Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Croatian medical journal
Artificial intelligence enables scale, consistency, and rigor in forensic identity inference.
Advances in forensic genomics, which include massively parallel sequencing, dense single nucleotide polymorphism testing, and forensic genetic genealogy (FGG), have greatly expanded the range of cases in which DNA evidence can generate investigative leads. As a result, the primary limitations in modern forensic DNA analysis are no longer analytical sensitivity or marker availability, but the ability to reason consistently, transparently, and at scale over increasingly complex genetic, genealogical, and contextual information. Current forensic workflows remain predominantly human-centered, relying on manual reasoning that is difficult to standardize, reproduce, document, or scale across growing case inventories. The potential of artificial intelligence (AI) is described as an enabling layer for forensic identity inference, defined here as computational decision-support systems that structure, prioritize, and document reasoning over genetic associations, genealogical structures, and investigative context during identity hypothesis development. While formal statistical inference quantifies evidentiary weight, identity inference governs how evidence is explored, combined, and acted upon during investigations. AI-assisted systems can augment, but not replace, expert judgment by supporting scalable prioritization, relational reasoning, and systematic documentation of analytical decisions, while reducing bias. Properly designed AI-enabled systems offer a path to sustainably scaling FGG while supporting scientific rigor, accountability, and public trust.
15 June 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Benchmarking spectroscopic-chemometric models for human bloodstain time since deposition in tropical outdoor microenvironments
Reliable estimation of the time since deposition (TSD) of bloodstains remains a key challenge in forensic reconstruction, particularly under realistic outdoor conditions. This study benchmarks visible (Vis) reflectance, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and micro-Raman spectroscopy, each coupled with partial least squares (PLS) regression and artificial neural networks (ANN), for TSD estimation of human bloodstains aged up to 100 days under naturally fluctuating tropical outdoor microenvironments. Blood from multiple donors was deposited on glass slides and aged under two conditions: open-air exposure and enclosed storage in sealed plastic bags. Spectra were acquired from the outer ring of each stain, preprocessed by standard normal variate transformation and partitioned by donor into training and independent test sets. Vis reflectance primarily tracked haemoglobin chromophore changes via Soret and α/β bands, whereas ATR-FTIR highlighted protein secondary structure and hydration through amide I-III and O-H/N-H stretching features. Micro-Raman spectra emphasised porphyrin breathing and CC stretching modes, together with aromatic amino-acid and amide bands that remained informative even at late ageing stages. Across all techniques, PLS models provided robust quantitative performance (R2P up to 0.98, RMSEP typically ∼5-10 days, RPD > 3), with micro-Raman-PLS giving the highest predictive accuracy and enclosed exposure consistently improving model stability relative to open air. ANN models did not provide systematic advantages over PLS and were more sensitive to condition-specific variance. In the context of previous outdoor ATR-FTIR studies, these results show that established Vis-, ATR-FTIR- and Raman-based chemometric models can deliver competitive TSD estimates when calibrated within clearly defined environmental domains, underscoring the importance of donor-independent and environmental-aware validation for forensic interpretation.
7 June 2026
Read appraisal →Analytical chemistry
Human-to-Rat Validation of PMI Biomarkers: A Bidirectional Cross-Species Metabolomics Study Reveals Asymmetric Translation
Accurately estimating the post-mortem interval (PMI, also known as time since death) remains a major challenge in forensic science due to substantial biological and environmental variability in human cases. While animal models provide controlled conditions for identifying PMI-related metabolic changes, their translational value for human forensics is unclear. This study bridges controlled rat metabolomics with real-world human autopsy samples using LC-MS-based metabolomics and machine learning. We analyzed femoral blood from 435 medico-legal autopsies (PMI 1-10 days) and employed two complementary modeling strategies. An untargeted Lasso model using all 3450 detected features achieved R2 = 0.51 and identified 51 consistently selected PMI-informative molecular features. To assess cross-species robustness, we reanalysed rat samples using the same analytical platform. Eighteen of the 51 human-derived features were detected in rats, showing clear PMI-dependent trends and strong correlations with PMI. Two metabolites achieved level 2 annotation and matched known PMI biomarkers from previous animal studies, demonstrating meaningful human-to-rat biochemical overlap. In contrast, a targeted Lasso model based on biomarkers identified in rats performed poorly in humans (R2 = 0.10), underscoring limited direct rat-to-human translation. Overall, human-derived biomarkers validated robustly in the controlled rat model proved far more reliable. These findings highlight both the potential and limitations of cross-species PMI biomarker discovery and emphasize the need for improved metadata, especially temperature history, to advance PMI modeling in forensic practice.
27 May 2026
Read appraisal →Analytica chimica acta
Optimized HSSE-GC-MS and machine learning for forensic gender classification of human scent from clothing
BACKGROUND: Clothes are considered a valuable piece of evidence in forensic investigations because of the underlying information that can be extracted from them. Human scent is one kind of trace evidence that can be extracted from clothes, which can link a specific person or location to the crime. This study optimized a headspace sorptive extraction (HSSE) method coupled with gas chromatography-mass spectrometry in combination with machine learning for objective discrimination of gender from human scent traces on clothing for forensic applications. RESULTS: The HSSE was optimized using the Box-Behnken design and Response Surface Methodology to maximize the Euclidean distance between the total ion sum spectrum (TIS) of two independent samples. The optimized extraction parameters were determined at 100 °C extraction temperature, 180 min extraction time, and 5 × 5 cm cloth dimension at m/z range 101 - 250 which produced an R2 of 0.9874. Method validation demonstrated an acceptable absolute error of 6.60%. Intra- and inter-day precisions were calculated for each m/z from 101 to 250 to remove outliers from the dataset for the multivariate analysis. Using the optimized extraction method, 62 samples were analyzed and the pre-processed TIS from the samples were subjected to various supervised machine learning algorithms. Support vector machine (SVM) and partial least squares-discriminant analysis (PLS-DA) models achieved perfect 100% accuracy for gender classification in both training and test sets. Random forest (RF) and linear discriminant analysis (LDA) also showed high test set accuracies of 93% and 86%, respectively. SIGNIFICANCE: This integrated approach, combining optimized stir bar HSSE-GC-MS with different machine learning algorithms using the TIS untargeted approach, provides a fast and automatic gender classification from human scent traces on clothes that can be used as a rapid screening technique in forensic investigations to narrow down suspect or victim profiles.
22 May 2026
Read appraisal →mSystems
Insects shape the cadaver decomposition microbiome and postmortem interval estimation accuracy
The breakdown and recycling of carrion is a crucial ecological process that largely relies on a community of necrophagous insects and microbes. Recent work has shown that a specialized microbial network, likely dispersed throughout the environment by insects, assembles during cadaver decomposition to break down flesh regardless of climate and geography. Because of their broad distribution and successional nature, decomposer microbes have been used in machine learning models to predict the postmortem interval (PMI) of human remains, an important contribution to the field of forensics. How factors such as an indoor environment, which alters insect activity, impact the composition of microbial communities decomposing human remains is unclear. Here, we investigate the effects of enclosed shelter on microbial community assembly and successional patterns during human decomposition and provide important considerations for PMI modeling. Compared to outdoor cadavers, we show that indoor cadavers experienced delayed colonization of key decomposer microbes over the course of decomposition due to restricted insect access. Consequently, machine learning models trained on outdoor cadavers frequently underestimated the PMI of cadavers decomposing indoors. Delayed colonization by blow fly maggots (Diptera: Calliphoridae) was correlated with higher PMI prediction errors, suggesting that insects are an important source of microbial decomposers that drive PMI model predictions. Incorporating microbial data from indoor cadavers and insect activity into PMI models significantly improved prediction capabilities for both indoor and outdoor decomposition environments. Ultimately, we demonstrate the important role insects play in the maintenance and distribution of microbes that help to recycle vertebrate remains.IMPORTANCEMicrobes are critical for the decomposition and recycling of organic matter. Recently, microbiome-based models have shown promising performance in estimating the postmortem interval (PMI). However, many deaths occur indoors, yet no studies have investigated the impact of enclosed shelter on the cadaver microbiome in a controlled setting. Here, cadavers were decomposed indoors, and we found that blow fly maggots serve as an important source of decomposer taxa that significantly alter the cadaver microbiome following infestation. Notably, PMI estimation models trained on outdoor data sets failed to accurately predict the PMI when insect colonization was delayed. We show that incorporating 16S rRNA amplicon data from cadavers decomposing indoors, along with environmental variables, significantly improves PMI estimates, suggesting a microbiome-based forensic tool may be feasible across decomposition environments. Importantly, this research demonstrates the critical ecological role insects play in the dispersal of specialized microbes that are involved in the breakdown and recycling of vertebrate remains.
22 May 2026
Read appraisal →Journal of proteome research
Protein Biomarkers as Molecular Tools for Forensic Biofluid Identification
Forensic proteomics has rapidly established a significant role in forensics, particularly when DNA analysis is insufficient. Proteomics has been frequently recognized for helping reveal useful information about the origin, state and context of forensic samples. Proteins are considered crucial biomarkers in biological samples because of their ability to elucidate cellular functions and post-mortem alterations. Combining advanced mass spectrometry and bioinformatics has increased the importance of proteomics in forensic sciences. The current study examined the use of proteomic technologies, mass spectrometry, and sophisticated software across the key domains of forensic research. The procedure for Post-Mortem Interval (PMI) estimation and body fluid classification proved to be more precise because of the use of machine learning. Peptide biomarkers have helped identify various species by samples of blood, saliva, and semen, and recognize brain, muscles, and skin tissues. Despite significant advancements, the wide acceptance of forensic proteomics remains problematic due to intricate sample stability, high equipment costs, and strict legal standards. Recent advancements in analytical sensitivity, data interpretation tools, and collaborative efforts toward robust protocols position forensic proteomics as an indispensable component of the forensic toolkit. This review indicates the increasing relevance of proteomics in forensic applications, especially relating to PMI estimation, body fluid differentiation, and disease profiling. It promises to significantly enhance the depth of evidentiary interpretation and contribute to more precise and equitable outcomes in the criminal justice system. Proteomic biomarkers need further validation across a range of environments, and standardized protocols should be developed and tested to ensure that proteomics is suitable for forensic use in the courts.
6 Apr 2026
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