Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 32 appraisals
Clinica chimica acta; international journal of clinical chemistry
Cortisol as a stress biomarker: analytical perspectives and challenges
Point-of-care testing (POCT) has emerged as a transformative approach in modern healthcare for the rapid detection of physiological abnormalities through minimally or non-invasively collected samples. Biological matrices such as saliva, urine, hair, blood, and interstitial fluid contain clinically significant biomarkers that may serve as indicators of physiological disorders. Among these, cortisol is the key stress biomarker and exerts substantial effects on body metabolism, acting on both peripheral tissues and the CNS. This review comprehensively outlines the evolution of cortisol detection strategies, progressing from conventional laboratory-based immunoassays to advanced analytical platforms, including electrochemical biosensors, wearable devices, and microfluidic systems. Accurate and reliable detection of elevated cortisol levels is crucial for improving diagnostic, therapeutic, and preventive strategies for stress-related disorders. By tracing the analytical window, the work describes the detection of cortisol from traditional immunoassays to innovative biosensing platforms. Moreover, recent advances in nanomaterials, sensor design, and data integration have enabled continuous, on-site monitoring of cortisol levels, thereby enhancing their applicability across diverse clinical and non-clinical settings. This integration of physiological insight with technological advancement provides a comprehensive overview of developments in cortisol assessment, connecting fundamental endocrine science with practical diagnostic applications. The review underscores the potential of next-generation POCT systems to improve early diagnosis, therapeutic monitoring, and personalized healthcare through real-time biomarker analysis.
17 July 2026
Read appraisal →Metabolomics : Official journal of the Metabolomic Society
A metabolomic signatures in hyperuricemia: a systematic review
BACKGROUND: Hyperuricemia (HUA) is traditionally viewed as a disorder of purine metabolism. However, its broader metabolic alterations remain incompletely understood. Metabolomics provides a useful approach for exploring metabolite changes associated with HUA, but a comprehensive synthesis of existing findings is still lacking. AIM OF REVIEW: This systematic review and meta-analysis aimed to characterize the systemic metabolic signature of HUA beyond purine pathways. By synthesizing data from 27 metabolomics studies involving 12,335 participants, the study sought to identify consistent metabolite biomarkers and key dysregulated pathways to provide new insights for diagnosis and therapeutic targeting. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review included 27 metabolomics studies involving 12,335 participants and identified 1,187 metabolites reported in association with HUA. Qualitative synthesis showed 54 consistently elevated and 20 consistently decreased blood metabolites, mainly involving amino acids, lipid-related metabolites, energy-related compounds, vitamins and their derivatives, and purine nucleoside metabolites. The meta-analysis was limited to two eligible studies, with one study contributing most of the statistical weight; it suggested higher levels of Alanine, Leucine, Phenylalanine, and Tyrosine and lower Histidine levels in HUA. Pathway enrichment analysis highlighted "One carbon pool by folate," "Arginine biosynthesis," "Glutathione metabolism," and related amino acid and energy metabolism pathways. Overall, these findings suggest that HUA may be associated with metabolic perturbations beyond purine metabolism alone, but the candidate metabolites and pathways require further validation in longitudinal, standardized, and mechanistic studies.
13 July 2026
Read appraisal →Journal of proteome research
AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney
Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a great challenge. We developed a workflow that combines matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) data with AI-based digital pathology annotations of kidney functional tissue units (FTUs) to profile N-glycan distributions within biopsy tissues. This approach can generate molecular-level data relevant to diverse pathological outcomes, thereby aiding in the elucidation of disease mechanisms. As a proof-of-concept, we demonstrate that this AI-based digital pathology approach to MALDI data segmentation enables the detection and differentiation of N-glycosylation within FTUs of healthy kidney tissue. We then elucidated differences in N-glycosylation between the diseased kidney tissue samples from patients with different diagnoses. Sialic acid N-glycans, which have been linked to various kidney diseases, displayed enrichment in the glomeruli and tubules of tissues from patients diagnosed with diabetic kidney disease (DKD), whereas they were enriched in the tubules and arteries from patients with acute kidney injury (AKI), in comparison to healthy tissue. Furthermore, we found that polylactosamine N-glycans were enriched only in the AKI samples, indicating their potential roles in tubular injury and inflammation. This workflow has the potential to bridge the gap between region-specific glycosylation and its implications on FTUs in diseases, paving the way for targeted molecular imaging studies in the kidney and other tissues.
7 June 2026
Read appraisal →PloS one
Burden and patterns of dyslipidaemia among adult Ghanaians: A systematic review
BACKGROUND: Dyslipidaemia is a major modifiable risk factor for cardiovascular disease and has become an increasing public health concern in sub-Saharan Africa. Rapid urbanisation, dietary transitions, and sedentary lifestyles have contributed to the rising incidence of cardiometabolic diseases in Ghana. However, evidence on the overall burden and patterns of dyslipidaemia in adults remains unclear. This systematic review aimed to synthesise the evidence on the prevalence and lipid profile patterns of adult Ghanaians. METHODS: A comprehensive search of PubMed, Scopus, Web of Science, African Journals Online, Africa-Wide Information and African Index Medicus databases was conducted for studies published between January 1980 and November 2025. Eligible studies reported the prevalence or distribution of dyslipidaemia in Ghanaian adults (≥ 18 years). Two reviewers independently screened, extracted, and assessed the quality of included studies. Findings were synthesised narratively and summarised in descriptive tables and figures, due to heterogeneity in study designs and data reporting. RESULTS: Twenty-four studies published between 2003 and 2023, comprising approximately 11,400 participants, met the inclusion criteria. Across the included studies, the reported prevalence of dyslipidaemia ranged from 3.0% to 72.4%, reflecting differences in study populations, diagnostic criteria and study settings. Low high-density lipoprotein cholesterol (HDL-C) was the most frequent abnormality, followed by elevated total and low-density lipoprotein cholesterol (LDL-C), whereas hypertriglyceridaemia was the least common. Studies from southern and middle regions-particularly Ashanti, Brong-Ahafo, and Greater Accra-reported a higher dyslipidaemia prevalence than those from northern Ghana. Considerable variation in diagnostic criteria, study populations and sampling strategies limited comparability and precluded meta-analysis. CONCLUSION: Dyslipidaemia is common among adult Ghanaians, with low HDL-C emerging as the predominant abnormality. Methodological differences and sampling biases limit the precise estimation of the national burden, but the available evidence indicates a growing cardiometabolic risk. Future studies should prioritise community-based sampling and context-appropriate lipid thresholds to support evidence-based cardiovascular risk-reduction strategies.
30 May 2026
Read appraisal →Molecular biology reports
The role of non-coding RNAs in the coronary no-reflow phenomenon: a systematic review
Coronary no-reflow poses a challenge in interventional cardiology. Increasing attention has been directed toward the potential role of non-coding RNAs (ncRNAs) in predicting coronary no-reflow (CNR), with no prior systematic review available to date. We aimed to systematically review the available evidence on the potential clinical utility of ncRNAs in CNR and/or microvascular obstruction (MVO). Embase, MEDLINE Ultimate, PubMed, Scopus, and Web of Science were searched, last on February 9, 2026. Studies enrolling adult patients with CNR and/or MVO that evaluated the expression of ncRNAs or investigated genetic variants within ncRNA-related pathways were included. Risk of bias was not assessed. Upregulation of miR-1, miR-7a/b, miR-98-5p, miR-133a, miR-133b, miR-208a, miR-224-3p, miR-660-5p, long non-coding RNA (lncRNA) TUG1, lncRNA MALAT1, and downregulation of miR-30e, miR-126, miR-155, and miR-542-3p were reported in patients with STEMI with CNR and/or MVO compared with those without CNR/MVO. Almost all ncRNAs were reported by a single study, with the exception of miR-30e, which was consistently reported in three independent studies. Several ncRNAs may demonstrate good performance in identifying patients at risk of CNR and/or MVO occurrence. Nevertheless, the available evidence is scarce and limited by the low robustness of the reported findings. Circulating ncRNAs are promising biomarkers for CNR. Nevertheless, several limitations persist, highlighting the need for further studies with standardized methodology to validate the clinical utility of ncRNAs.
28 May 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 →Circulation journal : official journal of the Japanese Circulation Society
High Human Epididymis Protein 4 Levels Are Associated With Lower Systolic Function and Poor Prognosis in Heart Failure With Reduced Ejection Fraction
BACKGROUND: Human epididymis protein 4 (HE4) is a novel fibrosis marker expressed in activated myofibroblasts. Although we previously demonstrated its prognostic value in dilated cardiomyopathy, its significance in general heart failure with reduced ejection fraction (HFrEF) remains unclear. METHODS AND RESULTS: Serum HE4 concentrations were measured in 140 patients with HFrEF (left ventricular ejection fraction <40%) and followed for up to 3 years to assess cardiovascular (CV) death and heart failure hospitalization. Kaplan-Meier analysis revealed significantly higher CV event rates in the high HE4 group (above the median) than in the low HE4 group (log-rank P<0.001). Multivariate Cox regression analysis identified HE4 (ln[HE4]) as an independent predictor of adverse outcomes (hazard ratio 3.99; 95% confidence interval [CI] 2.05-7.75; P<0.001). In multivariate linear regression, HE4 was significantly associated with age, serum creatinine, plasma B-type natriuretic peptide, E/e', and global longitudinal strain (GLS; β=-0.083; 95% CI -0.111, -0.054; P<0.001). Combining HE4 and GLS demonstrated that patients with high HE4 and low GLS had the highest CV event rate (log-rank P<0.001). CONCLUSIONS: HE4 is an independent prognostic biomarker in HFrEF, and its association with GLS may reflect underlying myocardial dysfunction and fibrotic remodeling leading to adverse outcomes.
26 May 2026
Read appraisal →The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Integrated metabolic and inflammatory biomarkers for early association and prediction of adverse pregnancy outcomes: a multicenter validation study
BACKGROUND: Adverse pregnancy outcomes (APOs) affect long-term maternal and offspring health. Conventional metabolic markers, including the triglyceride-glucose (TyG) index, inadequately capture pregnancy-related immuno-inflammatory dysregulation. We propose a composite index, TyNIR, integrating metabolic and inflammatory parameters, and assess its associations with APOs and predictive performance using machine learning. METHODS: Data were collected from two tertiary centers in Fujian, China (2023-2025), including 1,741 pregnancies and an external validation cohort of 300. Early-pregnancy parameters were used to derive TyNIR. Primary outcomes were gestational diabetes mellitus (GDM), gestational hypertension (GH), preterm birth (PB), low birth weight (LBW), and premature rupture of membranes (PROM). Multivariable regression, subgroup, and interaction analyses assessed associations between TyNIR and APOs. A feature selection approach developed the PREMIER model, which was compared with TyG and other models and externally validated. RESULTS: TyNIR was positively associated with GDM, GH, and PB. After adjustment, the highest quartile showed increased risks, with odds ratios (ORs) of 3.86 (95% CI 2.33-6.39) for GDM, 3.05 (1.74-5.36) for GH, and 3.45 (1.24-9.66) for PB. No associations were observed for LBW or PROM. The PREMIER model achieved area under the curve (AUC) values of 0.710 ± 0.031, 0.736 ± 0.040, and 0.742 ± 0.073, outperforming TyG and other models. External validation confirmed consistent associations and superior predictive performance. CONCLUSIONS: TyNIR consistently predicts risks of GDM, GH, and PB. The TyNIR-based PREMIER model shows robust predictive performance with external validation. These findings support early identification of high-risk pregnancies and may guide personalized management of APOs.
22 May 2026
Read appraisal →Journal of neurology
Diagnostic and prognostic value of blood neurofilament light chain in ischemic stroke: an individual patient data meta-analysis
BACKGROUND: We aimed to conduct an individual patient data meta-analysis on blood neurofilament light chain (NfL) in ischemic stroke (IS) to enhance its clinical applicability. METHODS: We performed a systematic literature search of studies on blood NfL measured in adult patients within 30 days after IS onset and derived age- and BMI-adjusted Z-scores based on a previously published reference population of healthy controls. We collected clinical, radiological and biochemical parameters of IS patients and tested associations of NfL at defined timepoints after IS onset (D1: < 24 h; D2: 24-48 h; D3: 48-72 h; D4-5: 72-120 h; D6-7: 120-168 h; D8-30: > 168 h) with baseline characteristics and 3-month follow-up outcomes (modified Rankin Scale, mRS; survival). RESULTS: We included 4081 blood NfL values from 2872 participants (IS n = 1985, transient ischemic attack n = 88, healthy controls n = 799) of 18 published studies and 3 unpublished cohorts. In patients with IS, NfL Z-score progressively increased from D1 [median: 2.0 (IQR: 0.9-2.9)] to D6-7 [median: 3.5 (IQR: 3.0-3.8)], with discriminative ability being high for IS vs. controls (AUC: 0.79-0.97) and fair for IS vs. TIA (AUC: 0.64-0.80). Higher NfL Z-score at D1 was associated with greater risk of symptomatic intracranial hemorrhage (aOR = 1.33, p = 0.014) and, from D2 onwards, with larger infarct lesion volume (highest Spearman's rho: 0.795 at D6-7). NfL independently predicted a mRS > 2 (aOR = 1.31, p < 0.001) and mortality (aOR = 1.67, p < 0.001) at 3 months. CONCLUSIONS: Blood NfL level was progressively elevated after IS, could discriminate IS from healthy controls with high accuracy and had prognostic value for intra-hospital complications and 3-month clinical outcomes in IS.
18 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
Exploring clinical feasibility of zero-shot learning in a large language model for immunofixation electrophoresis image interpretation
INTRODUCTION: Immunofixation electrophoresis (IFE) is the gold standard method for detecting and typing monoclonal immunoglobulins, but its interpretation requires expert evaluation and remains susceptible to subjectivity and variability. Recent advances in multimodal large language models (LLMs) have enabled zero-shot visual reasoning without task-specific training. This study aimed to evaluate the feasibility and diagnostic performance of zero-shot multimodal LLMs for automated interpretation of IFE images. MATERIALS AND METHODS: A dataset of 487 immunofixation electrophoresis images representing ten semantic classes (AK, AL, GK, GL, K, L, LGL, MK, ML, and NONE) was retrospectively collected and annotated by expert laboratory specialists. Two multimodal LLMs, ChatGPT-5.2 and Gemini3 Pro, were evaluated using a zero-shot visual question answering (ZS-VQA) framework without additional training or fine-tuning. Each image was analyzed using two prompt configurations: a simple prompt and a detailed prompt with structured diagnostic guidance. Model performance was assessed using precision, recall, F1-score, and confusion matrix analysis. RESULTS: Gemini outperformed ChatGPT across most classes, particularly with detailed prompts, achieving high F1-scores in clinically relevant monoclonal categories such as MK (89.80), AK (84.75), and GK (71.54). Detailed prompting consistently improved recall and F1-scores, underscoring the importance of prompt design. In contrast, ChatGPT showed lower recall, F1-scores, and classification consistency. Both models performed less effectively in visually subtle patterns. CONCLUSIONS: Zero-shot multimodal LLMs, particularly Gemini, show promising potential for interpreting IFE images without task-specific training. However, performance variability and limitations in certain classes indicate that further optimization and validation are required before clinical implementation.
18 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
A laboratory-driven clinical decision support system algorithm for personalized and automated cardiovascular risk stratification in primary care patients.
OBJECTIVE: To develop and evaluate an automated clinical decision support system (CDSS) capable of computing both categorical and exact-percentage cardiovascular risk (CVR) in routine clinical practice. METHODS: We developed and implemented an automated CDSS (AlinIQ®) for CVR assessment within the Clinical Laboratory Department of Hospital Universitari i Politècnic La Fe (València, Spain), applied to patients referred from Primary Care. The analytical profile-requested either via automatic trigger or proactive clinician order-included total cholesterol, HDL-C, LDL-C, non-HDL-C, triglycerides, lipoprotein(a) [Lp(a)], serum creatinine, and estimated glomerular filtration rate. Additional required inputs were smoking status, systolic blood pressure, country of origin, and age at diabetes onset. The CDSS automatically computed SCORE2, SCORE2-OP, SCORE2-Diabetes, and SCORE2 Asia-Pacific, generating both categorical strata and exact-percentage CVR. Lp(a)-adjusted CVR was derived using coefficients from the Spanish Atherosclerosis Society. Following the ESC 2025 Focused Update, patients meeting predefined clinical criteria for moderate, high, or very high CVR were directly assigned to the corresponding category without SCORE calculation. The system also incorporated modifying risk factors, generating standardized interpretive comments and personalized lipid targets. RESULTS: Over one month, 2289 screenings were requested; 189 (8.3%) were excluded. A total of 171 patients (7.5%) were classified as high risk based on score-based calculation (164 [95.9%] triggered automatically), and 29 (1.6%) as very high risk (23 [79.3%] triggered automatically). 119 individuals (6.8%) were reclassified to a higher risk category after Lp(a)-based adjustment. CONCLUSION: This CDSS provides a scalable and reproducible framework for laboratory-driven cardiovascular prevention.
17 May 2026
Read appraisal →Computers in biology and medicine
Dual-model deep learning for Alzheimer's prognostication
Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical observations and provide no uncertainty quantification-rendering them impractical at the critical first-visit encounter when treatment decisions must be made. We developed PROGRESS (PRognostic Generalization from REsting Static Signatures), a dual-model deep learning framework that transforms a single baseline cerebrospinal fluid (CSF) biomarker assessment into actionable prognostic estimates without requiring prior clinical history. The framework addresses two complementary clinical questions: a probabilistic trajectory network predicts individualized cognitive decline parameters with calibrated uncertainty bounds that achieve near-nominal coverage, enabling honest prognostic communication rather than false precision; and a deep survival model estimates time-to-conversion from mild cognitive impairment to dementia. Using data from over 3000 participants across 43 Alzheimer's Disease Research Centers in the National Alzheimer's Coordinating Center database, PROGRESS substantially outperforms existing approaches including Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction. Risk stratification identifies patient groups with seven-fold differences in conversion rates, enabling clinically meaningful treatment prioritization. Leave-one-center-out validation demonstrates robust generalizability, with survival discrimination remaining strong across all held-out clinical sites despite heterogeneous measurement conditions spanning four decades of assay technologies. By combining superior survival prediction with trustworthy trajectory uncertainty quantification, PROGRESS bridges the gap between biomarker measurement and personalized clinical decision-making-providing the prognostic timeline that current staging approaches cannot offer.
15 May 2026
Read appraisal →Molecular omics
Placental metabolomics for assessment of pregnancy complications: a systematic review
Metabolomics is a powerful tool for understanding biological function at the cellular level. While this omics technology has found application in areas ranging from toxicology to cancer, its potential for the study of human pregnancy complications is in its infancy. Proper placental function is critical for a healthy pregnancy and assessing placental metabolism offers a direct window into placental physiology. This review aims to analyse placental metabolomics in pregnancy complications in humans. A systematic search was completed using PubMed and Google Scholar, and the Covidence software was used for the screening process. Following title and abstract screening and full-text review, 32 articles remained and were included in this review. Multiple articles performed metabolomics using mass spectrometry and/or nuclear magnetic resonance to study pre-eclampsia, gestational hypertension, foetal growth restriction, maternal obesity, gestational diabetes mellitus, intrahepatic cholestasis of pregnancy, and preterm birth. A subset of pregnancy complications was investigated in only a single study each (human immunodeficiency virus in pregnancy, neural tube defects, chromosomal disorders, chronic hypoxia, and recurrent pregnancy loss). Univariate analyses identified potential biomarkers of pregnancy complications. Multivariate analyses demonstrated that the placental global metabolic profiles could separate healthy and complicated pregnancies and identified metabolic pathways (e.g. amino acid metabolism, lipid and phospholipid metabolism) to explain pathogenesis and the mechanisms of disease. We highlight knowledge gaps and future directions, emphasizing the need for data replication and inclusion of variables such as biological sex in the field of placental metabolomics.
11 May 2026
Read appraisal →Analytical chemistry
FiLM-Enhanced Biologically Informed Neural Networks for Multiclass Omics Analysis and Biomarker Discovery
High-throughput proteomics enables detailed molecular phenotyping but poses challenges for predictive modeling and interpretation due to high dimensionality, sparsity, and nonlinear interactions. Biologically informed neural networks (BINNs) address these challenges by embedding pathway knowledge into network architectures, providing interpretable models of complex omics data. We present BINN-FiLM, which extends conventional BINNs by integrating feature-wise linear modulation, enabling task-specific scaling and shifting of pathway-level activations while preserving a fixed Reactome-based hierarchy. Unlike BINNs that share all pathway parameters across classes, BINN-FiLM captures class-dependent biological rewiring, making it particularly effective for multiclass classification tasks. Multiclass problems are reformulated as multitask binary learning, allowing pathway activations to adapt to disease-specific contexts. We evaluated BINN-FiLM on three multiclass proteomic data sets, and BINN-FiLM consistently outperformed conventional BINNs and standard machine learning models. FiLM modulation parameters revealed task-specific pathway activity, and SHAP-based interpretation identified key proteins and pathways driving disease discrimination.
11 May 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy with machine-learning classification for the prediction of stereotactic radiotherapy induced treatment toxicity in high-risk localised prostate cancer
Radiotherapy can lead to late-onset toxicity, to varying extents between individuals due to differences in radiosensitivity. Predicting which patients are most at risk is key to augmenting the therapeutic window. However, the underlying biological mechanisms remain poorly understood, and current experimental methods often lack clinical applicability. This study employs Raman spectroscopy to analyse biochemical profiles in peripheral lymphocytes and plasma, aiming to monitor radiotherapeutic response and predict intrinsic radiosensitivity in high-risk localised prostate cancer patients treated with stereotactic radiotherapy. Partial-least squares discriminant analysis classification of Raman spectra at baseline (n = 20) from post-hormone therapy (n = 19), mid-treatment (pre-4th fraction; n = 21) and 3-months after treatment (n = 18) returned mean area under the curve values ranging from 0.88 to 0.93. Ensemble classifiers applied to imbalanced late toxicity datasets (grade 0-1, n = 16; grade 2+, n = 4) yielded mean F1 scores of 0.74 (random forest, lymphocytes) and 0.69 (AdaBoost, plasma); metrics based on best performing model for minority-class. Classical least squares lymphocyte and plasma toxicity models identified major concentration differences in amino acids, proteins, lipids, DNA and related biomolecules (p < 0.05). These findings demonstrate the potential of Raman spectroscopy as a minimally invasive, objective tool for classifying blood-based biochemical profiles across radiotherapy treatment time points and distinguishing patients with late grade 0-1 and grade 2+ toxicity.
10 May 2026
Read appraisal →Journal of affective disorders
Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.
BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes and progression to dementia. Evidence indicates a bidirectional depression-cognition relationship, but objective biological tools to capture severity and this interplay are scarce. METHODS: This study compared 110 patients with LLD between depressive episodes and remission phases. Based on untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis of plasma samples, we identified key metabolites and developed four predictive machine learning models (GLMNet, LDA, Naive Bayes, and cTree). Additionally, Spearman rank correlation analysis and mediation analysis were conducted to further investigate the relationships and mediating effects of the key metabolites. RESULTS: The diagnostic model based on key metabolites selected by the random forest algorithm showed good discriminatory performance in distinguishing LLD Episodes (AUC = 0.824). Tridecanoylcarnitine (Car(13:0)), PC(P-16:0/22:6), and SM(d18:1/22:0) were significantly downregulated during the depressive episode. Tridecanoylcarnitine (Car(13:0)) negatively correlated with depressive severity (p < 0.001) and positively with Mini-Mental State Examination scores. PC(P-16:0/22:6) was associated with both emotional and cognitive impairments. Mediation analysis supported that Tridecanoylcarnitine (Car(13:0)) and PC(P-16:0/22:6) partially mediated the depression-cognition relationship, explaining 22.0 % and 26.9 % of the variance, respectively (p < 0.05). CONCLUSION: This study reveals specific lipid metabolic dysregulation in LLD and identifies key metabolites significantly associated with both depressive severity and cognitive function. It further supports their mediating role in the comorbidity between depression and cognitive impairment. These metabolites may serve as potential targets for simultaneously regulating depression and cognition. The machine learning model developed provides a new auxiliary tool for the objective assessment of LLD.
4 May 2026
Read appraisal →FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Lysosomal Membrane Proteins: Key Regulators of Glucose Metabolism and Its Associated Diseases
Glucose homeostasis, which is critical for maintaining energy supply and health, involves glycogen metabolism, glycolysis, and gluconeogenesis. Lysosomal membrane proteins (LMPs) play core roles in regulating these processes. However, no focused systematic review of the topic has been reported. This review provides an in-depth analysis of the central roles of LMPs in glucose metabolism, focusing on the regulation of glucose homeostasis and their potential effects on metabolic diseases through the regulation of autophagy, signaling networks, specialized transporter functions, and other relevant mechanisms. In general, LMPs play core roles in lysosomal biosynthesis, and an in-depth study of their relationship with glucose metabolism could significantly highlight the important contribution of lysosomes in the development of related diseases.
3 May 2026
Read appraisal →Cell reports. Medicine
A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease
Accurate, non-invasive liver fibrosis detection is essential for chronic liver disease management, particularly with rising metabolic dysfunction-associated liver disease (MASLD) and chronic hepatitis B (CHB). While the Fibrosis-4 (FIB-4) index is widely used, its performance for advanced fibrosis is limited. We develop Met-FIB using metabolomics and machine learning, integrating FIB-4 parameters (age, aspartate aminotransferase, alanine aminotransferase, and platelet count) with tyrosine and taurocholic acid identified in a CHB discovery cohort (n = 3,251). Validation includes one CHB cohort (n = 729) and two MASLD cohorts (n = 149, n = 155). Met-FIB outperforms FIB-4, FibroScan, and other serum markers across all fibrosis stages. In CHB, Met-FIB achieves 96.3% rule-out sensitivity and 85.4% rule-in specificity for significant fibrosis, with rule-in specificity reaching 98.6% and 98.8% for advanced fibrosis and cirrhosis. In MASLD, corresponding values are 93.9% and 90.2% for significant fibrosis, with >97.9% specificity for late-stage disease. Met-FIB demonstrates clinical utility for non-invasive fibrosis staging across diverse etiologies.
25 Apr 2026
Read appraisal →Renal failure
Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study
Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and accurate prediction remains challenging due to the lack of reliable biomarkers. Urinary small-molecule metabolomics offers a promising approach to capture the dynamic metabolic changes during the progression of S-AKI. In this two-center prospective observational study, we enrolled 360 children from both centers. Urine samples were collected within 24h after hospitalized children diagnosed with sepsis, stored at -80 °C, and analyzed using gas chromatography-mass spectrometry (GC-MS). Based on urinary metabolic fingerprints (U-MF), we developed and validated a machine learning model for early prediction of S-AKI. The Shapley Additive Explanations (SHAP) algorithm was applied to visually explain the optimal model. A panel of 10 metabolites was selected as common discriminative features. Among the 4 machine learning models evaluated, the support vector machine (SVM) demonstrated the best performance in both the discovery cohort (AUC 0.94, 95% CI: 0.91-0.98) and the external validation cohort (AUC 0.89, 95% CI: 0.82-0.96), enabling early prediction of S-AKI within 24 h. Furthermore, the U-MF panel was integrated into an open-access online platform to facilitate clinical translation. Our findings suggest that U-MF combined with machine learning holds promise as a robust and noninvasive approach with potential utility for early prediction of S-AKI in pediatric patients.
24 Apr 2026
Read appraisal →PloS one
A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis.
Hypothyroidism, caused by reduced thyroid-hormone production, is often difficult to diagnose because its symptoms are non-specific and overlap with other disorders. We developed FusionNet-CXG, a hybrid deep-learning model that couples CNN, eXtended LSTM, and GRU components for hypothyroidism prediction. Experiments were performed on a public dataset (3,772 records; 30 features). Class imbalance was mitigated using SMOTE-NC, and performance was estimated using 10 repetitions of 5-fold stratified cross-validation, yielding 50 fold-level evaluations to obtain stable results. Across folds, FusionNet-CXG achieved 0.9394 mean accuracy, F1 = 0.906, and AUC-ROC = 0.94, and it exceeded CNN+LSTM and CNN + BiLSTM baselines under the same protocol. The results indicate that combining local feature extraction with recurrent modelling is effective for this task. To support interpretability, SHAP was used to quantify feature influence on the predictions. Future work will focus on external validation using more diverse real-world cohorts and incorporating temporal clinical data to improve clinical relevance.
23 Apr 2026
Read appraisal →Annals of medicine
Comparative evaluation of oxidative stress biomarkers F2-isoprostanes and 8-OHdG in Parkinson's disease and Type 2 Diabetes Mellitus: a systematic review and meta-analysis of human studies
BACKGROUND: Oxidative stress is central to type 2 diabetes mellitus (T2DM) and Parkinson's disease (PD). However, the utility of biomarkers for lipid peroxidation (F2-isoprostanes) and DNA damage (8-OHdG) in the comorbidity of PD and T2DM remains unclear. METHODS: We conducted a systematic review and meta-analysis of 54 unique studies of human subjects aged ≥ 50 years (n = 7,521: 3,522 with T2DM, 722 with PD, and 3,277 controls), measuring biomarkers in serum, plasma, or leukocytes. Mixed-effects models quantified standardized differences (Hedges' g) across subgroups. RESULTS: In T2DM, F2-isoprostanes (g = 1.60, 95% CI: 0.95-2.25) and 8-OHdG (g = 2.64, 95% CI: 2.13-3.14) were markedly elevated (p < 0.001). Stronger effects were observed in younger cohorts and serum/plasma samples, with complications like nephropathy exhibiting extreme oxidative stress (g = 5.24). In PD, 8-OHdG was moderately elevated (g = 0.78, 95% CI: 0.18-1.39; p = 0.011), particularly in randomized controlled trials and plasma samples, whereas F2-isoprostanes were not significantly elevated (g = 0.47, 95% CI: -0.43-1.38). High heterogeneity in T2DM (I2 > 90%) reflected methodological variability. CONCLUSION: Distinct profiles - both markers elevated in T2DM but only 8-OHdG in PD - underscore 8-OHdG's potential in PD-T2DM comorbidity. Future research should focus on standardized assays, multi-compartmental or multi-modal sampling, and longitudinal studies to clarify mechanisms and therapeutic targets.
21 Apr 2026
Read appraisal →International journal of biometeorology
Random forest-based medical geography: geographical distribution of reference values for LP-PLA2 in healthy adults in China
Lipoprotein-associated phospholipase A2 (LP-PLA2), a vascular-specific inflammatory marker, is associated with coronary heart disease (CHD), a highly prevalent condition among middle-aged and older adults. It explores the relationship between environmental factors in environmentscience, geography, statistics and medicine and the reference value of LP-PLA2 in China. A geographic distribution prediction model for Lp-PLA2 medical reference values was developed using optimized machine learning approaches. The support vector machine (SVM) model employed a polynomial kernel function, with hyperparameters tuned via grid search and cross-validation to determine the optimal classifier configuration. Concurrently, the random forest algorithm was applied to mitigate overfitting and perform regression analysis on the dataset. Further considering the influence of geographical factors on the 100 LP-PLA2 medical reference values in city and county units from 2000 to 2024, using 24 geographical factors as independent variables, to fit 2317 LP-PLA2 medical reference values, and then explore the spatial variation characteristics of LP-PLA2 medical reference values. Further spatial interpolation was visualized on the national map. The medical reference value of LP-PLA2 was significantly correlated with longitude, altitude, annual average wind speed, topsoil clay percentage, topsoil reference capacity, topsoil organic matter content, and total convertible amount of topsoil, respectively. The geospatial distribution map shows that the whole value was high in the east and low in the west, with significant differences in longitude. The geographical characteristics of any given region, when analyzed through either the SVM prediction model or the derived geographical distribution map, enable precise determination of region-specific Lp-PLA2 reference values. This establishes an evidence-based foundation for clinical decision-making and research design in healthcare institutions and medical research organizations.
20 Apr 2026
Read appraisal →Archives of disease in childhood. Fetal and neonatal edition
Cerebral fuels within the first week of life in very preterm infants: a cohort study
BACKGROUND: Ketones and lactate may contribute towards overall cerebral fuel availability in term infants, yet the availability of such cerebral fuels in very preterm infants is unclear. We undertook a prespecified substudy to explore ketone and lactate concentrations in the first week of life in infants recruited to the REACT trial (real-time continuous glucose monitoring in the newborn): an international multicentre randomised controlled trial of 182 very low birth weight infants investigating the use of continuous glucose monitoring in glycaemic care. METHODS: Ketone and lactate measurements were prospectively collected over the first week of life using the Nova Biomedical point-of-care meter. A longitudinal analysis was undertaken to explore lactate and ketone concentration trends across time and their relationships with blood glucose, baseline demographics, nutritional support and insulin treatment. RESULTS: Data were available for 168 infants (85 females) including 2902 blood glucose, 2084 ketone and 2017 lactate samples. The mean (SD) gestational age was 27.4 (2.0) weeks. Lactate concentrations were higher initially, with mean (SD) 1.72 (1.26) mmol/L on day 2 and lowered to 1.19 (1.1) mmol/L on day 7. Ketone concentrations remained consistently low at 0.1 mmol/L. Neither simultaneous blood glucose concentrations, macronutrient intake nor receipt of insulin was consistently related to ketone or lactate concentrations. CONCLUSION: In this cohort of very preterm infants, there were persistently low concentrations of ketones and relatively higher concentrations of lactate throughout the first week of life. Future research should evaluate changes in these metabolites during episodes of acute hypoglycaemia or hyperglycaemia over more prolonged periods of neonatal intensive care.
20 Apr 2026
Read appraisal →Medicine
Saturation effect of anion gap in predicting long-term mortality in critically ill patients with chronic obstructive pulmonary disease (COPD): A retrospective cohort study based on the MIMIC-IV database
The relationship between anion gap (AG) and long-term mortality in intensive care unit has been widely reported, but whether this association exists in critically ill patients with chronic obstructive pulmonary disease (COPD) is still unknown. The data of this study were collected from the Medical Information Mart for Intensive Care-IV. First of all, we used the Cox regression analysis and Kaplan-Meier curves to measure the relationship between AG and 365-day mortality for critically ill patients with COPD. Next, a restricted cubic spline was used to analyze the relationship between AG and mortality. Finally, age, sex, weight, hypertension, type 2 diabetes mellitus, heart failure, myocardial infarction, and chronic kidney disease were considered for subgroup analysis. A total of 2594 eligible subjects were sampled, of which 36.24% died within 365 days of intensive care unit admission. Cox regression analysis, after adjusting for confounders, demonstrated a significant association between AG and 365-day mortality in patients with COPD (hazard ratio = 1.03, 95% confidence interval: 1.01-1.05. P < .05). Stratifying AG into quartiles revealed higher levels of AG associated with an increased risk of death (Q1: 1.00, Q2: 1.34 [1.09-1.66], Q3: 1.44 [1.17-1.78], and Q4: 1.49 [1.19-1.87]). Additionally, restricted cubic spline analysis indicated a nonlinear relationship, with a critical value of AG at 14 mmol/L. Subgroup analysis highlighted AG as a significant predictor of long-term mortality in COPD patients across different subgroups, with an interaction effect observed in the subgroup with type 2 diabetes mellitus. In critically ill patients with COPD, there was a significant positive association between AG and 365-day mortality. In addition, there is a saturation effect at AG of 14 mmol/L.
20 Apr 2026
Read appraisal →Journal of the American College of Surgeons
Predictive Model for Mild Autonomous Cortisol Secretion in Patients Evaluated in Artificial Intelligence-Driven Adrenal Incidentaloma Clinic
BACKGROUND: Mild autonomous cortisol secretion (MACS) is present in approximately 20% to 50% of adrenal incidentalomas. These patients do not exhibit the clinical manifestations of overt Cushing's syndrome, and differentiation from nonfunctional adrenal incidentalomas (NFAI) is typically made after a low-dose dexamethasone suppression test. The objective of this study was to develop predictive models to distinguish MACS from NFAI using clinical and radiological parameters. STUDY DESIGN: This retrospective study included patients evaluated in an adrenal incidentaloma clinic between February 2022 and August 2024 who were diagnosed with either NFAI or MACS. Demographic characteristics, medical and medication history, and radiological features were collected. Patients were randomly divided into training and test cohorts in a 3:1 ratio. Predictive models for MACS were developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) algorithms. RESULTS: A total of 397 patients were included, with 297 allocated to the training set and the remaining 100 to the test set. The mean age was 62.3 years, and 55% of participants (n = 220) were women. MACS was present in 34% of the study population (n = 136). The most influential predictive factors of MACS were BMI, Posterior Adiposity Index, and the number of antihypertensive medications. The LASSO and RF models achieved discrimination with area under the curve values of 0.686 and 0.736, respectively. At Youden Index thresholds balancing sensitivity and selectivity, the LASSO model had 58.8% sensitivity, 75.8% specificity, and 70% accuracy, whereas the RF model had 64.7% sensitivity, 75.8% specificity, and 72% accuracy. CONCLUSIONS: Predictive models incorporating clinical and radiological characteristics offer a promising approach for distinguishing MACS from NFAI in patients with adrenal incidentalomas.
17 Apr 2026
Read appraisal →Scientific reports
Predicting post-stroke functional outcome using explainable machine learning and integrated data
Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated a diverse set of clinical characteristics and blood biomarkers with multiple machine learning models to predict 3-month functional outcome after AIS, and used explainable artificial intelligence to identify key drivers of performance. Models were trained on 506 patients aged 18-69 years with AIS enrolled at four stroke units in western Sweden. We compared extreme gradient boosting, multilayer perceptron (MLP), and L1- and L2-regularized logistic regression. Feature importance was assessed with Shapley additive global explanations. Of the 506 patients, 105 had an unfavorable outcome (modified Rankin Scale score > 2). All models showed high area under the curve (AUROC, 0.900-0.906). The MLP achieved the highest precision-recall performance (AUPRC, 0.773 ± 0.080) and sensitivity (0.655 ± 0.096), though with lower specificity (0.920 ± 0.035). Stroke severity (NIH Stroke Scale score) was the dominant predictor across models. Among biomarkers, brain-derived tau (BD-tau) was most informative, followed by inflammation-related plasma proteins. In conclusion, machine learning accurately predicted functional outcome after AIS. BD-tau and inflammation-related proteins contributed predictive information above stroke severity, suggesting a potential for blood biomarkers to enhance individualized prognostication after AIS.
17 Apr 2026
Read appraisal →Analytical chemistry
Dual-Signal Amplification Strategy for Synchronous Monitoring of Stress Hormones and Metabolic Indicators
The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challenge for wearable sensors, primarily due to the ultralow abundance of biomarkers in sweat and the vulnerability of weak electrochemical signals to interference. Herein, we report a wearable electrochemical sensor based on a dual-signal amplification strategy for the synchronous and highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA). The CuFe-PBA/CC substrate provides a platform featuring high surface area, electrocatalytic activity, and dual signal amplification capabilities, which are the primary sources of high sensitivity. The MIP layer enhances the effective concentration of target substances near active sites through a local enrichment effect. Combined with the substrate's high catalytic activity, this amplifies the electrochemical signal specific to the target. It synergizes with an exogenous redox probe ([Fe(CN)6]3-/4-) to establish a dual-amplification pathway, which enhances the current output to the milliampere level and substantially improves the signal-to-noise ratio. To ensure the optimal performance of the sensor, the preparation parameters were optimized via the integration of machine learning. This integrated sensing system exhibits high selectivity, superior sensitivity (with detection limits of 44.78 fM for cortisol, 0.064 μM for Glu, and 0.089 μM for UA), and excellent stability (retaining 88% of the initial signal after 100 cycles of mechanical deformation). Furthermore, we fabricated the sensor into a watch-like form factor, integrated it with high-precision wearable electronic modules, and achieved real-time wireless detection of sweat cortisol, Glu, and UA in ex vivo sweat monitoring experiments. This work provides a novel solution for objective stress assessment and metabolic status monitoring, holding promise for advancing long-term health management applications.
15 Apr 2026
Read appraisal →Epigenetics
The role of R-loops-mediated epigenetic regulation in genome stability maintenance and disease pathogenesis: a systematic review.
This systematic review synthesizes current evidence linking R-loops - associated processes with epigenetic regulation, genome stability, and human disease, integrating findings across DNA/RNA/protein modifications and 3D genome organization, and highlighting key mechanistic and technical gaps relevant to future studies, including longer-term translational exploration. Following PRISMA guidelines (Registration No.: INPLASY202560055), PubMed and ScienceDirect databases were searched through 18 January 2026. Two independent reviewers conducted blinded screening via the Rayyan platform. From 1290 initial articles, 71 studies met inclusion criteria. Methodological rigour was assessed using a modified SYRCLE risk-of-bias tool. Thematic synthesis identifies R-loops as recurrent intermediates linking transcription, chromatin regulation, and genome maintenance; feedback-like relationships are reported in selected systems. They spatially impede DNMTs to sustain promoter hypomethylation while recruiting TET enzymes for demethylation, and RNA modifications (e.g. m5C/m6A) may tune hybrid stability and, in some contexts, influence effector recruitment. R-loops facilitate the recruitment of histone-modifying complexes (PRC1/PRC2, G9a/GLP), thereby influencing chromatin landscapes and 3D genome architecture - including associations with TAD boundary insulation and with LLPS-related repair compartments in specific contexts. Dysregulation of R-loop homeostasis links to disease-related epigenetic alterations and genome instability with distinct disease-, locus- and cell-state specificity, and its context-dependent roles in diverse disorders involve transcriptional silencing, DNA damage, replication-transcription conflicts, repair pathways, chromatin regulation and mitochondrial genome maintenance. Integrating DRIP-seq with epigenomic profiling and functional tests supports mapping of context-specific relationships.
14 Apr 2026
Read appraisal →Molecular biomedicine
Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification
Atherosclerosis, a major cause of adverse cardiovascular events and mortality rates worldwide, stems from sustained lipid accumulation and subsequent chronic inflammation within the arterial walls. An early identification of patients at risk is crucial to prevent life-threatening thrombotic events and provide effective and personalized treatments. Leveraging the power of machine learning (ML) to enhance diagnostics and biomarker discovery, we applied a high-throughput approach using five ML classification algorithms (MLCA), integrating clinical and serum proteomic data from patients with carotid atherosclerotic stenosis (AT, n:60), dyslipidemic patients (DLP, n:55), and healthy controls (HC, n:66). As a result, a robust 6-protein panel (B2M, GPV, MMP9, PLF4, TSP1, and FB isoforms) was identified with a ROC-AUC value > 0.9 for all algorithms applied, highly discriminating AT patients compared to DLP or CTRL. The levels of these proteins were further validated in an independent external cohort, including patients presenting with acute atherothrombotic stroke, corroborating the potential of this panel as biomarker for atherosclerosis severity. In addition, the combined clinical-proteomic ML approach provided a more accurate patient stratification than the clinical or proteomic analysis alone. Mechanistically, the identified biomarkers highlight the importance of platelet activation, uncontrolled angiogenesis and intraplaque haemorrhage in the atherosclerotic process, underscoring the need for multipathway therapies to prevent unwanted thrombotic events.
12 Apr 2026
Read appraisal →Heart (British Cardiac Society)
Subphenogroups of acute heart failure with preserved ejection fraction: comprehensive proteomics and pathway analysis
BACKGROUND: Heterogeneity of heart failure with preserved ejection fraction (HFpEF) results in significant challenges for treatment development. Identifying and characterising distinct HFpEF phenogroups may aid in tailoring therapeutic strategies for these patients. The objective of this study was to assess proteomic patterns of HFpEF phenogroups identified through a machine-learning-based clustering model, with the aim of uncovering specific biological pathways associated with each phenogroup. METHODS: This study represents a post-hoc analysis of the ongoing Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a multicentre prospective observational study of hospitalised patients with acute decompensated HFpEF. Of the overall cohort (N=1238), this study analysed 198 patients with HFpEF with available proteomics data. These patients were classified into four phenogroups using the machine-learning-based clustering model. The SomaScan assay V.4.1 was used to measure levels of >7000 plasma proteins, and subsequent pathway analysis was conducted to determine the biological differences among the phenogroups. RESULTS: We identified four distinct phenogroups: Phenogroup 1 ('rhythm trouble'), Phenogroup 2 ('ventricular-arterial uncoupling'), Phenogroup 3 ('low output and systemic congestion') and Phenogroup 4 ('systemic failure'). The proteomics revealed distinct protein expression profiles among the phenogroups, with ribonuclease 4, tax1-binding protein 1, regenerating islet-derived protein 3-gamma and alpha-1-antichymotrypsin being the most significant markers to specific identified phenogroups. Pathway analysis suggested differences in immune response, autonomic activation, cellular homeostasis and tissue repair mechanisms across the phenogroups. CONCLUSIONS: Using a comprehensive plasma proteomics approach, our study identified distinct proteomic profiles of HFpEF phenogroups, which in turn suggest specific underlying biological processes. These profiles suggest the involvement of inflammatory activation, tissue injury and regenerative responses, immune modulation and systemic stress signalling as key components of HFpEF pathophysiology. TRIAL REGISTRATION NUMBER: UMIN-CTR ID: UMIN000021831.
12 Apr 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
Read appraisal →Nat Commun
Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy
Proteomics offers an unprecedented opportunity to characterize and predict diabetic retinopathy (DR) with minimal invasiveness. Here we examine this in 10,873 individuals with (pre)diabetes from two ethnically distinct cohorts. By simultaneous profiling of ~3000 proteins, we identify 668 associations with mechanistically plausible directionality that constitute a comprehensive DR proteomic landscape with linkages to retinal tomographic structure and genetic predisposition, pointing to established and novel biological pathways conferring DR risk. Integrating DR proteomic profile markedly improves predictive performance beyond clinical and genetic predictors, with plexin B2, growth differentiation factor 15, and renin emerging as top proteins validated across cohorts and linked to retinal microvascular degeneration in Guangzhou Diabetic Eye Study (GDES) based on SS-OCTA. A parsimonious panel of these three proteins alone achieves comparable performance in predicting DR development and progression, while renin is confirmed as a causal promoter through genetic analyses. Our findings highlight the potential of large-scale proteomics in elucidating DR pathogenesis and advancing biomarker discovery, with broad implications for early detection and intervention.
1 Oct 2025
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