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J Genet Couns. 2014;23(4):594–603.",{"doi":373},"10.1007\u002Fs10897-013-9683-2",{"id":18,"text":375,"url":18,"identifiers":376},"Jamal SM, Joon‐Ho Y, Jessica XC, Karin MD, Jessie HC, Holly KT, et al. Practices and policies of clinical exome sequencing providers: analysis and implications. Am J Med Genet Part A. 2013;161(6):935–50.",{"doi":377},"10.1002\u002Fajmg.a.35942",{"id":18,"text":379,"url":18,"identifiers":380},"Henderson GE, Wolf SM, Kuczynski K, Joffe S, Sharp RR, Parsons DW, et al. The challenge of informed consent and return of results in translational genomics: empirical analysis and recommendations. J Law Med Ethics. 2014;42(3):344–55.",{"doi":381},"10.1111\u002Fjlme.12151",{"id":18,"text":383,"url":18,"identifiers":384},"Prince AER, Conley J, Davis A, Lazaro-Muñoz G, Cadigan RJ. Automatic placement of genomic research results in medical records: Do researchers have a duty? Should participants have a choice? J Law Med Ethics. 2015 (in press).",{"doi":385},"10.1111\u002Fjlme.12323",{"id":18,"text":387,"url":18,"identifiers":388},"Hazin R, Brothers KB, Malin BA, Koenig BA, Sanderson SC, Rothstein MA, et al. Ethical, legal, and social implications of incorporating genomic information into electronic health records. Genet Med. 2013;15(10):810–6.",{"doi":389},"10.1038\u002Fgim.2013.117",{"id":18,"text":391,"url":18,"identifiers":392},"Facio FM, Sapp JC, Linn A, Biesecker LG. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research. BMC Genom. 2011;5:45.",{"doi":393},"10.1186\u002F1755-8794-5-45",{"id":18,"text":395,"url":18,"identifiers":396},"Burke W, Evans BJ, Jarvik GP. Return of results: ethical and legal distinctions between research and clinical care. Am J Med Genet Part C. 2014;166C(1):105–11.",{"doi":397},"10.1002\u002Fajmg.c.31393",{"id":18,"text":399,"url":18,"identifiers":400},"Green RC, Berg JS, Berry GT, Biesecker LG, Dimmock DP, Evans JP, et al. Exploring concordance and discordance for return of incidental findings from clinical sequencing. Genet Med. 2012;14(4):405–10.",{"doi":401},"10.1038\u002Fgim.2012.21",{"id":18,"text":403,"url":18,"identifiers":404},"Lemke AA, Bick D, Dimmock D, Simpson P, Veith R. Perspectives of clinical genetics professionals toward genome sequencing and incidental findings: a survey study. Clin Genet. 2013;84(3):230–6.",{"doi":405},"10.1111\u002Fcge.12060",{"id":18,"text":407,"url":18,"identifiers":408},"Yu J-HH, Harrell TM, Jamal SM, Tabor HK, Bamshad MJ. Attitudes of genetics professionals toward the return of incidental results from exome and whole-genome sequencing. Am J Hum Genet. 2014;95(1):77–84.",{"doi":409},"10.1016\u002Fj.ajhg.2014.06.004",{"id":18,"text":411,"url":18,"identifiers":412},"Reiff M, Mueller R, Mulchandani S, Spinner NB, Pyeritz RE, Bernhardt BA. A qualitative study of healthcare providers’ perspectives on the implications of genome-wide testing in pediatric clinical practice. J Genet Couns. 2014;23(4):474–88.",{"doi":413},"10.1007\u002Fs10897-013-9653-8",{"id":18,"text":415,"url":18,"identifiers":416},"Townsend A, Adam S, Birch PH, Lohn Z, Rousseau F, Friedman JM. “I want to know what’s in Pandora’s Box”: comparing stakeholder perspectives on incidental findings in clinical whole genomic sequencing. Am J Med Genet Part A. 2012;158A(10):2519–25.",{"doi":417},"10.1002\u002Fajmg.a.35554",{"id":18,"text":419,"url":18,"identifiers":420},"Brandt DS, Shinkunas L, Hillis SL, Daack-Hirsch SE, Driessnack M, Downing NR, et al. A closer look at the recommended criteria for disclosing genetic results: perspectives of medical genetic specialists, genomic researchers, and institutional review board chairs. J Genet Couns. 2013;22(4):544–53.",{"doi":421},"10.1007\u002Fs10897-013-9583-5",{"id":18,"text":423,"url":18,"identifiers":424},"Berg JS, Adams M, Nassar N, Bizon C, Lee K, Schmitt CP, et al. An informatics approach to analyzing the incidentalome. Genet Med. 2012;15(1):36–44.",{"doi":425},"10.1038\u002Fgim.2012.112",{"id":18,"text":427,"url":18,"identifiers":428},"• Biesecker LG. Opportunities and challenges for the integration of massively parallel genomic sequencing into clinical practice: lessons from the ClinSeq project. Gen Med. 2012;14(4):393–8. Discussion of early clinical experiences with NGS.",{"doi":429},"10.1038\u002Fgim.2011.78",{"id":18,"text":431,"url":18,"identifiers":432},"Berg JS, Amendola LM, Eng C, Van Allen E, Gray SW, Wagle N, et al. Processes and preliminary outputs for identification of actionable genes as incidental findings in genomic sequence data in the Clinical Sequencing Exploratory Research Consortium. Genet Med. 2013;15(11):860–7.",{"doi":433},"10.1038\u002Fgim.2013.133",{"id":18,"text":435,"url":18,"identifiers":436},"McLaughlin HM, Ceyhan-Birsoy O, Christensen KD, Kohane IS, Krier J, Lane WJ, et al. A systematic approach to the reporting of medically relevant findings from whole genome sequencing. BMC Med Genet. 2013;15:134.",{"doi":437},"10.1186\u002Fs12881-014-0134-1",{"id":18,"text":439,"url":18,"identifiers":440},"• Christenhusz GM, Devriendt K, Dierickx K. Disclosing incidental findings in genetics contexts: a review of the empirical ethical research. Eur J Med Genet. 2013;56(10):529–40. Review of published studies on secondary findings and proposes a decision-making schematic for use in the disclosure.",{"doi":441},"10.1016\u002Fj.ejmg.2013.08.006",{"id":18,"text":443,"url":18,"identifiers":444},"Anderson JA, Hayeems RZ, Shuman C, Szego MJ, Monfared N, Bowdin S, et al. Predictive genetic testing for adult-onset disorders in minors: a critical analysis of the arguments for and against the 2013 ACMG guidelines. Clin Genet. 2015;87(4):301–10.",{"doi":445},"10.1111\u002Fcge.12460",{"id":18,"text":447,"url":18,"identifiers":448},"•• American Academy of Pediatrics Committee On Bioethics, Committee On Genetics and the American College Of Medical Genetics and Genomic Social, Ethical, And Legal Issues Committee. Policy statement: ethical and policy issues in genetic testing and screening of children. Pediatrics. 2013;131(3):620–2. This policy statement was developed collaboratively by the AAP and the ACMG to make recommendation about genetic testing and screening in children and published prior to the ACMG recommendations on reporting of incidental findings.",{"doi":449},"10.1542\u002Fpeds.2012-3680",{"id":18,"text":451,"url":18,"identifiers":452},"Christenhusz GM, Devriendt K, Van Esch H, Dierickx K. Ethical signposts for clinical geneticists in secondary variant and incidental finding disclosure discussions. Med Health Care Philos. 2014. doi: 10.1007\u002Fs11019-014-9611-8 .",{},{"id":18,"text":454,"url":18,"identifiers":455},"Daack-Hirsch S, Driessnack M, Hanish A, Johnson VA, Shah LL, Simon CM, et al. ‘Information is information’: a public perspective on incidental findings in clinical and research genome-based testing. Clin Genet. 2013;84(1):11–8.",{"doi":456},"10.1111\u002Fcge.12167",{"id":18,"text":458,"url":18,"identifiers":459},"•• Christenhusz GM, Devriendt K, Van Esch H, Dierickx K. Focus group discussions on secondary variants and next-generation sequencing technologies. Eur J Med Genet. 2015;58(4):249–57. Summarizes data from 8 diverse focus groups about communication of results available after diagnostic genomic sequencing of children.",{"doi":460},"10.1016\u002Fj.ejmg.2015.01.007",{"id":18,"text":462,"url":18,"identifiers":463},"Green RC, Roberts JS, Cupples LA, Relkin NR, Whitehouse PJ, Brown T, et al. Disclosure of APOE genotype for risk of Alzheimer’s disease. NEJM. 2009;361(3):245–54.",{"doi":464},"10.1056\u002FNEJMoa0809578",{"id":18,"text":466,"url":18,"identifiers":467},"Roche MI. Moving toward NextGenetic counseling. Genet Med. 2012;14(9):777–8.",{"doi":468},"10.1038\u002Fgim.2012.84",{"id":18,"text":470,"url":18,"identifiers":471},"Janssens C. The hidden harm behind the return of results from personal genome services: a need for rigorous and responsible evaluation. Genet Med. 2014. doi: 10.1038\u002Fgim.2014.169 .",{},{"id":18,"text":473,"url":18,"identifiers":474},"•• Grubs R, Parker, L and Hamilton, R. Subtle. Psychosocial sequelae of genetic test results. Curr Genet Med Rep. 2014. A review of subtle psychosocial sequelae following result disclosure that is difficult to assess using existing quantitative measures but has relevance for decision-making about predictive genetic testing.",{"doi":475},"10.1007\u002Fs40142-014-0053-7",{"id":18,"text":477,"url":18,"identifiers":478},"Townsend A, Adam S, Birch PH, Friedman JM. Paternalism and the ACMG recommendations on genomic incidental findings: patients seen but not heard. Genet Med. 2013;15(9):751–2.",{"doi":479},"10.1038\u002Fgim.2013.105",{"id":18,"text":481,"url":18,"identifiers":482},"Facio FM, Eidem H, Fisher T, Brooks S, Linn A, Kaphingst KA, et al. Intentions to receive individual results from whole-genome sequencing among participants in the ClinSeq study. Eur J Hum Genet. 2013;21(3):261–5.",{"doi":483},"10.1038\u002Fejhg.2012.179",{"id":18,"text":485,"url":18,"identifiers":486},"Sapp JC, Dong D, Stark C, Ivey LE, Hooker G, Biesecker LG, et al. Parental attitudes, values, and beliefs toward the return of results from exome sequencing in children. Clin Genet. 2014;85(2):120–6.",{"doi":487},"10.1111\u002Fcge.12254",{"id":18,"text":489,"url":18,"identifiers":490},"Clift K, Halverson C, Friskdal A, Kumbamu A, Sharp RR, McCormick J. Patients’ views on incidental findings from clinical exome sequencing. Appl Transl Genom. 2015;4:38–43.",{"doi":491},"10.1016\u002Fj.atg.2015.02.005",{"id":18,"text":493,"url":18,"identifiers":494},"Prince AE, Roche MI. Genetic information, non-discrimination, and privacy protections in genetic counseling practice. J Genet Couns. 2014;23(6):891–902.",{"doi":495},"10.1007\u002Fs10897-014-9743-2",{"id":18,"text":497,"url":18,"identifiers":498},"Burke W, Trinidad SB, Clayton EW. Seeking genomic knowledge: the case for clinical restraint. Hastings Law J. 2013;64(6):1650–64.",{},{"id":18,"text":500,"url":18,"identifiers":501},"Sharp RR. Downsizing genomic medicine: approaching the ethical complexity of whole-genome sequencing by starting small. Genet Med. 2011;13(3):191–4.",{"doi":502},"10.1097\u002FGIM.0b013e31820f603f",{"id":18,"text":504,"url":18,"identifiers":505},"• Shahmirzadi L, Chao EC, Palmaer E, Parra MC, Tang S, Gonzalez KD. Patient decisions for disclosure of secondary findings among the first 200 individuals undergoing clinical diagnostic exome sequencing. Genet Med. 2014;16(5):395–9. Experience of a commercial laboratory in assessing preferences for secondary findings results for families undergoing diagnostic exome sequencing.",{"doi":506},"10.1038\u002Fgim.2013.153",{"id":18,"text":508,"url":18,"identifiers":509},"Bergner AL, Bollinger J, Raraigh KS, Tichnell C, Murray B, Blout CL, et al. Informed consent for exome sequencing research in families with genetic disease: the emerging issue of incidental findings. Am J Med Genet Part A. 2014;164A(11):2745–52.",{"doi":510},"10.1002\u002Fajmg.a.36706",{"id":18,"text":512,"url":18,"identifiers":513},"Goddard KA, Whitlock EP, Berg JS, Williams MS, Webber EM, Webster JA, et al. Description and pilot results from a novel method for evaluating return of incidental findings from next-generation sequencing technologies. Genet Med. 2013;15(9):721–8.",{"doi":514},"10.1038\u002Fgim.2013.37",{"id":18,"text":516,"url":18,"identifiers":517},"• Regier DA, Peacock SJ, Pataky R, van der Hoek K, Jarvik GP, Hoch J, et al. Societal preferences for the return of incidental findings from clinical genomic sequencing: a discrete-choice experiment. CMAJ. 2015;187(6):E190–97. An attempt to estimate personal utility for information from secondary genomic findings by a discrete choice method.",{"doi":518},"10.1503\u002Fcmaj.140697",{"id":18,"text":520,"url":18,"identifiers":521},"Bennette CS, Trinidad SB, Fullerton SM, Patrick D, Amendola L, Burke W, et al. Return of incidental findings in genomic medicine: measuring what patients value–development of an instrument to measure preferences for information from next-generation testing (IMPRINT). Genet Med. 2013;15(11):873–81.",{"doi":522},"10.1038\u002Fgim.2013.63",{"id":18,"text":524,"url":18,"identifiers":525},"Tabor HK, Berkman BE, Hull SC, Bamshad MJ. Genomics really gets personal: how exome and whole genome sequencing challenge the ethical framework of human genetics research. Am J Med Genet Part A. 2011;155A(12):2916–24.",{"doi":526},"10.1002\u002Fajmg.a.34357",{"id":18,"text":528,"url":18,"identifiers":529},"Heshka JT, Palleschi C, Howley H, Wilson B, Wells PS. A systematic review of perceived risks, psychological and behavioral impacts of genetic testing. Genet Med. 2007;10(1):19–32.",{"doi":530},"10.1097\u002FGIM.0b013e31815f524f",{"id":18,"text":532,"url":18,"identifiers":533},"Stacey D, Legare F, Col N, Bennett C, Barry M, Eden K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2014. doi: 10.1002\u002F14651858.CD001431 .",{},{"id":18,"text":535,"url":18,"identifiers":536},"Birch PH. Interactive e-counselling for genetics pre-test decisions: where are we now? Clin Genet. 2015;87(3):209–17.",{"doi":537},"10.1111\u002Fcge.12430",{"id":18,"text":539,"url":18,"identifiers":540},"Kaphingst KA, Facio FM, Cheng MRR, Brooks S, Eidem H, Linn A, et al. Effects of informed consent for individual genome sequencing on relevant knowledge. Clin Genet. 2012;82(5):408–15.",{"doi":541},"10.1111\u002Fj.1399-0004.2012.01909.x",{"id":18,"text":543,"url":18,"identifiers":544},"Waisbren S, Back D, Liu C, Kalia S, Ringer S, Holm IA, et al. Parents are interested in newborn genomic testing during the early postpartum period. Genet Med. 2014. doi: 10.1038\u002Fgim.2014.139 .",{},{"id":18,"text":546,"url":18,"identifiers":547},"Bombard Y, Miller FA, Hayeems RZ, Barg C, Cressman C, Carroll JC, et al. Public views on participating in newborn screening using genome sequencing. Eur J Hum Genet. 2014;22(11):1248–54.",{"doi":548},"10.1038\u002Fejhg.2014.22",{"id":18,"text":550,"url":18,"identifiers":551},"Elwyn G, Frosch D, Volandes AE, Edwards A, Montori VM. Investing in deliberation: a definition and classification of decision support interventions for people facing difficult health decisions. Med Decis Mak. 2009;30(6):701–11.",{"doi":552},"10.1177\u002F0272989X10386231",false,{"id":555,"createTime":556,"updateTime":557,"relativeEntities":558,"slug":559,"properties":560,"entityType":120,"verifyStatus":121,"verifyTime":557,"verifyNote":122,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":569,"fullTextUrl":18,"authors":570,"publicationType":167,"publisherRelationship":599,"citationCount":18,"citationInfo":18,"publishDate":625,"publishYear":626,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":553},"9c093cb8-62a0-4f9f-9a6c-c2242782626e","2024-01-16T22:29:37.739+00:00","2025-01-12T23:44:03.751+00:00",[],"Direct-to-Consumer-Genetic-Testing-and-Personal-Genomics-Services-A-Review-of-Recent-Empirical-Studies",{"references":561,"abstract":563,"title":565,"doi":567},{"VOID":562},"Hamilton A. Best inventions of 2008. In: time. 2008. http:\u002F\u002Fwww.time.com\u002Ftime\u002Fspecials\u002Fpackages\u002Farticle\u002F0,28804,1852747_1854493_1854113,00.html. Accessed 28 Apr 2013.\nYeager A. News 2008. Nature. 2008;2008(456):844–5.\nHudson K, Javitt G, Burke W, Byers P. ASHG statement* on direct-to-consumer genetic testing in the United States. Obstet Gynecol. 2007;110:1392–5.\nAmerican College of Medical Genetics. ACMG Statement on Direct-to-Consumer Genetic Testing. 2008. http:\u002F\u002Fwww.acmg.net\u002F\u002FAM\u002FTemplate.cfm?Section=Terms_and_Conditions&termsreturnurl=Section=Policy_Statements&Template=\u002FCM\u002FContentDisplay.cfm&ContentID=2975. Accessed 28 Apr 2013.\n23andMe. 2013. https:\u002F\u002Fwww.23andme.com\u002F. Accessed 28 Apr 2013.\nAllison M. Direct-to-consumer genomics reinvents itself. Nat Biotechnol. 2012;30:1027–9.\nUnited States Government Accountability Office. Misleading test results are further complicated by deceptive marketing and other questionable practices. http:\u002F\u002Fwww.gao.gov\u002Fproducts\u002FGAO-10-847T. Accessed 28 Apr 2013.\nBorry P, Van Hellemondt RE, Sprumont D, Jales CFD, Rial-Sebbag E, Spranger TM, Curren L, Kaye J, Nys H, Howard H. Legislation on direct-to-consumer genetic testing in seven European countries. Eur J Hum Genet. 2012;20:715–21.\nNational Society of Genetic Counselors. Position Statements. http:\u002F\u002Fwww.nsgc.org\u002FMedia\u002FPositionStatements\u002Ftabid\u002F330\u002FDefault.aspx#DTC. Accessed 28 Apr 2013.\nMaves MD. Re: molecular and clinical genetics panel of the medical devices advisory committee; notice of meeting [Docket No. FDA-2011-N-0066]. http:\u002F\u002Fwww.ama-assn.org\u002Fresources\u002Fdoc\u002Fwashington\u002Fconsumer-genetic-testing-letter.pdf. Accessed 28 Apr 2013.\nSingleton A, Erby LH, Foisie KV, Kaphingst KA. Informed choice in direct-to-consumer genetic testing (DTCGT) websites: a content analysis of benefits, risks, and limitations. J Genet Couns. 2012;21:433–9.\nArribas-Ayllon M, Sarangi S, Clarke A. Promissory accounts of personalisation in the commercialisation of genomic knowledge. Commun Med. 2011;8:53–66.\nHarris A, Kelly SE, Wyatt S. Counseling customers: emerging roles for genetic counselors in the direct-to-consumer genetic testing market. J Genet Couns. 2012;22(2):277–88.\nFinney Rutten LJ, Gollust SE, Naveed S, Moser RP. Increasing public awareness of direct-to-consumer genetic tests: health care access, Internet use, and population density correlates. J Cancer Epidemiol. 2012;2012:309109.\nHall TO, Renz AD, Snapinn KW, Bowen DJ, Edwards KL. Awareness and uptake of direct-to-consumer genetic testing among cancer cases, their relatives, and controls: The Northwest Cancer Genetics Network. Genet Test Mol Biomarkers. 2012;16:744–8.\nKolor K, Duquette D, Zlot A, Foland J, Anderson B, Giles R, Wrathall J, Khoury MJ. Public awareness and use of direct-to-consumer personal genomic tests from four state population-based surveys, and implications for clinical and public health practice. Genet Med. 2012;14:860–7.\nOrtiz AP, López M, Flores LT, Soto-Salgado M, Finney Rutten LJ, Serrano-Rodriguez RA, Hesse BW, Tortolero-Luna G. Awareness of direct-to-consumer genetic tests and use of genetic tests among Puerto Rican adults, 2009. Prev Chronic Dis. 2011;8:A110.\nLangford AT, Resnicow K, Roberts JS, Zikmund-Fisher BJ. Racial and ethnic differences in direct-to-consumer genetic tests awareness in HINTS 2007: sociodemographic and numeracy correlates. J Genet Couns. 2012;21:440–7.\nGollust SE, Gordon ES, Zayac C, Griffin G, Christman MF, Pyeritz RE, Wawak L, Bernhardt BA. Motivations and perceptions of early adopters of personalized genomics: perspectives from research participants. Public Health Genomics. 2012;15:22–30.\nSu Y, Howard HC, Borry P. Users’ motivations to purchase direct-to-consumer genome-wide testing: an exploratory study of personal stories. J Community Genet. 2011;2:135–46.\nGray SW, Hornik RC, Schwartz JS, Armstrong K. The impact of risk information exposure on women’s beliefs about direct-to-consumer genetic testing for BRCA mutations. Clin Genet. 2012;81:29–37.\nSweeny K, Legg AM. Predictors of interest in direct-to-consumer genetic testing. Psychol Health. 2011;26:1259–72.\nKaphingst KA, McBride CM, Wade C, Alford SH, Reid R, Larson E, Baxevanis AD, Brody LC. Patients’ understanding of and responses to multiplex genetic susceptibility test results. Genet Med. 2012;14:681–7.\nGordon ES, Griffin G, Wawak L, Pang H, Gollust SE, Bernhardt BA. “It’s not like judgment day”: public understanding of and reactions to personalized genomic risk information. J Genet Couns. 2012;21:423–32.\n• Kaufman DJ, Bollinger JM, Dvoskin RL, Scott JA. Risky business: risk perception and the use of medical services among customers of DTC personal genetic testing. J Genet Couns. 2012;21:413–422. This article reports on findings from a large online survey (n = 1048) of DTC-GT customers. Results suggest that consumers initiate various health-related behaviors in response to their personal genetic test results and that these actions are correlated with baseline perceptions of health risks.\nLeighton JW, Valverde K, Bernhardt BA. The general public’s understanding and perception of direct-to-consumer genetic test results. Public Health Genomics. 2012;15:11–21.\nJames KM, Cowl CT, Tilburt JC, Sinicrope PS, Robinson ME, Frimannsdottir KR, Tiedje K, Koenig BA. Impact of direct-to-consumer predictive genomic testing on risk perception and worry among patients receiving routine care in a preventive health clinic. Mayo Clin Proc. 2011;86:933–40.\nBansback N, Sizto S, Guh D, Anis AH. The effect of direct-to-consumer genetic tests on anticipated affect and health-seeking behaviors: a pilot survey. Genet Test Mol Biomarkers. 2012;16:1165–71.\nDar-Nimrod I, Zuckerman M, Duberstein PR. The effects of learning about one’s own genetic susceptibility to alcoholism: a randomized experiment. Genet Med. 2013;15:132–8.\n•• Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. 2011;364:524–534. This article reports on findings from a longitudinal study of over 2,000 individuals who received DTC-GT test results. Findings suggest little evidence of adverse psychological reactions to test results and no increase from baseline in health behaviors (e.g., physical activity, dietary fat intake) or screening.\nEgglestone C, Morris A, O’Brien A. Effect of direct-to-consumer genetic tests on health behaviour and anxiety: a survey of consumers and potential consumers. J Genet Couns. 2013;. doi:10.1007\u002Fs10897-013-9582-6.\nDohany L, Gustafson S, Ducaine W, Zakalik D. Psychological distress with direct-to-consumer genetic testing: a case report of an unexpected BRCA positive test result. J Genet Couns. 2012;21:399–401.\n• Francke U, Dijamco C, Kiefer AK, Eriksson N, Moiseff B, Tung JY, Mountain JL. Dealing with the unexpected: Consumer responses to direct-access BRCA mutation testing. PeerJ. 2013;1:e8. This report from 23andMe investigators details the experiences of consumers who learned from DTC-GT that they were positive for a BRCA mutation. Findings suggest that these unexpected results led in certain cases to risk reduction surgeries and identification of other high-risk family members.\nBloss CS, Wineinger NE, Darst BF, Schork NJ, Topol EJ. Impact of direct-to-consumer genomic testing at long term follow-up. J Med Genet. 2013;50:393–400.\n• Reid RJ, McBride CM, Alford SH, Price C, Baxevanis AD, Brody LC, Larson EB. Association between health-service use and multiplex genetic testing. Genet Med. 2012;14:852–859. This study of Multiplex Initiative participants suggests that receipt of multiplex genetic test results does not result in a significant increase from baseline in terms of participants’ health service utilization.\nPowell KP, Christianson CA, Cogswell WA, Dave G, Verma A, Eubanks S, Henrich VC. Educational needs of primary care physicians regarding direct-to-consumer genetic testing. J Genet Couns. 2012;21:469–78.\nPowell KP, Cogswell WA, Christianson CA, Dave G, Verma A, Eubanks S, Henrich VC. Primary care physicians’ awareness, experience and opinions of direct-to-consumer genetic testing. J Genet Couns. 2012;21:113–26.\nHock KT, Christensen KD, Yashar BM, Roberts JS, Gollust SE, Uhlmann WR. Direct-to-consumer genetic testing: an assessment of genetic counselors’ knowledge and beliefs. Genet Med. 2011;13:325–32.\nBrett GR, Metcalfe SA, Amor DJ, Halliday JL. 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Perception of direct-to-consumer genetic testing and direct-to-consumer advertising of genetic tests among members of a large managed care organization. J Genet Couns. 2012;21:448–61.\nRoberts ME, Riegert-Johnson DL, Thomas BC. Self diagnosis of Lynch syndrome using direct to consumer genetic testing: a case study. J Genet Couns. 2011;20:327–9.\nVayena E, Gourna E, Streuli J, Hafen E, Prainsack B. Experiences of early users of direct-to-consumer genomics in Switzerland: an exploratory study. Public Health Genomics. 2012;15:352–62.\nCorpas M. A family experience of personal genomics. J Genet Couns. 2012;21:386–91.\nAustin SEA, Hegele RA. Clinical implications of direct-to-consumer genetic testing for cardiovascular disease risk. Can J Cardiol. 2011;27:682–4.\nSaunders KH, Nazareth S, Pressman PI. Case report: BRCA in the Ashkenazi population: are current testing guidelines too exclusive? Hered Cancer Clin Pract. 2011;9:3.\nSturm AC, Manickam K. Direct-to-consumer personal genomic testing: a case study and practical recommendations for “genomic counseling”. J Genet Couns. 2012;21:402–12.",{"EN":564},"Direct-to-consumer genetic testing (DTC-GT) has sparked much controversy and undergone dramatic changes in its brief history. Debates over appropriate health policies regarding DTC-GT would benefit from empirical research on its benefits, harms, and limitations. We review the recent literature (2011-present) and summarize findings across (1) content analyses of DTC-GT websites, (2) studies of consumer perspectives and experiences, and (3) surveys of relevant health care providers. Findings suggest that neither the health benefits envisioned by DTC-GT proponents (e.g., significant improvements in positive health behaviors) nor the worst fears expressed by its critics (e.g., catastrophic psychological distress and misunderstanding of test results, undue burden on the health care system) have materialized to date. However, research in this area is in its early stages and possesses numerous key limitations. We note needs for future studies to illuminate the impact of DTC-GT and thereby guide practice and policy regarding this rapidly evolving approach to personal genomics.",{"EN":566},"Direct-to-Consumer Genetic Testing and Personal Genomics Services: A Review of Recent Empirical Studies",{"VOID":568},"10.1007\u002Fs40142-013-0018-2","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-013-0018-2",[571,587],{"id":572,"sortIndex":19,"researcher":18,"roles":573,"affiliations":575,"properties":584},"34b2bf25-9b76-4e4a-bc18-7f8a33aea1ad",[574],"AUTHOR",[576],{"id":18,"sortIndex":19,"affiliation":577,"properties":18},{"id":578,"createTime":579,"updateTime":579,"relativeEntities":580,"slug":18,"properties":581,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"3a875165-e5bf-4564-acc0-c4a9ea3390e1","2023-12-09T04:03:29.690+00:00",[],{"title":582},{"VI":583},"Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, USA",{"title":585},{"VI":586},"J. Scott Roberts",{"id":588,"sortIndex":73,"researcher":18,"roles":589,"affiliations":590,"properties":596},"dc5318a9-956a-432d-bff9-b1f1fbb439fc",[574],[591],{"id":18,"sortIndex":19,"affiliation":592,"properties":18},{"id":578,"createTime":579,"updateTime":579,"relativeEntities":593,"slug":18,"properties":594,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":595},{"VI":583},{"title":597},{"VI":598},"Jenny Ostergren",{"url":569,"publisher":600,"properties":620},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":601,"slug":10,"properties":602,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":605,"manageAffiliations":606,"indexDatabases":607,"url":18,"thumbnailPath":18,"statistic":615,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":603,"title":604},{"VOID":13},{"EN":15},[],[],[608],{"id":44,"indexDatabase":609,"url":59,"indexYears":18,"academicFieldIds":614,"indexDatabaseRanking":18},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":610,"label":611,"description":612,"key":55,"publicationTags":613,"standard":18},[],{"EN":51,"VI":51},{"VI":53,"EN":54},[57,58],[61],{"impactFactor":19,"impactFactorByYear":616,"i10Index":72,"i10IndexLast5Year":73,"totalPublication":74,"totalPublicationByYear":617,"totalCitation":79,"totalCitationByYear":618,"totalCitationPerPublication":87,"totalCitationPerPublicationByYear":619,"hindexLast5Year":72,"hindex":72},{"2014":64,"2015":65,"2016":66,"2017":67,"2018":68,"2019":69,"2020":70,"2021":71},{"2013":76,"2014":77,"2015":78,"2016":77,"2017":66,"2018":78,"2019":66,"2020":77},{"2013":81,"2014":82,"2015":83,"2016":84,"2017":85,"2018":86,"2019":82},{"2013":89,"2014":85,"2015":90,"2016":91,"2017":92,"2018":93,"2019":94},{"volume":621,"pages":623},{"VOID":622},"1",{"VOID":624},"182-200","2013-07-12",2013,{"id":628,"createTime":629,"updateTime":630,"relativeEntities":631,"slug":632,"properties":633,"entityType":120,"verifyStatus":121,"verifyTime":630,"verifyNote":122,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":642,"fullTextUrl":18,"authors":643,"publicationType":167,"publisherRelationship":698,"citationCount":18,"citationInfo":18,"publishDate":724,"publishYear":725,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":553},"747feb70-0977-4dff-9028-1c4f5722005c","2024-02-15T20:43:27.610+00:00","2024-12-26T23:40:41.425+00:00",[],"Admixture-Genetics-and-Complex-Diseases-in-Latin-Americans-and-US-Hispanics",{"references":634,"abstract":636,"title":638,"doi":640},{"VOID":635},"Salzano FM, Bortolini MC. 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Adv Pharmacol. 2018;83:133–54.",{"EN":637},"We review population and epidemiological genetics studies in Latin Americans and US Hispanics\u002FLatinos, who are product of admixture between Europeans, Africans, and Native Americans. Admixture studies identified a Southeastern-Africa\u002FBantu ancestry component more prevalent in Brazil. Contrasting dynamics of Native American introgression into admixed populations were inferred: > 400 years ago in Brazil, where Native American populations were decimated afterwards, and concentrated on the last 300 years in Peru, a predominantly indigenous country. Associations have been reported between phenotypes and individual ancestry, including subcomponents of Native American ancestry, such as gallbladder cancer with Chilean-Mapuche ancestry and lung function with a west-east component of Native American ancestry in Mexico. Individuals from Latin America are underrepresented in genome-wide association studies (GWAS), despite an important increase in their inclusion during the last quinquennium. GWAS and admixture mapping have found variants associated with anthropometric, cardiovascular, immunological, hematological, neurological, and endocrine-related traits\u002Fdiseases, as well as cancer. In the interface between Mendelian diseases and ancestry, a GWAS identified Venezuelan-specific modifiers of Huntington’s disease onset. The mutational landscape of hereditary breast cancer has been better characterized. Moreover, next-generation sequencing is allowing the identification of new mutations with different ancestral origins in different Latin American populations. The tsunami of new genetic markers and statistical methods allow us to consider three levels of biogeographic ancestry in Latin Americans: populational, individual, and local chromosome admixture. The challenge is how to integrate these levels of ancestry to understand the genetic architecture of diseases.",{"EN":639},"Admixture, Genetics and Complex Diseases in Latin Americans and US Hispanics",{"VOID":641},"10.1007\u002Fs40142-018-0151-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-018-0151-z",[644,659,671,683],{"id":645,"sortIndex":19,"researcher":18,"roles":646,"affiliations":647,"properties":656},"d8561784-94dc-4f7e-a470-f12698f81676",[574],[648],{"id":18,"sortIndex":19,"affiliation":649,"properties":18},{"id":650,"createTime":651,"updateTime":651,"relativeEntities":652,"slug":18,"properties":653,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"745b8f5c-3ce5-40f3-8191-47e6fd4dbba0","2024-01-09T10:54:51.637+00:00",[],{"title":654},{"VI":655},"Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil",{"title":657},{"VI":658},"Giordano Soares-Souza",{"id":660,"sortIndex":66,"researcher":18,"roles":661,"affiliations":662,"properties":668},"85f4816b-b646-4d79-826f-f3af2649b86d",[574],[663],{"id":18,"sortIndex":19,"affiliation":664,"properties":18},{"id":650,"createTime":651,"updateTime":651,"relativeEntities":665,"slug":18,"properties":666,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":667},{"VI":655},{"title":669},{"VI":670},"Eduardo 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The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.",{"EN":1084},"Opportunities for Artificial Intelligence in Advancing Precision Medicine",{"VOID":1086},"10.1007\u002Fs40142-019-00177-4","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-019-00177-4",[1089],{"id":1090,"sortIndex":19,"researcher":18,"roles":1091,"affiliations":1092,"properties":1113},"a9054c59-dc4f-4d69-9fe1-f363e6b570d1",[574],[1093,1103],{"id":1094,"sortIndex":73,"affiliation":1095,"properties":1102},"1c79fb3c-5fc1-4bad-9740-968ab26fc03f",{"id":1096,"createTime":1097,"updateTime":1097,"relativeEntities":1098,"slug":18,"properties":1099,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"44ac7ab0-1068-42d0-8ede-e68ae9105dcc","2024-02-09T04:42:12.568+00:00",[],{"title":1100},{"VI":1101},"School of Life Sciences Weihenstephan, Technical University München, Freising, Germany",{},{"id":18,"sortIndex":19,"affiliation":1104,"properties":18},{"id":1105,"createTime":1106,"updateTime":1107,"relativeEntities":1108,"slug":1109,"properties":1110,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0531e8fc-4e9a-4238-ab80-6c85db3e3112","2024-02-20T02:27:36.963+00:00","2025-06-11T18:23:31.694+00:00",[],"Cancer-Systems-Biology-Institute-of-Computational-Biology-Helmholtz-Zentrum-M%C3%BCnchen-M%C3%BCnchen-Germany",{"title":1111},{"VI":1112},"Cancer Systems Biology, Institute of Computational Biology, Helmholtz Zentrum München, München, Germany",{"title":1114},{"VI":1115},"Fabian V. 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Selective genetic overlap between amyotrophic lateral sclerosis and diseases of the frontotemporal dementia spectrum. JAMA Neurol. 2018;75(7):860–875.\nBroce I, Karch CM, Wen N, Fan CC, Wang Y, Hong Tan C, et al. Immune-related genetic enrichment in frontotemporal dementia: an analysis of genome-wide association studies. PLoS Med Public Libr Sci. 2018;e1002487:15.",{"EN":1705},"In this review, we highlight recent advances in the human genetics of frontotemporal dementia (FTD). In addition to providing a broad survey of genes implicated in FTD in the last several years, we also discuss variation in genes implicated in both hereditary leukodystrophies and risk for FTD (e.g., TREM2, TMEM106B, CSF1R, AARS2, NOTCH3). Over the past 5 years, genetic variation in approximately 50 genes has been confirmed or suggested to cause or influence risk for FTD and FTD-spectrum disorders. We first give background and discuss recent findings related to C9ORF72, GRN, and MAPT, the genes most commonly implicated in FTD. We then provide a broad overview of other FTD-associated genes and go on to discuss new findings in FTD genetics in East Asian populations, including pathogenic variation in CHCHD10, which may represent a frequent cause of disease in Chinese populations. Finally, we consider recent insights gleaned from genome-wide association and genetic pleiotropy studies. Recent genetic discoveries highlight cellular pathways involving autophagy, the endolysosomal system, and neuroinflammation and reveal an intriguing overlap between genes that confer risk for leukodystrophy and FTD.",{"EN":1707},"Recent Advances in the Genetics of Frontotemporal Dementia",{"VOID":1709},"10.1007\u002Fs40142-019-0160-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-019-0160-6",[1712,1728,1740,1752,1764],{"id":1713,"sortIndex":76,"researcher":18,"roles":1714,"affiliations":1715,"properties":1725},"41430f79-68e7-4c71-beeb-ec8f36ffade4",[574],[1716],{"id":18,"sortIndex":19,"affiliation":1717,"properties":18},{"id":1718,"createTime":1719,"updateTime":1719,"relativeEntities":1720,"slug":1721,"properties":1722,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"deb1126f-1095-4ab9-b805-f9e390e24d1a","2024-04-06T16:01:04.231+00:00",[],"Memory-and-Aging-Center-Department-of-Neurology-University-of-California-San-Francisco-San-Francisco-USA",{"title":1723},{"VI":1724},"Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, USA",{"title":1726},{"VI":1727},"Jennifer S. Yokoyama",{"id":1729,"sortIndex":19,"researcher":18,"roles":1730,"affiliations":1731,"properties":1737},"0d805618-4506-475d-bbe1-3e2689998b61",[574],[1732],{"id":18,"sortIndex":19,"affiliation":1733,"properties":18},{"id":1718,"createTime":1719,"updateTime":1719,"relativeEntities":1734,"slug":1721,"properties":1735,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1736},{"VI":1724},{"title":1738},{"VI":1739},"Daniel W. Sirkis",{"id":1741,"sortIndex":77,"researcher":18,"roles":1742,"affiliations":1743,"properties":1749},"eb70e981-c44d-417f-9c31-991715497699",[574],[1744],{"id":18,"sortIndex":19,"affiliation":1745,"properties":18},{"id":1718,"createTime":1719,"updateTime":1719,"relativeEntities":1746,"slug":1721,"properties":1747,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1748},{"VI":1724},{"title":1750},{"VI":1751},"Luke W. Bonham",{"id":1753,"sortIndex":73,"researcher":18,"roles":1754,"affiliations":1755,"properties":1761},"d1147b81-6e8a-458a-946c-6c6aab10d481",[574],[1756],{"id":18,"sortIndex":19,"affiliation":1757,"properties":18},{"id":1718,"createTime":1719,"updateTime":1719,"relativeEntities":1758,"slug":1721,"properties":1759,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1760},{"VI":1724},{"title":1762},{"VI":1763},"Ethan G. Geier",{"id":1765,"sortIndex":66,"researcher":18,"roles":1766,"affiliations":1767,"properties":1776},"5c85a8d2-6a1d-43bc-822f-935511a2f4d8",[574],[1768],{"id":18,"sortIndex":19,"affiliation":1769,"properties":18},{"id":1770,"createTime":1771,"updateTime":1771,"relativeEntities":1772,"slug":18,"properties":1773,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a384be95-dc85-4374-8e1d-9897a2e58768","2024-01-12T23:22:03.163+00:00",[],{"title":1774},{"VI":1775},"Hope Center for Neurological Disorders, Department of Psychiatry, Washington University School of Medicine, St. Louis, USA",{"title":1777},{"VI":1778},"Celeste M. Karch",{"url":1710,"publisher":1780,"properties":1800},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1781,"slug":10,"properties":1782,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1785,"manageAffiliations":1786,"indexDatabases":1787,"url":18,"thumbnailPath":18,"statistic":1795,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":1783,"title":1784},{"VOID":13},{"EN":15},[],[],[1788],{"id":44,"indexDatabase":1789,"url":59,"indexYears":18,"academicFieldIds":1794,"indexDatabaseRanking":18},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":1790,"label":1791,"description":1792,"key":55,"publicationTags":1793,"standard":18},[],{"EN":51,"VI":51},{"VI":53,"EN":54},[57,58],[61],{"impactFactor":19,"impactFactorByYear":1796,"i10Index":72,"i10IndexLast5Year":73,"totalPublication":74,"totalPublicationByYear":1797,"totalCitation":79,"totalCitationByYear":1798,"totalCitationPerPublication":87,"totalCitationPerPublicationByYear":1799,"hindexLast5Year":72,"hindex":72},{"2014":64,"2015":65,"2016":66,"2017":67,"2018":68,"2019":69,"2020":70,"2021":71},{"2013":76,"2014":77,"2015":78,"2016":77,"2017":66,"2018":78,"2019":66,"2020":77},{"2013":81,"2014":82,"2015":83,"2016":84,"2017":85,"2018":86,"2019":82},{"2013":89,"2014":85,"2015":90,"2016":91,"2017":92,"2018":93,"2019":94},{"volume":1801,"pages":1802},{"VOID":1139},{"VOID":1803},"41-52","2019-01-30",{"id":1806,"createTime":1807,"updateTime":1808,"relativeEntities":1809,"slug":1810,"properties":1811,"entityType":120,"verifyStatus":121,"verifyTime":1808,"verifyNote":122,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1820,"fullTextUrl":18,"authors":1821,"publicationType":167,"publisherRelationship":1872,"citationCount":18,"citationInfo":18,"publishDate":1897,"publishYear":1143,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":553},"8495bb28-a246-499b-992b-1ad0e9432a5f","2024-01-09T11:02:15.049+00:00","2025-01-10T23:11:15.861+00:00",[],"Protective-Variants-in-Alzheimer-s-Disease",{"references":1812,"abstract":1814,"title":1816,"doi":1818},{"VOID":1813},"Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL. 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Public Library of Science. 2018;14:e1007427.\nCuccaro D, De Marco EV, Cittadella R, Cavallaro S. Copy number variants in Alzheimer’s disease. J Alzheimers Dis. 2017;55:37–52.",{"EN":1815},"Over the last decade, over 40 loci have been associated with risk of Alzheimer’s disease (AD). However, most studies have either focused on identifying risk loci or performing unbiased screens without a focus on protective variation in AD. Here, we provide a review of known protective variants in AD and their putative mechanisms of action. Additionally, we recommend strategies for finding new protective variants. Recent Genome-Wide Association Studies have identified both common and rare protective variants associated with AD. These include variants in or near APP, APOE, PLCG2, MS4A, MAPT-KANSL1, RAB10, ABCA1, CCL11, SORL1, NOCT, SCL24A4-RIN3, CASS4, EPHA1, SPPL2A, and NFIC. There are very few protective variants with functional evidence and a derived allele with a frequency below 20%. Additional fine mapping and multi-omic studies are needed to further validate and characterize known variants as well as specialized genome-wide scans to identify novel variants.",{"EN":1817},"Protective Variants in Alzheimer’s Disease",{"VOID":1819},"10.1007\u002Fs40142-019-0156-2","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-019-0156-2",[1822,1842,1857],{"id":1823,"sortIndex":19,"researcher":18,"roles":1824,"affiliations":1825,"properties":1839},"100d2feb-8681-40de-b99c-7fdf80d65c58",[574],[1826],{"id":1827,"sortIndex":19,"affiliation":1828,"properties":1836},"eed2dcb7-7b7a-4e94-a72c-d18db9a5de55",{"id":1829,"createTime":1830,"updateTime":1830,"relativeEntities":1831,"slug":1832,"properties":1833,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"742e9c1d-6af3-49ee-be26-57f0067acea5","2024-12-27T18:18:04.084+00:00",[],"Department-of-Neuroscience-Icahn-School-of-Medicine-at-Mount-Sinai-New-York-United-States",{"title":1834},{"EN":1835},"Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States",{"title":1837},{"VI":1838},"Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA",{"title":1840},{"VI":1841},"Shea J. Andrews",{"id":1843,"sortIndex":77,"researcher":18,"roles":1844,"affiliations":1845,"properties":1854},"2733338c-59d6-41a4-8412-d10f81b9349a",[574],[1846],{"id":1847,"sortIndex":19,"affiliation":1848,"properties":1852},"27d260a2-9532-4fb8-9b30-e1e6f4303a13",{"id":1829,"createTime":1830,"updateTime":1830,"relativeEntities":1849,"slug":1832,"properties":1850,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1851},{"EN":1835},{"title":1853},{"VI":1838},{"title":1855},{"VI":1856},"Alison Goate",{"id":1858,"sortIndex":73,"researcher":18,"roles":1859,"affiliations":1860,"properties":1869},"1bbeb0d1-127d-4092-a195-a8de32f86c6d",[574],[1861],{"id":1862,"sortIndex":19,"affiliation":1863,"properties":1867},"79809a74-ec8f-4505-97b0-8a9d1e1ab77f",{"id":1829,"createTime":1830,"updateTime":1830,"relativeEntities":1864,"slug":1832,"properties":1865,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1866},{"EN":1835},{"title":1868},{"VI":1838},{"title":1870},{"VI":1871},"Brian Fulton-Howard",{"url":1820,"publisher":1873,"properties":1893},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1874,"slug":10,"properties":1875,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1878,"manageAffiliations":1879,"indexDatabases":1880,"url":18,"thumbnailPath":18,"statistic":1888,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":1876,"title":1877},{"VOID":13},{"EN":15},[],[],[1881],{"id":44,"indexDatabase":1882,"url":59,"indexYears":18,"academicFieldIds":1887,"indexDatabaseRanking":18},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":1883,"label":1884,"description":1885,"key":55,"publicationTags":1886,"standard":18},[],{"EN":51,"VI":51},{"VI":53,"EN":54},[57,58],[61],{"impactFactor":19,"impactFactorByYear":1889,"i10Index":72,"i10IndexLast5Year":73,"totalPublication":74,"totalPublicationByYear":1890,"totalCitation":79,"totalCitationByYear":1891,"totalCitationPerPublication":87,"totalCitationPerPublicationByYear":1892,"hindexLast5Year":72,"hindex":72},{"2014":64,"2015":65,"2016":66,"2017":67,"2018":68,"2019":69,"2020":70,"2021":71},{"2013":76,"2014":77,"2015":78,"2016":77,"2017":66,"2018":78,"2019":66,"2020":77},{"2013":81,"2014":82,"2015":83,"2016":84,"2017":85,"2018":86,"2019":82},{"2013":89,"2014":85,"2015":90,"2016":91,"2017":92,"2018":93,"2019":94},{"volume":1894,"pages":1895},{"VOID":1139},{"VOID":1896},"1-12","2019-01-24",{"id":1899,"createTime":1900,"updateTime":1900,"relativeEntities":1901,"slug":18,"properties":1902,"entityType":120,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1911,"fullTextUrl":18,"authors":1912,"publicationType":167,"publisherRelationship":1928,"citationCount":18,"citationInfo":18,"publishDate":1953,"publishYear":1143,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":553},"a032ed5f-eb55-439d-b5b1-f34f4c587862","2024-01-15T23:10:30.398+00:00",[],{"references":1903,"abstract":1905,"title":1907,"doi":1909},{"VOID":1904},"Uusitalo E, Leppävirta J, Koffert A, Suominen S, Vahtera J, Vahlberg T, et al. 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Neuro-Oncology. 2014;16(2):292–7. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fneuonc\u002Fnot150.\nKarajannis MA, Legault G, Hagiwara M, Ballas MS, Brown K, Nusbaum AO, et al. Phase II trial of lapatinib in adult and pediatric patients with neurofibromatosis type 2 and progressive vestibular schwannomas. Neuro-Oncology. 2012;14(9):1163–70. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fneuonc\u002Fnos146.\nOsorio DS, Hu J, Mitchell C, Allen JC, Stanek J, Hagiwara M, et al. Effect of lapatinib on meningioma growth in adults with neurofibromatosis type 2. J Neuro-Oncol. 2018;139(3):749–55. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11060-018-2922-5.",{"EN":1906},"Neurofibromatosis types 1 and 2 (NF1 & NF2) are complex genetic diseases that provide challenges in diagnosis, monitoring, clinical management and genetic counselling. This review highlights these challenges and provides insight into the general and specialist management considerations. Multidisciplinary care with a focus on evidence based interventions and quality of life outcomes has benefited patients. Anti-VEGF therapy has recently altered the management paradigm for NF2. Other novel molecularly targeted therapies are being trialled in both NF1 and NF2. Improved understanding of associated risks and natural history has informed screening regimes. Both diseases have significant associated morbidity and mortality, with the prognosis for some patients with NF2 particularly poor. A holistic and multidisciplinary approach provides the best model of care.",{"EN":1908},"Management and Screening in Neurofibromatosis Types 1 and 2",{"VOID":1910},"10.1007\u002Fs40142-019-00165-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-019-00165-8",[1913],{"id":1914,"sortIndex":19,"researcher":18,"roles":1915,"affiliations":1916,"properties":1925},"c69712c0-b972-4afe-b5d3-6aa871473d69",[574],[1917],{"id":18,"sortIndex":19,"affiliation":1918,"properties":18},{"id":1919,"createTime":1920,"updateTime":1920,"relativeEntities":1921,"slug":18,"properties":1922,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"452c443f-78f3-4fe4-be97-31f08ded1fff","2024-01-15T23:10:30.416+00:00",[],{"title":1923},{"VI":1924},"National Neurofibromatosis Service, Guy’s & St Thomas’ NHS Foundation Trust, London, UK",{"title":1926},{"VI":1927},"Adam C. Shaw",{"url":1911,"publisher":1929,"properties":1949},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1930,"slug":10,"properties":1931,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1934,"manageAffiliations":1935,"indexDatabases":1936,"url":18,"thumbnailPath":18,"statistic":1944,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":1932,"title":1933},{"VOID":13},{"EN":15},[],[],[1937],{"id":44,"indexDatabase":1938,"url":59,"indexYears":18,"academicFieldIds":1943,"indexDatabaseRanking":18},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":1939,"label":1940,"description":1941,"key":55,"publicationTags":1942,"standard":18},[],{"EN":51,"VI":51},{"VI":53,"EN":54},[57,58],[61],{"impactFactor":19,"impactFactorByYear":1945,"i10Index":72,"i10IndexLast5Year":73,"totalPublication":74,"totalPublicationByYear":1946,"totalCitation":79,"totalCitationByYear":1947,"totalCitationPerPublication":87,"totalCitationPerPublicationByYear":1948,"hindexLast5Year":72,"hindex":72},{"2014":64,"2015":65,"2016":66,"2017":67,"2018":68,"2019":69,"2020":70,"2021":71},{"2013":76,"2014":77,"2015":78,"2016":77,"2017":66,"2018":78,"2019":66,"2020":77},{"2013":81,"2014":82,"2015":83,"2016":84,"2017":85,"2018":86,"2019":82},{"2013":89,"2014":85,"2015":90,"2016":91,"2017":92,"2018":93,"2019":94},{"volume":1950,"pages":1951},{"VOID":1139},{"VOID":1952},"92-101","2019-05-02",{"id":1955,"createTime":1956,"updateTime":1957,"relativeEntities":1958,"slug":1959,"properties":1960,"entityType":120,"verifyStatus":121,"verifyTime":1957,"verifyNote":122,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1969,"fullTextUrl":18,"authors":1970,"publicationType":167,"publisherRelationship":2008,"citationCount":18,"citationInfo":18,"publishDate":2033,"publishYear":626,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":553},"23d804bc-523c-4739-93be-952874c52a1e","2024-01-27T09:10:04.320+00:00","2025-01-01T22:34:27.151+00:00",[],"State-of-the-Art-Technologies-to-Interrogate-Genetic-Genomic-Components-of-Drug-Response",{"references":1961,"abstract":1963,"title":1965,"doi":1967},{"VOID":1962},"Hong K-W, Oh B. 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Real-time DNA sequencing from single polymerase molecules. Science. 2009;323:133–8.\nDerrington IM, et al. Nanopore DNA sequencing with MspA. Proc Natl Acad Sci USA. 2010;107:16060–5.\nSchuster SC. Next-generation sequencing transforms today’s biology. Nat Methods 2008;5:16–8.\nAndries K, Verhasselt P, Guillemont J, et al. A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science. 2005;307:223–7.\nGizer IR, Ficks C, Waldman ID. Candidate gene studies of ADHD: a meta analytic review. Hum Genet. 2009;126:51–90.\n• Ashley EA, Butte AJ, Wheeler MT, et al: Clinical assessment incorporating a personal genome. Lancet 2010;375:1525–35. This study illustrates that whole-genome sequencing can yield useful and clinically relevant information for individual patients. Above mentioned study aimed to undertake an integrated analysis of a complete human genome in a clinical context. Also develop methods integrating genetic and clinical data will assist clinical decision making and represent a large step towards individualized medicine.\nPan S, Knowles JW. Exploring predisposition and treatment response—the promise of genomics. Prog Cardiovasc Dis. 2012;55(1):56–63.\n•• Chen R, Mias GI, Li-Pook-Than J, et al: Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012;148:1293–1307. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity. Detailed omics profiling coupled with genome sequencing can provide molecular and physiological information of medical significance. This approach can be generalized for personalized health monitoring and medicine.\nLeary RJ, et al. Development of personalized tumor biomarkers using massively parallel sequencing. Sci Transl Med. 2010;2:20ra14.\nBueno R, et al. Second generation sequencing of the mesothelioma tumor genome. PLoS One. 2010;5:e10612.\nVictoria JG, et al. Viral nucleic acids in live-attenuated vaccines: detection of minority variants and an adventitious virus. J Virol. 2010;84:6033–40.\nOnions D, Kolman J. Massively parallel sequencing, a new method for detecting adventitious agents. Biologicals. 2010;38:377–80.\nLey TJ, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature. 2008;456:66–72.\nKim PM, et al. Analysis of copy number variants and segmental duplications in the human genome: evidence for a change in the process of formation in recent evolutionary history. Genome Res. 2008;18:1865–74.\n• Roukos D. Integrative deep-sequencing analysis of cancer samples: discoveries and clinical challenges. Pharmacogenomics J. 2013;13(3):205–8. This article discuss the results of the first published studies with deep-sequencing and transcriptomic analysis data of clinical samples. It also emphasizes the potential and challenges to adopt new genomic discoveries into novel robust biomarkers and cancer targets. Step-by-step advances in transcriptome, proteome, epigenome and interactome analysis will lead to patients stratification and personalized health care through systems science-based integrative genome profile.\n• Soldi R, Cohen AL, Cheng L, et al. A genomic approach to predict synergistic combinations for breast cancer treatment. Pharmacogenomics J. 2013;13(1):94–104. Importantly, these studies provide an example of how genomic analysis of drug-response profiles can be used to design rational drug combinations for cancer treatment. Together, these results highlight a novel therapeutic combination rationally designed by genomic analysis. Importantly, it has been shown that analysis of gene expression profiles can identify synergistic drug combinations.\n• Man M, Close SL, Shaw AD, et al. Beyond single-marker analyses: mining whole genome scans for insights into treatment responses in severe sepsis. Pharmacogenomics J. 2013;13(3):218–26. This article, in contrast, evidence for gene–gene interactions were identified for sepsis treatment responses with genetic biomarkers dominating models for predicting therapeutic responses, yielding candidates for replication in other cohorts. These novel approaches have led to the affirmation of clinical variables as important prognostic markers and the possible identification of epistatic interactions in the drug response of severe sepsis.\nRamsingh G, et al. Complete characterization of the microRNAome in a patient with acute myeloid leukemia. Blood. 2010;116:5316–26.\nLe T, et al. Low-abundance HIV drug-resistant viral variants in treatment experienced persons correlate with historical antiretroviral use. PLoS One. 2009;4:e6079.\nTowner JS, et al. Newly discovered Ebola virus associated with hemorrhagic fever outbreak in Uganda. PLoS Pathog. 2008;4(11):e1000212.\nLevin JZ, Berger MF, Adiconis X, et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol. 2009;10:R115.\nStratton, M. Genome resequencing and genetic variation. Nat Biotechnol. 2008;26:65–66.\nGinsburg GS, Willard HF. Genomic and personalized medicine: foundations and applications. Transl Res. 2009;154(6):277–87.\nCooper GM, et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood. 2008;112:1022–7.\nTrevino LR, et al. Germline genetic variation in an organic anion transporter polypeptide associated with methotrexate pharmacokinetics and clinical effects. J Clin Oncol. 2009;27:5972–8.\nTeichert M, et al. A genome-wide association study of acenocoumarol maintenance dosage. Hum Mol Genet. 2009;18:3758–68.\nGe D, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature. 2009;461:399–401.\n• Suppiah V, et al. IL28B is associated with response to chronic hepatitis C interferon-α and ribavirin therapy. Nature Genet. 2009;41:1100–4. This study suggests that host genetics may be useful for the prediction of drug response, and they also support the investigation of the role of IL28B in the treatment of HCV and in other diseases treated with IFN-alpha. Ultimately studies on the genetics of drug response should lead to the development of new therapeutic approaches, including tailoring of clinical management on the basis of host genotype.\nShuldiner AR, et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA. 2009;302:849–57.\n• Nicoletti P, et al. Genome wide pharmacogenetics of bisphosphonate -induced osteonecrosis of the jaw: the role of RBMS3. Oncologist 2012,17: 279–287. This study conducted a genome-wide association study to search for genetic variants with a large effect size that increase the risk for Bisphosphonate-related osteonecrosis of the jaw (BRONJ). Study findings suggest that genetic susceptibility plays a role in the pathophysiology of BRONJ, with RBMS3 having a significant effect in the risk.\nDaly AK, et al. HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat Genet. 2009;41:816–9.\nChantarangsu S, et al. Genome-wide association study identifies variations in 6p21.3 associated with nevirapine-induced rash. Clin Infect Dis. 2011;53:341–8.\n•• Tantisira KG, et al. Genome wide association between GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J Med. 2011;365:1173–83. In this article, genome-wide association study of more than 530,000 SNPs revealed a novel functional SNP, rs37973 that decreases the expression of GLCCI1, a gene influencing the pharmacologic response to inhaled glucocorticoids in asthma.\nIngle JN, et al. Genome-wide associations and functional genomic studies of musculoskeletal adverse events in women receiving aromatase inhibitors. J Clin Oncol. 2010;28:4674–82.\nOzeki T, et al. Genome-wide association study identifies HLAA*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum Mol Genet. 2011;20:1034–41.\nInnocenti F, et al. A genome-wide association study of overall survival in pancreatic cancer patients treated with gemcitabine in CALGB 80303. Clin Cancer Res. 2012;18:577–84.\n• Tantisira KG, et al. Genome-wide association identifies the T gene as a novel asthma pharmacogenetic locus. Am J Respir Cri. Care Med. 2012;185:1286–91. In this study, Genome-wide association studies (GWAS) can rapidly identify novel pharmacogenetic loci. Genome-wide association has identified the T gene as a novel pharmacogenetic locus for inhaled corticosteroid response in asthma. In conclusion, GWAS study of ICS response in SHARP has allowed the identification of a novel pharmacogenetic locus, the T gene.\nSrinivasan Y, et al. Genome-wide association study of epirubicin-induced leukopenia in Japanese patients. Pharmacogenet Genomics. 2011;21:552–8.\nTanaka Y, et al. Genome-wide association study identified ITPA\u002FDDRGK1 variants reflecting thrombocytopenia in pegylated interferon and ribavirin therapy for chronic hepatitis C. Hum Mol Genet. 2011;20:3507–16.\nChung CM, et al. A genome-wide association study identifies new loci for ACE activity: potential implications for response to ACE inhibitor. Pharmacogenomics J. 2010;10:537–44.\nSinger JB, et al. A genome-wide study identifies HLA alleles Associated with lumiracoxib-related liver injury. Nat Genet. 2010;42:711–4.\nLucena MI, et al. Susceptibility to amoxicillin–clavulanate induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology. 2011;141:338–47.\nKim J-H, et al. Genome-wide and follow-up studies identify CEP68 gene variants associated with risk of aspirin-intolerant asthma. PLoS One. 2010;5:e13818.\nTohkin M, et al. A whole-genome association study of major determinants for allopurinol-related Stevens–Johnson syndrome and toxic epidermal necrolysis in Japanese patients. Pharmacogenomics J. 2011. doi:10.1038\u002Ftpj.2011.41.\nAberg K, et al. Genome-wide association study of antipsychotic induced QTc interval prolongation. Pharmacogenomics J. 2012;12:165–72.\nHuang RS, et al. Platinum sensitivity-related germline polymorphism discovered via a cell-based approach and analysis of its association with outcome in ovarian cancer patients. Clin Cancer Res. 2011;17:5490–500.\nMetzker ML, et al. Sequencing technologies—the next generation. Nat Rev Genet. 2010;11(1):31–46.",{"EN":1964},"Over the last decade, the study of genomes has rapidly advanced to the point that genomic research now serves as the basis for many medical decisions and public health initiatives. Genetic variation is likely to contribute substantially to the variation in drug response observed across human populations. Genomic tools such as sequence variation and, more specifically, personal genome sequencing incorporating genome-wide association studies and next-generation sequences enable the precise prediction and treatment of disease. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures and dose selection of therapeutic drugs are the important issues in personalized medicine. In order to make personalized medicine effective, these genomic techniques must be standardized and integrated into health systems and clinical workflow for prediction of disease incidence, genetic and environmental information to optimize the medical care and outcomes for each patient. Technology continues to lead the field of personalized medicine since the interpretation of the human genome is progressing as the cost and duration of genomic sequencing continue to decrease sharply. This review aimed at understanding the technologies that reveal how the changes that occur within the genome can alter their functions and the genomic variations that constitute individual susceptibility to diseases and responses to therapy.",{"EN":1966},"State-of-the-Art Technologies to Interrogate Genetic\u002FGenomic Components of Drug Response",{"VOID":1968},"10.1007\u002Fs40142-013-0022-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40142-013-0022-6",[1971,1996],{"id":1972,"sortIndex":73,"researcher":18,"roles":1973,"affiliations":1974,"properties":1993},"05ed4f7d-6fd5-4727-8764-3bfa51e38d1f",[574],[1975,1985],{"id":1976,"sortIndex":73,"affiliation":1977,"properties":1984},"66c8c8ab-bf22-4485-8a5a-92f09f0a65ff",{"id":1978,"createTime":1979,"updateTime":1979,"relativeEntities":1980,"slug":18,"properties":1981,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"255aa242-fad4-457c-893d-b1255d28d08c","2023-12-25T16:31:58.724+00:00",[],{"title":1982},{"VI":1983},"Department of Pathology and Laboratory Medicine, University of Rochester, Rochester, USA",{},{"id":18,"sortIndex":19,"affiliation":1986,"properties":18},{"id":1987,"createTime":1988,"updateTime":1988,"relativeEntities":1989,"slug":18,"properties":1990,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"47206a48-0214-4a31-9713-cd9ca54795a1","2024-01-27T09:10:04.349+00:00",[],{"title":1991},{"VI":1992},"Center for Genetic & Genomic Medicine, Faculty of Basic Medicine, Zhejiang University School of Medicine and James D. Watson Institute of Genome Sciences, Hangzhou, China",{"title":1994},{"VI":1995},"Ming Qi",{"id":1997,"sortIndex":19,"researcher":18,"roles":1998,"affiliations":1999,"properties":2005},"7038e5bd-4011-4249-aeec-a82aca4e8cf9",[574],[2000],{"id":18,"sortIndex":19,"affiliation":2001,"properties":18},{"id":1987,"createTime":1988,"updateTime":1988,"relativeEntities":2002,"slug":18,"properties":2003,"entityType":39,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":2004},{"VI":1992},{"title":2006},{"VI":2007},"Santasree Banerjee",{"url":1969,"publisher":2009,"properties":2029},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2010,"slug":10,"properties":2011,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":2014,"manageAffiliations":2015,"indexDatabases":2016,"url":18,"thumbnailPath":18,"statistic":2024,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":2012,"title":2013},{"VOID":13},{"EN":15},[],[],[2017],{"id":44,"indexDatabase":2018,"url":59,"indexYears":18,"academicFieldIds":2023,"indexDatabaseRanking":18},{"id":46,"createTime":47,"updateTime":48,"relativeEntities":2019,"label":2020,"description":2021,"key":55,"publicationTags":2022,"standard":18},[],{"EN":51,"VI":51},{"VI":53,"EN":54},[57,58],[61],{"impactFactor":19,"impactFactorByYear":2025,"i10Index":72,"i10IndexLast5Year":73,"totalPublication":74,"totalPublicationByYear":2026,"totalCitation":79,"totalCitationByYear":2027,"totalCitationPerPublication":87,"totalCitationPerPublicationByYear":2028,"hindexLast5Year":72,"hindex":72},{"2014":64,"2015":65,"2016":66,"2017":67,"2018":68,"2019":69,"2020":70,"2021":71},{"2013":76,"2014":77,"2015":78,"2016":77,"2017":66,"2018":78,"2019":66,"2020":77},{"2013":81,"2014":82,"2015":83,"2016":84,"2017":85,"2018":86,"2019":82},{"2013":89,"2014":85,"2015":90,"2016":91,"2017":92,"2018":93,"2019":94},{"volume":2030,"pages":2031},{"VOID":622},{"VOID":2032},"150-161","2013-07-09"]