Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out using ATR-FTIR spectroscopy and chemometric approach

Exploration and application of microorganisms related to the inference of the time since deposition (TsD) in semen and blood stains

Determining the time since deposition (TsD) of body fluid stains can provide crucial criminal information to forensic researchers. Although there are studies on inferring residual time through DNA and RNA markers, this requires high sample quality, and microorganisms, as a new type of marker with individual and tissue identification capabilities, have the potential for body fluid recognition and TsD inference. Blood and semen are the most common types of bodily fluid stains at crime scenes, but research on the inference of the TsD of these two types of stains through microorganisms still needs to be explored. Thus, this study collected samples of body fluid stains exposed indoors for up to 56 days and selected several microorganisms that were both liquid specific and related to residual time inference in blood (Methylobacterium and Sphingomonas) and semen (Gardnerella) stains via 16 S rRNA high-throughput sequencing. Furthermore, the microorganisms' ability to infer TsD was verified using qPCR in validation group samples stored under the same conditions, and two multiple logistic regression models were constructed. The average absolute deviation of differences between the predicted and actual retention times of the three types of body fluids in the test set using two estimation methods was 2.15 and 2.06 days, respectively. In conclusion, this study has discovered four novel microorganisms related to the retention time of blood and semen and has preliminarily constructed the TsD prediction models, providing a new direction for future forensic research on the inference of TsD in blood and semen stains.

A review on forensic analysis of bio fluids (blood, semen, vaginal fluid, menstrual blood, urine, saliva): Spectroscopic and non-spectroscopic technique

The accurate detection, identification, and analysis of biofluids at crime scenes play a critical role in forensic investigations. Various biofluids, such as blood, semen, vaginal fluid, menstrual blood, urine, and saliva, can be crucial evidence. In a murder case involving a knife attack, for instance, bloodstains from both the victim and perpetrator might be present. Sexual assault cases often involve the analysis of semen and vaginal secretions. Biofluid analysis employs a two-tiered approach: presumptive tests for initial identification and confirmatory tests for definitive analysis. This review article focuses on six key biofluids and their forensic significance. In this review, we comprehensively explore the relevant analytical techniques, including non-spectroscopic methods like immunoassays, spot tests, and cytokine profiling, alongside spectroscopic techniques such as Infrared (IR) spectroscopy, Mass Spectrometry (MS), and Raman Spectroscopy (RS).

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques

Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are found in urine samples from patients with RCC. In this study, we propose to investigate the use of Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) on dried urine samples for distinguishing RCC. We analyzed dried urine samples from 49 patients with RCC, confirmed by histopathology, and 39 healthy donors using ATR-FTIR spectroscopy. The vibrational bands of the dried urine were identified by comparing them with spectra from dried artificial urine, individual urine components, and dried artificial urine spiked with urine components. Urea dominated all spectra, but smaller intensity peaks, corresponding to creatinine, phosphate, and uric acid, were also identified. Statistically significant differences between the FTIR spectra of the two groups were obtained only for creatinine, with lower intensities for RCC cases. The discrimination of RCC was performed through Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM). Using PCA-LDA, we achieved a higher discrimination accuracy (82%) (using only six Principal Components to avoid overfitting), as compared to SVM (76%). Our results demonstrate the potential of urine ATR-FTIR combined with machine learning techniques for RCC discrimination. However, further studies, especially of other urological diseases, must validate this approach.

Are Aptamer-Based Biosensors the Future of the Detection of the Human Gut Microbiome?-A Systematic Review and Meta-Analysis

The gut microbiome is shaped early in life by dietary and lifestyle factors. Specific compounds in the gut affect the growth of different bacterial species and the production of beneficial or harmful byproducts. Dysbiosis of the gut microbiome has been linked to various diseases resulting from the presence of harmful bacteria and their byproducts. Existing methods for detecting microbial species, such as microscopic observation and molecular biological techniques, are costly, labor-intensive, and require skilled personnel. Biosensors, which integrate a recognition element, transducer, amplifier, signal processor, and display unit, can convert biological events into electronic signals. This review provides a comprehensive and systematic survey of scientific publications from 2018 to June 2024, obtained from ScienceDirect, PubMed, and Scopus databases. The aim was to evaluate the current state-of-the-art and identify knowledge gaps in the application of aptamer biosensors for the determination of gut microbiota. A total of 13 eligible publications were categorized based on the type of study: those using microbial bioreceptors (category 1) and those using aptamer bioreceptors (category 2) for the determination of gut microbiota. Point-of-care biosensors are being developed to monitor changes in metabolites that may lead to disease. They are well-suited for use in the healthcare system and offer an excellent alternative to traditional methods. Aptamers are gaining attention due to their stability, specificity, scalability, reproducibility, low production cost, and low immunogenicity. While there is limited research on using aptamers to detect human gut microbiota, they show promise for providing accurate, robust, and cost-effective diagnostic methods for monitoring the gut microbiome.

Succession changes of microbial community for inferring the time since deposition of saliva

Saliva is a common biological examination material at crime scenes and has high application value in forensic case investigations. It can reflect the suspect's time of crime at the scene and provide evidence of the suspect's criminal facts. Even though many researchers have proposed their experimental protocols for estimating the time since deposition (TsD) of saliva, there is still a relative lack of research on the use of microorganisms to estimate TsD. In the current study, the succession change of microbial community in saliva with different TsD values was explored to discern the microbial markers related to TsD of saliva. We gathered saliva samples from six unrelated healthy Han individuals living in Guizhou, China and exposed these samples to indoor conditions at six time points (0, 1, 3, 7, 15, and 28 days). Temporal changes of microbial compositions in these samples were investigated by 16S rRNA sequencing (V3-V4 regions). By assessing temporal variation patterns of microbial abundance at the genus level, four bacteria (Brucella, Prevotella, Pseudomonas, and Fusobacterium) were observed to show good time dependence in these samples. In addition, the hierarchical clustering and principal co-ordinates analysis results revealed that these saliva samples could be classified into t-short (≤7 days) and t-long (>7 days) groups. In the end, the random forest model was developed to predict the TsD of these samples. For the model, the root mean square error, R2, and mean absolute error between predicted and actual TsD values were 1.5213, 0.9851, and 1.1969, respectively. To sum up, we identified TsD-related microbial markers in saliva samples, which could be viewed as valuable markers for inferring the TsD of saliva.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy

Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.

An Emerging Strategy for Muscle Evanescent Trauma Discrimination by Spectroscopy and Chemometrics

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.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect's allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis

Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed.