Estimating the time since deposition (TsD) in saliva stains using temporal changes in microbial markers

Dobay, 2019, Microbiome-based body fluid identification of samples exposed to indoor conditions, Forensic Sci. Int. Genet., 40, 105, 10.1016/j.fsigen.2019.02.010 Sirker, 2016, A 17-month time course study of human RNA and DNA degradation in body fluids under dry and humid environmental conditions, Int. J. Leg. Med., 130, 1431, 10.1007/s00414-016-1373-9 Kapoor, 2018, Salivary signature in forensic profiling: a scoping review, J. Forensic Dent. Sci., 10, 123, 10.4103/jfo.jfds_30_18 Anderson, 2005, A method for determining the age of a bloodstain, Forensic Sci. Int., 148, 37, 10.1016/j.forsciint.2004.04.071 Bauer, 2003, Quantification of RNA degradation by semi-quantitative duplex and competitive RT-PCR: a possible indicator of the age of bloodstains?, Forensic Sci. Int., 138, 94, 10.1016/j.forsciint.2003.09.008 Matsuoka, 1995, Estimation of bloodstain age by rapid determinations of oxyhemoglobin by use of oxygen electrode and total hemoglobin, Biol. Pharm. Bull., 18, 1031, 10.1248/bpb.18.1031 Hanson, 2010, A blue spectral shift of the hemoglobin soret band correlates with the age (time since deposition) of dried bloodstains, PLoS One, 5, 10.1371/journal.pone.0012830 Doty, 2016, A Raman “spectroscopic clock” for bloodstain age determination: the first week after deposition, Anal. Bioanal. Chem., 408, 3993, 10.1007/s00216-016-9486-z Rajamannar, 1977, Determination of the age of bloodstains using immunoelectrophoresis, J. Forensic Sci., 22, 159, 10.1520/JFS10381J Anderson, 2011, Multivariate analysis for estimating the age of a bloodstain, J. Forensic Sci., 56, 186, 10.1111/j.1556-4029.2010.01551.x Ackermann, 2010, Estimating trace deposition time with circadian biomarkers: a prospective and versatile tool for crime scene reconstruction, Int. J. Leg. Med., 124, 387, 10.1007/s00414-010-0457-1 Asaghiar, 2020, Evaluating the use of hypoxia sensitive markers for body fluid stain age prediction, Sci. Justice, 60, 547, 10.1016/j.scijus.2020.09.001 Alshehhi, 2019, Estimating time since deposition using quantification of RNA degradation in body fluid-specific markers, Forensic Sci. Int., 298, 58, 10.1016/j.forsciint.2019.02.046 Díez López, 2021, Estimating the time since deposition of saliva stains with a targeted bacterial DNA approach: a proof-of-principle study, Front. Microbiol., 12, 10.3389/fmicb.2021.647933 Salzmann, 2019, Transcription and microbial profiling of body fluids using a massively parallel sequencing approach, Forensic Sci. Int. Genet., 43, 10.1016/j.fsigen.2019.102149 Salzmann, 2021, Assessing time dependent changes in microbial composition of biological crime scene traces using microbial RNA markers, Forensic Sci. Int. Genet., 53, 10.1016/j.fsigen.2021.102537 P. Amplicon, P. Clean-Up, P. Index, 16s metagenomic sequencing library preparation, Illumina. com., 2013. Zhang, 2017, Altered gut microbiota in a mouse model of Alzheimer’s disease, J. Alzheimers Dis., 60, 1241, 10.3233/JAD-170020 Kechin, 2017, cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing, J. Comput. Biol., 24, 1138, 10.1089/cmb.2017.0096 Caporaso, 2010, QIIME allows analysis of high-throughput community sequencing data, Nat. Methods, 7, 335, 10.1038/nmeth.f.303 Chong, 2020, Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data, Nat. Protoc., 15, 799, 10.1038/s41596-019-0264-1 Edgar, 2011, UCHIME improves sensitivity and speed of chimera detection, Bioinformatics, 27, 2194, 10.1093/bioinformatics/btr381 Metcalf, 2016, Microbial community assembly and metabolic function during mammalian corpse decomposition, Science, 351, 158, 10.1126/science.aad2646 Edgar, 2010, Search and clustering orders of magnitude faster than BLAST, Bioinformatics, 26, 2460, 10.1093/bioinformatics/btq461 DeSantis, 2006, Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB, Appl. Environ. Microbiol., 72, 5069, 10.1128/AEM.03006-05 Edwards, 2018, Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice, PLoS Biol., 16, 10.1371/journal.pbio.2003862 Paradis, 2004, APE: analyses of phylogenetics and evolution in R language, Bioinformatics, 20, 289, 10.1093/bioinformatics/btg412 Liaw, 2002, Classification and regression by randomForest, R N., 2, 18 Leys, 2013, Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median, J. Exp. Soc. Psychol., 49, 764, 10.1016/j.jesp.2013.03.013 Moon, 2011, A robust mean absolute deviation model for portfolio optimization, Comput. Oper. Res., 38, 1251, 10.1016/j.cor.2010.10.020 Hao, 2021, Predicting human age by detecting DNA methylation status in hair, Electrophoresis, 42, 1255, 10.1002/elps.202000349 Maestroni, 2020, Is rotator cuff related shoulder pain a multidimensional disorder?, Explor. Study, 20, 297 Feng, 2018, Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males, Forensic Sci. Int. Genet., 35, 38, 10.1016/j.fsigen.2018.03.009 Kubista, 2006, The real-time polymerase chain reaction, Mol. Asp. Med., 27, 95, 10.1016/j.mam.2005.12.007 Esposito, 2019, Setup of quantitative PCR for oral Neisseria spp, Eval. Celiac Dis. Diagn., 10 Bachtiar, 2018, Relationship between Candida albicans and Streptococcus mutans in early childhood caries, evaluated by quantitative PCR, F1000Res, 7, 1645, 10.12688/f1000research.16275.2 Dong, 2019, Succession of oral microbiota community as a tool to estimate postmortem interval, Sci. Rep., 9, 13063, 10.1038/s41598-019-49338-z The Human Microbiome Project Consortium, 2012, Structure, function and diversity of the healthy human microbiome, Nature, 486, 207, 10.1038/nature11234 Hanson, 2016, Characterization of the bacterial and fungal microbiome in indoor dust and outdoor air samples: a pilot study, Environ. Sci. Process Impacts, 18, 713, 10.1039/C5EM00639B Zhang, 2016, Environmental changes affect the assembly of soil bacterial community primarily by mediating stochastic processes, Glob. Change Biol., 22, 198, 10.1111/gcb.13080 Metcalf, 2013, A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system, Elife, 2, 10.7554/eLife.01104 Giampaoli, 2017, Informativeness of NGS analysis for vaginal fluid identification, J. Forensic Sci., 62, 192, 10.1111/1556-4029.13222 Smidt, 2015, Comparison of detection methods for vaginal lactobacilli, Benef. Microbes, 6, 747, 10.3920/BM2014.0154 Zhang, 2016, Variable selection with stepwise and best subset approaches, Ann. Transl. Med., 4, 136, 10.21037/atm.2016.03.35 Hampton-Marcell, 2020, Detecting personal microbiota signatures at artificial crime scenes, Forensic Sci. Int., 313, 10.1016/j.forsciint.2020.110351 Oh, 2016, Temporal stability of the human skin microbiome, Cell, 165, 854, 10.1016/j.cell.2016.04.008 Yang, 2019, Cigarette smoking and oral microbiota in low-income and African-American populations, J. Epidemiol. Community Health, 73, 1108, 10.1136/jech-2019-212474