Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

BACKGROUND: Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. PURPOSE: This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Developing an interpretation model for body fluid identification.

Criminal investigations, particularly sexual assaults, frequently require the identification of body fluid type in addition to body fluid donor to provide context. In most cases this can be achieved by conventional methods, however, in certain scenarios, alternative molecular methods are required. An example of this is the detection of menstrual fluid and vaginal material, which are not able to be identified using conventional techniques. Endpoint reverse-transcription PCR (RT-PCR) is currently used for this purpose to amplify body fluid specific messenger RNA (mRNA) transcripts in forensic casework. Real-time quantitative reverse-transcription PCR (RT-qPCR) is a similar method but utilises fluorescent markers to generate quantitative results in the form of threshold cycle (Cq) values. Despite the uncertainty surrounding body fluid identification, most interpretation guidelines utilise categorical statements. Probabilistic modelling is more realistic as it reflects biological variation as well as the known performance of the method. This research describes the application of various machine learning models to single-source mRNA profiles obtained by RT-qPCR and assesses their performance. Multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) were used to discriminate between the following body fluid categories: saliva, circulatory blood, menstrual fluid, vaginal material, and semen. We identified that the performance of MLR was somewhat improved when the quantitative dataset of the original Cq values was used (overall accuracy of approximately 0.95) rather than presence/absence coded data (overall accuracy of approximately 0.94). This indicates that the quantitative information obtained by RT-qPCR amplification is useful in assigning body fluid class. Of the three classification methods, MLR performed the best. When we utilised receiver operating characteristic curves to observe performance by body fluid class, it was clear that all methods found difficulty in classifying menstrual blood samples. Future work will involve the modelling of body fluid mixtures, which are common in samples analysed as part of sexual assault investigations.

Exosomes from uterine fluid promote capacitation of human sperm.

Background: Extracellular vesicles (EVs) are membrane-bound vesicles containing various proteins, lipids, and nucleic acids. EVs are found in many body fluids, such as blood and urine. The release of EVs can facilitate intercellular communication through fusion with the plasma membrane or endocytosis into the recipient cell or through internalization of the contents. Recent studies have reported that EVs isolated from human endometrial epithelial cells (EECs) promote sperm fertilization ability. EVs from uterine flushing fluid more closely resemble the physiological condition of the uterus. However, it is unclear whether EVs derived directly from uterine flushing fluid have the same effect on sperm. This study aimed to research the effect of EVs from uterine flushing fluid on sperm. Methods: EVs were isolated from the uterine flushing fluid. The presence of EVs was confirmed by nanoparticle tracking analysis (NTA), Western blot, and transmission electron microscopy (TEM). EVs were incubated with human sperm for 2 h and 4 h. The effects of EVs on sperm were evaluated by analyzing acrosome reaction, sperm motility, and reactive oxygen species (ROS). Results: The EVs fractions isolated from the uterine fluid were observed in cup-shaped vesicles of different sizes by TEM. All isolated vesicles contained similar numbers of vesicles in the expected size range (30-200 nm) by NTA. CD9 and CD63 were detected in EVs by western blot. Comparing the motility of the two groups incubated sperm motility significantly differed at 4 h. The acrosome reactions were promoted by incubating with EVs significantly. ROS were increased in sperm incubated with EVs. Conclusion: Our results showed EVs present in the uterine fluid. Acrosome reactions and ROS levels increased in human sperm incubated with EVs. EVs from uterine fluid can promote the capacitation of human sperm. The increased capacitation after sperm interaction with EVs suggests a possible physiological effect during the transit of the uterus.

Uterine fluid microRNAs in repeated implantation failure.

Recurrent implantation failure (RIF) is a significant obstacle in assisted reproductive procedures, primarily because of compromised receptivity. As such, there is a need for a dependable and accurate clinical test to evaluate endometrial receptiveness, particularly during embryo transfer. MicroRNAs (miRNAs) have diverse functions in the processes of implantation and pregnancy. Dysregulation of miRNAs results in reproductive diseases such as recurrent implantation failure (RIF). The endometrium secretes several microRNAs (miRNAs) during the implantation period, which could potentially indicate whether the endometrium is suitable for in vitro fertilization (IVF). The goal of this review is to examine endometrial miRNAs as noninvasive biomarkers that successfully predict endometrium receptivity in RIF.

Unveiling Morphine: A Rapid and Selective Fluorescence Sensor for Forensic and Medical Analysis.

Opioid use, particularly morphine, is linked to CNS-related disorders, comorbidities, and premature death. Morphine, a widely abused opioid, poses a significant global health threat and serves as a key metabolite in various opioids. Here, we present a turn-off fluorescent sensor capable of detecting morphine with exceptional sensitivity and speed in various samples. The fluorescent sensor was developed through the dimerization process of 7-methoxy-1-tetralone and subsequent demethylation to produce the final product. Despite morphine possessing inherent fluorophoric properties and emitting light in an approximately similar wavelength as the sensor's fluorescent blue light, the introduction of the target molecule (morphine) in the presence of the sensor caused a reduction in the sensor's fluorescence intensity, which is attributable to the formation of the sensor-morphine complex. By utilizing this fluorescence quenching sensor, the chemo-selective detection of morphine becomes highly feasible, encompassing a linear range from 0.008 to 40 ppm with an impressive limit of detection of 8 ppb. Consequently, this molecular probe demonstrates a successful application in determining trace amounts of morphine within urine, yielding satisfactory analytical results. The study also explores the effect of several variables on the sensor's response and optimizes the detection of morphine in urine using a response surface methodology with a central composite design.

Assessment of Blood and Semen Detection and DNA Collection from Swabs up to Three Months after Deposition on Five Different Cloth Materials.

Body fluid identification plays a crucial role in criminal investigations. Because of their presence in many cases, blood and semen are the most relevant body fluids in forensic sciences. Based on antigen-antibody reactions binding unique proteins for each body fluid, serological assays represent one of the most rapid and highly specific tests for blood and semen. Currently, few studies have assessed the factors affecting body fluid identification by applying these assays. This work aimed to study the effect of different fabrics from clothes and time since deposition on identification through immunochromatographic tests for blood and semen, DNA isolation, and STR profiling from these samples. Body fluids were deposited on black- and white-dyed denim and cotton fabrics, and on leather. Afterward, blood and semen were sampled at 1 day, 30 days, and 90 days after deposition and identified by using the SERATEC® HemDirect Hemoglobin Test and the PSA Semiquant and SERATEC® BLOOD CS and SEMEN CS tests, respectively. Laboratory and crime scene tests presented similar performances for the detection of blood and semen stains on every tested fabric. No differences were found on band intensities between timepoints for all fabrics. It was possible to recover and identify blood and semen samples up to three months after deposition and to obtain full STR profiles from all the tested fabrics. Both body fluid STR profiles showed differences in their quality between 1 and 90 days after deposition for all fabrics except for black cotton for semen samples. Future research will expand the results, assessing body fluid identification on other substrates and under different environmental conditions.

Body fluid contamination in the context of an adverse analytical finding in doping: About a case involving ostarine.

Ostarine, also known as MK-2866 or enobosarm, is a selective androgen receptor modulator (SARM). It has anabolic properties and as such is widely used in doping, accounting in 2021 for 25 % of the adverse analytical findings (AAF) among the class S1.2 "Other anabolic agents" of products banned by the World Anti-Doping Agency, to which it belongs. But in some cases, it can be responsible for an AAF following contamination. We report the case of an athlete who contaminated herself by exchanging body fluids while kissing her boyfriend, who took 25 mg per day of MK-2866 for 9 days prior to the athlete's AAF (urinary concentration evaluated at 13 ng/mL) without her knowledge. Both subjects came to our lab for hair testing. The athlete's hair was black and slightly frizzy. Six segments of 2 cm then 7 × 3 cm (33 cm) were analysed and showed increasing concentrations, from 2 pg/mg on the first segment to 17.8 pg/mg on the last segment. The boyfriend's hair, light-brown, analyzed on 4 × 2 cm, also showed increasing values, from 65 to 143 pg/mg. These gradients of concentration in the hair's athlete and in her boyfriend were compatible with external contamination of the hair, confirmed by analysis of washing baths, pillowcases (150 pg on each), and the athlete's hairbrush (250 pg). Fingernails were also contaminated, with 21 pg/mg in the athlete and 1041 pg/mg in the boyfriend, with highly contaminated washing baths, and toenails were less contaminated, with 2 pg/mg in the athlete and 17.3 pg/mg in the boyfriend. Urine samples taken 35 days after the start of MK-2866 treatment showed a value of 3690 ng/mL in the boyfriend and 5.7 ng/mL in the athlete. After 6 days off, these concentrations were 3.3 ng/mL and 0.1 ng/mL, respectively. A controlled transfer study was carried out 12 days after discontinuation (urine concentrations returned to negative level). After administration of 17 mg (the 25 mg/mL vial having been controlled at 17 mg/mL), urine samples were taken from the boyfriend and the athlete (n = 10 for each) for more than 25 h after they had been living normally with each other (regular kissing in particular). The boyfriend's urine concentrations ranged from 681 ng/mL to 12822 ng/mL (Tmax = 8:30 hrs), and the athlete's from 0.3 ng/mL to 13 ng/mL with Tmax = 8:30 hrs, i.e. at 22:30 hrs, which corresponded exactly to the time of collection of the urine that showed AAF, with a similar concentration. The dose ingested by the athlete was estimated at 15 µg. These results demonstrate the transfer of ostarine via body fluids between two subjects, with a high risk of AAF in one athlete, as observed in our case.

A portable EIS-based biosensor for the detection of microcystin-LR residues in environmental water bodies and simulated body fluids.

Due to the eutrophication of water bodies around the world, there is a drastic increase in harmful cyanobacterial blooms leading to contamination of water bodies with cyanotoxins. Chronic exposure to cyanotoxins such as microcystin leads to oxidative stress, inflammation, and liver damage, and potentially to liver cancer. We developed a novel and easy-to-use electrochemical impedance spectroscopy-based immunosensor by fabricating stencil-printed conductive carbon-based interdigitated microelectrodes and immobilising them with cysteamine-capped gold nanoparticles embedded in polyaniline. It has been also coupled with a custom handheld device enabling regular on-site assessment, especially in resource-constrained situations encountered in developing countries. The sensor is able to detect microcystin-LR up to 0.1 µg L-1, having a linear response between 0.1 and 100 µg L-1 in lake and river water and in serum and urine samples. In addition to being inexpensive, easy to fabricate, and sensitive, it also has very good selectivity.

Mass spectrometry-based proteomic analysis of biological stains identifies body fluids specific markers.

The identification of biological stains and their tissue resource is an important part of forensic research. Current methods suffer from several limitations including poor sensitivity and specificity, trace samples, and sample destruction. In this study, we profiled the proteomes of menstrual blood, peripheral blood, saliva, semen, and vaginal fluid with mass spectrometry technology. Tissue-enhanced and tissue-specific proteins of each group have been proposed as potential biomarkers. These candidate proteins were further annotated and screened through the combination with the Human Protein Atlas database. Our data not only validates the protein biomarkers reported in previous studies but also identifies novel candidate biomarkers for human body fluids. These candidates lay the foundation for the development of rapid and specific forensic examination methods.

Combining Thermal Desorption with Selected Ion Flow Tube Mass Spectrometry for Analyses of Breath Volatile Organic Compounds.

An instrument integrating thermal desorption (TD) to selected ion flow tube mass spectrometry (SIFT-MS) is presented, and its application to analyze volatile organic compounds (VOCs) in human breath is demonstrated for the first time. The rationale behind this development is the need to analyze breath samples in large-scale multicenter clinical projects involving thousands of patients recruited in different hospitals. Following adapted guidelines for validating analytical techniques, we developed and validated a targeted analytical method for 21 compounds of diverse chemical class, chosen for their clinical and biological relevance. Validation has been carried out by two independent laboratories, using calibration standards and real breath samples from healthy volunteers. The merging of SIFT-MS and TD integrates the rapid analytical capabilities of SIFT-MS with the capacity to collect breath samples across multiple hospitals. Thanks to these features, the novel instrument has the potential to be easily employed in clinical practice.

The optimization and comparison of two high-throughput faecal headspace sampling platforms: the microchamber/thermal extractor and hi-capacity sorptive extraction probes (HiSorb).

Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.

Automated 2D peak detection in gas chromatography-ion mobility spectrometry through persistent homology.

BACKGROUND: Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful analytical technique which has gained widespread use in a variety of fields. Detecting peaks in GC-IMS data is essential for chemical identification. Topological data analysis (TDA) has the ability to record alterations in topology throughout the entire spectrum of GC-IMS data and retain this data in diagrams known as persistence diagrams. To put it differently, TDA naturally identifies characteristics such as mountains, volcanoes, and their higher-dimensional equivalents within the original data and measures their significance. RESULTS: In the present contribution, a novel approach based on persistent homology (a flagship technique of TDA) is suggested for automatic 2D peak detection in GC-IMS. For this purpose, two different GC-IMS data examples (urine and olive oil) are used to show the performance of the proposed method. The outputs of the algorithm are GC-IMS chromatogram with detected peaks, persistence plot showing the importance (intensity) of the detected peaks and a table with retention times (RT), drift times (DT), and persistence scores of detected peaks. The RT and DT can be used for identification of the peaks and persistence scores for quantitation. Additionally, watershed segmentation is applied to the GC-IMS images to index individual peaks and segment overlapping compounds allowing for a more accurate identification and quantification of individual peaks. SIGNIFICANCE: Inspection of the results for GC-IMS datasets showed the accurate and reliable performance of the proposed strategy based on persistent homology for automatic 2D GC-IMS peak detection for qualitative and quantitative analysis. In addition, this approach can be easily extended to other types of hyphenated chromatographic and/or spectroscopic data.

Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis.

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjustedP-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

Predicting nitrogen excretion of cattle kept under tropical and subtropical conditions using semimechanistic models.

The present study aims at evaluating whether current semimechanistic models developed for temperate cattle systems can be adopted for cattle under (sub-) tropical husbandry systems to adequately (accurately and precisely) predict total nitrogen (TN), urine nitrogen (UN), faecal nitrogen (FN) excretion and its partition into different FN fractions. Selected models were built based on the feeding recommendations for ruminants of the British (Model A), German (Model G) and French (INRA; Model I) system. Model evaluation was conducted using eight nitrogen balance studies performed in El Salvador, Kenya and Peru (n = 392 individual observations including lactating cows, heifers and steers). Concordance correlation coefficient, root mean square errors (RMSE), and mean biases were estimated to evaluate the models' adequacy in predicting nitrogen excretion. Input variables causing greatest variation in nitrogen excretion prediction were identified by a sensitivity analysis and adjusted. Model G was able to adequately (i.e., RMSE of <25% of observed mean, systematic error of <5% of the mean square error) predict TN excretion through a compensation between overestimation of UN excretion and underestimation of FN excretion. None of the models were able to adequately predict UN, FN, and different FN fractions. Model I adequately predicted FN (RMSE = 18%) when duodenal microbial crude protein flow was increased, and the intercept used to predict FN excretion was reduced from 4.30 to 3.82 g of nitrogen per kilogram of dry matter intake. These adjustments, however, were not sufficient to predict adequately UN excretion (RMSE = 38%), individual FN fractions (RMSE > 56%), and TN (RMSE = 22%) excretion, by Model I.

Application to several suspected poisoning cases of a validated analytical method for the determination of muscarine in human biological matrices using liquid chromatography with high-resolution mass spectrometry detection.

Despite prevention efforts, many cases of mushroom poisoning are reported around the world every year. Among the different toxins implicated in these poisonings, muscarine may induce parasympathetic neurological damage. Muscarine poisonings are poorly reported in the current literature, implying a lack of available data on muscarine concentrations in human matrices. A validated liquid chromatography with high-resolution mass spectrometry detection (Orbitrap technology) method was developed to determine muscarine concentrations in human urine, plasma, and whole blood samples. Muscarine was determined using 100 µL of biological fluids, and precipitation was used for sample preparation. Liquid chromatography-mass spectrometry was performed using an Accucore Phenyl-X analytical column with the electrospray source in positive ion mode. Muscarine was quantitated in parallel reaction monitoring (PRM) mode with D9-muscarine as the internal standard. The method was validated successfully over the concentration range 0.1-100 µg/L for plasma and whole blood and 1-100 µg/L for urine, with acceptable precision and accuracy (<13.5%), including the lower limit of quantification. Ten real cases of suspected muscarine poisoning were successfully confirmed with this validated method. Muscarine concentrations in these cases ranged from 0.12 to 14 µg/L in whole blood,

Does Aggregation of Therapeutic IgGs in PBS Offer a True Picture of What Happens in Models Derived from Human Body Fluids?

Therapeutic proteins such as monoclonal antibodies (mAb) are known to form aggregates due to various factors. Phosphate buffered saline (PBS), human serum, and human serum filtrate (HSF) are some of the models used to analyze mAb stability in physiologically relevant in-vitro conditions. In this study, aggregation of mAb in PBS and models derived from body fluids seeded with mAb samples subjected to various stresses were compared. Samples containing mAb subjected to pH, temperature, UV light, stirring, and interfacial agitation stress were seeded into different models for 2 case studies. In the first case study, %HMW (high molecular weight species) of mAb in PBS and HSF were compared using size exclusion chromatography. It was found that change in %HMW was higher in PBS compared to HSF. For example, PBS containing mAb that was subjected to UV light stress showed change in HMW by >10 % over 72 h, but the change was <5 % in HSF. In second case study, aggregates particles of FITC tagged mAb were monitored in PBS and serum using fluorescence microscope image processing. It was found that PBS and serum containing mAb subjected to stirring and interfacial agitation resulted in aggregates of >2 µm size, and average size and percentage number of particles having >10 µm size was higher in serum compared to PBS at all analysis time point. Overall, it was found that aggregation of mAb in PBS was different from that in human body fluids. Second case study also showed the importance of advanced strategies for further characterization of mAb in serum.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples.

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

Estimation of time since deposition of semen stain on different fabric types using ATR-FTIR spectroscopy and chemometrics.

Various body fluids such as blood, semen, vaginal secretions, and saliva are frequently encountered at crime scene. In cases of sexual assault, semen stains are one of the most reliable evidence of biological origin. In this study, our objective was to develop a method for estimating the time since deposition of semen stains on five different fabric types using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy, with a focus on a time frame of up to 8 weeks. Semen samples from six different volunteers were dripped onto five distinct fabric materials, and ATR-FTIR measurements were obtained at 17 different time points. Principal component analysis (PCA) and partial least squares (PLS) methods were employed to differentiate semen stains on various fabric samples and estimate the age of semen stains. Models constructed using PCA and PLSR achieved high R2 values and low root-mean-square error (RMSE). While the performance varies depending on fabric types, it was observed that age estimation of semen stains can be made within following intervals: 0.39-0.76 days for 0-7 day range, 2.59-3.38 days for the 1-8 week range, and 3.98-8.1 days for the 0-56 day range. This study demonstrates the effectiveness of using ATR-FTIR spectroscopy in combination with chemometrics to estimate the age of human semen stains on various fabric types based on time-dependent spectral changes.

(Un)Reliable detection of menstrual blood in forensic casework - evaluation of the Seratec® PMB test with mock samples.

The identification of the type of body fluid in crime scene evidence may be crucial, so that the efforts are high to reduce the complexity of these analyses and to minimize time and costs. Reliable immunochromatographic rapid tests for specific and sensitive identification of blood, saliva, urine and sperm secretions are already routinely used in forensic genetics. The recently introduced Seratec® PMB test is said to detect not only hemoglobin, but also differentiate menstrual blood from other secretions containing blood (cells) by detecting D-dimers. In our experimental set-up, menstrual blood could be reliably detected in mock forensic samples. Here, the result was independent of sample age and extraction buffer volume. It was also successfully demonstrated that all secretions without blood cells were negative for both, hemoglobin (P) and D-dimer (M). However, several blood cell-containing secretions/tissues comprising blood (injury), nasal blood, postmortem blood and wound crust also demonstrated positive results for D-dimer (M) and were therefore false positives. For blood (injury) and nasal blood, this result was reproduced for different extraction buffer volumes. The results of this study clearly demonstrate that the Seratec® PMB test is neither useful nor suitable for use in forensic genetics because of the great risk of false positive results which can lead to false conclusions, especially in sexual offense or violent acts.

A combined molecular approach utilizing microbial DNA and microRNAs in a qPCR multiplex for the classification of five forensically relevant body fluids.

Body fluid identification is an essential step in the forensic biology workflow that can assist DNA analysts in determining where to collect DNA evidence. Current presumptive tests lack the specificity that molecular techniques can achieve; therefore, molecular methods, including microRNA (miRNA) and microbial signature characterization, have been extensively researched in the forensic community. Limitations of each method suggest combining molecular markers to increase the discrimination efficiency of multiple body fluids from a single assay. While microbial signatures have been successful in identifying fluids with high bacterial abundances, microRNAs have shown promise in fluids with low microbial abundance (blood and semen). This project synergized the benefits of microRNAs and microbial DNA to identify multiple body fluids using DNA extracts. A reverse transcription (RT)-qPCR duplex targeting miR-891a and let-7g was validated, and miR-891a differential expression was significantly different between blood and semen. The miRNA duplex was incorporated into a previously reported qPCR multiplex targeting 16S rRNA genes of Lactobacillus crispatus, Bacteroides uniformis, and Streptococcus salivarius to presumptively identify vaginal/menstrual secretions, feces, and saliva, respectively. The combined classification regression tree model resulted in the presumptive classification of five body fluids with 94.6% overall accuracy, now including blood and semen identification. These results provide proof of concept that microRNAs and microbial DNA can classify multiple body fluids simultaneously at the quantification step of the current forensic DNA workflow.