Choi KM, Zissler A, Kim E, Ehrenfellner B, Cho E, Lee SI, Steinbacher P, Yun KN, Shin JH, Kim JY, Stoiber W, Chung H, Monticelli FC, Kim JY, Pittner S (2019) Postmortem proteomics to discover biomarkers for forensic PMI estimation. Int J Legal Med 133(3):899–908. https://doi.org/10.1007/s00414-019-02011-6
Article PubMed PubMed Central Google Scholar
van Daalen MA, de Kat DS, Oude Grotebevelsborg BF, de Leeuwe R, Warnaar J, Oostra RJ, WL MD-H (2017) An aquatic decomposition scoring method to potentially predict the Postmortem Submersion Interval of Bodies Recovered from the North Sea. J Forensic Sci 62(2):369–373. https://doi.org/10.1111/1556-4029.13258
Perez-Carceles MD, del Pozo S, Sibon A, Noguera JA, Osuna E, Vizcaya MA, Luna A (2012) Serum biochemical markers in drowning: diagnostic efficacy of Strontium and other trace elements. Forensic Sci Int 214(1–3):159–166. https://doi.org/10.1016/j.forsciint.2011.07.047
Article CAS PubMed Google Scholar
Zilg B, Alkass K, Berg S, Druid H (2016) Interpretation of postmortem vitreous concentrations of sodium and chloride. Forensic Sci Int 263:107–113. https://doi.org/10.1016/j.forsciint.2016.04.006
Article CAS PubMed Google Scholar
Zimmerman KA, Wallace JR (2008) The potential to determine a postmortem submersion interval based on algal/diatom diversity on decomposing mammalian carcasses in brackish ponds in Delaware. J Forensic Sci 53(4):935–941. https://doi.org/10.1111/j.1556-4029.2008.00748.x
Piette MH, De Letter EA (2006) Drowning: still a difficult autopsy diagnosis. Forensic Sci Int 163(1–2):1–9. https://doi.org/10.1016/j.forsciint.2004.10.027
Schneppe S, Dokter M, Bockholdt B (2021) Macromorphological findings in cases of death in water: a critical view on drowning signs. Int J Legal Med 135(1):281–291. https://doi.org/10.1007/s00414-020-02469-9
Locci E, Stocchero M, Noto A, Chighine A, Natali L, Napoli PE, Caria R, De-Giorgio F, Nioi M, d’Aloja E (2019) A (1)H NMR metabolomic approach for the estimation of the time since death using aqueous humour: an animal model. Metabolomics 15(5):76. https://doi.org/10.1007/s11306-019-1533-2
Article CAS PubMed Google Scholar
Levi H, Elkon R, Shamir R (2021) DOMINO: a network-based active module identification algorithm with reduced rate of false calls. Mol Syst Biol 17(1):e9593. https://doi.org/10.15252/msb.20209593
Article CAS PubMed PubMed Central Google Scholar
Cheng S, Shah SH, Corwin EJ, Fiehn O, Fitzgerald RL, Gerszten RE, Illig T, Rhee EP, Srinivas PR, Wang TJ, Jain M, American Heart Association Council on Functional G, Translational B, Council C, Stroke N, Council on, Clinical C, Stroke C (2017) Potential Impact and Study Considerations of Metabolomics in Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association. Circ Cardiovasc Genet 10 (2). https://doi.org/10.1161/HCG.0000000000000032
Hollywood K, Brison DR, Goodacre R (2006) Metabolomics: current technologies and future trends. Proteomics 6(17):4716–4723. https://doi.org/10.1002/pmic.200600106
Article CAS PubMed Google Scholar
Wei Z, Dong Z, Jia J, Liang X, Wang T, Hu M, Fu S, Yun K (2021) Application of Q-TOF-MS based metabonomics techniques to analyze the plasma metabolic profile changes on rats following death due to acute intoxication of phorate. Int J Legal Med 135(4):1437–1447. https://doi.org/10.1007/s00414-021-02532-z
Zhang FY, Wang LL, Dong WW, Zhang M, Tash D, Li XJ, Du SK, Yuan HM, Zhao R, Guan DW (2022) A preliminary study on early postmortem submersion interval (PMSI) estimation and cause-of-death discrimination based on nontargeted metabolomics and machine learning algorithms. Int J Legal Med. https://doi.org/10.1007/s00414-022-02783-4
Poloz YO, O’Day DH (2009) Determining time of death: temperature-dependent postmortem changes in calcineurin A, MARCKS, CaMKII, and protein phosphatase 2A in mouse. Int J Legal Med 123(4):305–314. https://doi.org/10.1007/s00414-009-0343-x
Janssen I, Heymsfield SB, Wang ZM, Ross R (2000) Skeletal muscle mass and distribution in 468 men and women aged 18–88 year. J Appl Physiol (1985) 89(1):81–88. https://doi.org/10.1152/jappl.2000.89.1.81
Article CAS PubMed Google Scholar
Du T, Lin Z, Xie Y, Ye X, Tu C, Jin K, Xie J, Shen Y (2018) Metabolic profiling of femoral muscle from rats at different periods of time after death. PLoS ONE 13(9):e0203920
Article PubMed PubMed Central Google Scholar
Lu XJ, Li J, Wei X, Li N, Dang LH, An GS, Du QX, Jin QQ, Cao J, Wang YY, Sun JH (2023) A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques. Int J Legal Med 137(1):237–249. https://doi.org/10.1007/s00414-022-02844-8
Pesko BK, Weidt S, McLaughlin M, Wescott DJ, Torrance H, Burgess K, Burchmore R (2020) Postmortomics: the potential of untargeted metabolomics to highlight markers for Time since Death. #N/A 24(11):649–659. https://doi.org/10.1089/omi.2020.0084
Wang LL, Zhang FY, Dong WW, Wang CL, Liang XY, Suo LL, Jian C, Zhang M, Guo XS, Jiang PH, Guan DW, Zhao R (2020) A novel approach for the forensic diagnosis of drowning by microbiological analysis with next-generation sequencing and unweighted UniFrac-based PCoA. Int J Legal Med 134(6):2149–2159. https://doi.org/10.1007/s00414-020-02358-1
Katajamaa M, Oresic M (2007) Data processing for mass spectrometry-based metabolomics. J Chromatogr 1158(1–2):318–328. https://doi.org/10.1016/j.chroma.2007.04.021
Banoei MM, Vogel HJ, Weljie AM, Yende S, Angus DC, Winston BW (2020) Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP). #N/A 24(1):461. https://doi.org/10.1186/s13054-020-03147-3
Breiman L (2001) Random forests. #N/A 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Cutler A, Cutler DR, Stevens JR (2012) Ensemble machine learning. Ensemble Mach Learn
Zysset-Burri DC, Keller I, Berger LE, Largiader CR, Wittwer M, Wolf S, Zinkernagel MS (2020) Associations of the intestinal microbiome with the complement system in neovascular age-related macular degeneration. NPJ Genom Med 5:34. https://doi.org/10.1038/s41525-020-00141-0
Article CAS PubMed PubMed Central Google Scholar
Kimura N, Aso Y, Yabuuchi K, Ishibashi M, Hori D, Sasaki Y, Nakamichi A, Uesugi S, Fujioka H, Iwao S, Jikumaru M, Katayama T, Sumi K, Eguchi A, Nonaka S, Kakumu M, Matsubara E (2019) Modifiable lifestyle factors and cognitive function in older people: a cross-sectional observational study. Front Neurol 10:401. https://doi.org/10.3389/fneur.2019.00401
Article PubMed PubMed Central Google Scholar
Wang M, Gui X, Wu L, Tian S, Wang H, Xie L, Wu W (2020) Amino acid metabolism, lipid metabolism, and oxidative stress are associated with post-stroke depression: a metabonomics study. BMC Neurol 20(1):250. https://doi.org/10.1186/s12883-020-01780-7
Article CAS PubMed PubMed Central Google Scholar
Kim TJ, Park JG, Ahn SK, Kim KW, Choi J, Kim HY, Ha SH, Seo WD, Kim JK (2020) Discrimination of Adzuki Bean (Vigna angularis) geographical origin by targeted and non-targeted metabolite profiling with gas chromatography time-of-flight Mass Spectrometry. Metabolites 10(3). https://doi.org/10.3390/metabo10030112
Cao J, Li J, Gu Z, Niu JJ, An GS, Jin QQ, Wang YY, Huang P, Sun JH (2023) Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia. Int J Legal Med 137(1):169–180. https://doi.org/10.1007/s00414-022-02816-y
Bonicelli A, Mickleburgh HL, Chighine A, Locci E, Wescott DJ, Procopio N (2022) The ‘ForensOMICS’ approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics. #N/A 11. https://doi.org/10.7554/eLife.83658
Aiello D, Luca F, Siciliano C, Frati P, Fineschi V, Rongo R, Napoli A (2021) Analytical Strategy for MS-Based thanatochemistry to Estimate Postmortem interval. J Proteome Res 20(5):2607–2617. https://doi.org/10.1021/acs.jproteome.0c01038
Article CAS PubMed Google Scholar
Claudia-Ferreira A, Barbosa DJ, Saegeman V, Fernandez-Rodriguez A, Dinis-Oliveira RJ, Freitas AR, On Behalf Of The Escmid Study Group Of F, Post-Mortem Microbiology E (2023) The future is now: unraveling the expanding potential of Human (Necro)Microbiome in Forensic investigations. Microorganisms 11(10). https://doi.org/10.3390/microorganisms11102509
Chighine A, Locci E, Nioi M, d’Aloja E (2021) Looking for Post-mortem Metabolomic standardization: waiting for Godot-the importance of Post-mortem interval in forensic metabolomics. Chem Res Toxicol 34(9):1946–1947. https://doi.org/10.1021/acs.chemrestox.1c00211
Comments (0)