Dataset Information

Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

ABSTRACT: An assessment of levels of Insulin in cadaveric fluids, to estimate the postmortem interval (PMI) was carried out. To profile postmortem changes of Insulin, it was extracted at different intervals i.e. (0, 3, 6, 12, 24 h), from the heart of 22 human cadavers. The cases included were the subjects of accidental deaths without any prior history of disease and their exact time of death was known. Immunoanalyzer Cobas e-411 instrument was used to detect the relationship between the amount of Insulin and PMI. Level of Insulin was measured in cardiac blood. Statically, significant correlations between levels of Insulin and PMI were studied and correlation coefficients were calculated. SPSS (version 12.0) was used for statistical analysis. Insulin levels in cadaver blood are correlated significantly with PMI with a p value of <0.001. When insulin level increases by 1 unit the duration decreases by 0.93 units. The least square regression line is: [Duration(Y)=22.71-0.93 Insulin level (X)].

SUBMITTER: Kumar S

PROVIDER: S-EPMC4781973 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

Kumar Sachil S Verma Anoop K AK

Data in brief 20160302

An assessment of levels of Insulin in cadaveric fluids, to estimate the postmortem interval (PMI) was carried out. To profile postmortem changes of Insulin, it was extracted at different intervals i.e. (0, 3, 6, 12, 24 h), from the heart of 22 human cadavers. The cases included were the subjects of accidental deaths without any prior history of disease and their exact time of death was known. Immunoanalyzer Cobas e-411 instrument was used to detect the relationship between the amount of Insulin ...[more]

PMID: 26977436

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Project description:Microbial succession has been suggested to supplement established postmortem interval (PMI) estimation methods for human remains. Due to limitations of entomological and morphological PMI methods, microbes are an intriguing target for forensic applications as they are present at all stages of decomposition. Previous machine learning models from soil necrobiome data have produced PMI error rates from two and a half to six days; however, these models are built solely on amplicon sequencing of biomarkers (e.g., 16S, 18S rRNA genes) and do not consider environmental factors that influence the presence and abundance of microbial decomposers. This study builds upon current research by evaluating the inclusion of environmental data on microbial-based PMI estimates from decomposition soil samples. Random forest regression models were built to predict PMI using relative taxon abundances obtained from different biological markers (bacterial 16S, fungal ITS, 16S-ITS combined) and taxonomic levels (phylum, class, order, OTU), both with and without environmental predictors (ambient temperature, soil pH, soil conductivity, and enzyme activities) from 19 deceased human individuals that decomposed on the soil surface (Tennessee, USA). Model performance was evaluated by calculating the mean absolute error (MAE). MAE ranged from 804 to 997 accumulated degree hours (ADH) across all models. 16S models outperformed ITS models (p = 0.006), while combining 16S and ITS did not improve upon 16S models alone (p = 0.47). Inclusion of environmental data in PMI prediction models had varied effects on MAE depending on the biological marker and taxonomic level conserved. Specifically, inclusion of the measured environmental features reduced MAE for all ITS models, but improved 16S models at higher taxonomic levels (phylum and class). Overall, we demonstrated some level of predictability in soil microbial succession during human decomposition, however error rates were high when considering a moderate population of donors.

Dataset Information

Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

Publications

Estimation of postmortem interval using the data of insulin level in the cadaver׳s blood.

Similar Datasets

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