Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents.

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

The baru (Dipteryx alata Vog.), a fruit native to the Cerrado biome, is well-known for its almonds, which are extensively exploited and exported. Unfortunately, the remaining parts of this fruit are often discarded. This study investigates the fixed chemical constituents of the baru, including the bark, pulp, endocarp, and almonds, using the PS-MS technique in positive and negative ionization modes. Notably, this research presents the first chemical profile of baru almonds in both their raw and roasted states. The analysis identified 57 compounds reported for the first time in a baru and 24 common compounds. The majority of these compounds are classified as flavonoids. In both ionization modes, the peel exhibited a higher proportion of phenolic compounds, although the chemical compounds varied among the peel, pulp, almond, and endocarp. These findings highlight the perspective of bioeconomy and biotechnology. By staggering baru fruit production alongside extractivists, we can optimize the utilization of all parts of the fruit. Furthermore, given the knowledge of the biological properties of flavonoids and the baru composition, we recommend additional studies to analyze their potential in preventing chronic non-communicable diseases.

DNA typing of latent fingerprints is highly desirable to increase chances of individualization. We recovered DNA from Cyanoacrylate (CA) fumed fingerprints and used both GlobalFiler™ and ForenSeq™ DNA Signature Prep kits for DNA typing. For GlobalFiler™, samples were processed using a protocol modified for Low Template (LT)-DNA samples (half-volume reactions, 30 cycles) while for ForenSeq™ DNA Signature Prep, samples were processed using a standard protocol and fluorometer-based library quantitation. We evaluated genotyping success and quality of profiles in terms of completeness, Peak Height Ratio/Allele Coverage Ratio, presence of PCR artifacts and drop-in alleles. With GlobalFiler™, average autosomal STR (aSTR) profile completeness was 44.4% with 2-20 pg, 54.3% with 22-60 pg, and 95% with 64-250 pg DNA input. CODIS uploadable profiles were obtained in 2/10, 3/11, and 11/12 samples in these ranges. With ForenSeq™ DNA Signature Prep, average aSTR profile completeness was 19.7% with 1-20 pg and 45.2% with 22-47 pg but increased to 78.3% with 68-122 pg and 86.7% with 618-1000 pg DNA input. Uploadable profiles were obtained in 0/12, 4/11, 4/7, and 3/3 samples for these ranges. Results show very high sensitivity using both kits. Half-volume reactions and 30 cycles had minimal negative effect on Globalfiler™ profile quality, providing support for wider use after validation experiments to routinely improve results from LT samples. A standard protocol for the ForenSeq™ DNA Signature Prep kit was also highly successful with LT DNA obtained from CA-fumed fingerprints with additional information from isometric STR alleles and other markers.

BACKGROUND: Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation.
OBJECTIVE: The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality.
METHODS: Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a \"component-target-pathway\" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed.
RESULTS: Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g.
CONCLUSIONS: The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine.

This computational protocol describes how to use pyPGCF, a python software package that runs in the linux environment, in order to analyze bacterial genomes and perform: (i) phylogenomic analysis, (ii) species demarcation, (iii) identification of the core proteins of a bacterial genus and its individual species, (iv) identification of species-specific fingerprint proteins that are found in all strains of a species and, at the same time, are absent from all other species of the genus, (v) functional annotation of the core and fingerprint proteins with eggNOG, and (vi) identification of secondary metabolite biosynthetic gene clusters (smBGCs) with antiSMASH. This software has already been implemented to analyze bacterial genera and species that are important for plants (e.g., Pseudomonas, Bacillus, Streptomyces). In addition, we provide a test dataset and example commands showing how to analyze 165 genomes from 55 species of the genus Bacillus. The main advantages of pyPGCF are that: (i) it uses adjustable orthology cut-offs, (ii) it identifies species-specific fingerprints, and (iii) its computational cost scales linearly with the number of genomes being analyzed. Therefore, pyPGCF is able to deal with a very large number of bacterial genomes, in reasonable timescales, using widely available levels of computing power.

The use of collaborative exercises (CE) and proficiency tests (PT) as part of the governance programme for any forensic science laboratory has become commonplace and recommended by several international organisations. Traditionally these have been discipline-specific exercises testing a laboratory\'s ability in a single area of forensic science. However, the \"real\" world is normally more complex and, in many instances, forensic material must be examined for a number of different evidence types. This article summarises the concepts, planning, design, preparation, implementation, co-ordination and evaluation of the 2022 Multidisciplinary Collaborative Exercise (2022-MdCE) covering a range of forensic disciplines, specifically DNA, fingerprint, documents and handwriting. The exercise consisted of a questioned letter with typescript text and a signature. In addition, the letter contained a visible bloody fingermark in the area of the signature, a visible staining in the lower left-hand corner, a latent fingermark and an indented impression. The analysis of the results showed that, in the investigation of the bloody fingermark, the priority was given to the DNA examination. Some critical issues emerged in relation to the biological (DNA)/ink sampling strategies when applied before fingermark visualisation. Another outcome of the exercise has been to demonstrate the importance of indented impressions, which have been underestimated by a significant number of participants. As setters, more in-depth studies are needed to produce consistent samples. This concerns all the disciplined involved but especially DNA and fingermarks. Based on this exercise, it is believed that this approach to testing of forensic disciplines allows the analysis of good practice within the various scientific areas, as well as scrutinising the process and sequence of events for examining the material within a forensic laboratory in the best conservative way for all kind of evidences.

Latent fingerprints at crime scenes are frequently recovered using forensic gel-lifters, which can help to preserve the crime scene and to enhance visualisation of traces such as blood or paint. In addition to providing fingerprint ridge detail, additional chemical information can also be recovered from gel lifts that may prove pertinent to an investigation. However, while DNA and metal ions have been shown to be able to be detected in gel-lifted fingerprints, the determination of other types of chemical information such as the presence of drugs in gel-lifted prints has not been previously shown. This study demonstrates the application of an ambient ionisation method, sheath flow probe electrospray ionisation-mass spectrometry (sfPESI-MS), to the direct analysis of gel-lifted fingerprints. A model drug compound (zolpidem) is successfully detected from gel-lifted prints from three different surface types: glass, metal, and paper. The surface activity-based separation associated with probe electrospray approaches is shown to resolve zolpidem ions from background phthalate species, significantly enhancing the response obtained from the gel-lifter. A depletion series experiment shows that the drug residue can be detected with up to 100% efficiency after eight consecutive contacts; however, detection efficiency drops to 20% after 30 contacts. The developed approach has potential application to analysis of historical gel-lifters to obtain additional chemical information.

The field of forensic anthropology is characterised by its ongoing development and growth. Forensic anatomy is a burgeoning discipline that focuses on the analysis and identification of both preserved and unpreserved human body parts, both in deceased individuals and the living. This subject plays a crucial role in establishing the four key factors of forensic anthropology, namely sex, age, race, and height. The objective of this research endeavour was to evaluate the significance of anatomical information in the process of forensic age estimation. The researchers established the inclusion criteria in accordance with the globally recognised Population, Intervention, Comparison, Outcome (PICOS) framework, as advised by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. The research included many methodologies in order to ascertain the age. Upon conducting a comprehensive review of the existing literature pertaining to anatomical knowledge in the field of forensic age estimate, we have identified many notable applications. These include the utilisation of various anatomical features such as the dental pulp chamber, fingerprints, acetabulum, sternal end of the fourth rib, as well as hand and wrist bones for the purpose of age estimation. It is important for anatomists and other forensic scientists to engage in collaborative efforts to facilitate the exchange of ideas and ensure thorough investigations. This cooperation is particularly crucial in areas where anatomical sciences play a significant role in forensic science and investigation. Nevertheless, in order to mitigate the potential for estimating error, it is still advisable to use a multi-factorial evaluation approach that involves examining many body areas.

Fingerprints with similar morphological characteristics but from different individuals can lead to errors in individual identification, especially when dealing with large databases containing millions of fingerprints. To address this issue and enhance the accuracy of similar fingerprint identification, the use of the likelihood ratio (LR) model for quantitative evaluation of fingerprint evidence has emerged as an effective research method. In this study, the LR fingerprint evidence evaluation model was established by using mathematical statistical methods, such as parameter estimation and hypothesis testing. This involved various steps, including database construction, scoring, fitting, calculation, and visual evaluation. Under the same-source conditions, the optimal parameter methods selected by different number of minutiae are gamma and Weibull distribution, while normal, Weibull, and lognormal distributions were the fitting parameters selected for minutiae configurations. The fitting parameters selected by different number of minutiae under different-source conditions are lognormal distribution, and the parameter methods selected for different minutiae configurations include Weibull, gamma, and lognormal distributions. The results of the LR model showed increased accuracy as the number of minutiae increased, indicating strong discriminative and corrective power. However, the accuracy of the LR evaluation based on different configurations was comparatively lower. In addition, the LR models with different numbers of minutiae outperformed those with different minutiae configurations. Our study shows that the use of LR models based on parametric methods is favoured in reducing the risk of fingerprint evidence misidentification, improving the quantitative assessment methods of fingerprint evidence, and promoting fingerprint identification from experience to science.
UNASSIGNED: Likelihood ratio (LR) method based on parameter estimation was applied to scientific evaluation of fingerprint evidence with excellent discriminatory and calibration capabilities.Both the number of minutiae and configuration of minutiae have significant effects on the score-based LR method.Fingerprints from the same source contain many different patterns of deformation.Databases containing 10 million fingerprints from different sources have been used for building the LR model.