiltrating lobular carcinoma of the breast: tumor characteristics and clinical outcome. 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Oncotarget. 2017;8(48):83734.",{"doi":1252},"10.18632\u002Foncotarget.19634",false,{"id":1255,"createTime":1256,"updateTime":1257,"relativeEntities":1258,"slug":1259,"properties":1260,"entityType":147,"verifyStatus":148,"verifyTime":1272,"verifyNote":150,"languages":1273,"translateLanguages":1274,"viewCount":106,"primaryUrl":1275,"fullTextUrl":23,"authors":1276,"publicationType":231,"publisherRelationship":1370,"citationCount":106,"citationInfo":1431,"publishDate":23,"publishYear":23,"citationAnalyzeStatus":22,"lastCitationAnalyze":23,"indexDatabases":1433,"openAccess":23,"references":1434,"isForceReanalyzing":1253},"b9e3bf1d-7282-4476-8ac2-39b1672cbf72","2024-04-18T01:28:43.094+00:00","2026-09-05T08:12:06.798+00:00",[],"Enhancing-osteogenic-differentiation-of-dental-pulp-stem-cells-through-rosuvastatin-loaded-niosomes-optimized-by-Box-Behnken-design-and-modified-by-hyaluronan-a-novel-strategy-for-improved-efficiency",{"openalex":1261,"abstract":1263,"title":1265,"pm":1268,"doi":1270},{"VOID":1262},"W4391256361",{"EN":1264},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>Bone tissue engineering necessitates a stem cell source capable of osteoblast differentiation and mineralized matrix production. Dental pulp stem cells (DPSCs), a subtype of mesenchymal stem cells from human teeth, present such potential but face challenges in osteogenic differentiation. This research introduces an innovative approach to bolster DPSCs’ osteogenic potential using niosomal and hyaluronan modified niosomal systems enriched with rosuvastatin. While rosuvastatin fosters bone formation by regulating bone morphogenetic proteins and osteoblasts, its solubility, permeability, and bioavailability constraints hinder its bone regeneration application. Using a Box-Behnken design, optimal formulation parameters were ascertained. Both niosomes were analyzed for size, polydispersity, zeta potential, and other parameters. They displayed average sizes under 275 nm and entrapment efficiencies exceeding 62%. Notably, niosomes boosted DPSCs’ cell viability and osteogenic marker expression, suggesting enhanced differentiation and bone formation. Conclusively, the study underscores the potential of both niosomal systems in ameliorating DPSCs’ osteogenic differentiation, offering a promising avenue for bone tissue engineering and regeneration.\u003C\u002Fjats:p>\n                \u003Cjats:p>\u003Cjats:bold>Graphical Abstract\u003C\u002Fjats:bold>\u003C\u002Fjats:p>",{"EN":1266,"VI":1267},"Enhancing osteogenic differentiation of dental pulp stem cells through rosuvastatin loaded niosomes optimized by Box-Behnken design and modified by hyaluronan: a novel strategy for improved efficiency","Tăng cường biệt hóa tạo xương của tế bào gốc tủy răng bằng niosome nạp rosuvastatin được tối ưu hóa bằng thiết kế Box-Behnken và biến tính bằng hyaluronan: chiến lược mới nhằm nâng cao hiệu quả",{"VOID":1269},"38279117",{"VOID":1271},"10.1186\u002Fs13036-024-00406-7","2024-12-18T05:14:56.721+00:00",[152],[154],"https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13036-024-00406-7",[1277,1294,1313,1332,1351],{"id":1278,"sortIndex":106,"researcher":23,"roles":1279,"affiliations":1280,"properties":1289,"displayName":1291,"givenName":23,"familyName":23},"08bc3268-242d-4823-8f8a-d7f8070a5db7",[],[1281],{"id":1282,"sortIndex":106,"affiliation":1283,"properties":23},"63932a27-c9a6-4bab-b2d3-436fe78db275",{"id":1282,"createTime":23,"updateTime":23,"relativeEntities":1284,"slug":23,"properties":1285,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1288,"statistic":23},[],{"title":1286},{"VI":1287},"Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran",[],{"title":1290,"openalex":1292},{"EN":1291},"Zaynab Sadeghi Ghadi",{"VOID":1293},"A5076528431",{"id":1295,"sortIndex":178,"researcher":23,"roles":1296,"affiliations":1297,"properties":1306,"displayName":1310,"givenName":23,"familyName":23},"021a6881-49c5-487c-b2f2-3c8917968f42",[],[1298],{"id":1299,"sortIndex":106,"affiliation":1300,"properties":23},"18ea3470-4612-4f5e-8d6a-322007c81367",{"id":1299,"createTime":23,"updateTime":23,"relativeEntities":1301,"slug":23,"properties":1302,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1305,"statistic":23},[],{"title":1303},{"VI":1304},"Student Research Committee, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran",[],{"orcid":1307,"title":1309,"openalex":1311},{"VOID":1308},"https:\u002F\u002Forcid.org\u002F0000-0001-7554-0181",{"EN":1310},"Mohammad Hossein Asadi",{"VOID":1312},"A5045184397",{"id":1314,"sortIndex":196,"researcher":23,"roles":1315,"affiliations":1316,"properties":1325,"displayName":1329,"givenName":23,"familyName":23},"07866436-0458-477b-a842-d030d879f081",[],[1317],{"id":1318,"sortIndex":106,"affiliation":1319,"properties":23},"f4a5676f-27fc-47b8-a1b9-c7f47ea80745",{"id":1318,"createTime":23,"updateTime":23,"relativeEntities":1320,"slug":23,"properties":1321,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1324,"statistic":23},[],{"title":1322},{"EN":1323},"Cellular and Molecular Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, Iran",[],{"orcid":1326,"title":1328,"openalex":1330},{"VOID":1327},"https:\u002F\u002Forcid.org\u002F0000-0002-0747-2321",{"EN":1329},"Younes 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W, et al. Stem cells: past, present, and future. Stem Cell Res Ther. 2019;10(1):1–22.",{"doi":1438},"10.1186\u002Fs13287-019-1165-5",{"id":23,"text":1440,"url":23,"identifiers":1441},"Ravindran S, Huang CC, George A. Extracellular matrix of dental pulp stem cells: applications in pulp tissue engineering using somatic MSCs. Front Physiol. 2014;4:395.",{"doi":1442},"10.3389\u002Ffphys.2013.00395",{"id":23,"text":1444,"url":23,"identifiers":1445},"Kamble PR, Shaikh KS, Chaudhari PD. Application of liquisolid technology for enhancing solubility and dissolution of rosuvastatin. Adv Pharm Bull. 2014;4(2):197–204.",{},{"id":23,"text":1447,"url":23,"identifiers":1448},"Sadeghi Ghadi Z, et al. Preparation, characterization and in vivo evaluation of novel hyaluronan containing niosomes tailored by Box-Behnken design to co-encapsulate curcumin and quercetin. Eur J Pharm Sci. 2019;130:234–46.",{"doi":1449},"10.1016\u002Fj.ejps.2019.01.035",{"id":23,"text":1451,"url":23,"identifiers":1452},"Sadeghi Ghadi Z, Ebrahimnejad P. Curcumin entrapped hyaluronan containing niosomes: preparation, characterisation and in vitro\u002Fin vivo evaluation. J Microencapsul. 2019;36(2):169–79.",{"doi":1453},"10.1080\u002F02652048.2019.1617360",{"id":23,"text":1455,"url":23,"identifiers":1456},"Marianecci C, et al. Niosomes from 80s to present: the state of the art. Adv Colloid Interface Sci. 2014;205:187–206.",{"doi":1457},"10.1016\u002Fj.cis.2013.11.018",{"id":23,"text":1459,"url":23,"identifiers":1460},"Yousefi V, et al. Synthesis and application of magnetic@ layered double hydroxide as an anti-inflammatory drugs nanocarrier. J Nanobiotechnol. 2020;18:1–11.",{"doi":1461},"10.1186\u002Fs12951-020-00718-y",{"id":23,"text":1463,"url":23,"identifiers":1464},"Samiei M, et al. Osteogenic\u002Fodontogenic bioengineering with co-administration of simvastatin and hydroxyapatite on poly caprolactone based nanofibrous scaffold. Adv Pharm Bull. 2016;6(3):353.",{"doi":1465},"10.15171\u002Fapb.2016.047",{"id":23,"text":1467,"url":23,"identifiers":1468},"Potdar PD, Jethmalani YD. Human dental pulp stem cells: Applications in future regenerative medicine. World J Stem Cells. 2015;7(5):839.",{"doi":1469},"10.4252\u002Fwjsc.v7.i5.839",{"id":23,"text":1471,"url":23,"identifiers":1472},"Ma L, et al. Maintained properties of aged dental pulp stem cells for superior periodontal tissue regeneration. Aging Dis. 2019;10(4):793.",{"doi":1473},"10.14336\u002FAD.2018.0729",{"id":23,"text":1475,"url":23,"identifiers":1476},"Kassem MA, et al. Maximizing the therapeutic efficacy of Imatinib mesylate-loaded niosomes on human Colon adenocarcinoma using Box-Behnken design. J Pharm Sci. 2017;106(1):111–22.",{"doi":1477},"10.1016\u002Fj.xphs.2016.07.007",{"id":23,"text":1479,"url":23,"identifiers":1480},"Sadeghi-Ghadi Z, et al. Improved oral delivery of quercetin with hyaluronic acid containing niosomes as a promising formulation. J Drug Target. 2021;29(2):225–34.",{"doi":1481},"10.1080\u002F1061186X.2020.1830408",{"id":23,"text":1483,"url":23,"identifiers":1484},"Sadeghi-Ghadi Z, et al. Improving antibacterial efficiency of Curcumin in magnetic polymeric nanocomposites. J Pharmaceu Innov. 2022;18(1):13–28.",{"doi":1485},"10.1007\u002Fs12247-022-09619-z",{"id":23,"text":1487,"url":23,"identifiers":1488},"Hosseini K, Soofiyani SR, Zamiri RE, Farjami A, Dilmaghani A, Mahdavi M, Tarhriz V, Yousefi V. Layered double hydroxide nanostructures as drug-carriers in treatment of breast cancer. Nanomedicine Journal. 2022;9(2):95–106. https:\u002F\u002Fdoi.org\u002F10.22038\u002Fnmj.2022.63097.1661.",{"doi":1489},"10.22038\u002Fnmj.2022.63097.1661",{"id":23,"text":1491,"url":23,"identifiers":1492},"Prabhu P, et al. Development and evaluation of norfloxacin loaded maltodextrin based proniosomes. Int J Res Pharm Sci. 2012;3:176–9.",{},{"id":23,"text":1494,"url":23,"identifiers":1495},"Mansouri E, et al. Intercalation and release of an anti-inflammatory drug into designed three-dimensionally layered double hydroxide nanostructure via calcination–reconstruction route. Adsorption. 2020;26:835–42.",{"doi":1496},"10.1007\u002Fs10450-020-00217-4",{"id":23,"text":1498,"url":23,"identifiers":1499},"Sharifi F, Jahangiri M, Ebrahimnejad P. Synthesis of novel polymeric nanoparticles (methoxy-polyethylene glycol-chitosan\u002Fhyaluronic acid) containing 7-ethyl-10-hydroxycamptothecin for colon cancer therapy: in vitro, ex vivo and in vivo investigation artificial cells. Nanomedicine, and Biotechnology. 2021;49(1):367–80.",{},{"id":23,"text":1501,"url":23,"identifiers":1502},"Taleghani AS, et al. Adsorption and controlled release of iron-chelating drug from the amino-terminated PAMAM\u002Fordered mesoporous silica hybrid materials. J Drug Deliv Sci Technol. 2020;56: 101579.",{"doi":1503},"10.1016\u002Fj.jddst.2020.101579",{"id":23,"text":1505,"url":23,"identifiers":1506},"Ajlan SA, et al. Osteogenic differentiation of dental pulp stem cells under the influence of three different materials. BMC Oral Health. 2015;15(1):1–10.",{"doi":1507},"10.1186\u002Fs12903-015-0113-8",{"id":23,"text":1509,"url":23,"identifiers":1510},"Sabbagh J, et al. Differences in osteogenic and odontogenic differentiation potential of DPSCs and SHED. J Dent. 2020;101: 103413.",{"doi":1511},"10.1016\u002Fj.jdent.2020.103413",{"id":23,"text":1513,"url":23,"identifiers":1514},"van Tonder A, Joubert AM, Cromarty AD. Limitations of the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay when compared to three commonly used cell enumeration assays. BMC Res Notes. 2015;8:47.",{"doi":1515},"10.1186\u002Fs13104-015-1000-8",{"id":23,"text":1517,"url":23,"identifiers":1518},"Abasi M, et al. 7SK small nuclear RNA transcription level down-regulates in human tumors and stem cells. Med Oncol. 2016;33(11):128.",{"doi":1519},"10.1007\u002Fs12032-016-0841-x",{"id":23,"text":1521,"url":23,"identifiers":1522},"Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002;30(9):e36.",{"doi":1523},"10.1093\u002Fnar\u002F30.9.e36",{"id":23,"text":1525,"url":23,"identifiers":1526},"Sadeghi-Ghadi Z, Ebrahimnejad P, Talebpour Amiri F, Nokhodchi A. Improved oral delivery of quercetin with hyaluronic acid containing niosomes as a promising formulation. J Drug Target. 2021;29(2):225–34. https:\u002F\u002Fdoi.org\u002F10.1080\u002F1061186X.2020.1830408.",{"doi":1481},{"id":23,"text":1528,"url":23,"identifiers":1529},"Maestrelli F, et al. Effect of preparation technique on the properties and in vivo efficacy of benzocaine-loaded ethosomes. J Liposome Res. 2009;19(4):253–60.",{"doi":1530},"10.3109\u002F08982100902788408",{"id":23,"text":1532,"url":23,"identifiers":1533},"Md U, Ghuge P, Jain B. Niosomes: a novel trend of drug delivery. Eur J Biomed Pharma Sci (EJBPS). 2017;4(7):436–42.",{},{"id":23,"text":1535,"url":23,"identifiers":1536},"Marianecci C, et al. Niosomes from 80s to present: the state of the art. Adv Colloid Interface Sci. 2013;205:187–206.",{"doi":1457},{"id":23,"text":1538,"url":23,"identifiers":1539},"Sarfraz RM, et al. Development and evaluation of rosuvastatin calcium based microparticles for solubility enhancement: an in vitro study. Adv Polym Technol. 2017;36(4):433–41.",{"doi":1540},"10.1002\u002Fadv.21625",{"id":23,"text":1542,"url":23,"identifiers":1543},"Salih OS, Samein LH, Ali WK. Formulation and in vitro evaluation of rosuvastatin calcium niosomes. Int J Pharm Pharm Sci. 2013;5(4):525–35.",{},{"id":23,"text":1545,"url":23,"identifiers":1546},"Manconi M, et al. Chitosan and hyaluronan coated liposomes for pulmonary administration of curcumin. Int J Pharm. 2017;525(1):203–10.",{"doi":1547},"10.1016\u002Fj.ijpharm.2017.04.044",{"id":23,"text":1549,"url":23,"identifiers":1550},"Sharma SC. ZnO nano-flowers from Carica papaya milk: degradation of alizarin Red-S dye and antibacterial activity against Pseudomonas aeruginosa and Staphylococcus aureus. Optik. 2016;127:6498–512.",{"doi":1551},"10.1016\u002Fj.ijleo.2016.04.036",{"id":1553,"createTime":1554,"updateTime":1555,"relativeEntities":1556,"slug":1557,"properties":1558,"entityType":147,"verifyStatus":148,"verifyTime":1570,"verifyNote":150,"languages":1571,"translateLanguages":1572,"viewCount":106,"primaryUrl":1573,"fullTextUrl":23,"authors":1574,"publicationType":231,"publisherRelationship":1766,"citationCount":122,"citationInfo":1822,"publishDate":23,"publishYear":23,"citationAnalyzeStatus":290,"lastCitationAnalyze":1824,"indexDatabases":1825,"openAccess":23,"references":1826,"isForceReanalyzing":1253},"74576bbc-0c7e-473d-a9ad-84060557e77a","2024-04-11T22:17:26.452+00:00","2026-09-05T03:11:47.083+00:00",[],"Melatonin-and-endothelial-cell-loaded-alginate-fibrin-hydrogel-promoted-angiogenesis-in-rat-cryopreserved-thawed-ovaries-transplanted-to-the-heterotopic-sites",{"openalex":1559,"abstract":1561,"title":1563,"pm":1566,"doi":1568},{"VOID":1560},"W4361224091",{"EN":1562},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:sec>\n                \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>Ischemic niche can promote follicular atresia following the transplantation of cryopreserved\u002Fthawed ovaries to the heterotopic sites. Thus, the promotion of blood supply is an effective strategy to inhibit\u002Freduce the ischemic damage to ovarian follicles. Here, the angiogenic potential of alginate (Alg) + fibrin (Fib) hydrogel enriched with melatonin (Mel) and CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> endothelial cells (ECs) was assessed on encapsulated cryopreserved\u002Fthawed ovaries following transplantation to heterotopic sites in rats.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>Alg + Fib hydrogel was fabricated by combining 2% (w\u002Fv) sodium Alg, 1% (w\u002Fv) Fib, and 5 IU thrombin at a ratio of 4: 2: 1, respectively. The mixture was solidified using 1% CaCl\u003Cjats:sub>2\u003C\u002Fjats:sub>. Using FTIR, SEM, swelling rate, and biodegradation assay, the physicochemical properties of Alg + Fib hydrogel were evaluated. The EC viability was examined using an MTT assay. Thirty-six adult female rats (aged between 6 and 8 weeks) with a normal estrus cycle were ovariectomized and enrolled in this study. Cryopreserved\u002Fthawed ovaries were encapsulated in Alg + Fib hydrogel containing 100 µM Mel + CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs (2 × 10\u003Cjats:sup>4\u003C\u002Fjats:sup> cells\u002Fml) and transplanted into the subcutaneous region. Ovaries were removed after 14 days and the expression of Ang-1, and Ang-2 was monitored using real-time PCR assay. The number of vWF\u003Cjats:sup>+\u003C\u002Fjats:sup> and α-SMA\u003Cjats:sup>+\u003C\u002Fjats:sup> vessels was assessed using IHC staining. Using Masson’s trichrome staining, fibrotic changes were evaluated.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>FTIR data indicated successful interaction of Alg with Fib in the presence of ionic cross-linker (1% CaCl\u003Cjats:sub>2\u003C\u002Fjats:sub>). Data confirmed higher biodegradation and swelling rates in Alg + Fib hydrogel compared to the Alg group (p < 0.05). Increased viability was achieved in encapsulated CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs compared to the control group (p < 0.05). IF analysis showed the biodistribution of Dil\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs within hydrogel two weeks after transplantation. The ratio of Ang-2\u002FAng-1 was statistically up-regulated in the rats that received Alg + Fib + Mel hydrogel compared to the control-matched groups (p < 0.05). Based on the data, the addition of Mel and CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs to Alg + Fib hydrogel reduced fibrotic changes. Along with these changes, the number of vWF\u003Cjats:sup>+\u003C\u002Fjats:sup> and α-SMA\u003Cjats:sup>+\u003C\u002Fjats:sup> vessels was increased in the presence of Mel and CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>Co-administration of Alg + Fib with Mel and CD144\u003Cjats:sup>+\u003C\u002Fjats:sup> ECs induced angiogenesis toward encapsulated cryopreserved\u002Fthawed ovarian transplants, resulting in reduced fibrotic changes.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":1564,"VI":1565},"Melatonin and endothelial cell-loaded alginate-fibrin hydrogel promoted angiogenesis in rat cryopreserved\u002Fthawed ovaries transplanted to the heterotopic sites","Hydrogel alginate-fibrin nạp melatonin và tế bào nội mô thúc đẩy hình thành mạch ở buồng trứng chuột cống được bảo quản lạnh\u002Frã đông ghép vào các vị trí dị vị",{"VOID":1567},"36978096",{"VOID":1569},"10.1186\u002Fs13036-023-00343-x","2025-02-07T19:04:18.231+00:00",[152],[154],"https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13036-023-00343-x",[1575,1594,1613,1630,1645,1662,1682,1698,1715,1732,1749],{"id":1576,"sortIndex":106,"researcher":23,"roles":1577,"affiliations":1578,"properties":1587,"displayName":1591,"givenName":23,"familyName":23},"d1004a9d-b30d-483c-8d0a-a525a9ded711",[],[1579],{"id":1580,"sortIndex":106,"affiliation":1581,"properties":23},"13c9a4de-b42e-4061-8914-4ccda05fe80d",{"id":1580,"createTime":23,"updateTime":23,"relativeEntities":1582,"slug":23,"properties":1583,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1586,"statistic":23},[],{"title":1584},{"EN":1585},"Department of Anatomical Sciences, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, 5166714766, 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Iran",[],{"orcid":1607,"title":1609,"openalex":1611},{"VOID":1608},"https:\u002F\u002Forcid.org\u002F0000-0001-7637-930X",{"EN":1610},"Azizeh Rahmani Del Bakhshayesh",{"VOID":1612},"A5010920128",{"id":1614,"sortIndex":196,"researcher":23,"roles":1615,"affiliations":1616,"properties":1625,"displayName":1627,"givenName":23,"familyName":23},"0ee84687-9b48-49c7-a00b-ef8b70011f9d",[],[1617],{"id":1618,"sortIndex":106,"affiliation":1619,"properties":23},"f0cac020-7250-4ef9-9aa6-8a41ad0386a3",{"id":1618,"createTime":23,"updateTime":23,"relativeEntities":1620,"slug":23,"properties":1621,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1624,"statistic":23},[],{"title":1622},{"VI":1623},"Department of Anatomical Sciences, Faculty of Medicine, Tarbiat Modares University, Tehran, Iran",[],{"title":1626,"openalex":1628},{"EN":1627},"Zahra 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Z, Babaei E, Rezaie Nezhad Zamani A, Rahbarghazi R, Azeez HJ. Curcumin-enriched Gemini surfactant nanoparticles exhibited tumoricidal effects on human 3D spheroid HT-29 cells in vitro. Cancer Nanotechnol. 2021;12(1):1–15.",{"doi":1830},"10.1186\u002Fs12645-020-00074-4",{"id":23,"text":1832,"url":23,"identifiers":1833},"Donnez J, Dolmans M, Demylle D, Jadoul P, Pirard C, Squifflet J, Martinez-Madrid B, Van Langendonckt A. Restoration of ovarian function after orthotopic (intraovarian and periovarian) transplantation of cryopreserved ovarian tissue in a woman treated by bone marrow transplantation for sickle cell anaemia: case report. Hum Reprod. 2006;21(1):183–8.",{"doi":1834},"10.1093\u002Fhumrep\u002Fdei268",{"id":23,"text":1836,"url":23,"identifiers":1837},"Marin L, Bedoschi G, Kawahara T, Oktay KH. History, evolution and current state of ovarian tissue auto-transplantation with cryopreserved tissue: a successful translational research journey from 1999 to 2020. Reproductive Sci. 2020;27(4):955–62.",{"doi":1838},"10.1007\u002Fs43032-019-00066-9",{"id":23,"text":1840,"url":23,"identifiers":1841},"Letourneau JM, Ebbel EE, Katz PP, Katz A, Ai WZ, Chien AJ, Melisko ME, Cedars MI, Rosen MP. Pretreatment fertility counseling and fertility preservation improve quality of life in reproductive age women with cancer. Cancer. 2012;118(6):1710–7.",{"doi":1842},"10.1002\u002Fcncr.26459",{"id":23,"text":1844,"url":23,"identifiers":1845},"Oktay K. Ovarian tissue cryopreservation and transplantation: preliminary findings and implications for cancer patients. Hum Reprod Update. 2001;7(6):526–34.",{"doi":1846},"10.1093\u002Fhumupd\u002F7.6.526",{"id":23,"text":1848,"url":23,"identifiers":1849},"Izadpanah M, Rahbarghazi R, Seghinsara AM, Abedelahi A. (2022) Novel Approaches Used in Ovarian Tissue Transplantation for Fertility Preservation: Focus on Tissue Engineering Approaches and Angiogenesis Capacity. Reproductive sciences (Thousand Oaks, Calif). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43032-022-01048-0",{"doi":1850},"10.1007\u002Fs43032-022-01048-0",{"id":23,"text":1852,"url":23,"identifiers":1853},"Ebrahimi F, Zavareh S, Nasiri M. (2022) The Combination of Estradiol and N-acetylcysteine improves folliculogenesis and angiogenesis of mice ovarian autografts by reducing inflammation and oxidative stress.",{"doi":1854},"10.21203\u002Frs.3.rs-2069273\u002Fv1",{"id":23,"text":1856,"url":23,"identifiers":1857},"Izadpanah M, Rahbarghazi R, Seghinsara AM, Abedelahi A. (2022) Novel approaches used in ovarian tissue transplantation for fertility preservation: Focus on tissue engineering approaches and angiogenesis capacity.Reproductive Sciences:1–12",{"doi":1850},{"id":23,"text":1859,"url":23,"identifiers":1860},"Dath C, Dethy A, Van Langendonckt A, Van Eyck AS, Amorim CA, Luyckx V, Donnez J, Dolmans MM. Endothelial cells are essential for ovarian stromal tissue restructuring after xenotransplantation of isolated ovarian stromal cells. Hum Reprod. 2011;26(6):1431–9. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fhumrep\u002Fder073.",{"doi":1861},"10.1093\u002Fhumrep\u002Fder073",{"id":23,"text":1863,"url":23,"identifiers":1864},"Wong R, Donno R, Leon-Valdivieso CY, Roostalu U, Derby B, Tirelli N, Wong JK. Angiogenesis and tissue formation driven by an arteriovenous loop in the mouse. Sci Rep. 2019;9(1):10478. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-019-46571-4.",{"doi":1865},"10.1038\u002Fs41598-019-46571-4",{"id":23,"text":1867,"url":23,"identifiers":1868},"Lueckgen A, Garske DS, Ellinghaus A, Mooney DJ, Duda GN, Cipitria A. Enzymatically-degradable alginate hydrogels promote cell spreading and in vivo tissue infiltration. Biomaterials. 2019;217:119294.",{"doi":1869},"10.1016\u002Fj.biomaterials.2019.119294",{"id":23,"text":1871,"url":23,"identifiers":1872},"Nemati S, Alizadeh Sardroud H, Baradar Khoshfetrat A, Khaksar M, Ahmadi M, Amini H, Saberianpour S, Delkhosh A, Akbar Movassaghpour A, Rahbarghazi R. The effect of alginate–gelatin encapsulation on the maturation of human myelomonocytic cell line U937. J Tissue Eng Regen Med. 2019;13(1):25–35.",{"doi":1873},"10.1002\u002Fterm.2765",{"id":23,"text":1875,"url":23,"identifiers":1876},"Firouzi N, Baradar Khoshfetrat A, Kazemi D. Enzymatically gellable gelatin improves nano-hydroxyapatite‐alginate microcapsule characteristics for modular bone tissue formation. J biomedical Mater Res Part A. 2020;108(2):340–50.",{"doi":1877},"10.1002\u002Fjbm.a.36820",{"id":23,"text":1879,"url":23,"identifiers":1880},"Ahmed TA, Dare EV, Hincke M. Fibrin: a versatile scaffold for tissue engineering applications. Tissue Eng Part B: Reviews. 2008;14(2):199–215.",{"doi":1881},"10.1089\u002Ften.teb.2007.0435",{"id":23,"text":1883,"url":23,"identifiers":1884},"Chiti MC, Dolmans M-M, Donnez J, Amorim C. Fibrin in reproductive tissue engineering: a review on its application as a biomaterial for fertility preservation. Ann Biomed Eng. 2017;45(7):1650–63.",{"doi":1885},"10.1007\u002Fs10439-017-1817-5",{"id":23,"text":1887,"url":23,"identifiers":1888},"Robinson M, Douglas S, Michelle Willerth S. Mechanically stable fibrin scaffolds promote viability and induce neurite outgrowth in neural aggregates derived from human induced pluripotent stem cells. Sci Rep. 2017;7(1):1–9.",{"doi":1889},"10.1038\u002Fs41598-017-06570-9",{"id":23,"text":1891,"url":23,"identifiers":1892},"Maltaris T, Dimmler A, Müller A, Hoffmann I, Beckmann MW, Dittrich R. Comparison of two freezing protocols in an open freezing system for cryopreservation of rat ovarian tissue. J Obstet Gynecol Res. 2006;32(3):273–9.",{"doi":1893},"10.1111\u002Fj.1447-0756.2006.00398.x",{"id":23,"text":1895,"url":23,"identifiers":1896},"Mahmoudi Asl M, Rahbarghazi R, Beheshti R, Alihemmati A, Aliparasti MR, Abedelahi A. Effects of different vitrification solutions and protocol on follicular ultrastructure and revascularization of Autografted Mouse Ovarian tissue. Cell J. 2021;22(4):491–501. https:\u002F\u002Fdoi.org\u002F10.22074\u002Fcellj.2021.6877.",{"doi":1897},"10.22074\u002Fcellj.2021.6877",{"id":23,"text":1899,"url":23,"identifiers":1900},"Ahmadian S, Sheshpari S, Pazhang M, Bedate AM, Beheshti R, Abbasi MM, Nouri M, Rahbarghazi R, Mahdipour M. Intra-ovarian injection of platelet-rich plasma into ovarian tissue promoted rejuvenation in the rat model of premature ovarian insufficiency and restored ovulation rate via angiogenesis modulation. Reproductive Biology and Endocrinology. 2020;18(1):78. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12958-020-00638-4.",{"doi":1901},"10.1186\u002Fs12958-020-00638-4",{"id":23,"text":1903,"url":23,"identifiers":1904},"Hemadi M, Abolhassani F, Akbari M, Sobhani A, Pasbakhsh P, Ährlund-Richter L, Modaresi MH, Salehnia M. Melatonin promotes the cumulus–oocyte complexes quality of vitrified–thawed murine ovaries; with increased mean number of follicles survival and ovary size following heterotopic transplantation. Eur J Pharmacol. 2009;618(1–3):84–90.",{"doi":1905},"10.1016\u002Fj.ejphar.2009.07.018",{"id":23,"text":1907,"url":23,"identifiers":1908},"Tavana S, Valojerdi MR, Eimani H, Abtahi NS, Fathi R. Auto-transplantation of whole rat ovary in different transplantation sites. In: Veterinary Research Forum, 2017. Faculty of Veterinary Medicine, Urmia University, Urmia, Iran, p 275",{},{"id":23,"text":1910,"url":23,"identifiers":1911},"Donnez J, Dolmans M-M, Pellicer A, Diaz-Garcia C, Serrano MS, Schmidt KT, Ernst E, Luyckx V, Andersen CY. Restoration of ovarian activity and pregnancy after transplantation of cryopreserved ovarian tissue: a review of 60 cases of reimplantation. Fertil Steril. 2013;99(6):1503–13.",{"doi":1912},"10.1016\u002Fj.fertnstert.2013.03.030",{"id":23,"text":1914,"url":23,"identifiers":1915},"Alshaikh AB, Padma AM, Dehlin M, Akouri R, Song MJ, Brännström M, Hellström M. Decellularization and recellularization of the ovary for bioengineering applications; studies in the mouse. Reproductive Biology and Endocrinology. 2020;18(1):1–10.",{"doi":1916},"10.1186\u002Fs12958-020-00630-y",{"id":23,"text":1918,"url":23,"identifiers":1919},"Hassani A, Avci ÇB, Kerdar SN, Amini H, Amini M, Ahmadi M, Sakai S, Bagca BG, Ozates NP, Rahbarghazi R, Khoshfetrat AB. Interaction of alginate with nano-hydroxyapatite-collagen using strontium provides suitable osteogenic platform. J Nanobiotechnol. 2022;20(1):310. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12951-022-01511-9.",{"doi":1920},"10.1186\u002Fs12951-022-01511-9",{"id":23,"text":1922,"url":23,"identifiers":1923},"Deepthi S, Jayakumar R. Alginate nanobeads interspersed fibrin network as in situ forming hydrogel for soft tissue engineering. Bioactive Mater. 2018;3(2):194–200. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.bioactmat.2017.09.005.",{"doi":1924},"10.1016\u002Fj.bioactmat.2017.09.005",{"id":23,"text":1926,"url":23,"identifiers":1927},"Vorwald CE, Gonzalez-Fernandez T, Joshee S, Sikorski P, Leach JK. Tunable fibrin-alginate interpenetrating network hydrogels to support cell spreading and network formation. Acta Biomater. 2020;108:142–52. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.actbio.2020.03.014.",{"doi":1928},"10.1016\u002Fj.actbio.2020.03.014",{"id":23,"text":1930,"url":23,"identifiers":1931},"Rahimi G, Isachenko V, Kreienberg R, Sauer H, Todorov P, Tawadros S, Mallmann P, Nawroth F, Isachenko E. Re-vascularisation in human ovarian tissue after conventional freezing or vitrification and xenotransplantation. Eur J Obstet Gynecol Reproductive Biology. 2010;149(1):63–7.",{"doi":1932},"10.1016\u002Fj.ejogrb.2009.11.015",{"id":23,"text":1934,"url":23,"identifiers":1935},"Yang H, Lee HH, Lee HC, Ko DS, Kim SS. Assessment of vascular endothelial growth factor expression and apoptosis in the ovarian graft: can exogenous gonadotropin promote angiogenesis after ovarian transplantation? Fertil Steril. 2008;90(4):1550–8.",{"doi":1936},"10.1016\u002Fj.fertnstert.2007.08.086",{"id":23,"text":1938,"url":23,"identifiers":1939},"Fagiani E, Christofori G. Angiopoietins in angiogenesis. Cancer Lett. 2013;328(1):18–26.",{"doi":1940},"10.1016\u002Fj.canlet.2012.08.018",{"id":23,"text":1942,"url":23,"identifiers":1943},"Brindle NP, Saharinen P, Alitalo K. Signaling and functions of angiopoietin-1 in vascular protection. Circul Res. 2006;98(8):1014–23.",{"doi":1944},"10.1161\u002F01.RES.0000218275.54089.12",{"id":23,"text":1946,"url":23,"identifiers":1947},"Cummings MJ, Bakamutumaho B, Price A, Owor N, Kayiwa J, Namulondo J, Byaruhanga T, Jain K, Postler TS, Muwanga M. HIV infection drives pro-inflammatory immunothrombotic pathway activation and organ dysfunction among adults with sepsis in Uganda. AIDS. 2023;37(2):233–45.",{"doi":1948},"10.1097\u002FQAD.0000000000003410",{"id":23,"text":1950,"url":23,"identifiers":1951},"Man L, Park L, Bodine R, Ginsberg M, Zaninovic N, Schattman G, Schwartz RE, Rosenwaks Z, James D. (2018) Co-transplantation of human ovarian tissue with engineered endothelial cells: a cell-based strategy combining accelerated perfusion with direct paracrine delivery.JoVE (Journal of Visualized Experiments)(135):e57472",{"doi":1952},"10.3791\u002F57472",{"id":23,"text":1954,"url":23,"identifiers":1955},"Soybİr G, Topuzlu C, OdabaŞ Ö, Dolay K, Bİlİr A, KÖksoy F. The effects of melatonin on angiogenesis and wound healing. Surg Today. 2003;33(12):896–901.",{"doi":1956},"10.1007\u002Fs00595-003-2621-3",{"id":23,"text":1958,"url":23,"identifiers":1959},"Mirza-Aghazadeh‐Attari M, Reiter RJ, Rikhtegar R, Jalili J, Hajalioghli P, Mihanfar A, Majidinia M, Yousefi B. Melatonin: an atypical hormone with major functions in the regulation of angiogenesis. IUBMB Life. 2020;72(8):1560–84.",{"doi":1960},"10.1002\u002Fiub.2287",{"id":23,"text":1962,"url":23,"identifiers":1963},"Rahbarghazi A, Siahkouhian M, Rahbarghazi R, Ahmadi M, Bolboli L, Keyhanmanesh R, Mahdipour M, Rajabi H. Role of melatonin in the angiogenesis potential; highlights on the cardiovascular disease. J Inflamm. 2021;18(1):4. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12950-021-00269-5.",{"doi":1964},"10.1186\u002Fs12950-021-00269-5",{"id":1966,"createTime":1967,"updateTime":1968,"relativeEntities":1969,"slug":1970,"properties":1971,"entityType":147,"verifyStatus":148,"verifyTime":1982,"verifyNote":150,"languages":23,"translateLanguages":23,"viewCount":106,"primaryUrl":1983,"fullTextUrl":23,"authors":1984,"publicationType":231,"publisherRelationship":2077,"citationCount":23,"citationInfo":23,"publishDate":2138,"publishYear":2139,"citationAnalyzeStatus":2140,"lastCitationAnalyze":2141,"indexDatabases":2142,"openAccess":23,"references":23,"isForceReanalyzing":1253},"eb6d27e7-3929-4bce-815d-5c89c33df579","2023-12-08T09:34:02.693+00:00","2026-07-22T10:07:35.393+00:00",[],"Heparinized-chitosan-stabilizes-the-bioactivity-of-BMP-2-and-potentiates-the-osteogenic-efficacy-of-demineralized-bone-matrix",{"abstract":1972,"title":1974,"gsPaper":1976,"references":1978,"doi":1980},{"EN":1973},"Demineralized bone matrix (DBM), an allograft bone processed to better expose osteoinductive factors such as bone morphogenetic proteins (BMPs), is increasingly used for clinical bone repair. However, more extensive use of DBM is limited by its unpredictable osteoinductivity and low bone formation capacity. Commercial DBM products often employ polymeric carriers to enhance handling properties but such carriers generally do not possess bioactive functions. Heparin is a highly sulfated polysaccharide and is shown to form a stable complex with growth factors to enhance their bioactivities. In this study, a new heparinized synthetic carrier for DBM is developed based on photocrosslinking of methacrylated glycol chitosan and heparin conjugation. Heparinized chitosan exerts protective effects on BMP bioactivity against physiological stressors related to bone fracture healing. It also enhances the potency of BMPs by inhibiting the activity of BMP antagonist, noggin. Moreover, heparinized chitosan is effective to deliver bone marrow stromal cells and DBM for enhanced osteogenesis by sequestering and localizing the cell-produced or DBM-released BMPs. This research suggests an essential approach of developing a new hydrogel carrier to stabilize the bioactivity of BMPs and improve the clinical efficacy of current bone graft therapeutics for accelerated bone repair.",{"EN":1975},"Heparinized chitosan stabilizes the bioactivity of BMP-2 and potentiates the osteogenic efficacy of demineralized bone matrix",{"VOID":1977},"[]",{"VOID":1979},"Pape HC, Evans A, Kobbe P. Autologous bone graft: properties and techniques. J Orthop Trauma. 2010;24(Suppl 1):S36–40.\nLadd AL, Pliam NB. Use of bone-graft substitutes in distal radius fractures. J Am Acad Orthop Surg. 1999;7:279–90.\nKuhls R, Werner-Rustner M, Küchler I, Soost F. Human demineralised bone matrix as a bone substitute for reconstruction of cystic defects of the lower jaw. Cell Tissue Bank. 2001;2:143–53.\nWildemann B, Kadow-Romacker A, Haas NP, Schmidmaier G. Quantification of various growth factors in different demineralized bone matrix preparations. J Biomed Mater Res A. 2007;81:437–42.\nPieske O, Wittmann A, Zaspel J, Löffler T, Rubenbauer B, Trentzsch H, et al. Autologous bone graft versus demineralized bone matrix in internal fixation of ununited long bones. J Trauma Manag Outcomes. 2009;3:11.\nChesmel KD, Branger J, Wertheim H, Scarborough N. Healing response to various forms of human demineralized bone matrix in athymic rat cranial defects. J Oral Maxillofac Surg. 1998;56:857–63 discussion 864-855.\nShehadi JA, Elzein SM. Review of commercially available demineralized bone matrix products for spinal fusions: a selection paradigm. Surg Neurol Int. 2017;8:203.\nFrancis CS, Mobin SS, Lypka MA, Rommer E, Yen S, Urata MM, et al. rhBMP-2 with a demineralized bone matrix scaffold versus autologous iliac crest bone graft for alveolar cleft reconstruction. Plast Reconstr Surg. 2013;131:1107–15.\nJames AW, LaChaud G, Shen J, Asatrian G, Nguyen V, Zhang XL, et al. A review of the clinical side effects of bone morphogenetic Protein-2. Tissue Engineering Part B Reviews. 2016;22:284–97.\nTang TT, Xu XL, Dai KR, Yu CF, Yue B, Lou JR. Ectopic bone formation of human bone morphogenetic protein-2 gene transfected goat bone marrow-derived mesenchymal stem cells in nude mice. Chin J Traumatol. 2005;8:3–7.\nChoudhry OJ, Christiano LD, Singh R, Golden BM, Liu JK. Bone morphogenetic protein-induced inflammatory cyst formation after lumbar fusion causing nerve root compression case report. J Neurosurg Spine. 2012;16:296–301.\nTachi K, Takami M, Zhao B, Mochizuki A, Yamada A, Miyamoto Y, et al. Bone morphogenetic protein 2 enhances mouse osteoclast differentiation via increased levels of receptor activator of NF-kappa B ligand expression in osteoblasts. Cell Tissue Res. 2010;342:213–20.\nSottile V, Seuwen K. Bone morphogenetic protein-2 stimulates adipogenic differentiation of mesenchymal precursor cells in synergy with BRL 49653 (rosiglitazone). FEBS Lett. 2000;475:201–4.\nJeon O, Song SJ, Yang HS, Bhang S-H, Kang S-W, Sung MA, et al. Long-term delivery enhances in vivo osteogenic efficacy of bone morphogenetic protein-2 compared to short-term delivery. Biochem Biophys Res Commun. 2008;369:774–80.\nRider CC. Heparin\u002Fheparan sulphate binding in the TGF-beta cytokine superfamily. Biochem Soc Trans. 2006;34:458–60.\nRuppert R, Hoffmann E, Sebald W. Human bone morphogenetic protein 2 contains a heparin-binding site which modifies its biological activity. Eur J Biochem. 1996;237:295–302.\nTakada T, Katagiri T, Ifuku M, Morimura N, Kobayashi M, Hasegawa K, et al. Sulfated polysaccharides enhance the biological activities of bone morphogenetic proteins. J Biol Chem. 2003;278:43229–35.\nKuo WJ, Digman MA, Lander AD. Heparan sulfate acts as a bone morphogenetic protein coreceptor by facilitating ligand-induced receptor hetero-oligomerization. Mol Biol Cell. 2010;21:4028–41.\nGroppe J, Greenwald J, Wiater E, Rodriguez-Leon J, Economides AN, Kwiatkowski W, et al. Structural basis of BMP signalling inhibition by the cystine knot protein noggin. Nature. 2002;420:636–42.\nPaine-Saunders S, Viviano BL, Economides AN, Saunders S. Heparan sulfate proteoglycans retain noggin at the cell surface: a potential mechanism for shaping bone morphogenetic protein gradients. J Biol Chem. 2002;277:2089–96.\nZhao BH, Katagiri T, Toyoda H, Takada T, Yanai T, Fukuda T, et al. Heparin potentiates the in vivo ectopic bone formation induced by bone morphogenetic protein-2. J Biol Chem. 2006;281:23246–53.\nKumar M, Muzzarelli RAA, Muzzarelli C, Sashiwa H, Domb AJ. Chitosan chemistry and pharmaceutical perspectives. Chem Rev. 2004;104:6017–84.\nKim S, Cui ZK, Fan JB, Fartash A, Aghaloo TL, Lee M. Photocrosslinkable chitosan hydrogels functionalized with the RGD peptide and Phosphoserine to enhance Osteogenesis. J Mater Chem B. 2016;4:5289–98.\nCao LY, Werkmeister JA, Wang J, Glattauer V, McLean KM, Liu CS. Bone regeneration using photocrosslinked hydrogel incorporating rhBMP-2 loaded 2-N, 6-O-sulfated chitosan nanoparticles. Biomaterials. 2014;35:2730–42.\nTian M, Yang Z, Kuwahara K, Nimni ME, Wan C, Han B. Delivery of demineralized bone matrix powder using a thermogelling chitosan carrier. Acta Biomater. 2012;8:753–62.\nHu J, Hou Y, Park H, Choi B, Hou S, Chung A, et al. Visible light Crosslinkable chitosan hydrogels for tissue engineering. Acta Biomater. 2012;8:1730–8.\nLogithKumar R, KeshavNarayan A, Dhivya S, Chawla A, Saravanan S, Selvamurugan N. A review of chitosan and its derivatives in bone tissue engineering. Carbohydr Polym. 2016;151:172–88.\nKim S, Cui ZK, Koo B, Zheng J, Aghaloo T, Lee M. Chitosan-lysozyme conjugates for enzyme-triggered hydrogel degradation in tissue engineering applications. ACS Appl Mater Interfaces. 2018;10:41138–45.\nGazzerro E, Gangji V, Canalis E. Bone morphogenetic proteins induce the expression of noggin, which limits their activity in cultured rat osteoblasts. J Clin Invest. 1998;102:2106–14.\nKenley R, Marden L, Turek T, Jin L, Ron E, Hollinger JO. Osseous regeneration in the rat CALVARIUM using novel delivery systems for recombinant human bone morphogenetic PROTEIN-2 (RHBMP-2). J Biomed Mater Res. 1994;28:1139–47.\nMarden LJ, Hollinger JO, Chaudhari A, Turek T, Schaub RG, Ron E. Recombinant human bone morphogenetic protein-2 is superior to demineralized bone matrix in repairing craniotomy defects in rats. J Biomed Mater Res. 1994;28:1127–38.\nOrtega N, Behonick D, Stickens D, Werb Z. How proteases regulate bone morphogenesis. Ann N Y Acad Sci. 2003;995:109–16.\nVu TH, Werb Z. Matrix metalloproteinases: effectors of development and normal physiology. Genes Dev. 2000;14:2123–33.\nTchetverikov I, Lohmander LS, Verzijl N, Huizinga TW, TeKoppele JM, Hanemaaijer R, et al. MMP protein and activity levels in synovial fluid from patients with joint injury, inflammatory arthritis, and osteoarthritis. Ann Rheum Dis. 2005;64:694–8.\nKochetkova EA, Ugaĭ LG, Maĭstrovskaia IV, Buria KA, Nevzorova VA. Role of matrix metalloproteinase-9 in the pathogenesis of osteoporosis in patients with chronic obstructive pulmonary disease. Ter Arkh. 2012;84:37–40.\nThrailkill K, Cockrell G, Simpson P, Moreau C, Fowlkes J, Bunn RC. Physiological matrix metalloproteinase (MMP) concentrations: comparison of serum and plasma specimens. Clin Chem Lab Med. 2006;44:503–4.\nMasuhara K, Nakai T, Yamaguchi K, Yamasaki S, Sasaguri Y. Significant increases in serum and plasma concentrations of matrix metalloproteinases 3 and 9 in patients with rapidly destructive osteoarthritis of the hip. Arthritis Rheum. 2002;46:2625–31.\nHu Z, Ma C, Liang Y, Zou S, Liu X. Osteoclasts in bone regeneration under type 2 diabetes mellitus. 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Biomacromolecules. 2007;8:3758–66.\nSabokbar A, Millett PJ, Myer B, Rushton N. A rapid, quantitative assay for measuring alkaline phosphatase activity in osteoblastic cells in vitro. Bone Miner. 1994;27:57–67.\nCui Z-K, Kim S, Baljon JJ, Wu BM, Aghaloo T, Lee M. Microporous methacrylated glycol chitosan-montmorillonite nanocomposite hydrogel for bone tissue engineering. Nat Commun. 2019;10:3523.",{"VOID":1981},"10.1186\u002Fs13036-020-0231-y","2024-06-25T19:51:22.234+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13036-020-0231-y",[1985,2001,2014,2027,2040,2053],{"id":1986,"sortIndex":106,"researcher":23,"roles":1987,"affiliations":1989,"properties":1998,"displayName":2000,"givenName":23,"familyName":23},"33c03c59-5790-44a8-af26-599f8035867a",[1988],"AUTHOR",[1990],{"id":1991,"sortIndex":106,"affiliation":1992,"properties":23},"64d80587-ddaa-4e1f-9b6b-38ccd2a5f0f1",{"id":1991,"createTime":23,"updateTime":23,"relativeEntities":1993,"slug":23,"properties":1994,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":1997,"statistic":23},[],{"title":1995},{"VI":1996},"Division of Advanced Prosthodontics, University of California, Los Angeles, USA",[],{"title":1999},{"VI":2000},"Soyon Kim",{"id":2002,"sortIndex":178,"researcher":23,"roles":2003,"affiliations":2004,"properties":2011,"displayName":2013,"givenName":23,"familyName":23},"97854093-4d35-49f9-a112-e82981640cbb",[1988],[2005],{"id":1991,"sortIndex":106,"affiliation":2006,"properties":23},{"id":1991,"createTime":23,"updateTime":23,"relativeEntities":2007,"slug":23,"properties":2008,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2010,"statistic":23},[],{"title":2009},{"VI":1996},[],{"title":2012},{"VI":2013},"Jiabing 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Lee",{"id":2028,"sortIndex":24,"researcher":23,"roles":2029,"affiliations":2030,"properties":2037,"displayName":2039,"givenName":23,"familyName":23},"b2b25c53-b5d9-4349-9f8b-27ab142c0849",[1988],[2031],{"id":1991,"sortIndex":106,"affiliation":2032,"properties":23},{"id":1991,"createTime":23,"updateTime":23,"relativeEntities":2033,"slug":23,"properties":2034,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2036,"statistic":23},[],{"title":2035},{"VI":1996},[],{"title":2038},{"VI":2039},"Chen Chen",{"id":2041,"sortIndex":122,"researcher":23,"roles":2042,"affiliations":2043,"properties":2050,"displayName":2052,"givenName":23,"familyName":23},"5452ab52-c97c-47e0-aa88-f103590d1fb3",[1988],[2044],{"id":1991,"sortIndex":106,"affiliation":2045,"properties":23},{"id":1991,"createTime":23,"updateTime":23,"relativeEntities":2046,"slug":23,"properties":2047,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2049,"statistic":23},[],{"title":2048},{"VI":1996},[],{"title":2051},{"VI":2052},"Ksenia Bubukina",{"id":2054,"sortIndex":1664,"researcher":23,"roles":2055,"affiliations":2056,"properties":2074,"displayName":2076,"givenName":23,"familyName":23},"c34b86d0-f461-46b2-86ed-f5768d9b8415",[1988],[2057,2063],{"id":1991,"sortIndex":106,"affiliation":2058,"properties":23},{"id":1991,"createTime":23,"updateTime":23,"relativeEntities":2059,"slug":23,"properties":2060,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2062,"statistic":23},[],{"title":2061},{"VI":1996},[],{"id":2064,"sortIndex":178,"affiliation":2065,"properties":2071},"6619b04b-47dc-4ddb-8599-944d8a5863a4",{"id":2064,"createTime":23,"updateTime":23,"relativeEntities":2066,"slug":23,"properties":2067,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2070,"statistic":23},[],{"title":2068},{"VI":2069},"Department of Bioengineering, University of California, Los Angeles, United States",[],{"title":2072},{"VI":2073},"Department of Bioengineering, University of California, Los Angeles, USA",{"title":2075},{"VI":2076},"Min Lee",{"url":1983,"publisher":2078,"properties":2133},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2079,"slug":10,"properties":2080,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":2085,"manageAffiliations":2102,"indexDatabases":2113,"url":104,"thumbnailPath":23,"statistic":2128,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":2081,"eissn":2082,"issn":2083,"title":2084},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[2086,2090,2094,2098],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":2087,"label":2088,"description":2089,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":2091,"label":2092,"description":2093,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},{"id":39,"createTime":23,"updateTime":23,"relativeEntities":2095,"label":2096,"description":2097,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":42},{},{"id":45,"createTime":23,"updateTime":23,"relativeEntities":2099,"label":2100,"description":2101,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":48},{},[2103,2108],{"id":52,"createTime":23,"updateTime":23,"relativeEntities":2104,"slug":23,"properties":2105,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2107,"statistic":23},[],{"title":2106},{"EN":56},[],{"id":59,"createTime":23,"updateTime":23,"relativeEntities":2109,"slug":23,"properties":2110,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2112,"statistic":23},[],{"title":2111},{"EN":63},[],[2114,2121],{"id":67,"indexDatabase":2115,"url":80,"indexYears":23,"academicFieldIds":2120,"indexDatabaseRanking":23},{"id":69,"createTime":23,"updateTime":23,"relativeEntities":2116,"label":2117,"description":2118,"key":76,"publicationTags":2119,"standard":23},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82,83],{"id":85,"indexDatabase":2122,"url":96,"indexYears":97,"academicFieldIds":2127,"indexDatabaseRanking":103},{"id":87,"createTime":23,"updateTime":23,"relativeEntities":2123,"label":2124,"description":2125,"key":93,"publicationTags":2126,"standard":23},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100,101,102],{"impactFactor":106,"impactFactorByYear":2129,"i10Index":106,"i10IndexLast5Year":106,"totalPublication":108,"totalPublicationByYear":2130,"totalCitation":106,"totalCitationByYear":2131,"totalCitationPerPublication":106,"totalCitationPerPublicationByYear":2132,"hindexLast5Year":106,"hindex":106},{},{"2007":24,"2008":110,"2009":111,"2010":112,"2011":113,"2012":114,"2013":115,"2014":116,"2015":117,"2016":118,"2017":119,"2018":118,"2019":120,"2020":114,"2021":113,"2022":115,"2023":121,"2024":122},{},{},{"pages":2134,"volume":2136},{"VOID":2135},"1-12",{"VOID":2137},"14","2020-03-06",2020,"ERROR_IN_GET_PLATFORM_ID","2026-07-22T10:07:35.392+00:00",[103,78],{"id":2144,"createTime":2145,"updateTime":2146,"relativeEntities":2147,"slug":2148,"properties":2149,"entityType":147,"verifyStatus":148,"verifyTime":2159,"verifyNote":150,"languages":23,"translateLanguages":23,"viewCount":106,"primaryUrl":2160,"fullTextUrl":23,"authors":2161,"publicationType":231,"publisherRelationship":2190,"citationCount":23,"citationInfo":23,"publishDate":2251,"publishYear":2252,"citationAnalyzeStatus":2140,"lastCitationAnalyze":2253,"indexDatabases":2254,"openAccess":23,"references":23,"isForceReanalyzing":1253},"017ec16c-f443-4ef5-8163-4be19cff9858","2024-01-19T12:45:20.144+00:00","2026-07-21T21:03:32.671+00:00",[],"Effect-of-tumor-shape-and-size-on-drug-delivery-to-solid-tumors",{"abstract":2150,"title":2152,"gsPaper":2154,"references":2155,"doi":2157},{"EN":2151},"Tumor shape and size effect on drug delivery to solid tumors are studied, based on the application of the governing equations for fluid flow, i.e., the conservation laws for mass and momentum, to physiological systems containing solid tumors. The discretized form of the governing equations, with appropriate boundary conditions, is developed for predefined tumor geometries. The governing equations are solved using a numerical method, the element-based finite volume method. Interstitial fluid pressure and velocity are used to show the details of drug delivery in a solid tumor, under an assumption that drug particles flow with the interstitial fluid. Drug delivery problems have been most extensively researched in spherical tumors, which have been the simplest to examine with the analytical methods. With our numerical method, however, more complex shapes of the tumor can be studied. The numerical model of fluid flow in solid tumors previously introduced by our group is further developed to incorporate and investigate non-spherical tumors such as prolate and oblate ones. Also the effects of the surface area per unit volume of the tissue, vascular and interstitial hydraulic conductivity on drug delivery are investigated.",{"EN":2153},"Effect of tumor shape and size on drug delivery to solid tumors",{"VOID":1977},{"VOID":2156},"Jain RK: Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy. Science. 2005, 307: 58-62. 10.1126\u002Fscience.1104819.\nBaxter LT, Jain RK: Transport of fluid and macromolecules in tumors. (I) Role of interstitial pressure and convection. Microvasc Res. 1989, 37: 77-104. 10.1016\u002F0026-2862(89)90074-5.\nJain RK, Tong RT, Munn LL: Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis. Cancer Res. 2007, 67 (6): 2729-2735. 10.1158\u002F0008-5472.CAN-06-4102.\nJain RK, Ward-Hartley K: Tumor blood flow-characterization, modifications, and role in hyperthermia. IEEE Trans Sonics and Ultrason. 1984, 31:\nJain RK: Determinants of tumor blood flow: A review. Cancer Res. 1988a, 48: 2641-2658.\nJones PL, Gallagher BM, Sands H: Autoradiographic analysis of monoclonal antibody distribution in human colon and breast tumor xenografts. Cancer Immunology Immunotherapy. 1986, 22 (2): 139-143.\nSands H, Jones PL, Shah SA, Plame D, Vessella RL, Gallagher BM: Correlation of vascular permeability and blood flow with monoclonal antibody uptake by human clouser and renal cell xenografts. Cancer Res. 1988, 48: 188-193.\nGoldacre RJ, Sylven B: On the access of blood-borne dyes to various tumor regions. Brit J Cancer. 1962, 16: 306-322. 10.1038\u002Fbjc.1962.36.\nBlakeslee S: Impenetrable tumors found to block even the newest cancer agents. The New York Times. 1989\nJain RK: Transport of molecules in the tumor interstitium: A review. Cancer Res. 1987a, 47: 3039-3051.\nJain RK: Transport of molecules across tumor vasculature. Cancer Metastasis Rev. 1987b, 6: 559-594. 10.1007\u002FBF00047468.\nJain RK, Baxter LT: Mechanisms of heterogeneous distribution of monoclonal antibodies and other macromolecules in tumors: significance of elevated interstitial pressure. Cancer Res. 1988, 48: 7022-7032.\nJain RK: Transvascular and interstitial transport in tumors. Vascular Endothelium in Health and Disease. 1988b, 215-220.\nSoltani M, Chen P: Numerical modeling of fluid flow in solid tumors. PLoS ONE. 2011, 6 (6): e20344-10.1371\u002Fjournal.pone.0020344.\nWang CH, Li J: Three dimensional simulation of IgG delivery to tumors. Chemical Engineering Science. 1998, 53 (20): 3579-3600. 10.1016\u002FS0009-2509(98)00173-0.\nTan WHK, Lee T, Wang CH: Simulation of intratumoral release of Etanidazole: Effects on the size of surgical opening. Journal of Pharmaceutical Science. 2003, 92 (4): 773-789. 10.1002\u002Fjps.10351.\nGoh YMF, Kong HL, Wang CH: Simulation of the delivery of doxorubicin to hepatoma. Pharmaceutical Research. 2001, 18 (6): 761-770. 10.1023\u002FA:1011076110317.\nTeo CS, Tan WHK, Lee T, Wang CH: Transient interstitial fluid flow in brain tumors: Effects on drug delivery. Chemical Engineering Science. 2005, 60 (17): 4803-4821. 10.1016\u002Fj.ces.2005.04.008.\nTan WHK, Wang FJ, Lee T, Wang CH: Computer simulation of the delivery of Etanidazole to brain tumor PLGA wafers: Comparison between linear and double burst release systems. Biotechnology and Bioengineering. 2003, 82 (3): 278-288. 10.1002\u002Fbit.10571.\nWang CH, Li J, Teo CS, Lee T: The delivery of BCNU to brain tumors. Journal of Controlled Release. 1999, 61 (1-2): 21-41. 10.1016\u002FS0168-3659(99)00098-X.\nPozrikidis C, Farrow DA: A model of fluid flow in solid tumors. Ann Biomed Eng. 2003, 31 (2): 181-194.\nChapman SJ, Shipley RJ, Jawad R: Multiscale modeling of fluid transport in tumors. Bulletin of Mathematical Biology. 2008, 70: 2334-2357. 10.1007\u002Fs11538-008-9349-7.\nBaxter LT, Jain RK: Transport of fluid and macromolecules in tumors. (II) Role of heterogeneous perfusion and lymphatics. Microvasc Res. 1990, 40: 246-263. 10.1016\u002F0026-2862(90)90023-K.\nBaxter LT, Jain RK: Transport of fluid and macromolecules in tumors. (III) Role of binding and metabolism. Microvasc Res. 1991, 41: 5-23. 10.1016\u002F0026-2862(91)90003-T.\nWiig H, Tveit E, Hultborn R, Reed RK, Weiss L: Interstitial fluid pressure in DMBA-induced rat mammary tumors. Stand J C\u002Fin Lab Invest. 1982, 42: 159-164. 10.3109\u002F00365518209168067.\nYoung JS, Lumsden CE, Stalker AL: The significance of the tissue pressure of normal testicular and of neoplastic (Brown-Pearce carcinoma) tissue in the rabbit. J Pathol Bacteriol. 1950, 62: 313-333. 10.1002\u002Fpath.1700620303.\nEl-Kareh AW, Secomb TW: Effect of increasing vascular hydraulic conductivity on delivery of macromolecular drugs to tumor cells. Int J Radiation Oncology Biol Phys. 1995, 32 (5): 1419-1423. 10.1016\u002F0360-3016(95)00110-K.\nCampbell NA: Biology. 1996, The Benjamin\u002FCummings Publishing Co\nSalathe EP, An K: A mathematical analysis of fluid movement across capillary walls. Microvasc Res. 1976, 11: 1-23. 10.1016\u002F0026-2862(76)90072-8.\nScheidegger AE: The physics of flow through porous media. 1963, Univ. of Toronto Press\nGross JF, Popel AS: Mathematical models of transport phenomena in normal and neoplastic tissue, Tumor Blood Circulation. 1979, CRC Press, h. i peterson edition\nde Boer R: Theory of porous media: Highlights in the historical development and current state. 2000, Springer\nBear J: Dynamics of fluids in porous media. 1988, Dover Publications\nStarling EH: On the absorption of fluids from the connective tissue space. J Physiol. 1896, 19: 312-326.\nCurry FE: Mechanics and thermodynamics of transcapillary exchange, Handbook of Physiology, Section 2: The Cardiovascular System. 1984, Amer. Physiol Soc\nGullino PM: Extracellular compartments of solid tumors, Cancer. 1975, Plenum\nButler TP, Grantham FH, Gullino PM: Bulk transfer of fluid in the interstitial compartment of mammary tumors. Cancer Res. 1975, 35: 3084-3088.\nSoltani M, Chen P: Shape design of internal flow with minimum pressure loss. Advanced Science Letters. 2009, 2: 347-355. 10.1166\u002Fasl.2009.1044.\nLinninger AA, Somayaji MR, Erickson T, Guo X, Penn RD: Computational methods for predicting drug transport in anisotropic and heterogeneous brain tissue. Journal of Biomechanics. 2008, 41: 2176-2187. 10.1016\u002Fj.jbiomech.2008.04.025.\nLinninger AA, Somayaji MR, Zhang L, Hariharan MS, Penn RD: Rigorous mathematical modeling techniques for optimal delivery of macromolecules to the brain. IEEE Transactions on Biomedical Engineering. 2008, 55 (9): 2303-2313.\nKhawli LA, LeBerthon B, Charak BS, Mazumder A, Epstein AL: Enhanced tumor uptake of monoclonal antibodies induced by a novel vasoactive immunoconjugate. Antib Immunoconjug Radiopharm. 1991, 4: 205-\nLeBerthon B, Khawli LA, Alauddin M, Miller GK, Charak BS, Mazumder A, Epstein AL: Enhanced tumor uptake of macromolecules induced by a novel vasoactive interleukin 2 immunoconjugate. Cancer Res. 1991, 51: 2694-2698.\nCope DA, Dewhirst MW, Friedman HS, Bigner DD, Zalutsky MR: Enhanced delivery of a monoclonal antibody F(ab’)2 fragment to subcutaneous human glioma xenografts using local hyperthermia. Cancer Res. 1990, 50: 1803-1809.\nIvanchenko O, Sindhwani N, Linninger AA: Experimental techniques for studying poroelasticity in brain phantom gels under high flow microinfusion. Journal of Biomechanical Engineering. 2010, 132 (5): 051008-10.1115\u002F1.4001164.",{"VOID":2158},"10.1186\u002F1754-1611-6-4","2024-06-26T20:48:56.321+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002F1754-1611-6-4",[2162,2177],{"id":2163,"sortIndex":106,"researcher":23,"roles":2164,"affiliations":2165,"properties":2174,"displayName":2176,"givenName":23,"familyName":23},"d5d18b23-17e0-47c5-ae5c-006166b3f78d",[1988],[2166],{"id":2167,"sortIndex":106,"affiliation":2168,"properties":23},"9ec94541-04d4-4300-b986-e625d7218c40",{"id":2167,"createTime":23,"updateTime":23,"relativeEntities":2169,"slug":23,"properties":2170,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2173,"statistic":23},[],{"title":2171},{"VI":2172},"Waterloo Institute for Nanotechnology, Department of Chemical Engineering, University of Waterloo, Waterloo, Canada",[],{"title":2175},{"VI":2176},"M Soltani",{"id":2178,"sortIndex":178,"researcher":23,"roles":2179,"affiliations":2180,"properties":2187,"displayName":2189,"givenName":23,"familyName":23},"2f6dfae7-ab21-48b7-8111-281456ac03cd",[1988],[2181],{"id":2167,"sortIndex":106,"affiliation":2182,"properties":23},{"id":2167,"createTime":23,"updateTime":23,"relativeEntities":2183,"slug":23,"properties":2184,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2186,"statistic":23},[],{"title":2185},{"VI":2172},[],{"title":2188},{"VI":2189},"Pu Chen",{"url":2160,"publisher":2191,"properties":2246},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2192,"slug":10,"properties":2193,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":2198,"manageAffiliations":2215,"indexDatabases":2226,"url":104,"thumbnailPath":23,"statistic":2241,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":2194,"eissn":2195,"issn":2196,"title":2197},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[2199,2203,2207,2211],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":2200,"label":2201,"description":2202,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":2204,"label":2205,"description":2206,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},{"id":39,"createTime":23,"updateTime":23,"relativeEntities":2208,"label":2209,"description":2210,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":42},{},{"id":45,"createTime":23,"updateTime":23,"relativeEntities":2212,"label":2213,"description":2214,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":48},{},[2216,2221],{"id":52,"createTime":23,"updateTime":23,"relativeEntities":2217,"slug":23,"properties":2218,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2220,"statistic":23},[],{"title":2219},{"EN":56},[],{"id":59,"createTime":23,"updateTime":23,"relativeEntities":2222,"slug":23,"properties":2223,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2225,"statistic":23},[],{"title":2224},{"EN":63},[],[2227,2234],{"id":67,"indexDatabase":2228,"url":80,"indexYears":23,"academicFieldIds":2233,"indexDatabaseRanking":23},{"id":69,"createTime":23,"updateTime":23,"relativeEntities":2229,"label":2230,"description":2231,"key":76,"publicationTags":2232,"standard":23},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82,83],{"id":85,"indexDatabase":2235,"url":96,"indexYears":97,"academicFieldIds":2240,"indexDatabaseRanking":103},{"id":87,"createTime":23,"updateTime":23,"relativeEntities":2236,"label":2237,"description":2238,"key":93,"publicationTags":2239,"standard":23},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100,101,102],{"impactFactor":106,"impactFactorByYear":2242,"i10Index":106,"i10IndexLast5Year":106,"totalPublication":108,"totalPublicationByYear":2243,"totalCitation":106,"totalCitationByYear":2244,"totalCitationPerPublication":106,"totalCitationPerPublicationByYear":2245,"hindexLast5Year":106,"hindex":106},{},{"2007":24,"2008":110,"2009":111,"2010":112,"2011":113,"2012":114,"2013":115,"2014":116,"2015":117,"2016":118,"2017":119,"2018":118,"2019":120,"2020":114,"2021":113,"2022":115,"2023":121,"2024":122},{},{},{"pages":2247,"volume":2249},{"VOID":2248},"1-15",{"VOID":2250},"6","2012-12-01",2012,"2026-07-21T21:03:32.670+00:00",[103,78],{"id":2256,"createTime":2257,"updateTime":2258,"relativeEntities":2259,"slug":2260,"properties":2261,"entityType":147,"verifyStatus":148,"verifyTime":2271,"verifyNote":150,"languages":23,"translateLanguages":23,"viewCount":106,"primaryUrl":2272,"fullTextUrl":23,"authors":2273,"publicationType":231,"publisherRelationship":2435,"citationCount":23,"citationInfo":23,"publishDate":2496,"publishYear":2497,"citationAnalyzeStatus":2140,"lastCitationAnalyze":2498,"indexDatabases":2499,"openAccess":23,"references":23,"isForceReanalyzing":1253},"6bf6132e-91e3-4013-8134-6dd893500d26","2024-01-29T05:19:55.528+00:00","2026-07-10T10:29:58.154+00:00",[],"Development-of-anti-aflatoxin-B1-nanobodies-from-a-novel-mutagenesis-derived-synthetic-library-for-traditional-Chinese-medicine-and-foods-safety-testing",{"abstract":2262,"title":2264,"gsPaper":2266,"references":2267,"doi":2269},{"EN":2263},"The main commercially available methods for detecting small molecules of mycotoxins in traditional Chinese medicine (TCM) and functional foods are enzyme-linked immunosorbent assay and mass spectrometry. Regarding the development of diagnostic antibody reagents, effective methods for the rapid preparation of specific monoclonal antibodies are inadequate. In this study, a novel synthetic phage-displayed nanobody Golden Glove (SynaGG) library with a glove-like cavity configuration was established using phage display technology in synthetic biology. We applied this unique SynaGG library on the small molecule aflatoxin B1 (AFB1), which has strong hepatotoxicity, to isolate specific nanobodies with high affinity for AFB1. These nanobodies exhibit no cross-reactivity with the hapten methotrexate, which is recognized by the original antibody template. By binding to AFB1, two nanobodies can neutralize AFB1-induced hepatocyte growth inhibition. Using molecular docking, we found that the unique non-hypervariable complementarity-determining region 4 (CDR4) loop region of the nanobody was involved in the interaction with AFB1. Specifically, the CDR4’s positively charged amino acid arginine directed the binding interaction between the nanobody and AFB1. We then rationally optimized the interaction between AFB1 and the nanobody by mutating serine at position 2 into valine. The binding affinity of the nanobody to AFB1 was effectively improved, and this result supported the use of molecular structure simulation for antibody optimization. In summary, this study revealed that the novel SynaGG library, which was constructed through computer-aided design, can be used to isolate nanobodies that specifically bind to small molecules. The results of this study could facilitate the development of nanobody materials to detect small molecules for the rapid screening of TCM materials and foods in the future.",{"EN":2265},"Development of anti-aflatoxin B1 nanobodies from a novel mutagenesis-derived synthetic library for traditional Chinese medicine and foods safety testing",{"VOID":1977},{"VOID":2268},"Hamers-Casterman C, Atarhouch T, Muyldermans S, Robinson G, Hamers C, Songa EB, et al. Naturally occurring antibodies devoid of light chains. Nature. 1993;363(6428):446–8.\nWard ES, Gussow D, Griffiths AD, Jones PT, Winter G. Binding activities of a repertoire of single immunoglobulin variable domains secreted from Escherichia coli. Nature. 1989;341(6242):544–6.\nSteeland S, Vandenbroucke RE, Libert C. Nanobodies as therapeutics: big opportunities for small antibodies. Drug Discov Today. 2016;21(7):1076–113.\nMorrison C. Nanobody approval gives domain antibodies a boost. Nat Rev Drug Discov. 2019;18(7):485–7.\nIezzi ME, Policastro L, Werbajh S, Podhajcer O, Canziani GA. Single-domain antibodies and the promise of modular targeting in Cancer imaging and treatment. Front Immunol. 2018;9:273.\nBelanger K, Iqbal U, Tanha J, MacKenzie R, Moreno M, Stanimirovic D. Single-domain antibodies as therapeutic and imaging agents for the treatment of CNS diseases. Antibodies (Basel). 2019;8(2):27.\nFouladi M, Sarhadi S, Tohidkia M, Fahimi F, Samadi N, Sadeghi J, et al. Selection of a fully human single domain antibody specific to helicobacter pylori urease. Appl Microbiol Biotechnol. 2019;103(8):3407–20.\nAl Qaraghuli MM, Palliyil S, Broadbent G, Cullen DC, Charlton KA, Porter AJ. Defining the complementarities between antibodies and haptens to refine our understanding and aid the prediction of a successful binding interaction. BMC Biotechnol. 2015;15:99.\nNoel F, Malpertuy A, de Brevern AG. Global analysis of VHHs framework regions with a structural alphabet. Biochimie. 2016;131:11–9.\nSpinelli S, Frenken LG, Hermans P, Verrips T, Brown K, Tegoni M, et al. Camelid heavy-chain variable domains provide efficient combining sites to haptens. Biochemistry. 2000;39(6):1217–22.\nLadenson RC, Crimmins DL, Landt Y, Ladenson JH. Isolation and characterization of a thermally stable recombinant anti-caffeine heavy-chain antibody fragment. Anal Chem. 2006;78(13):4501–8.\nFanning SW, Horn JR. An anti-hapten camelid antibody reveals a cryptic binding site with significant energetic contributions from a nonhypervariable loop. Protein Sci. 2011;20(7):1196–207.\nKucukcakan B, Hayrulai-Musliu Z. Challenging role of dietary aflatoxin B1 exposure and hepatitis B infection on risk of hepatocellular carcinoma. Open Access Maced J Med Sci. 2015;3(2):363–9.\nBrase S, Encinas A, Keck J. Nising CF: chemistry and biology of mycotoxins and related fungal metabolites. Chem Rev. 2009;109(9):3903–90.\nHamid AS, Tesfamariam IG, Zhang Y, Zhang ZG. Aflatoxin B1-induced hepatocellular carcinoma in developing countries: geographical distribution, mechanism of action and prevention. Oncol Lett. 2013;5(4):1087–92.\nKew MC. Aflatoxins as a cause of hepatocellular carcinoma. J Gastrointest Liver Dis. 2013;22:305–10.\nSmela ME, Currier SS, Bailey EA, Essigmann JM. The chemistry and biology of aflatoxin B(1): from mutational spectrometry to carcinogenesis. Carcinogenesis. 2001;22(4):535–45.\nGefen T, Vaya J, Khatib S, Rapoport I, Lupo M, Barnea E, et al. The effect of haptens on protein-carrier immunogenicity. Immunology. 2015;144(1):116–26.\nSalvador JP, Vasylieva N, Gonzalez-Garcia I, Jin M, Caster R, Siegel JB, et al. Nanobody-based lateral flow immunoassay for the rapid detection of aflatoxin B1 in almond Milk. ACS Food Sci Technol. 2022;2(8):1276–82.\nNie Y, Li S, Zhu J, Hu R, Liu M, He T, et al. Chemical shift assignments of a camelid nanobody against aflatoxin B1. Biomol NMR Assign. 2019;13(1):75–8.\nHe T, Wang Y, Li P, Zhang Q, Lei J, Zhang Z, et al. Nanobody-based enzyme immunoassay for aflatoxin in agro-products with high tolerance to cosolvent methanol. Anal Chem. 2014;86(17):8873–80.\nKunkel TA, Roberts JD, Zakour RA. Rapid and efficient site-specific mutagenesis without phenotypic selection. Methods Enzymol. 1987;154:367–82.\nSidhu SS, Weiss GA. Constructing phage display libraries by oligonucleotide-directed mutagenesis. In: Clackson T, Lowman HB, editors. Phage display: a practical approach. 1st ed. Oxford: Oxford University Press; 2004. p. 27–41.\nBarbas CF 3rd, Kang AS, Lerner RA, Benkovic SJ. Assembly of combinatorial antibody libraries on phage surfaces: the gene III site. Proc Natl Acad Sci U S A. 1991;88(18):7978–82.\nLin TY, Tsai TH, Chen CT, Yang TW, Chang FL, Lo YN, et al. Generation of avian-derived anti-B7-H4 antibodies exerts a blockade effect on the immunosuppressive response. Exp Anim. 2021;70(3):333–43.\nKhajeh S, Tohidkia MR, Aghanejad A, Mehdipour T, Fathi F, Omidi Y. Phage display selection of fully human antibody fragments to inhibit growth-promoting effects of glycine-extended gastrin 17 on human colorectal cancer cells. Artif Cells Nanomed Biotechnol. 2018;46(sup2):1082–90.",{"VOID":2270},"10.1186\u002Fs13036-023-00350-y","2024-06-24T01:00:26.193+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13036-023-00350-y",[2274,2316,2331,2344,2359,2374,2398,2413],{"id":2275,"sortIndex":106,"researcher":23,"roles":2276,"affiliations":2277,"properties":2313,"displayName":2315,"givenName":23,"familyName":23},"510df85d-3458-4a05-ba85-70e3adaf6316",[1988],[2278,2286,2295,2304],{"id":2279,"sortIndex":106,"affiliation":2280,"properties":23},"14ae059d-d6b9-4a99-87e8-55fc42dca838",{"id":2279,"createTime":23,"updateTime":23,"relativeEntities":2281,"slug":23,"properties":2282,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2285,"statistic":23},[],{"title":2283},{"VI":2284},"TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan",[],{"id":2287,"sortIndex":178,"affiliation":2288,"properties":2294},"7a71fe9b-19f2-4de5-8836-791bcf2707c8",{"id":2287,"createTime":23,"updateTime":23,"relativeEntities":2289,"slug":23,"properties":2290,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2293,"statistic":23},[],{"title":2291},{"VI":2292},"Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan",[],{},{"id":2296,"sortIndex":196,"affiliation":2297,"properties":2303},"2e71037e-eb86-45d9-ac5c-805571297b05",{"id":2296,"createTime":23,"updateTime":23,"relativeEntities":2298,"slug":23,"properties":2299,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2302,"statistic":23},[],{"title":2300},{"VI":2301},"Ph.D. Program in Drug Discovery and Development 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Medicine, Ministry of Health and Welfare, Taipei, Taiwan",[],{},{"title":2433},{"VI":2434},"Keng-Chang 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the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets. These characteristics have a negative impact on the performance of most classifiers when used to classify cancer using genomic datasets. This paper introduces Reduced Noise-Autoencoder (RN-Autoencoder) for pre-processing imbalanced genomic datasets for precise cancer classification. Firstly, RN-Autoencoder solves the curse of dimensionality problem by utilizing the autoencoder for feature reduction and hence generating new extracted data with lower dimensionality. In the next stage, RN-Autoencoder introduces the extracted data to the well-known Reduced Noise-Synthesis Minority Over Sampling Technique (RN- SMOTE) that efficiently solve the problem of class imbalance in the extracted data. RN-Autoencoder has been evaluated using different classifiers and various imbalanced datasets with different imbalance ratios. The results proved that the performance of the classifiers has been improved with RN-Autoencoder and outperformed the performance with original data and extracted data with percentages based on the classifier, dataset and evaluation metric. Also, the performance of RN-Autoencoder has been compared to the performance of the current state of the art and resulted in an increase up to 18.017, 19.183, 18.58 and 8.87% in terms of test accuracy using colon, leukemia, Diffuse Large B-Cell Lymphoma (DLBCL) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. RN-Autoencoder is a model for cancer classification using imbalanced gene expression datasets. It utilizes the autoencoder to reduce the high dimensionality of the gene expression datasets and then handles the class imbalance using RN-SMOTE. RN-Autoencoder has been evaluated using many different classifiers and many different imbalanced datasets. The performance of many classifiers has improved and some have succeeded in classifying cancer with 100% performance in terms of all used metrics. In addition, RN-Autoencoder outperformed many recent works using the same datasets.",{"EN":2510},"RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data",{"VOID":1977},{"VOID":2513},"Tabakhi S, Najafi A, Ranjbar R, Moradi P. Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing. 2015;168:1024–36. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2015.05.022.\nC Devi Arockia Vanitha, D Devaraj, M Venkatesulu. Gene expression data classification using Support Vector Machine and mutual information-based gene selection. Procedia Comput Sci. 2014;47(C):13–21. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2015.03.178.\nDas Sarma S, Deng DL, Duan LM. Machine learning meets quantum physics. Phys Today. 2019;72(3):48–54. https:\u002F\u002Fdoi.org\u002F10.1063\u002FPT.3.4164.\nA Limshuebchuey, R Duangsoithong, T Windeatt. Redundant feature identification and redundancy analysis for causal feature selection. In 2015 8th Biomedical Engineering International Conference (BMEiCON). 2015:1–5. https:\u002F\u002Fdoi.org\u002F10.1109\u002FBMEiCON.2015.7399532.\nAAGS Danasingh, A alias Balamurugan Subramanian, JL Epiphany. Identifying redundant features using unsupervised learning for high-dimensional data. SN Appl Sci. 2020;2(8):1367. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-020-3157-6.\nL Chen, S Wang. Automated feature weighting in naive bayes for high-dimensional data classification. In Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM ’12. 2012:1243. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2396761.2398426.\nTran B, Xue B, Zhang M. Genetic programming for feature construction and selection in classification on high-dimensional data. Memetic Comput. 2016;8(1):3–15. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12293-015-0173-y.\nBonev B, Escolano F, Cazorla M. Feature selection, mutual information, and the classification of high-dimensional patterns. Pattern Anal Appl. 2008;11(3–4):309–19. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10044-008-0107-0.\nChandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng. 2014;40(1):16–28. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2013.11.024.\nCai J, Luo J, Wang S, Yang S. Feature selection in machine learning: A new perspective. Neurocomputing. 2018;300:70–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2017.11.077.\nSolorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF. A review of unsupervised feature selection methods. Artif Intell Rev. 2020;53(2):907–48. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10462-019-09682-y.\nU Shaham, O Lindenbaum, J Svirsky, Y Kluger. Deep unsupervised feature selection by discarding nuisance and correlated features. 2021. Available: http:\u002F\u002Farxiv.org\u002Fabs\u002F2110.05306.\nGu S, Cheng R, Jin Y. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput. 2018;22(3):811–22. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00500-016-2385-6.\nAlhenawi E, Al-Sayyed R, Hudaib A, Mirjalili S. Feature selection methods on gene expression microarray data for cancer classification: a systematic review. Comput Biol Med. 2022;140: 105051. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compbiomed.2021.105051.\nMishra D, Sharma S. Performance analysis of dimensionality reduction techniques: a comprehensive Review. Adv Mech Eng. 2021;639–651:2021. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-981-16-0942-8_60.\nEngel D, Hüttenberger L, Hamann B. A survey of dimension reduction methods for high-dimensional data analysis and visualization. OpenAccess Ser Informatics. 2012;27:135–49. https:\u002F\u002Fdoi.org\u002F10.4230\u002FOASIcs.VLUDS.2011.135.\nHira ZM, Gillies DF. A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinformatics. 2015;2015:1–13. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2015\u002F198363.\nJia W, Sun M, Lian J, Hou S. Feature dimensionality reduction: a review. Complex Intell Syst. 2022;8(3):2663–93. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40747-021-00637-x.\nChawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2022;16:321–57. Available: https:\u002F\u002Farxiv.org\u002Fpdf\u002F1106.1813.pdf%0A. http:\u002F\u002Fwww.snopes.com\u002Fhorrors\u002Finsects\u002Ftelamonia.asp\nBlagus R, Lusa L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics. 2013;14:106. https:\u002F\u002Fdoi.org\u002F10.1186\u002F1471-2105-14-106.\nMacIejewski, J. Stefanowski. Local neighbourhood extension of SMOTE for mining imbalanced data. IEEE SSCI 2011 Symp. Ser Comput Intell - CIDM 2011 2011 IEEE Symp. Comput Intell Data Min. 2011:104–111. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCIDM.2011.5949434\nCheng K, Zhang C, Yu H, Yang X, Zou H, Gao S. Grouped SMOTE with noise filtering mechanism for classifying imbalanced data. IEEE Access. 2019;7:170668–81. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2955086.\nRivera WA. Noise reduction a priori synthetic over-sampling for class imbalanced data sets. Inf Sci (Ny). 2017;408:146–61. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ins.2017.04.046.\nArafa A, El-Fishawy N, Badawy M, Radad M. RN-SMOTE: reduced noise SMOTE based on DBSCAN for enhancing imbalanced data classification. J King Saud Univ Comput Inf Sci. 2022;34(8):5059–74. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jksuci.2022.06.005.\nXW Liang, AP Jiang, T Li, YY Xue, GT Wang. LR-SMOTE — An improved unbalanced data set oversampling based on K-means and SVM. Knowledge-Based Syst. 2020;196. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.knosys.2020.105845.\nLi B, Han B, Qin C. Application of large-scale L 2-SVM for microarray classification. J Supercomputing. 2022;78(2):2265–86. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-021-03962-7.\nKakati T, Bhattacharyya DK, Kalita JK, Norden-Krichmar TM. DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning. 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Int J Mol Sci. 2021;22:11919. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms222111919.\nA Abid, MF Balin, J Zou. Concrete Autoencoders for Differentiable Feature Selection and Reconstruction. 2019. Available: http:\u002F\u002Farxiv.org\u002Fabs\u002F1901.09346\nS Majumder, Yogita, V Pal, A Yadav, A Chakrabarty. Performance analysis of deep learning models for binary classification of cancer gene expression data. J Healthc Eng. 2022;2022.https:\u002F\u002Fdoi.org\u002F10.1155\u002F2022\u002F1122536.\nSaberi-Movahed F, et al. Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection. Knowl Based Syst. 2022;256: 109884. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.knosys.2022.109884.\nA Bustamam, Z Rustam, AA Selly, NA Wibawa, D Sarwinda, N Husna. Lung cancer classification based on support vector machine-recursive feature elimination and artificial bee colony. 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(Accessed 6 Dec 2022).\nhttps:\u002F\u002Fwww.openml.org\u002Fd\u002F1165. (Accessed 6 Dec 2022).\nhttps:\u002F\u002Fwww.openml.org\u002Fd\u002F1145. (Accessed 6 Dec 2022).\nhttps:\u002F\u002Fwww.openml.org\u002Fd\u002F1158. (Accessed 6 Dec 2022).\nD Pandit, J Dhodiya, Y Patel. Molecular cancer classification on microarrays gene expression data using wavelet-based deep convolutional neural network. Int J Imaging Syst Technol. 2022:1–19. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fima.22780.\nUzma, F Al-Obeidat, A Tubaishat, B Shah, Z Halim. Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data. Neural Comput Appl. 2020;4. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-020-05101-4.\nhttps:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets\u002FBreast+Cancer+Wisconsin+(Diagnostic). (Accessed 6 Nov 2022).\nSamieinasab M, Torabzadeh SA, Behnam A, Aghsami A, Jolai F. Meta-Health Stack: A new approach for breast cancer prediction. Healthcare Analytics. 2022;2: 100010. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.health.2021.100010.\nSingh D, Nigam R, Mittal R, Nunia M. Information retrieval using machine learning from breast cancer diagnosis. Multimed Tools Appl. 2022. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-022-13550-3.\nhttps:\u002F\u002Fseer.cancer.gov\u002Fdata\u002F. (Accessed 6 Dec 2022).\nS Bacha, O Taouali. A novel machine learning approach for breast cancer diagnosis. Measurement (Lond). 2022;187. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.measurement.2021.110233.\nTong F. \"A Comprehensive Comparison of Neural Network-Based Feature Selection Methods in Biological Omics Datasets\". In 2021 4th International Conference on Signal Processing and Machine Learning. 2021 pp. 77-81. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3483207.3483220.\nDanaee P, Ghaeini R, Hendrix DA. 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A Transfer-Learning Approach to Feature Extraction from Cancer Transcriptomes with Deep Autoencoders. 2019:912–924. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-20521-8_74\nWang Y, Yao H, Zhao S. Auto-encoder based dimensionality reduction. Neurocomputing. 2016;2016(184):232–42. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2015.08.104.\nVS Ngairangbam, M Spannowsky, M Takeuchi. Anomaly detection in high-energy physics using a quantum autoencoder. Physical Review D. 2022;105(9). https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevD.105.095004\nMujkic E, Philipsen MP, Moeslund TB, Christiansen MP, Ravn O. Anomaly detection for agricultural vehicles using autoencoders. Sensors. 2022;22(10):3608. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs22103608.\nZhou H, Yu K, Zhang X, Wu G, Yazidi A. Contrastive autoencoder for anomaly detection in multivariate time series”. 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(Accessed 6 Nov 2022).\nhttps:\u002F\u002Fweb.archive.org\u002Fweb\u002F20080207153800\u002Fhttp:\u002F\u002Fresearch.i2r.a-star.edu.sg\u002Frp\u002F. (Accessed 6 Nov 2022).\nA Arafa, M Radad, M Badawy, NE Fishawy. Regularized Logistic Regression Model for Cancer Classification. In 2021 38th National Radio Science Conference (NRSC), 2021:251–261. https:\u002F\u002Fdoi.org\u002F10.1109\u002FNRSC52299.2021.9509831.\nAA Arafa, M Radad, M Badawy, N El-Fishawy. Logistic regression hyperparameter optimization for cancer classification. Menoufia J Electron Eng Res. 2022;31(1):1–8. https:\u002F\u002Fdoi.org\u002F10.21608\u002Fmjeer.2021.70512.1034.",{"VOID":2515},"10.1186\u002Fs13036-022-00319-3","2024-05-12T19:40:54.898+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13036-022-00319-3",[2519,2534,2547,2560],{"id":2520,"sortIndex":106,"researcher":23,"roles":2521,"affiliations":2522,"properties":2531,"displayName":2533,"givenName":23,"familyName":23},"2571f5f1-d22e-4622-9786-df7a54be8c1f",[1988],[2523],{"id":2524,"sortIndex":106,"affiliation":2525,"properties":23},"4addf8c0-d1c4-451b-bcd1-6d5808ce34e7",{"id":2524,"createTime":23,"updateTime":23,"relativeEntities":2526,"slug":23,"properties":2527,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2530,"statistic":23},[],{"title":2528},{"VI":2529},"Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt",[],{"title":2532},{"VI":2533},"Ahmed Arafa",{"id":2535,"sortIndex":178,"researcher":23,"roles":2536,"affiliations":2537,"properties":2544,"displayName":2546,"givenName":23,"familyName":23},"c4dd6508-488c-48e1-bb4c-2bc3976a6784",[1988],[2538],{"id":2524,"sortIndex":106,"affiliation":2539,"properties":23},{"id":2524,"createTime":23,"updateTime":23,"relativeEntities":2540,"slug":23,"properties":2541,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2543,"statistic":23},[],{"title":2542},{"VI":2529},[],{"title":2545},{"VI":2546},"Nawal El-Fishawy",{"id":2548,"sortIndex":196,"researcher":23,"roles":2549,"affiliations":2550,"properties":2557,"displayName":2559,"givenName":23,"familyName":23},"fbcc470b-55eb-401b-873b-d8dcdd31b35c",[1988],[2551],{"id":2524,"sortIndex":106,"affiliation":2552,"properties":23},{"id":2524,"createTime":23,"updateTime":23,"relativeEntities":2553,"slug":23,"properties":2554,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2556,"statistic":23},[],{"title":2555},{"VI":2529},[],{"title":2558},{"VI":2559},"Mohammed 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delivery approaches serve as a platform to modify gene expression of a cell population with applications including functional genomics, tissue engineering, and gene therapy. The delivery of exogenous genetic material via nonviral vectors has proven to be less toxic and to cause less of an immune response in comparison to viral vectors, but with decreased efficiency of gene transfer. Attempts have been made to improve nonviral gene transfer efficiency by modifying physicochemical properties of gene delivery vectors as well as developing new delivery techniques. In order to further improve and understand nonviral gene delivery, our approach focuses on the cell-material interface, since materials are known to modulate cell behavior, potentially rendering cells more responsive to nonviral gene transfer. In this study, self-assembled monolayers of alkanethiols on gold were employed as model biomaterial interfaces with varying surface chemistries. NIH\u002F3T3 mouse fibroblasts were seeded on the modified surfaces and transfected using either lipid- or polymer- based complexing agents. Transfection was increased in cells on charged hydrophilic surfaces presenting carboxylic acid terminal functional groups, while cells on uncharged hydrophobic surfaces presenting methyl terminations demonstrated reduced transfection for both complexing agents. Surface–induced cellular characteristics that were hypothesized to affect nonviral gene transfer were subsequently investigated. Cells on charged hydrophilic surfaces presented higher cell densities, more cell spreading, more cells with ellipsoid morphologies, and increased quantities of focal adhesions and cytoskeleton features within cells, in contrast to cell on uncharged hydrophobic surfaces, and these cell behaviors were subsequently correlated to transfection characteristics. Extracellular influences on nonviral gene delivery were investigated by evaluating the upregulation and downregulation of transgene expression as a function of the cell behaviors induced by changes in the cells’ microenvronments. This study demonstrates that simple surface modifications can lead to changes in the efficiency of nonviral gene delivery. In addition, statistically significant differences in various surface-induced cell characteristics were statistically correlated to transfection trends in fibroblasts using both lipid and polymer mediated DNA delivery approaches. The correlations between the evaluated complexing agents and cell behaviors (cell density, spreading, shape, cytoskeleton, focal adhesions, and viability) suggest that polymer-mediated transfection is correlated to cell morphological traits while lipid-mediated transfection correlates to proliferative characteristics.",{"EN":2646},"The role of surface chemistry-induced cell characteristics on nonviral gene delivery to mouse fibroblasts",{"VOID":2648},"[\"14197846077993846810\"]",{"VOID":2650},"10.1186\u002F1754-1611-6-17","2024-05-03T09:23:07.995+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002F1754-1611-6-17",[2654,2678],{"id":2655,"sortIndex":106,"researcher":23,"roles":2656,"affiliations":2657,"properties":2675,"displayName":2677,"givenName":23,"familyName":23},"30540dec-f84e-4789-b70a-766200a826f7",[1988],[2658,2666],{"id":2659,"sortIndex":106,"affiliation":2660,"properties":23},"3c346a7a-125a-4e3c-b245-90b45fa08fee",{"id":2659,"createTime":23,"updateTime":23,"relativeEntities":2661,"slug":23,"properties":2662,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2665,"statistic":23},[],{"title":2663},{"VI":2664},"Biomedical Engineering Program, University of Nebraska-Lincoln, Lincoln, USA",[],{"id":2667,"sortIndex":178,"affiliation":2668,"properties":2674},"c03511de-911f-49ba-9cef-98deda0ce3db",{"id":2667,"createTime":23,"updateTime":23,"relativeEntities":2669,"slug":23,"properties":2670,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":2673,"statistic":23},[],{"title":2671},{"VI":2672},"Center for Nanohybrid Functional Materials, University of Nebraska-Lincoln, Lincoln, USA",[],{},{"title":2676},{"VI":2677},"Tadas 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Molecular Biology of the Cell 1999, 10: 2493-2506.",{"doi":2925},{"id":23,"text":3025,"url":3026,"identifiers":3027},"Sang HX, Pisarev VM, Chavez J, Robinson S, Guo YJ, Hatcher L, Munger C, Talmadge CB, Solheim JC, Singh RK, Talmadge JE: Murine mammary adenocarcinoma cells transfected with p53 and\u002For Flt3L induce antitumor immune responses. Cancer Gene Therapy 2005, 12: 427-437. 10.1038\u002Fsj.cgt.7700809","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsj.cgt.7700809",{"mag":3028,"openalex":3029,"pm":3030,"doi":3031},"2112394028","W2112394028","15678151","10.1038\u002Fsj.cgt.7700809",{"id":23,"text":3033,"url":3034,"identifiers":3035},"Engler A, Bacakova L, Newman C, Hategan A, Griffin M, Discher D: Substrate compliance versus ligand density in cell on gel responses. Biophys J 2004, 86: 617-628. 10.1016\u002FS0006-3495(04)74140-5","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0006-3495(04)74140-5",{"mag":3036,"pmc":3037,"openalex":3038,"pm":3039,"doi":3040},"1974170815","1303831","W1974170815","14695306","10.1016\u002Fs0006-3495(04)74140-5",{"id":23,"text":3042,"url":3043,"identifiers":3044},"Kumar S, Maxwell IZ, Heisterkamp A, Polte TR, Lele TP, Salanga M, Mazur E, Ingber DE: Viscoelastic retraction of single living stress fibers and its impact on cell shape, cytoskeletal organization, and extracellular matrix mechanics. Biophys J 2006, 90: 3762-3773. 10.1529\u002Fbiophysj.105.071506","https:\u002F\u002Fdoi.org\u002F10.1529\u002Fbiophysj.105.071506",{"mag":3045,"pmc":3046,"openalex":3047,"pm":3048,"doi":3049},"2171906125","1440757","W2171906125","16500961","10.1529\u002Fbiophysj.105.071506",{"id":23,"text":3051,"url":3052,"identifiers":3053},"Xia N, Thodeti CK, Hunt TP, Xu Q, Ho M, Whitesides GM, Westervelt R, Ingber DE: Directional control of cell motility through focal adhesion positioning and spatial control of Rac activation. FASEB J 2008, 22: 1649-1659. 10.1096\u002Ffj.07-090571","https:\u002F\u002Fdoi.org\u002F10.1096\u002Ffj.07-090571",{"mag":3054,"openalex":3055,"pm":3056,"doi":3057},"2127053984","W2127053984","18180334","10.1096\u002Ffj.07-090571",{"id":23,"text":3059,"url":23,"identifiers":3060},"Dowdy S, Wearden S, Chilko D: Statistics for Research. 3rd edition. Hoboken, NJ: Wiley; 2004.",{},{"id":3062,"text":3063,"url":3064,"identifiers":3065},"6be477c8-e1ac-4967-89e5-e1159c49c9dc","Arima Y, Iwata H: Effect of wettability and surface functional groups on protein adsorption and cell adhesion using well-defined mixed self-assembled monolayers. Biomaterials 2007, 28: 3074-3082. 10.1016\u002Fj.biomaterials.2007.03.013","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0142961207002323",{"doi":3066},"10.1016\u002Fj.biomaterials.2007.03.013",{"id":23,"text":3068,"url":3069,"identifiers":3070},"Goda T, Miyahara Y: Detection of Microenvironmental Changes Induced by Protein Adsorption onto Self-Assembled Monolayers using an Extended Gate-Field Effect Transistor. Anal Chem 2010, 82: 1803-1810. 10.1021\u002Fac902401y","https:\u002F\u002Fdoi.org\u002F10.1021\u002Fac902401y",{"mag":3071,"openalex":3072,"pm":3073,"doi":3074},"2059227473","W2059227473","20141107","10.1021\u002Fac902401y",{"id":23,"text":3076,"url":3077,"identifiers":3078},"Mitragotri S, Lahann J: Physical approaches to biomaterial design. Nat Mater 2009, 8: 15-23. 10.1038\u002Fnmat2344","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnmat2344",{"mag":3079,"pmc":3080,"openalex":3081,"pm":3082,"doi":3083},"2070166910","2793340","W2070166910","19096389","10.1038\u002Fnmat2344",{"id":3085,"text":3086,"url":3087,"identifiers":3088},"8f9bc7b1-6ee9-4885-9fdf-eed7783d4c7e","Adler AF, Leong KW: Emerging links between surface nanotechnology and endocytosis: Impact on nonviral gene delivery. Nano Today 2010, 5: 553-569. 10.1016\u002Fj.nantod.2010.10.007","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS174801321000143X",{"doi":3089},"10.1016\u002Fj.nantod.2010.10.007",{"id":2921,"text":3091,"url":2923,"identifiers":3092},"Wilke M, Fortunati E, vandenBroek M, Hoogeveen AT, Scholte BJ: Efficacy of a peptide-based gene delivery system depends on mitotic activity. Gene Therapy 1996, 3: 1133-1142.",{"doi":2925},{"id":3094,"text":3095,"url":3096,"identifiers":3097},"6e93a3f6-3d1f-4687-99a2-d6d8fec8b5f0","Schweikl H, Muller R, Englert C, Hiller KA, Kujat R, Nerlich M, Schmalz G: Proliferation of osteoblasts and fibroblasts on model surfaces of varying roughness and surface chemistry. Journal of Materials Science-Materials in Medicine 2007, 18: 1895-1905. 10.1007\u002Fs10856-007-3092-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10856-007-3092-8",{"doi":3098},"10.1007\u002Fs10856-007-3092-8",{"id":23,"text":3100,"url":3101,"identifiers":3102},"Li S, Huang L: Nonviral gene therapy: promises and challenges. Gene Therapy 2000, 7: 31-34. 10.1038\u002Fsj.gt.3301110","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsj.gt.3301110",{"mag":3103,"openalex":3104,"pm":3105,"doi":3106},"2027022352","W2027022352","10680013","10.1038\u002Fsj.gt.3301110",{"id":23,"text":3108,"url":23,"identifiers":3109},"Saravia V, Toca-Herrera JL: Substrate Influence on Cell Shape and Cell Mechanics: HepG2 Cells Spread on Positively Charged Surfaces. Microsc Res Tech 2009, 72: 957-964. 10.1002\u002Fjemt.20742",{"doi":3110},"10.1002\u002Fjemt.20742",{"id":2921,"text":3112,"url":2923,"identifiers":3113},"Plautz SA, Boanca G, Riethoven J-JM, Pannier AK: Microarray analysis of gene expression profiles in cells transfected with nonviral vectors. Molecular therapy : the journal of the American Society of Gene Therapy 2011, 19: 2144-2151.",{"doi":2925},{"id":2921,"text":3115,"url":2923,"identifiers":3116},"Segal G, Lee W, Arora PD, McKee M, Downey G, McCulloch CAG: Involvement of actin filaments and integrins in the binding step in collagen phagocytosis by human fibroblasts. Journal of Cell Science 2001, 114: 119-129.",{"doi":2925},{"id":3118,"text":3119,"url":3120,"identifiers":3121},"7331a5c9-69d2-49a7-bc66-7a998fc1136f","Pompe T, Renner L, Werner C: Nanoscale features of fibronectin fibrillogenesis depend on protein-substrate interaction and cytoskeleton structure. Biophys J 2005, 88: 527-534. 10.1529\u002Fbiophysj.104.048074","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0006349505731271",{"doi":3122},"10.1529\u002Fbiophysj.104.048074",{"id":3124,"text":3125,"url":3126,"identifiers":3127},"e519cfa6-04d9-4499-8fae-a43f237481f9","Keselowsky BG, Collard DM, Garcia AJ: Surface chemistry modulates focal adhesion composition and signaling through changes in integrin binding. Biomaterials 2004, 25: 5947-5954. 10.1016\u002Fj.biomaterials.2004.01.062","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0142961204001176",{"doi":3128},"10.1016\u002Fj.biomaterials.2004.01.062",{"id":23,"text":3130,"url":3131,"identifiers":3132},"Michael KE, Dumbauld DW, Burns KL, Hanks SK, Garcia AJ: Focal Adhesion Kinase Modulates Cell Adhesion Strengthening via Integrin Activation. Molecular Biology of the Cell 2009, 20: 2508-2519. 10.1091\u002Fmbc.E08-01-0076","https:\u002F\u002Fdoi.org\u002F10.1091\u002Fmbc.e08-01-0076",{"mag":3133,"pmc":3134,"openalex":3135,"pm":3136,"doi":3137},"2114270638","2675629","W2114270638","19297531","10.1091\u002Fmbc.e08-01-0076",{"id":23,"text":3139,"url":3140,"identifiers":3141},"Geiger RC, Taylor W, Glucksberg MR, Dean DA: Cyclic stretch-induced reorganization of the cytoskeleton and its role in enhanced gene transfer. Gene Therapy 2006, 13: 725-731. 10.1038\u002Fsj.gt.3302693","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsj.gt.3302693",{"mag":3142,"pmc":3143,"openalex":3144,"pm":3145,"doi":3146},"2074939226","4150916","W2074939226","16437132","10.1038\u002Fsj.gt.3302693",{"id":23,"text":3148,"url":3149,"identifiers":3150},"Dumbauld DW, Michael KE, Hanks SK, Garcia AJ: Focal adhesion kinase-dependent regulation of adhesive forces involves vinculin recruitment to focal adhesions. Biology of the Cell 2010, 102: 203-213. 10.1042\u002FBC20090104","https:\u002F\u002Fdoi.org\u002F10.1042\u002Fbc20090104",{"mag":3151,"pmc":3152,"openalex":3153,"pm":3154,"doi":3155},"2021160320","4915345","W2021160320","19883375","10.1042\u002Fbc20090104",{"id":23,"text":3157,"url":3158,"identifiers":3159},"Bengali Z, Pannier AK, Segura T, Anderson BC, Jang JH, Mustoe TA, Shea LD: Gene delivery through cell culture substrate adsorbed DNA complexes. Biotechnol Bioeng 2005, 90: 290-302. 10.1002\u002Fbit.20393","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fbit.20393",{"mag":3160,"pmc":3161,"openalex":3162,"pm":3163,"doi":3164},"2114567767","2648408","W2114567767","15800863","10.1002\u002Fbit.20393",{"id":3166,"createTime":3167,"updateTime":3168,"relativeEntities":3169,"slug":3170,"properties":3171,"entityType":147,"verifyStatus":148,"verifyTime":3181,"verifyNote":150,"languages":23,"translateLanguages":23,"viewCount":106,"primaryUrl":3182,"fullTextUrl":23,"authors":3183,"publicationType":231,"publisherRelationship":3199,"citationCount":23,"citationInfo":23,"publishDate":3259,"publishYear":3260,"citationAnalyzeStatus":2140,"lastCitationAnalyze":3261,"indexDatabases":3262,"openAccess":23,"references":23,"isForceReanalyzing":1253},"4bbe5ff3-1c3d-4e0e-80a3-348f04a07ece","2024-02-08T04:45:46.000+00:00","2025-12-08T08:21:58.845+00:00",[],"Authentic-teaching-and-learning-through-synthetic-biology",{"abstract":3172,"title":3174,"gsPaper":3176,"references":3177,"doi":3179},{"EN":3173},"Synthetic biology is an emerging engineering discipline that, if successful, will allow well-characterized biological components to be predictably and reliably built into robust organisms that achieve specific functions. Fledgling efforts to design and implement a synthetic biology curriculum for undergraduate students have shown that the co-development of this emerging discipline and its future practitioners does not undermine learning. Rather it can serve as the lynchpin of a synthetic biology curriculum. Here I describe educational goals uniquely served by synthetic biology teaching, detail ongoing curricula development efforts at MIT, and specify particular aspects of the emerging field that must develop rapidly in order to best train the next generation of synthetic biologists.",{"EN":3175},"Authentic teaching and learning through synthetic biology",{"VOID":1977},{"VOID":3178},"The Fantasticks Book by Tom Jones. Lyrics by Tom Jones. Music by Harvey Schmidt. Based on Les Romanesques by Edmond Rostand. Opened 5\u002F3\u002F1960.\nCommittee on the Engineer of 2020, Phase II, Committee on Engineering Education, National Academy of Engineering In Educating the Engineer of 2020: Adapting Engineering Education to the New Century. The National Academies Press; 2005.\nCommittee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century, National Research Council In BIO2010: Transforming Undergraduate Education for Future Research Biologists. The National Academies Press; 2003.\nSmith SM: Reforming undergrad biology curriculum. Science 2002, 298: 747. 10.1126\u002Fscience.298.5594.747b\nGross LJ: Interdisciplinarity and the undergraduate biology curriculum: finding a balance. Cell Biol Educ 2004, 3: 85-87. 10.1187\u002Fcbe.04-03-0040\nCech TR, Rubin GM: Nurturing interdisciplinary research. Nature Structural & Molecular Biology 2004, 11: 1166-1169. 10.1038\u002Fnsmb1204-1166\nAres M Jr: Interdisciplinary research and the undergraduate biology student. Nature Structural & Molecular Biology 2004, 11: 1170-1172. 10.1038\u002Fnsmb1204-1170\nSB2.0 Declaration[http:\u002F\u002Fdspace.mit.edu\u002Fhandle\u002F1721.1\u002F32982]\nRai A, Boyle J: Synthetic Biology: Caught between Property Rights, the Public Domain, and the Commons. PLoS Biol 2007,5(3):e58. 10.1371\u002Fjournal.pbio.0050058\nHenkel J, Maurer SM: The economics of synthetic biology. Molecular Systems Biology 2007, 3: 117. 10.1038\u002Fmsb4100161\nCommission on Behavioral and Social Sciences and Education and the Committee on Learning Research and Educational Practice, National Research Council In How People Learn: Brain, Mind, Experience, and School: Expanded Edition. The National Academies Press; 2000.\nGardner HowardE: Intelligence Reframed: Multiple Intelligences for the 21st Century. Basic Books; 2000.\nIlulissat Statement2007. 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Cell Biol Educ 2005, 4: 19-23. 10.1187\u002Fcbe.04-11-0047\nSB1.0[http:\u002F\u002Fsyntheticbiology.org\u002FSynthetic_Biology_1.0.html]\nSB2.0[http:\u002F\u002Fpbd.lbl.gov\u002Fsbconf\u002F]\nSB3.0[http:\u002F\u002Fwww.syntheticbiology3.ethz.ch\u002F]\nGardner TS, Cantor CR, Collins JJ: Construction of a genetic toggle switch in Escherichia coli. Nature 2000, 403: 339-342. 10.1038\u002F35002131\nElowitz MB, Leibler S: A synthetic oscillatory network of transcriptional regulators. Nature 2000, 403: 335-338. 10.1038\u002F35002125\nMalcolm Campbell A, Heyer LaurieJ: Discovering Genomics, Proteomics and Bioinformatics Benjamin Cummings. 2007.\nCOMIC 1. Adventures in Synthetic Biology. Story by Drew Endy, Isadora Deese & The MIT Synthetic Biology Working Group Art by Chuck Wadey Nature 2005, 438: 449-453. [http:\u002F\u002Fwww.nature.com\u002Fnature\u002Fcomics\u002Fsyntheticbiologycomic\u002Findex.html] 10.1038\u002Fnature04342\nBioBuilder animations in progress. Story by Rebecca Adams, Isadora Deese, Drew Endy, Natalie Kuldell. Art and animations by Animated Storyboards[http:\u002F\u002Fwww.biobuilder.org]\niGEM 2006[http:\u002F\u002Fwww.igem2006.com\u002Findex.htm]\niGEM 2007[http:\u002F\u002Fparts.mit.edu\u002Figem07\u002Findex.php\u002FMain_Page]\nTeaching resources for iGEM[http:\u002F\u002Fparts.mit.edu\u002Figem07\u002Findex.php\u002FPodcasts]\nEau d'coli team wiki , in press.http:\u002F\u002Fopenwetware.org\u002Fwiki\u002FIGEM:MIT\u002F2006\u002FBlurb\nLevskaya A, Chevalier AA, Tabor JJ, Simpson ZB, Lavery LA, Levy M, Davidson EA, Scouras A, Ellington AD, Marcotte EM, Voigt CA: Synthetic biology: engineering Escherichia coli to see light. Nature 2005, 438: 441-442. 10.1038\u002Fnature04405\nIBE General Meeting 2006[http:\u002F\u002Fwww.ibe.org\u002Fmeetings\u002F2007\u002Findex.cgi]\nBrown University class in Synthetic Biological Systems[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002FBrown_Synthetic_Biology]\nDavidson College class called Reengineering Life: Synthetic Biology Seminar[http:\u002F\u002Fwww.bio.davidson.edu\u002Fcourses\u002Fsynthetic\u002Fsynthetic_seminar.html#schedule]\nHarvard Medical School nanocourse in Synthetic Biology: Cellular and Molecular Engineering[http:\u002F\u002Fidb.med.harvard.edu\u002Fcontent\u002Fview\u002F310\u002F27\u002F]\nMIT lab class 20.109(F07) Laboratory Fundamentals of Biological Engineering[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.109(F07)]\nUC Berkeley class in Implications and Applications of Synthetic Biology[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002FSynbio_2007]\n20.109(F07): Module 1\u002FGenome Engineering[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.109(F07):Module_1]\nChan LY, Kosuri S, Endy D: Refactoring bacteriophage T7. Mol Syst Biol 2005, 1: 2005.0018. 10.1038\u002Fmsb4100025\nNam KT, Kim DW, Yoo PJ, Chiang CY, Meethong N, Hammond PT, Chiang YM, Belcher AM: Virus-enabled synthesis and assembly of nanowires for lithium ion battery electrodes. Science 2006, 312: 885-888. 10.1126\u002Fscience.1122716\n20.109(F07): phage nanowire deposition to ITO patterned slide[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.109%28F07%29:_Phage_by_design%2C_pt2]\nPosfai G, Plunkett G 3rd, Feher T, Frisch D, Keil GM, Umenhoffer K, Kolisnychenko V, Stahl B, Sharma SS, de Arruda M, Burland V, Harcum SW, Blattner FR: Emergent properties of reduced-genome Escherichia coli. Science 2006, 312: 1044-1046. 10.1126\u002Fscience.1126439\n20.109(F07): Module 3\u002FBiomaterials Engineering[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.109_%28F07%29:_Phage_by_design]\nBE.109: Systems engineering[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002FBE.109:Systems_engineering]\nPetty NK, Evans TJ, Fineran PC, Salmond GP: Biotechnological exploitation of bacteriophage research. Trends Biotechnol 2007,25(1):7-15. 10.1016\u002Fj.tibtech.2006.11.003\n20.109(F07): Genome engineering assessment[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.109%28F07%29:_Genome_engineering_assessment]\n20.20: Introduction to biological engineering design[http:\u002F\u002Fopenwetware.org\u002Fwiki\u002F20.20]",{"VOID":3180},"10.1186\u002F1754-1611-1-8","2024-05-07T10:52:59.270+00:00","https:\u002F\u002Fjbioleng.biomedcentral.com\u002Farticles\u002F10.1186\u002F1754-1611-1-8",[3184],{"id":3185,"sortIndex":106,"researcher":23,"roles":3186,"affiliations":3187,"properties":3196,"displayName":3198,"givenName":23,"familyName":23},"110f142f-dd58-4637-90da-320e09af34ab",[1988],[3188],{"id":3189,"sortIndex":106,"affiliation":3190,"properties":23},"bdcd9fa2-e8bc-4d1b-ae5c-835868453849",{"id":3189,"createTime":23,"updateTime":23,"relativeEntities":3191,"slug":23,"properties":3192,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":3195,"statistic":23},[],{"title":3193},{"VI":3194},"MIT, Department of Biological Engineering, Cambridge, USA",[],{"title":3197},{"VI":3198},"Natalie Kuldell",{"url":3182,"publisher":3200,"properties":3255},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":3201,"slug":10,"properties":3202,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":3207,"manageAffiliations":3224,"indexDatabases":3235,"url":104,"thumbnailPath":23,"statistic":3250,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":3203,"eissn":3204,"issn":3205,"title":3206},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[3208,3212,3216,3220],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":3209,"label":3210,"description":3211,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":3213,"label":3214,"description":3215,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},{"id":39,"createTime":23,"updateTime":23,"relativeEntities":3217,"label":3218,"description":3219,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":42},{},{"id":45,"createTime":23,"updateTime":23,"relativeEntities":3221,"label":3222,"description":3223,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":48},{},[3225,3230],{"id":52,"createTime":23,"updateTime":23,"relativeEntities":3226,"slug":23,"properties":3227,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":3229,"statistic":23},[],{"title":3228},{"EN":56},[],{"id":59,"createTime":23,"updateTime":23,"relativeEntities":3231,"slug":23,"properties":3232,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":3234,"statistic":23},[],{"title":3233},{"EN":63},[],[3236,3243],{"id":67,"indexDatabase":3237,"url":80,"indexYears":23,"academicFieldIds":3242,"indexDatabaseRanking":23},{"id":69,"createTime":23,"updateTime":23,"relativeEntities":3238,"label":3239,"description":3240,"key":76,"publicationTags":3241,"standard":23},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82,83],{"id":85,"indexDatabase":3244,"url":96,"indexYears":97,"academicFieldIds":3249,"indexDatabaseRanking":103},{"id":87,"createTime":23,"updateTime":23,"relativeEntities":3245,"label":3246,"description":3247,"key":93,"publicationTags":3248,"standard":23},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100,101,102],{"impactFactor":106,"impactFactorByYear":3251,"i10Index":106,"i10IndexLast5Year":106,"totalPublication":108,"totalPublicationByYear":3252,"totalCitation":106,"totalCitationByYear":3253,"totalCitationPerPublication":106,"totalCitationPerPublicationByYear":3254,"hindexLast5Year":106,"hindex":106},{},{"2007":24,"2008":110,"2009":111,"2010":112,"2011":113,"2012":114,"2013":115,"2014":116,"2015":117,"2016":118,"2017":119,"2018":118,"2019":120,"2020":114,"2021":113,"2022":115,"2023":121,"2024":122},{},{},{"pages":3256,"volume":3258},{"VOID":3257},"1-6",{"VOID":1428},"2007-12-27",2007,"2025-12-08T08:21:58.844+00:00",[103,78],{"id":3264,"createTime":3265,"updateTime":3266,"relativeEntities":3267,"slug":3268,"properties":3269,"entityType":147,"verifyStatus":148,"verifyTime":3282,"verifyNote":150,"languages":23,"translateLanguages":23,"viewCount":106,"primaryUrl":3283,"fullTextUrl":23,"authors":3284,"publicationType":231,"publisherRelationship":3339,"citationCount":3395,"citationInfo":3396,"publishDate":2633,"publishYear":2497,"citationAnalyzeStatus":22,"lastCitationAnalyze":3266,"indexDatabases":3399,"openAccess":23,"references":23,"isForceReanalyzing":1253},"ece10c2f-d113-4b08-8073-1862e9f9ed2a","2024-04-06T14:18:22.516+00:00","2025-11-20T05:45:10.277+00:00",[],"Research-progress-on-black-phosphorus-hybrids-hydrogel-platforms-for-biomedical-applications",{"abstract":3270,"title":3272,"gsPaper":3274,"keywords":3276,"references":3278,"doi":3280},{"EN":3271},"Hydrogels, also known as three-dimensional, flexible, and polymer networks, are composed of natural and\u002For synthetic polymers with exceptional properties such as hydrophilicity, biocompatibility, biofunctionality, and elasticity. Researchers in biomedicine, biosensing, pharmaceuticals, energy and environment, agriculture, and cosmetics are interested in hydrogels. Hydrogels have limited adaptability for complicated biological information transfer in biomedical applications due to their lack of electrical conductivity and low mechanical strength, despite significant advances in the development and use of hydrogels. The nano-filler-hydrogel hybrid system based on supramolecular interaction between host and guest has emerged as one of the potential solutions to the aforementioned issues. Black phosphorus, as one of the representatives of novel two-dimensional materials, has gained a great deal of interest in recent years owing to its exceptional physical and chemical properties, among other nanoscale fillers. However, a few numbers of publications have elaborated on the scientific development of black phosphorus hybrid hydrogels extensively. In this review, this review thus summarized the benefits of black phosphorus hybrid hydrogels and highlighted the most recent biological uses of black phosphorus hybrid hydrogels. Finally, the difficulties and future possibilities of the development of black phosphorus hybrid hydrogels are reviewed in an effort to serve as a guide for the application and manufacture of black phosphorus -based hydrogels. Recent applications of black phosphorus hybrid hydrogels in biomedicine.\n                  \n                    \n                  \n                ",{"EN":3273},"Research progress on black phosphorus hybrids hydrogel platforms for biomedical applications",{"VOID":3275},"[\"4121009767431042576\"]",{"EN":3277},"",{"VOID":3279},"Wang J, Zhu M, Hu Y, Chen R, Hao Z, Wang Y, et al. Exosome-hydrogel system in bone tissue engineering: a promising therapeutic strategy. Macromol Biosci. 2022:e2200496-e.\nXing Y, Zeng B, Yang W. Light responsive hydrogels for controlled drug delivery. 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