Metabolites, as products of cellular metabolism, can provide a wealth of biological information and are less susceptible to degradation than other biomarkers due to their low molecular weight. Due to these properties, metabolites can be used as valuable biomarkers for forensic investigations. Knowing the timing of deposition of bloodstain could help to reconstruct crime scenes, draw conclusions about the time of the crime, and narrow down the circle of possible suspects. Previous studies have indicated that the concentration of some metabolites in blood is subject to circadian changes. However, the circadian metabolites of bloodstains have been still unclear. A total of sixty-four bloodstain samples were prepared under real conditions in three time categories (morning/noon (09:00 h ∼ 17:00 h), afternoon/evening (18:00 h ∼ 23:00 h) and night/early morning (24:00 h ∼ 08:00 h)). Fifty metabolites of bloodstains with significant differences were identified in the three time categories. Twenty-eight of these metabolites exhibited significant circadian changes. Finally, three independently contributing circadian metabolites were selected to build the logistic regression model, with an area under the curve of 0.91, 0.84 and 0.87 for the prediction of bloodstain deposition time in the morning/noon, afternoon/evening and night/early morning, respectively. The study indicated that circadian metabolites can be used for evaluating the timing of bloodstain deposition. This would provide a valuable perspective for analyzing the deposition time of biological traces in forensic investigations.

DNA degradation in biological material needs to be better understood. Bloodstains on washed clothing are disturbed by washing procedures, sometimes transferred to other fabrics, often with latent bloodstains and usually with significantly degraded DNA. The samples (cotton fabric with bloodstains) are divided into six main groups, depending on the washing method regarding water temperature (95, 60, and 30 °C) and the detergent use. After completing the washing process, samples were stored for a certain period (1 day to 6 months) and subsequently analyzed. Analyses were performed using standard protocols and commercial kits to measure the remaining DNA quantity (concentration) and DNA degradation index in the processed samples. Our results revealed that the high washing temperature (60 and 95 °C) and the application of detergent have a synergic action on DNA degradation, while at 30 °C this effect is absent. Furthermore, the effect of detergent on accelerated DNA degradation is observed about a month after the washing. This delayed effect of detergent has no explanation in current literature data. To obtain optimal results from the bloodstains, we recommended that the period from the crime event and attempted cleaning by a perpetrator to the laboratory analysis should be less than 1 month.

Monozygotic twins (MZTs) possess identical genomic DNA sequences and are usually indistinguishable through routine forensic DNA typing methods, which can be relevant in criminal and paternity cases. Recently, novel epigenetic methods involving DNA methylation and microRNA analysis have been introduced to differentiate MZTs. In this study, we explore the potential of using epigenetic markers, specifically circular RNAs (circRNAs), a type of non-coding RNA (ncRNA), to identify MZTs, and investigate the unique expression patterns of circRNAs within pairs of MZTs, enabling effective differentiation. Epigenetics regulates gene expression at the post-transcriptional level and plays a crucial role in cell growth and aging. CircRNAs, a recently characterized subclass of ncRNA, have a distinct covalent loop structure without the typical 5\' cap or 3\' tail. They have been reported to modulate various cellular processes and play roles in embryogenesis and eukaryotic development. To achieve this, we conducted a comprehensive circRNA sequencing analysis (circRNA-seq) using total RNA extracted from the blood samples of five pairs of MZTs. We identified a total of 15,257 circRNAs in all MZTs using circRNA-seq. Among them, 3, 21, 338, and 2967 differentially expressed circRNAs (DEcircRNAs) were shared among five, four, three, and two pairs of MZTs, respectively. Subsequently, we validated twelve selected DEcircRNAs using real-time quantitative polymerase chain reaction (RT-qPCR) assays, which included hsa_circ_0004724, hsa_circ_0054196, hsa_circ_004964, hsa_circ_0000591, hsa_circ_0005077, hsa_circ_0054853, hsa_circ_0054716, hsa_circ_0002302, hsa_circ_0004482, hsa_circ_0001103, novel_circ_0030288 and novel_circ_0056831. Among them, hsa_circ_0005077 and hsa_circ_0004482 exhibited the best performance, showing differences in 7 out of 10 pairs of MZTs. These twelve differentially expressed circRNAs also demonstrated strong discriminative power when tested on saliva samples from 10 pairs of MZTs. Notably, hsa_circ_0004724 displayed differential expression in 8 out of 10 pairs of MZTs in their saliva. Additionally, we evaluated the detection sensitivity, longitudinal temporal stability, and suitability for aged bloodstains of these twelve DEcircRNAs in forensic scenarios. Our findings highlight the potential of circRNAs as molecular markers for distinguishing MZTs, emphasizing their suitability for forensic application.

Estimating the time that bloodstains are left at a crime scene can provide invaluable evidence for law enforcement investigations, including determining the time of the crime, linking the perpetrator to the crime scene, narrowing the pool of possible suspects, and verifying witness statements. There have been some attempts to estimate the time since deposition of bloodstains, i.e., how much time has passed since the bloodstain was left at a crime scene. However, most studies focus on the time interval of days. As far as we know, previous study have been conducted to estimate the deposition time of blood within a 24-h day-night cycle. To date, there is a lack of studies on whether rhythmic mRNA of blood is suitable for bloodstain samples. In this study, we estimated the bloodstain deposition time within a 24-h day-night cycle based on the expression of messenger RNAs (mRNAs) by real-time quantitative polymerase chain reaction. Bloodstain samples were prepared from eight individuals at eight time points under real and uncontrolled conditions. Four mRNAs expressed rhythmically and were used to construct a regression model using the k-nearest neighbor (KNN) algorithm, resulting in a mean absolute error of 3.92 h. Overall, using the rhythmic mRNAs, a machine learning model was developed which has allowed us to predict the deposition time of bloodstains within the 24-h day-night cycle in East Asian populations. This study demonstrates that mRNA biomarkers can be used to estimate the bloodstain deposition time within a 24-h period. Furthermore, rhythmic mRNA biomarkers provide a potential method and perspective for estimating the deposition time of forensic traces in forensic investigation. Case samples in forensic analysis are usually limited or degraded, so the stability and sensitivity of rhythmic biomarkers need to be further investigated.

BACKGROUND: Blood and semen stains are the most common biological stains encountered at crime scenes. The washing of biological stains is a common application that perpetrators use to spoil the crime scene. With a structured experiment approach, this study aims to investigate the effects of washing with various chemicals on the ATR-FTIR detection of blood and semen stains on cotton.
METHODS: On cotton pieces, a total of 78 blood and 78 semen stains were applied, and each group of six stains was immersed or mechanically cleaned in water, 40% methanol, 5% sodium hypochlorite solution, 5% hypochlorous acid solution, 5 g/L soap dissolved pure water, and 5 g/L dishwashing detergent dissolved water. ATR-FTIR spectra gathered from all stains and analyzed with chemometric tools.
CONCLUSIONS: According to performance parameters of developed models, PLS-DA is a powerful tool for discrimination of washing chemical for both washed blood and semen stains. Results from this study show that FTIR is promising for use in detecting blood and semen stains that have become invisible to the naked eye due to washing of the findings.
CONCLUSIONS: Our approach allows blood and semen to be detected on cotton pieces using FTIR combined with chemometrics, even though it is not visible to the naked eye. Washing chemicals also can be distinguished via FTIR spectra of stains.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1 ng of genomic DNA and the entire procedure can be completed within 10 h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

Ergothioneine, which is a naturally occurring metabolite, generally accumulates in tissues and cells subjected to oxidative stress, owing to its structural stability at physiological pH; therefore, it has been attracting attention in various biomedical fields. Ergothioneine has also been suggested as a potential forensic marker, but its applicability has not yet been quantitatively validated. In this study, quantitative analysis of ergothioneine in bloodstains was conducted to estimate the age of bloodstains and that of bloodstain donors. Blood from youth and elderly participants was used to generate bloodstains. After extracting metabolites from the bloodstains under prevalent age conditions, ergothioneine levels were quantified by mass spectrometry via multiple reaction monitoring. The concentration of ergothioneine in day 0 bloodstains (fresh blood), was significantly higher in the elderly group than in the youth group, but it did not differ by sex. Statistically significant differences were observed between the samples from the two age groups on days 0, 5 and 7, and on days 2 and 3 compared with day 0. The findings suggest that ergothioneine can be used to estimate the age of bloodstains and of the donor; it could be useful as a potential marker in reconstructing crime scenes.

The volume of blood leaked from blood vessels may change due to evaporation of water under the natural influence of the external environment. Bloodstains and dried blood spots (DBS), which describes blood dried in the external environment, are similar in their production and their metabolite quantification profiles. In both bloodstain metabolite analysis in the forensic science field and DBS metabolite analysis in the clinical field, it is important to determine the volume of the origin blood as this affects metabolite quantification results. Therefore, the purpose of this study is to discover the internal standard metabolites that have quantitatively proportional relationships with origin blood volume and maintain constant concentrations even as the age of the bloodstain increases. As a result, the concentrations of L-isoleucine and L-phenylalanine increased in proportion to the origin blood volume of the bloodstain. The differences in concentration of L-isoleucine were significant in all volume comparisons except in the comparison between 65 μL and 85 μL. The differences in concentration of L-phenylalanine were significant in all volume comparisons except between 65 μL and 45 μL and between 65 μL and 85 μL. In addition, it was confirmed that both metabolites tended to maintain constant concentrations without being affected by bloodstain age as the volume became smaller. These internal standard metabolites can be used for estimating the origin blood volume of bloodstains during metabolite analysis of bloodstains and DBS and could provide a volume criterion for standardization when comparing metabolite quantification between samples.

Establishing a correlation between environmental variables and chemical change can significantly improve the quality of research in multiple fields. Among various environmental variables, temperature and humidity are closely related to the rate of chemical reactions. This study aimed to confirm changes in metabolite markers that were previously discovered in other temperature and humidity environment conditions and to confirm the possibility that they could act as markers. After blood collection from the subjects and bloodstain preparation, the quantitative values of the bloodstain metabolites were confirmed (when the age of the bloodstain was within a month) under eight environmental conditions (4 °C/30%, 4 °C/60%, 25 °C/30%, 25 °C/60%, 25 °C/90%, 40 °C/30%, 40 °C/60%, and 40 °C/90%). Age-of-bloodstain estimation models were constructed to confirm the applicability of bloodstain metabolites as markers for bloodstain age in various environments. The average concentration of metabolite markers exhibited a decreasing trend with the age of the bloodstain, which transformed into an increasing trend from day 7 onwards. In terms of temperature and humidity, 25 °C and 90%, respectively, showed the most dissimilar metabolite change pattern compared to other conditions. The age-of-bloodstain estimation models developed here have an R-square value of up to 0.92 for each condition and an R-square value of 0.71 when all environmental conditions were combined. The findings herein highlight the immense potential of blood metabolites for field application, confirming the possibility of predicting metabolite changes from the rates of their chemical reactions and validating the importance of metabolites as age-of-bloodstain markers under various environmental conditions.

Age prediction can provide important information about the contributors of biological evidence left at crime scenes. DNA methylation has been regarded as the most promising age-predictive biomarker. Measuring themethylation level at the genome-wide scaleis an important step to screen specific markers for forensic age prediction. In present study, we screened out five age-related CpG sites from the public EPIC BeadChip data and evaluated them in a training set (115 blood) by multiplex methylation SNaPshot assay. Through full subset regression, the five markers were narrowed down to three, namely cg10501210 (C1orf132), cg16867657 (ELOVL2), and cg13108341 (DNAH9), of which the last one was a newly discovered age-related CpG site. An age prediction model was built based on these three markers, explaining 86.8% of the variation of age with a mean absolute deviation (MAD) of 4.038 years. Then, the multiplex methylation SNaPshot assay was adjusted according to the age prediction model. Considering that bloodstains are one of the most common biological samples in practical cases, three validation sets composed of 30 blood, 30 fresh bloodstains and 30 aged bloodstains were used for evaluation of the age prediction model. The MAD of each set was estimated as 4.734, 4.490, and 5.431 years, respectively, suggesting that our age prediction model was applicable for age prediction for blood and bloodstains in Chinese Han population of 11-71 age. In general, this study describes a workflow of screening CpG markers from public chip data and presents a 3-CpG markers model for forensic age prediction.