Search

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

Microorganism plays a vital role in the decomposition of vertebrate remains in nature nutrient cycling, where the postmortem microbial succession patterns during decomposition remain unclear. Here, hierarchical clustering based on Manhattan distances analyzed the similarity and difference among postmortem intestine microbial succession patterns from microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns are obtained. Generally, the normal intestinal flora in the cecum gradually decreases with changes in living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied with oxygen consumption. Furthermore, a random forest regression model is developed to predict postmortem interval with the microbial succession trend data set. The model demonstrates a mean absolute error of 20.01 hours and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and Lachnospiraceae NK4A136 group are considered as significant biomarkers for this model according to the ranked list. This study explores microbial succession patterns in terms of relative abundances and variety, aiding in predicting postmortem intervals, offering some knowledge of microbial behaviors in decomposition ecology.

Polymeric surfactant can simultaneously thicken aquous phase and reduce water–oil interfacial tension (IFT), which makes it present a great application prospect in the oilfield taword enhanced oil recovery (EOR). There are two kinds of polymeric surfactant with different molecular structures are commonly used to realize the salt resistance, thermal endurance and shear resistance. However, the influence of the molecular structure of polymeric surfactant on their properties related on EOR are not well understood. In the work, we synthesized two kinds of polymeric surfactants with different molecular structure which respectively named intermolecular association molecule (IAM) and rigid chain molecule (RCM). Their physico-chemical properties, porous media transportation and EOR are compared with a series of experiments. It is found that the hydrodynamic characteristic sizes and retention of IAM are higher than those of RCM via the membrane filtration test and nuclear magnetic resonance (NMR) scan. These features make IAM have a larger resistance factor and residual resistance factor than RCM and hydrolyzed polyacrylamide (HPAM) in the same viscosity. Compare to HPAM with shear thinning, RCM presents a Newtonian fluid region and IAM presents a shear thickening region when the shear rate is lower than 1 s−1. The spatial structure of IAM can pack oil drop into its network, which make it easy to form oil-in-water-in-oil (O/W/O) emulsions. Moreover, the concentration and water–oil ratio (WOR) rarely affect the stability of IAM emulsions once the intermolecular association generated. The increamental oil recovery factor of IAM is 17.5% after water flooding in the three-layer core flooding experiments with the permeability of 500 mD, 1500 mD and 3000 mD at 55℃, which is higher than that of 14.5% for RCM and 11.4% for HPAM.

Hyperlipidemia is one kind of metabolic syndrome for which the treatment commonly includes simvastatin (SV). Individuals vary widely in statin responses, and growing evidence implicates gut microbiome involvement in this variability. However, the associated molecular mechanisms between metabolic improvement and microbiota composition following SV treatment are still not fully understood. In this study, combinatory approaches using ultrahigh-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS/MS)-based metabolomic profiling, PCR-denaturing gradient gel electrophoresis (PCR-DGGE), quantitative PCR (qPCR), and 16S rRNA gene sequencing-based gut microbiota profiling were performed to investigate the interplay of endogenous metabolites and the gut microbiota related to SV treatment. A total of 6 key differential endogenous metabolites were identified that affect the metabolism of amino acids (phenylalanine and tyrosine), unsaturated fatty acids (linoleic acid and 9-hydroxyoctadecadienoic acid (9-HODE)), and the functions of gut microbial metabolism. Moreover, a total of 22 differentially abundant taxa were obtained following SV treatment. Three bacterial taxa were identified to be involved in SV treatment, namely, Bacteroidaceae, Prevotellaceae, and Porphyromonadaceae. These findings suggested that the phenylalanine and tyrosine-associated amino acid metabolism pathways, as well as the linoleic acid and 9-HODE-associated unsaturated fatty acid metabolism pathways, which are involved in gut flora interactions, might be potential therapeutic targets for improvement in SV hypolipidemic efficacy. The mass spectrometric data have been deposited to MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp). Username: MSV000087842_reviewer. Password: hardworkingzsr.

Trauma is one of the most common conditions in the biomedical field. It is important to identify it quickly and accurately. However, when evanescent trauma occurs, it presents a great challenge to professionals. There are few reports on the establishment of a rapid and accurate trauma identification and prediction model. In this study, Fourier transform infrared spectroscopy (FTIR) and microscopic spectroscopy (micro-IR) combined with chemometrics were used to establish prediction models for the rapid identification of muscle trauma in humans and rats. The results of the average spectrum, principal component analysis (PCA) and loading maps showed that the differences between the rat muscle trauma group and the rat control group were mainly related to biological macromolecules, such as proteins, nucleic acids and carbohydrates. The differences between the human muscle trauma group and the human control group were mainly related to proteins, polysaccharides, phospholipids and phosphates. Then, a partial least squares discriminant analysis (PLS-DA) was used to evaluate the classification ability of the training and test datasets. The classification accuracies were 99.10% and 93.69%, respectively. Moreover, a trauma classification and recognition model of human muscle tissue was constructed, and a good classification effect was obtained. The classification accuracies were 99.52% and 91.95%. In conclusion, spectroscopy and stoichiometry have the advantages of being rapid, accurate and objective and of having high resolution and a strong recognition ability, and they are emerging strategies for the identification of evanescent trauma. In addition, the combination of spectroscopy and stoichiometry has great potential in the application of medicine and criminal law under practical conditions.

With the rapid digitalization and technological advancements of the modern society, the seriously increasing electromagnetic pollution has created stronger demand for advanced electromagnetic wave shielding and absorbing materials in both civilian and military fields. Multifunctional materials with ultra-lightweight, wide bandwidth, highly flexible and robustness are the future development direction to meet the applications. Graphene and MXene, as two typical 2D nanomaterials, have attracted considerable interests for the electromagnetic wave shielding and absorbing materials. More importantly, three dimensional (3D) cross-linked graphene or/and MXene based materials, where graphene or/and MXene sheets are interconnected into a macroscopic monolithic bulk material, can achieve excellent electromagnetic interference (EMI) shielding and microwave absorption (MA) performance, together with lightweight, exceptional mechanical properties and so on. This review addresses the construction methods of graphene and MXene based 3D cross-linked materials, and the effects of different fabrication methods toward the structure and EMI shielding and/or MA properties are discussed. Additionally, some perceptions and discussions are proposed for the future studies.

A Gram-negative, non-motile rod and strictly aerobic bacterium, designated as 18B16333

Once the body dies, the indigenous microbes of the host begin to break down the body from the inside and play a key role thereafter. This study aimed to investigate the probable shift in the composition of the rectal microbiota at different time intervals up to 15 days after death and to explore bacterial taxa important for estimating the time since death. At the phylum level, Proteobacteria and Firmicutes showed major shifts when checked at 11 different intervals and emerged at most of the postmortem intervals. At the species level, Enterococcus faecalis and Proteus mirabilis showed a downward and upward trend, respectively, after day 5 postmortem. The phylum-, family-, genus-, and species-taxon richness decreased initially and then increased considerably. The turning point occurred on day 9, when the genus, rather than the phylum, family, or species, provided the most information for estimating the time since death. We constructed a prediction model using genus-level data from high-throughput sequencing, and seven bacterial taxa, namely, Enterococcus, Proteus, Lactobacillus, unidentified Clostridiales, Vagococcus, unidentified Corynebacteriaceae, and unidentified Enterobacteriaceae, were included in this model. The abovementioned bacteria showed potential for estimating the shortest time since death.

A novel anaerobic maifanite-immobilized sludge reactor (AMSR) was employed to investigate the feasibility and performance of continuous hydrogen production for the treatment of pharmaceutical intermediate wastewater (PIW) at different organic loading rates (OLR) (from 12 to 96 g COD L−1 d−1) according to changes in the hydraulic retention time (HRT). A reactor without maifanite was also employed as a control. The results indicate that maifanite accelerates granular sludge formation and the AMSR presents more efficient and stable performance than the control in terms of the hydrogen production rate. In the AMSR, the highest hydrogen production rate of 11.2 ± 0.4 mmol L−1 h−1 was achieved at an optimum OLR of 72 g COD L−1 d−1. The main metabolic route for hydrogen production was ethanol-type fermentation, which was reflected in the relative abundance of E. harbinense, which was dominant for all of the OLRs. The maximum energy conversion efficiency in the dual production of hydrogen and ethanol was determined to be 24.5 kJ L−1 h−1 at an OLR of 72 g COD L−1 d−1.

The determination of cause of death (COD) is one of the most important tasks in forensic practice and is mainly based on macroscopical and microscopical morphological signatures. However, some CODs are hard to determine because the significant morphological signatures can be nonspecific, variable, subjective, or even absent in the real world. In this study, gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS)-based untargeted metabolomics was employed to obtain plasma metabolic profiles of rats that died from anaphylactic shock (AS), mechanical asphyxia (MA), or sudden cardiac death (SCD). The metabolic alterations of each COD group compared to the control group were investigated using a principal component analysis, partial least-squares discriminant analysis, the Wilcoxon test, and fold change analysis. A range of differential features was screened, and 11, 8, and 7 differential metabolites were finally verified for the AS, MA, and SCD groups, respectively. We proposed some explanations that may account for these metabolic differences, including glucose metabolism, the tricarboxylic acid cycle, glycolysis, lipid metabolism, creatinine catabolism, and purine metabolism. Next, for each COD, we used its differential metabolites, which were obtained through comparisons of each COD group to the control group and represented the metabolic changes of the individual COD, to perform a receiver operating characteristic (ROC) analysis to preliminarily evaluate their ability to discriminate each COD group from the other COD groups. We found that creatinine in the AS group and malic acid and uric acid in the MA group might represent some specific metabolic changes for the relevant COD with high areas under the curve in the ROC curve analysis. Moreover, the combination panel for AS or MA also showed a good ability to discriminate it from the others. However, SCD had fewer metabolic signatures and was relatively harder to discriminate from the other CODs in our work. The preliminary study demonstrates the feasibility of GC-HRMS-based untargeted metabolomics as a promising tool to reveal metabolic changes in different death processes and to determine the complex CODs.

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

Semen stain is one of the most important biological evidence at sexual crime scenes. Age estimation of human semen stains plays an important role in forensic work, and it is rarely studied due to lack of well-established methods. In this study, the technique called attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) coupled with advanced chemometric methods was employed to determine the age of semen stains on three different substrates: glass slides, tissues and fabric made of regenerated cellulose fibres up to 6 d. Partial least squares regression (PLSR) was used in conjunction with spectral analysis for age estimation, and the results generated high R2 values (cross-validation: 0.81, external validation: 0.74) but a narrow margin of error for root mean square error (RMSE) (RMSE of cross-validation: 0.77 d, RMSE of prediction: 1.02 d). Additionally, our results indicated the robustness of PLSR model was not weaken by the influence of different substrates in this study. Our results indicate that ATR-FTIR, combined with chemometric methods, shows great potential as a convenient and efficient tool for age estimation of semen stains. Moreover, the method could be applied to routine forensic investigations in the future.

Traumatic lung injury (TLI), which is a common mechanical injury, is receiving increasing attention because of its serious hazards. In forensic practices, accurately identifying TLI is of great importance for investigations and case trials. The main goal of this research was to identify TLI utilizing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics. The macroscopic appearance of lung tissue showed that identifying TLI in lung tissue at the decomposition stage is not feasible by only visualization, and significant pulmonary hypostasis was observed in the lungs regardless of whether the lung tissue was injured. Average spectra and principal component analysis (PCA) suggested that the biochemical difference between injured lung tissue samples from the TLI group and noninjured lung tissue samples from the negative control group was mainly attributed to the different structures and contents of proteins. Partial least squares discriminant analysis (PLS-DA) was then utilized to identify TLI with an accuracy of 96.4% and 98.6% based on the training set and the test set, respectively. Next, we focused on samples that were misclassified in the model and proposed that the misclassification could be caused by the pulmonary hypostasis effect. Therefore, two additional PCA and PLS-DA models were created to identify the pulmonary hypostatic areas between the TLI group and the negative control group and the nonpulmonary hypostatic areas between the TLI group and the negative control group. The PCA results indicated that the biochemical difference between the two groups was still associated with proteins, and the two PLS-DA models achieved 100% accuracy based on both the training and test sets. This result indicated that when pulmonary hypostasis was considered and the lung tissue was divided into pulmonary hypostatic areas and nonpulmonary hypostatic areas for separate comparisons, TLI identification was achieved with a greater accuracy than that obtained when the two areas were combined. This research confirms that the combined application of ATR-FTIR spectroscopy and chemometrics can be utilized to accurately identify TLI.

Oleanolic acid 3-O-β-d-glucopyranosyl-(1 → 3)-β-d-glucopyranoside (1) and oleanolic acid 3-O-β-d-glucopyranosyl-(1 → 3)-[α-l-arabinofuranosyl-(1 → 4)]-β-d-glucopyranoside (2), novel Panax stipulcanatus saponin analogues, were synthesized for the first time starting from commercially available oleanolic acid, d-glucose, and L-(+)-arabinose. Glycosyl N-phenyltrifluoroacetimidates as donors and two-step consecutive glycosylation reactions are crucial in the synthesis. In vitro antifungal activity results indicated that analogue 2 combined with fluconazole showed synergistic antifungal activity against fluconazole-resistant Candida albicans, with MIC

A Gram-stain-positive, facultatively anaerobic, non-motile, endospore-forming and rod-shaped bacterium, occurring singly or in pairs, designated TB2019T, was isolated from environmental monitoring samples of corridor air collected at the Tianjin Institute for Drug Control, Tianjin Province (PR China). The isolate was able to grow at 15–40 °C (optimum growth at 37 °C), pH 6.0–8.0 (pH 7.0) and in the presence of 0–2% (w/v) NaCl (0% NaCl). Comparison of 16S rRNA gene sequences indicated that TB2019T was most closely related to Paenibacillus typhae CGMCC 1.11012T (98.63%), Paenibacillus albidus Q4-3T (98.19%), Paenibacillus borealis DSM 13188T (97.55%), Paenibacillus helianthi P26ET (97.33%) and Paenibacillus odorifer DSM 15391T (97.19%). The digital DNA–DNA hybridization and the average nucleotide identity values between TB2019T and the five type strains mentioned above ranged from 20.7 to 25.0% and 75.2 to 81.3%, respectively, and the genomic DNA G+C content was 49.52 mol%. The diagnostic cell-wall sugar was ribose, and the diagnostic amino acid was meso-diaminopimelic acid. The polar lipids of TB2019T included diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, three unidentified aminophospholipids and one unidentified phospholipid. MK-7 was the predominant menaquinone, and anteiso-C15:0 (30.6%) was the major fatty acid. Based on the polyphasic taxonomic data, strain TB2019T represents a novel species of the genus Paenibacillus , for which the name Paenibacillus tianjinensis sp. nov. is proposed. The type strain is TB2019T (=CICC 25065T=JCM 34610T).

Aquaporins, the major facilitators of water transport across membranes, are involved in growth and development and adaptation to drought stress in plants. In this study, a plasma membrane intrinsic protein (SiPIP2;4) was cloned from Saussurea involucrata, a cold-tolerant hardy herb. The expression of SiPIP2;4 increased the stomatal density and sensitivity of tobacco (Nicotiana tabacum), thus, affecting the plant's growth and resistance to the diverse water environment. The higher stomatal density under well-watered conditions effectively promoted the photosynthetic rate, which led to the rapid growth of transgenic lines. The stomata in the transgenic lines responded more sensitively to the vapor pressure deficit than the wild-type under different levels of ambient humidity. Their stomatal apertures positively correlated with the ambient humidity. Under drought conditions, the overexpression of SiPIP2;4 promoted rapid stomatal closure, reduced water dissipation, and enhanced drought tolerance. These results indicate that SiPIP2;4 regulates the density and sensitivity of plant stomata, thus, playing an important role in balancing plant growth and stress tolerance. This suggests that SiPIP2;4 has the potential to serve as a genetic resource for crop improvement.

Zeolitic imidazolate framework-8 (ZIF-8) is regarded as an excellent adsorbent material for removing diverse toxic or hazardous gases. However, its milli- to nanoscale size typically restricts its applications. In this study, we demonstrate the ZIF-8 nanocrystals assembled on TiO2-coated activated carbon fiber felts via atomic layer deposition (ZIF-8@ALD-ACF felts), which can be used as a highly efficient adsorbent for HCHO removal. Using the TiO2-coated ACF felts as substrates via ALD can provide large number of growth sites for ZIF-8 nanocrystals, which results in fast and highly efficient removal of HCHO. Furthermore, ZIF-8@ALD-ACF felts achieve a highly efficient removal of HCHO under ultraviolet (UV) light. HCHO can be completely removed at high concentration within 110 min. Moreover, under UV light, it can be completely removed at low concentration within 6 min. The micropores in ACF and nanocages in ZIF-8 are suitable for small molecular HCHO uptaking and provide sufficient contact time for photocatalysis by TiO2. The ALD technology provides a useful methodology for the practical application of MOF-based materials.

Novel nanoscale lanthanide and mixed lanthanide metal-organic frameworks with thermo-sensitive polymer as organic links were successfully prepared. They were obtained through biomimetic mineralization approach. This is the first report of fabrication of polymer-metal-organic frameworks via biomimetic mineralization. The particle size was in the range from 200 to 500 nm. The photoluminescent properties of the materials have also been studied. The fluorescence intensity changed in different solvent (N, N-dimethylformamide, tetrahydrofuran, ethanol, acetone, dichloromethane, water), so solvent recognition can be achieved by using the polymer-metal-organic frameworks. The materials are also very sensitive to water (even trace water) in the mixed solutions composed of water and organic solvents. Especially the mixed lanthanide polymer-metal-organic frameworks can be designed to a ratiometric fluorescent nanosensor. The novel fluorescence detection materials have very important potential applications in the field of chemical industry.

In this study, two novel composite membranes containing nanoscale ZIF-8 and polyphenylene sulfide (PPS) nonwoven fabric were prepared via hydrothermal (PPS-ZIF-8) and biomimetic mineralization (PPS-ZIF-8-BSA; BSA, bovine serum albumin) approaches. The biomimetic mineralization approach in particular was extremely rapid and mild, and crystalline ZIF-8 was coated on the PPS substrate in only a few seconds at room temperature. The maximum iodine adsorption capacities of the PPS-ZIF-8 and PPS-ZIF-8-BSA membranes were 2.51 and 2.07 g/g, respectively. The composite fibrous membranes were able to capture trace iodine vapor under differential pressures ranging from 0 to 1000 Pa without almost any iodine vapor leakage. The composite membranes can be applied in harsh environments because of the excellent stability of ZIF-8 and the PPS high-performance fibers. This study provides a promising strategy to fabricate novel adsorption materials for the collection of radioactive iodine during nuclear waste disposal.

In traditional Chinese medicine, Lysimachiae Herba (LH) and Desmodii Styracifolii Herba (DSH) have been widely used for the treatment of calculi, but there is a certain focus in clinical application. Flavonoids as their pharmacologically active substances were focusly studied to make clear of their chemical compositions and reveal the similarities and differences between LH and DHS by analysis of characteristic marker components at the molecular level. An ultra high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS) approach based on metabolite profiling was established. The high-resolution data was acquired through data dependent acquisition (DDA) mode. Based on the targeted and untargeted analytical strategies, a total of 113 compounds were identified, of which 80 compounds existed in LH and 61 in DSH. Then multivariate statistical analysis was applied to further find the characteristic marker components, and a total number of 21 variables were screened as the valuable variables for discrimination. By matching with identified flavonoids, these 21 variables were corresponding to 15 flavonoids (including 6 from LH and 9 from DSH) which were firstly identified as the marker compounds. These results indicated that the UPLC-QTOF-MS/MS method with analysis strategy was a powerful tool for rapidly identification and screening of marker compounds of flavonoids between LH and DSH, and the 15 screened marker compounds provide a chemical basis for the further researches on the mechanisms of LH and DSH in the treatment of cholelithiasis and nephrolithiasis respectively.

The identification of antemortem and postmortem fractures is a critical and challenging task for forensic researchers. Based on our preliminary studies, we explored whether the combination of Fourier transform infrared spectroscopy (FTIR) and chemometrics can identify antemortem and postmortem fractures in complex environments. The impacts of the four environments on the bone spectrum were analyzed by principal component analysis (PCA). It was found that the bone degradation rate in the submerged and ground surface (GS) environments was higher than that in the buried and constant temperature and moisture (CTM) environments. Additionally, the bone degradation rate in buried environment higher than that in the CTM environment. The average spectrum, PCA and partial least squares discriminant analysis (PLS-DA) results all revealed that there were significant differences between the antemortem fracture and the remaining three groups in a complex environment. Compared with the antemortem fracture, the antemortem fracture control (AFC) and postmortem fracture control (PFC) tended to be more similar to the postmortem fracture. According to the loading plot, amide I and amide II were the main components that contributed to the identification of the antemortem fracture, AFC, postmortem fracture, and PFC. Finally, we established a differential model for the antemortem and postmortem fractures (an accuracy of 96.9%), and a differentiation model for the antemortem fracture, AFC, postmortem fracture, and PFC (an accuracy of 87.5%). In conclusion, FTIR spectroscopy is a reliable tool for the identification of antemortem and postmortem fractures in complex environments.

Determination of the cause of death for diabetic ketoacidosis (DKA), a common and fatal acute complication of diabetes mellitus, is a challenging forensic task owing to the lack of characteristic morphological findings at autopsy. In this study, Fourier-transform infrared (FTIR) microspectroscopy coupled with chemometrics was employed to characterize biochemical differences in pulmonary edema fluid from different causes of death to supplement conventional methods and provide an efficient postmortem diagnosis of DKA. With this aim, FTIR spectra in three different situations (DKA-caused death, other causes of death with diabetes history, and other causes of death without diabetes history) were measured. The results of principal component analysis indicated different spectral profiles between these three groups, which mainly exhibited variations in proteins. Subsequently, two binary classification models were established using an algorithm of partial least squares discriminant analysis (PLS-DA) to determine whether decedents had diabetes and whether the diabetic patients died from DKA. Satisfactory prediction results of PLS-DA models demonstrated good differentiation among these three groups. Therefore, it is feasible to make a postmortem diagnosis of DKA and detect diabetes history via FTIR microspectroscopic analysis of the pulmonary edema fluid.

The determination of cause of death is one of the most important tasks in forensic practice. However, asphyxia is a difficult cause of death to determine, especially when the deceased has an underlying disease that can lead to a sudden unexpected death, such as coronary atherosclerotic heart disease (CAHD, which is the leading cause of sudden cardiac death, SCD), because its determination is currently still based on an exclusion strategy. In this study, gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS)-based untargeted metabolomics was employed to obtain the pulmonary metabolic profiles of rats who died from asphyxia and SCD. First, fourteen metabolites were identified to investigate the mechanism of death from asphyxia, and we proposed some explanations that may account for these metabolic alterations, including the perturbation of amino acid metabolism, lipid metabolism, and energy metabolism (TCA cycle). Second, we discovered eight potential biomarkers to differentiate between asphyxia and SCD as the cause of death. The excellent classification performances of the eight individual biomarkers and their combination in fresh lung tissue were observed. Third, we also explored the relative change in the concentration of the eight metabolites and their classification performance in decomposed tissue (at 24 h postmortem). Lactic acid, pantothenic acid, and the combination of the eight biomarkers can be recognized as perfect classifiers to discriminate asphyxia from SCD even when decomposition has occurred. Our results showed that GC-HRMS-based untargeted metabolomics can be used as a promising tool to explore the metabolic alterations of the death process and to determine the cause of death.

Once the body dies, the inherent microbes of the host begin to break down from the inside and play a key role thereafter. It is hypothesized that after the death certain rectal microbes would change during the decomposition course in the body. This study aimed to investigate the probable shift in the composition of the rectal flora at different time intervals up to 15 days after death and to explore bacterial taxa important for estimating the time of death. At the phylum level, Proteobacteria and Firmicutes showed major shifts, when checked at 11 different intervals, and emerged at most of the postmortem intervals. At the species level, Enterococcus faecalis and Proteus mirabilis existed at most postmortem intervals; the former showed a downward trend after day 5 postmortem, while the latter showed an upward trend. There were obvious differences in bacterial community structure and richness at the phylum, genus, and species levels during the decomposition of the corpse of rats. The phylum, genus, and species taxa richness decreased initially and then increased significantly. The turning point came on day 9 when genus, rather than phylum or species, contained the most information for estimating the time of death. We constructed a prediction model using genus taxon data from high-throughput sequencing, which explained 87.2% of the time since the first sampling within 1 h. Seven bacteria, namely Enterococcus, Proteus, Lactobacillus, unidentified Clostridiales, Vagococcus, unidentified Corynebacteriaceae, and unidentified Enterobacteriaceae, were included in this model. The above-mentioned bacteria showed a promising future for estimating the shortest time of death and results of current study were agreeing with the proposed hypothesis.

Species identification is very important in forensic science case. However, the existing methods in forensic practice to identifying the species of bone and teeth are not objective, accurate or brief enough. We have reported the classification of bone species by Fourier transform infrared (FT-IR) and chemometric methods. Here we further use this method to realize the rapid detection of teeth species. 50 teeth samples from human and non-human (bovine, dog, rat, rabbit) were used in this study. Uncontrolled environment conditions were set to simulate real forensic casework. Teeth sample were prepared by grinding powder and pressed into KBr tablet, then the spectral data were collected. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used in the study. The internal and external validations of PLS-DA results were 97.1% and 93.3% accuracy, respectively. The results illustrate that FT-IR spectroscopy be used as a practical tool to identify species of unknown teeth.

In the current era of urbanization and industrialization, water pollution and energy generation are the main concerns in the climate change context. Microbial Fuel Cells (MFCs) utilize biodegradable carbon compounds in organic waste to produce an electric current.MFC technology provides sustainable solutions for renewable power systems and energy-efficient wastewater treatment for distributed electricity systems. However, in wastewater treatment, generating usable energy from the MFC remains a significant challenge for the system scale and implementation. Hence, in this paper, the Optimized Hybridized Mathematical Model (OHMM) has been proposed for wastewater treatment and energy generation using microbial fuel batteries. Furthermore, statistical analysis has a role in synthesizing knowledge of fuel cells, defining and limiting factors in electricity production. The findings show that the proposed OHMM method, Sensitivity Analysis, Uncertainty Analysis, and Accuracy reduces energy consumption and cost function compared to other existing methods. With more miniature steam, it reduces the amount of harmful smoke that power plants emit, preserves the natural resources of a planet, and protects habitats against destruction. They will lead to a healthier and happier world by taking action to reduce the battery.