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PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Does impulsivity predict treatment outcomes in PTSD with borderline personality disorder features? Results from a randomized clinical trial.

BACKGROUND: Trauma-focused psychotherapies are first-line treatments for posttraumatic stress disorder (PTSD). However, a substantial proportion of clients do not respond adequately or drop out of therapy prematurely. This has sparked interest in identifying individual-level predictors of treatment outcomes, including improvement in PTSD severity and dropout. Impulsivity may be a predictor because it may interfere with key therapeutic processes, such as cognitive restructuring and emotional processing. Consequently, we present a hypothesis-driven secondary analysis of a 15-month randomized clinical trial comparing Dialectical Behavior Therapy for PTSD (DBT-PTSD) and Cognitive Processing Therapy (CPT) in women with childhood abuse-related PTSD and borderline personality disorder features to test whether impulsivity, assessed at baseline, predicts PTSD improvement and dropout. We further explore whether the dimensions of impulsivity (non-planning, attentional impulsivity, and motor impulsivity) differentially affect the outcomes in DBT-PTSD vs. CPT. METHODS: A total of 193 cis women with PTSD related to childhood abuse and borderline personality disorder features were assessed using the Clinician-Administered PTSD Scale (CAPS) and the Barratt Impulsiveness Scale (BIS-10). Separate probit models and general linear models were applied to predict dropout and pre-to-post changes in PTSD severity (ΔCAPS) from total impulsivity and subscale scores, i.e. non-planning, attentional and motor impulsivity. RESULTS: Overall, dropout rates were higher for participants with higher baseline impulsivity scores (p = 0.049), particularly for those with higher non-planning impulsivity (p = 0.012). In participants randomized to CPT improvement in PTSD symptom severity (ΔCAPS) was negatively related to baseline total impulsivity (p = 0.021). In participants randomized to DBT-PTSD this relation was not significant. CONCLUSIONS: The results suggest that impulsivity may predict treatment outcomes. Specifically, patients with elevated impulsivity may be less likely to respond adequately to CPT. If replicated, these findings have implications for personalization of treatment.

Humans↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Analyzing salinity tolerance in grass carp (Ctenopharyngodon idella): Insights from genome-wide association study and genomic selection.

Grass carp (Ctenopharyngodon idella) is one of the most widely cultured freshwater fish species globally. However, the expansion of its farming scale faces severe limitation owing to freshwater scarcity; therefore, the development of strains with greater salinity tolerance is key for expanding production using brackish water resources. To investigate the genetic basis of salinity tolerance in grass carp, a genome-wide association study (GWAS) was conducted using 200 individuals representing extreme phenotypes, namely salinity-tolerant and salinity-sensitive groups. In total, 17 single nucleotide polymorphisms (SNPs) related to salinity tolerance were detected, which were distributed across 11 chromosomes. Through gene annotation, 38 candidate genes were obtained from these loci. Enrichment analysis revealed these candidate genes are primarily implicated in key biological processes, including osmotic regulation, energy metabolism, and stress responses. Analyses of different SNP densities revealed that the 5 K SNP density panel can balance prediction accuracy and computational efficiency. The BayesA model achieved the highest prediction accuracy under the GWAS_Evenly selection strategy, with substantial reductions in mean absolute error and mean square error. This study reveals the genetic mechanisms of salinity tolerance in grass carp, which might be optimized through genomic selection, and provides insights for selectively breeding new varieties with greater salinity tolerance.

Animals↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗

PubMed2026

Routine methods misidentify Serratia spp.: Limitations of MALDI-TOF MS revealed by whole-genome sequencing.

Accurate species-level identification within the genus Serratia remains challenging due to extensive phenotypic overlap and high genomic relatedness among closely related and recently described taxa. This study presents an evaluation of routine and genome-based identification approaches applied to clinical Serratia isolates, integrating phenotypic assays, MALDI-TOF MS (Bruker Daltonics), 16S rRNA gene sequencing, and Whole-Genome Sequencing (WGS). A total of 103 isolates collected from a teaching hospital were analyzed. WGS was performed on a subset of isolates. Conventional biochemical methods classified all isolates as Serratia marcescens, whereas MALDI-TOF MS identified 60.1% as S. marcescens, 11.6% as S. ureilytica, and 28.1% just at the genus level. Peak analysis from MALDI-TOF MS revealed specific peaks associated with S. marcescens and S. ureilytica, but limited discriminatory power. WGS of six isolates initially identified as S. ureilytica by MALDI-TOF MS revealed reclassification as Serratia sarumanii (n = 5) and Serratia montpellierensis (n = 1), supported by Average Nucleotide Identity (ANI), Average Amino Acid Identity (AAI), and Digital DNA-DNA Hybridization (dDDH) thresholds. In contrast, 16S rRNA analysis showed limited species-level resolution. Phylogenomic and SNP-based analyses confirmed these classifications with strong support. Overall, this study underscores the critical role of high-resolution genomic approaches for precise species identification and highlights the need for continuous expansion and curation of MALDI-TOF MS reference databases to support reliable clinical diagnostics and epidemiological surveillance of emerging Serratia species.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Non-motor symptoms and healthcare utilization before diagnosis of myasthenia gravis: a nationwide cohort study.

BACKGROUND: Non-motor symptoms have been reported prior to myasthenia gravis (MG) diagnosis. However, the temporal patterns of non-motor symptoms and healthcare utilization before MG diagnosis remain unclear. METHODS: We conducted a retrospective, population-based cohort study using the Korean National Health Insurance Service (KNHIS) database from 2011 to 2021. Incident MG cases were identified using the International Classification of Diseases, Tenth and Rare Intractable Disease codes. Individuals younger than 20  years or with missing health screening data were excluded. Each MG case was matched 1:10 by age, sex, and index date to controls. Non-motor symptoms and healthcare utilization were defined using operational criteria derived from KNHIS claims data. Rate ratios (RRs) and 95 % confidence intervals (CIs) were estimated across four prespecified intervals (0-1, 1-2, 2-5, and 5-10  years) before MG diagnosis. RESULTS: We included 8,355 MG patients and 83,550 controls (mean age, 53.7  years; male, 44 %). MG patients had higher rates of any non-motor symptoms over 10  years(RR 1.34; 95 % CI 1.30-1.39), with the sharpest increase in the year before diagnosis. Depression, anxiety, migraine, constipation, and insomnia consistently showed higher RRs across all intervals. Hospitalizations (RR 1.66; 95 % CI 1.61-1.71) and outpatient clinic visits (RR 1.10; 95 % CI 1.04-1.17) were consistently higher across 10  years, peaking during the 0-1 year before MG diagnosis. CONCLUSION: Non-motor symptoms and healthcare utilization increased years before MG diagnosis. Earlier recognition of these symptom patterns may facilitate timelier evaluation for MG and improve diagnostic pathways.

Humans↗

PubMed2026

Clinicopathological response and survival outcomes of HER2-low versus HER2-zero early breast Cancer: A systematic review and Meta-analysis.

BACKGROUND: Breast cancer is the most common malignant tumor in women. Human epidermal growth factor receptor 2 (HER2) is a key biomarker for classification and treatment. A subgroup with HER2-low expression has been identified, but existing evidence is heterogeneous. This systematic review and meta-analysis compared pathological response and survival outcomes between HER2-low and HER2-zero early-stage breast cancer to clarify prognostic features. METHODS: This study followed PRISMA guidelines and was registered in PROSPERO (CRD420251120506). PubMed, Embase, Web of Science, ClinicalTrials.gov, and major oncology conferences were searched through September 2025. Cohort studies of early-stage breast cancer comparing HER2-low (IHC 1+/2+ and ISH-negative) vs. HER2-zero with extractable pCR, DFS, or OS data were included. Studies involving HER2-positive patients or inconsistent definitions were excluded. Meta-analyses were performed using RevMan 5.3. RESULTS: Twenty-eight studies involving 115,182 patients were included. HER2-low patients showed significantly lower pCR rates (OR = 0.58, 95% CI: 0.52-0.65). DFS favored HER2-low (multivariate HR = 0.75, 95% CI: 0.69-0.83), especially in HR+ tumors, with a weaker effect in HR- cases. OS also favored HER2-low (HR = 0.80, 95% CI: 0.72-0.89), mainly driven by the HR- subgroup; no OS difference was seen in HR+ tumors. Sensitivity analyses and funnel plots indicated robust results with no apparent publication bias. Overall study quality was high (17 high-quality, 11 moderate-quality). CONCLUSION: HER2-low early breast cancer shows lower pCR after neoadjuvant therapy but better long-term survival. These findings support the clinical relevance of HER2-low as a biologically meaningful subgroup within HER2-negative disease, while its status as a stable and independent subtype still requires further validation through prospective studies, standardized testing, and multi-omics investigation.

Humans↗

PubMed2026

Neuroimaging anxious children and adolescents before and after cognitive behavioral therapy: a systematic review.

OBJECTIVE: This systematic review investigates brain changes in youths with anxiety disorders following cognitive behavioral therapy (CBT) and neural markers that predict CBT responses. METHODS: We conducted a systematic search using the electronic databases PubMed, Web of Science, and ProQuest. The inclusion deadline was set to October 27, 2025. We included fifteen peer-reviewed neuroimaging studies that examined the effects of CBT in youths under 19 years old with a primary clinical diagnosis of an anxiety disorder based on DSM-5 criteria. RESULTS: Although the existing literature is marked by substantial diversity in methods and outcomes, task-related neural response in the anterior cingulate cortex (ACC, 2/8, 25.0%), insula (1/8, 12.5%) increased from pre to post CBT and these changes were further correlated with clinical symptom improvements. Moreover, CBT outcomes were predicted by pre-treatment activity or connectivity in the ACC and amygdala (3/13, 23.0%). A smaller proportion of studies (2/13, 15.3%) found that activity or connectivity in the insula, precuneus/cuneus, postcentral gyrus, and activity or structure in the nucleus accumbens (NAcc) predicted response to CBT. The low consistency of these findings was driven by methodological variability, low reliability of the neural markers, and relatively small sample sizes. CONCLUSIONS: This review highlights promises of neural predictors and outcomes to enhance anxiety disorder treatments in children and adolescents, facilitating future personalized and effective CBT. Beyond this initial promise, the field is hindered by methodological inconsistencies and limited replications. While longitudinal and personalized approaches are important next steps, the central challenge remains: identifying neural markers that are both reliable and robust.

Adolescent↗

PubMed2026

Genome-wide identification and expression profiling of HSD3B and SDR42E1 genes in the Pacific oyster (Crassostrea gigas): potential associations with gonadal development.

Sex steroids are lipid-soluble signaling molecules that regulate sex differentiation, reproductive development and physiological homeostasis in animals. 3β-Hydroxysteroid dehydrogenase/Δ5-Δ4 isomerase (3β-HSD) is a key steroidogenic enzyme, whereas SDR42E1, an extended short-chain dehydrogenase/reductase, has been implicated in sterol- and steroid-related metabolism. However, the composition, evolutionary relationships and expression patterns of the HSD3B- and SDR42E1-related genes in bivalve gonadal development remain poorly characterized. In this study, five PF01073-containing genes, comprising three CgHsd3b and two CgSdr42e1 genes, were identified in the Pacific oyster Crassostrea gigas. Phylogenetic analysis separated the proteins into HSD3B-related and SDR42E1-related groups, and gene-structure and motif analyses indicated subfamily-level divergence. All five proteins retained the SDR domain but differed in exon-intron structure and motif composition. Each contained the extended-SDR TGxxGxxG motif, whereas exact classical [ST]GxxxGxG and NNAG motifs were absent. Tyr- and Lys-equivalent residues were conserved, while the HSD3B1 Ser-equivalent position contained Thr in two C. gigas proteins and Ser in one. These features support their classification as extended-SDR proteins but do not establish enzymatic activity or substrate specificity. The three CgHsd3b genes were dispersed on one chromosome, whereas CgSdr42e1-1 and CgSdr42e1-2 were adjacent on another chromosome, suggesting a possible local duplication event for the CgSdr42e1 pair. Public RNA-seq data showed distinct tissue- and gonadal-stage expression patterns, with several genes displaying gonad-biased or female-stage-associated expression. Independent RT-qPCR profiling of the representative genes CgHsd3b-3 and CgSdr42e1-1 detected stage-dependent expression, although tissue rankings differed from those in the public RNA-seq datasets. These differences may reflect the use of independent biological samples, tissue composition, normalization procedures, and platform-specific measurements. Because enzymatic assays, metabolite measurements, cellular localization, and functional perturbation were not performed, the results identify candidate genes whose expression is associated with gonadal development rather than demonstrating regulatory roles. This study provides a comparative framework for future functional investigation of sterol- and steroid-related metabolism in bivalves.

Animals↗

PubMed2026

Quo vadis, BGA? A collaborative EDNAP exercise on the challenges and progress in forensic biogeographical ancestry inference.

There is a broad consensus that forensic tests for the prediction of externally visible characteristics (EVC) and analysis of biogeographic ancestry (BGA) of an individual are technically reliable. However, interpretation of the results and population-specific genotype distribution patterns remains challenging. EVC and BGA analyses provide valuable information for population genetics studies and as investigative leads for criminal cases, as well as for historical and contemporary identification tests. However, inaccurate or incorrect predictions, for example, from subjective bias in the interpretations made, have the potential to misdirect police investigations. The legal situation regarding EVC and BGA testing varies by country: ranging from countries where it is explicitly prohibited, to those without specific regulations on biogeographic ancestry prediction, and others that have already enacted laws governing its use. The reluctance to utilize these analyses is not only due to legal restrictions and data protection concerns, but also to initial limited sets of sufficiently comprehensive forensic DNA assays. Forensic BGA marker panels typically contain up to ∼300 SNPs. This relatively small number of genetic markers, along with limited reference population data, complicates the interpretation of results from donors of unknown origin. This paper presents the results of a collaborative EDNAP study, which, for the first time, evaluated the approach to reporting EVC and BGA data between international laboratories. For the study, DNA from nine individuals with self-reported ancestry was collected and analysed using various forensic panels differing in the number and composition of ancestry-informative markers genotyped, comprising: the Precision ID mtDNA Whole Genome Panel, the VISAGE Basic Tool and the VISAGE Enhanced Tool for Appearance and Ancestry Prediction, and the Ion AmpliSeq™ PhenoTrivium Panel. To ensure full data protection, all SNP genotypes and uniparental marker haplotypes obtained were not shared with third parties. Instead, the genetic data were analysed using a range of commonly used population analysis software packages. These analysis outcomes were then distributed to twelve European forensic laboratories (both academic and law enforcement institutions), who were asked to prepare reports based on their interpretation of the phenotypes and ancestry they inferred from the analysis data. A questionnaire sent alongside the genetic information, aimed to evaluate which difficulties were encountered by the participants in processing the BGA analysis data they were given.

Humans↗

PubMed2025

Application of causal discovery of factors driving dissolved oxygen in estuarine environments.

Dissolved oxygen (DO) concentrations in estuarine bottom waters are a manifestation of multiple, interacting physical and biogeochemical processes, yet identifying their independent contributions remains challenging. Here, we analyze monthly water quality monitoring data from eight stations across Long Island Sound from 1994 to 2022 using a causal discovery framework (PCMCI+) and transformation of forcing variables. Our goal is to identify and isolate variables that causally influence bottom DO and improve predictive models by minimizing overfitting and multicollinearity. PCMCI+ reveals surface-layer temperature as the most important and consistent negative driver of bottom DO, followed by stratification. Wind events exhibit only brief relief by advection and mixing, while river discharge shows no direct causal link to DO, making it less influential than previously thought. Biogeochemical variables, including chlorophyll-a (Chl-a), nitrate and nitrite, and particulate carbon, influence DO through both contemporaneous and time-lagged pathways, often with signs that shift depending on the process. The derived models were evaluated by comparing skill scores, mean squared error, and Akaike Information Criterion. Both model types perform well, with coefficient of determination values exceeding 0.90 at multiple stations using only 3-5 predictors. Our analysis reveals that the best causal predictors are surface-layer temperature, stratification, Chl-a, and particle carbon. This approach provides a scalable framework for improving prediction models and understanding the mechanistic links that control the seasonal variability of DO in estuarine systems.

Estuaries↗

PubMed2026

Correlative analysis of endogenous miRNA expression profiles underlying brown planthopper adaptation to resistant rice.

The brown planthopper (Nilaparvata lugens Stål, BPH) is a major insect pest threatening global rice production. However, the molecular mechanisms underlying the adaptation of BPH populations with different virulence levels to resistant rice cultivars remain poorly understood. MicroRNAs (miRNAs), as key post-transcriptional regulators, play critical roles in host adaptation in herbivorous insects. In this study, we analyzed the miRNA expression profiles of a high-virulent population (IR56p) and a low-virulence population (TN1p) after feeding on susceptible (TN1) and resistant (IR56) rice cultivars. Our findings reveal distinct miRNA-mediated regulatory strategies employed by the two populations. The IR56p population showed downregulation of miRNAs including miR-10, miR-124, and miR-316, showing an inverse correlation with increased expression of predicted target genes involved in detoxification (carboxylesterase, UDP-glycosyltransferase) and effector function (calmodulin). In contrast, several miRNAs highly expressed in IR56p, including miR-307, miR-317, and miR-275, were predicted to target rice genes associated with hormone signaling, cell wall biosynthesis, and oxidative homeostasis, suggesting a possible but unproven inter-species regulatory role that requires functional validation. Collectively, these descriptive and correlative findings provide hypothesis generating insights into insect-plant coevolution and identifies candidate molecular targets for future functional validation and RNA interference-based pest management strategies.

Animals↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

Efficacy of the NMIC-150 system in identifying extended-spectrum beta-lactamases in clinical isolates.

Extended-spectrum beta-lactamases (ESBLs) are significant contributors to the growing global crisis of antimicrobial resistance. This study evaluated the performance of the NMIC-150 System for susceptibility testing of third-generation cephalosporins (3GCs) and assessed whether ceftazidime-avibactam and aztreonam-avibactam could identify ESBL-producing carbapenem-resistant Enterobacterales (CREs). A total of 278 non-duplicate clinical isolates (Klebsiella pneumoniae, E. coli, and Proteus mirabilis) were analyzed. Antimicrobial susceptibility was determined using reference broth microdilution (BMD) and the NMIC-150 System. ESBL production was defined as an ≥eight-fold reduction in the minimum inhibitory concentration (MIC) of 3GCs in the presence of clavulanic acid, according to CLSI criteria. Whole-genome sequencing was performed to characterize ESBL and carbapenemase genes among 3GC-resistant isolates. A Random Forest model was used to predict ESBL-producing isolates based on MIC values. The NMIC-150 System demonstrated over 90% categorical and essential agreement with BMD for ceftazidime and ceftriaxone, along with robust predictive performance via Random Forest analysis. These findings suggest that the NMIC-150 System is a reliable platform for 3GC susceptibility testing and that an ≥eight-fold MIC reduction with ceftazidime-avibactam or aztreonam-avibactam may serve as a phenotypic indicator of ESBL production in CRE isolates. In conclusion, the NMIC-150 System shows potential for routine antimicrobial resistance surveillance and may facilitate the rapid identification of ESBL-producing CREs in clinical settings.

Microbial Sensitivity Tests↗