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PubMed2026

Machine learning to differentiate colonization from infection in multidrug-resistant Gram-negative bacteria: implications for further research.

PURPOSE OF REVIEW: Machine learning has emerged as a promising tool to support antimicrobial decision-making in infectious diseases. In colonized patients, distinguishing multidrug-resistant Gram-negative bacteria (MDR-GNB) colonization from true infection remains a major clinical challenge, as both delayed appropriate therapy in severe infections and unnecessary broad-spectrum antimicrobial use may adversely affect patient outcomes and antimicrobial stewardship. This review discusses the current evidence on machine learning models for predicting or detecting MDR-GNB infection in colonized patients, highlights key methodological limitations of the available literature, and outlines future research priorities. RECENT FINDINGS: Current evidence specifically evaluating machine learning models beyond logistic regression in MDR-GNB-colonized patients remains limited. Overall, while machine learning may achieve encouraging discriminatory performance, important methodological limitations persist. Most notably, predictive models are frequently developed in heterogeneous populations that do not reflect the clinically relevant populations of colonized patients in which treatment decisions are made. Furthermore, improvements in predictive performance remain modest, possibly reflecting limited sample sizes and data granularity rather than insufficient algorithmic complexity. In our opinion, future advances could require multicenter datasets enriched with longitudinal clinical, microbiological, and genomic information, together with automated feature extraction from electronic health records. SUMMARY: The main challenge for machine learning in predicting MDR-GNB infection in colonized patients may lie not in developing increasingly sophisticated algorithms, but in generating clinically representative datasets and adopting rigorous methodological standards for model development, validation, calibration, and implementation. Future research should prioritize clinically meaningful target populations and demonstrate improvements in patient outcomes and antimicrobial stewardship beyond conventional measures of predictive performance.

antimicrobial resistance↗

PubMed2025

Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.

OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. METHOD: Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). RESULTS: The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. CONCLUSION: The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation. PLAIN LANGUAGE SUMMARY: This study followed over 7,500 adolescents for 4 years and tested 2 machine learning models using psychological, social, and brain data to identify those at risk of experiencing suicidal thoughts or behaviors. Both models predicted first-time suicidal thoughts or behaviors with moderate accuracy. Key risk factors that were identified included being female, experiencing sleep problems, and negative home and school environments. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Adolescent↗

PubMed2026

Antimicrobial resistance analysis of Klebsiella pneumoniae bloodstream infections based on a random forest algorithm: a longitudinal study based on data from tertiary hospitals in China from 2012 to 2023.

BACKGROUND: Bloodstream infections (BSIs) caused by Klebsiella pneumoniae pose a significant global health burden, complicated by rising antimicrobial resistance (AMR). This study aimed to characterize resistance patterns, identify predictors of carbapenem resistance, and develop a machine learning model to predict patient outcomes. METHODS: In a retrospective analysis of 109 279 K. pneumoniae BSIs from tertiary hospitals in China (2012-2023), 11 000 isolates underwent whole-genome sequencing (WGS) and antimicrobial susceptibility testing. Cox proportional hazards and logistic regression models identified predictors of 30-day mortality and carbapenem-resistant K. pneumoniae (CRKP), respectively. A random forest model predicted AMR trends and outcomes, evaluated by accuracy, precision, recall, and ROC-AUC using R Studio (R Studio, Inc., Boston, MA, USA). RESULTS: Carbapenem resistance occurred in 32.3% of isolates, with rates of 41.9% for third-generation cephalosporins and 41.2% for fluoroquinolones. Among sequenced isolates, ST11 with blaKPC was the dominant CRKP genotype (12.0%). blaKPC (OR 3.97, 95% CI 3.10-5.11) and blaNDM (OR 2.80, 95% CI 2.07-3.71) strongly predicted carbapenem resistance; ICU admission predicted 30-day mortality (HR 2.10, 95% CI 1.80-2.46, p<0.001). Mortality was higher in CRKP (40.2%) vs. susceptible cases (21.5%). The random forest model achieved 89.2% accuracy and 0.92 ROC-AUC, with drug share, age, and CRKP status as top predictors. CONCLUSIONS: CRKP, especially ST11-blaKPC, drives excess mortality. Key predictors highlight the urgency for enhanced AMR surveillance and targeted therapy.

Humans↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2026

Association of PCSK9 and CCL22 gene polymorphisms with myocardial infarction in a South Indian population.

Myocardial infarction (MI) remains a major global cause of morbidity and mortality, with a particularly high burden among individuals with type 2 diabetes mellitus (T2DM). Host genetic factors play a significant role in modulating individual susceptibility to MI by influencing lipid metabolism and immune-mediated inflammatory pathways. The proprotein convertase subtilisin/kexin type 9 (PCSK9) gene is a key regulator of cholesterol homeostasis, while C-C motif chemokine ligand 22 (CCL22) is involved in immune cell recruitment and vascular inflammation. In this study, we investigated the association of PCSK9 rs505151 and rs11591147 and CCL22 rs4359426 polymorphisms with MI risk in a South Indian population. This case-control study included 400 participants categorized into controls (n = 100), MI (n = 100), T2DM (n = 100), and MI with T2DM (n = 100). Significant differences in clinical and biochemical parameters, including lipid indices and cardiometabolic risk markers, were observed between groups (p < 0.05). Genetic analysis revealed a significant association between the PCSK9 rs505151 variant and MI susceptibility across allelic and genotypic distributions, with significant effects under dominant and recessive inheritance models. Multivariable logistic regression confirmed that the rs505151 risk genotype was independently associated with MI after adjustment for age, sex, body mass index, and smoking status. In contrast, PCSK9 rs11591147 was rare and showed no significant association. The CCL22 rs4359426 polymorphism showed limited evidence of association with MI, with a significant effect observed only under the dominant inheritance model. Furthermore, combined analysis using a genetic risk score suggested that cumulative genetic burden involving PCSK9 and CCL22 variants was associated with an increased risk of MI. Overall, our findings suggest that genetic variation in lipid-regulatory and immune-related pathways may contribute to MI susceptibility in South Indians. Further studies are warranted to validate these associations and clarify their biological and clinical relevance.

Humans↗

PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Humans↗

PubMed2025

MyESL: A Software for Evolutionary Sparse Learning in Molecular Phylogenetics and Genomics.

Evolutionary sparse learning uses supervised machine learning to build evolutionary models where genomic sites loci are parameters. It uses the Least Absolute Shrinkage and Selection Operator with bi-level sparsity to connect a specific phylogenetic hypothesis with sequence variation across genomic loci. The MyESL software addresses the need for open-source tools to perform evolutionary sparse learning analyses, offering features to preprocess input phylogenomic alignments, post-process output models to generate molecular evolutionary metrics, and make Least Absolute Shrinkage and Selection Operator regression adaptable and efficient for phylogenetic trees and alignments. The core of MyESL, which constructs models with logistic regressions using bi-level sparsity, is written in C++. Its input data preprocessing and result post-processing tools are developed in Python. Compared to other tools, MyESL is more computationally efficient and provides evolution-friendly inputs and outputs. These features have already enabled the use of MyESL in two phylogenomic applications, one to identify outlier sequences and fragile clades in inferred phylogenies and another to build genetic models of convergent traits. In addition to the use in a Python environment, MyESL is available as a standalone executable compatible across multiple platforms, which can be directly integrated into scripts and third-party software. The source code, executable, and documentation for MyESL are openly accessible at https://github.com/kumarlabgit/MyESL.

Phylogeny↗

PubMed2026

The association of 25-hydroxyvitamin D deficiency with neuroinflammation and prognosis in HIV-negative cryptococcal meningitis.

BACKGROUND: Cryptococcal meningitis (CM) in HIV-negative individuals is increasing, yet the role of vitamin D remains unclear. This study investigates serum 25-hydroxyvitamin D [25(OH)D] levels and their clinical implications in HIV-negative CM patients. METHODS: We conducted a retrospective case-control study of 93 HIV-negative CM patients and 191 healthy controls (HCs). Serum 25(OH)D levels, cerebrospinal fluid (CSF) fungal burden, cytokine profiles, the incidence of postinfectious inflammatory response syndrome (PIIRS), and one-year mortality were assessed. Bivariate logistic regression models identified predictors of mortality. RESULTS: CM patients had significantly lower serum 25(OH)D levels than HCs (18.33 vs. 23.69 ng/mL, p < 0.001), with a higher rate of deficiency (<20 ng/mL) in the CM group (59.14% vs. 34.03%, p < 0.001). Lower 25(OH)D levels were associated with elevated CSF levels of IL-6 and IL-8 (p < 0.05). Deficiency was linked to increased PIIRS incidence (43.64% vs. 21.05%, p = 0.028). Bivariate logistic regression showed a protective trend for 25(OH)D levels (OR 0.939, 95% CI 0.877-1.006, p = 0.075), although deficiency was not associated with higher mortality. CONCLUSIONS: Serum 25(OH)D deficiency is prevalent in HIV-negative CM patients and linked to neuroinflammation and increased risk of PIIRS. Serum 25(OH)D levels may serve as a useful prognostic marker, although further research is needed.

Humans↗

PubMed2026

Accelerated Biological Aging Increases the Risk of Head and Neck Cancer: Insights From Genetic Instruments of Epigenetic Clocks.

Epigenetic clocks are robust biomarkers of biological aging and have been associated with cancer susceptibility. However, the relationship between genetically predicted epigenetic age acceleration and head and neck cancer risk remains unclear. Using a large case-control study of 2189 head and neck squamous cell carcinoma (HNSCC) cases and 2189 age- and sex-matched controls, we investigated the associations between polygenic scores (PGSs) for multiple epigenetic clocks and HNSCC risk, and evaluated their potential causal roles using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS)-identified single nucleotide polymorphisms (SNPs) associated with four epigenetic clocks (HannumAge, HorvathAge, GrimAge, and PhenoAge) were used to construct clock-specific PGSs. Logistic regression models were applied to assess associations between PGSs and HNSCC risk, while MR analyses, including inverse-variance weighted (IVW), weighted median, and MR-Egger methods, were used to infer potential causal relationships. Among the 48 epigenetic clock-associated SNPs, 12 showed nominal associations with HNSCC risk, and one variant (rs2275558 in PBX1) remained significant after Bonferroni correction (OR = 0.67, 95% CI: 0.60-0.76). PGSs for all four epigenetic clocks were higher in cases than in controls. In logistic regression analyses, each standard deviation increase in HannumAge PGS was associated with a 25% higher risk of HNSCC (OR = 1.25, 95% CI: 1.10-1.41), whereas HorvathAge, GrimAge, and PhenoAge PGSs showed weaker positive associations (ORs ranging from 1.06 to 1.10). Individuals in the highest PGS quartile for all four epigenetic clocks exhibiting 14%-25% higher risk than those in the lower three quartiles. MR analyses supported potential causal effects of genetically predicted HannumAge (IVW OR = 1.24 per SD increase, 95% CI: 1.09-1.42) and GrimAge (IVW OR = 1.23 per SD increase, 95% CI: 0.98-1.56) on HNSCC risk, with consistent estimates in weighted median analyses. Our results highlight biological aging as a potential etiologic mechanism for HNSCC and suggest that epigenetic clock-related genetic profiles may improve HNSCC risk stratification.

Humans↗

PubMed2026

Genome-wide association analysis reveals specialization to hosts and niches in multiple species of the Lactobacillaceae.

The Lactobacillaceae inhabit diverse environments, but the extent of their habitat adaptation remains unclear and the colonization factors unknown. First, we applied multiple machine learning models to determine if we can distinguish strains of the same species isolated from two different habitats based on their gene content. Surprisingly, we show that no species is differentially adapted to the oral cavity versus the human gut, or food versus the human gut, while only Lactobacillus crispatus showed specialization to the human urogenital system versus human gut. We then asked which species of Lactobacillaceae are habitat-specialized and how they could be identified. Using multiple lifestyle predictors incorporated in logistic regression models, we found that Limosilactobacillus reuteri, Ligilactobacillus ruminis, L. salivarius, L. crispatus, and L. mucosae displayed the highest degrees of host specialization. Applying our microbial genome-wide association study tool, aurora, to these species identified genes encoding adhesins and bacteriocins as the strongest and most common adaptation factors. This work establishes a generalizable framework for identifying novel species-habitat pairs with strong evidence of specialization and for uncovering the genomic features underlying within-species host and habitat adaptation.

Humans↗

PubMed2024

Geospatial Analysis of Multilevel Socioenvironmental Factors Impacting the Campylobacter Burden among Infants in Rural Eastern Ethiopia: A One Health Perspective.

Increasing attention has focused on health outcomes of Campylobacter infections among children younger than 5 years in low-resource settings. Recent evidence suggests that colonization by Campylobacter species contributes to environmental enteric dysfunction, malnutrition, and growth faltering in young children. Campylobacter species are zoonotic, and factors from humans, animals, and the environment are involved in transmission. Few studies have assessed geospatial effects of environmental factors along with human and animal factors on Campylobacter infections. Here, we leveraged Campylobacter Genomics and Environmental Enteric Dysfunction project data to model multiple socioenvironmental factors on Campylobacter burden among infants in eastern Ethiopia. Stool samples from 106 infants were collected monthly from birth through the first year of life (December 2020-June 2022). Genus-specific TaqMan real-time polymerase chain reaction was performed to detect and quantify Campylobacter spp. and calculate cumulative Campylobacter burden for each child as the outcome variable. Thirteen regional environmental covariates describing topography, climate, vegetation, soil, and human population density were combined with household demographics, livelihoods/wealth, livestock ownership, and child-animal interactions as explanatory variables. We dichotomized continuous outcome and explanatory variables and built logistic regression models for the first and second halves of the infant's first year of life. Infants being female, living in households with cattle, reported to have physical contact with animals, or reported to have mouthed soil or animal feces had increased odds of higher cumulative Campylobacter burden. Future interventions should focus on infant-specific transmission pathways and create adequate separation of domestic animals from humans to prevent potential fecal exposures.

Humans↗

PubMed2024

Predictive modeling of gene mutations for the survival outcomes of epithelial ovarian cancer patients.

Epithelial ovarian cancer (EOC) has a low overall survival rate, largely due to frequent recurrence and acquiring resistance to platinum-based chemotherapy. EOC with homologous recombination (HR) deficiency has increased sensitivity to platinum-based chemotherapy because platinum-induced DNA damage cannot be repaired. Mutations in genes involved in the HR pathway are thought to be strongly correlated with favorable response to treatment. Patients with these mutations have better prognosis and an improved survival rate. On the other hand, mutations in non-HR genes in EOC are associated with increased chemoresistance and poorer prognosis. For this reason, accurate predictions in response to treatment and overall survival remain challenging. Thus, analyses of 360 EOC cases on NCI's The Cancer Genome Atlas (TCGA) program were conducted to identify novel gene mutation signatures that were strongly correlated with overall survival. We found that a considerable portion of EOC cases exhibited multiple and overlapping mutations in a panel of 31 genes. Using logistical regression modeling on mutational profiles and patient survival data from TCGA, we determined whether specific sets of deleterious gene mutations in EOC patients had impacts on patient survival. Our results showed that six genes that were strongly correlated with an increased survival time are BRCA1, NBN, BRIP1, RAD50, PTEN, and PMS2. In addition, our analysis shows that six genes that were strongly correlated with a decreased survival time are FANCE, FOXM1, KRAS, FANCD2, TTN, and CSMD3. Furthermore, Kaplan-Meier survival analysis of 360 patients stratified by these positive and negative gene mutation signatures corroborated that our regression model outperformed the conventional HR genes-based classification and prediction of survival outcomes. Collectively, our findings suggest that EOC exhibits unique mutation signatures beyond HR gene mutations. Our approach can identify a novel panel of gene mutations that helps improve the prediction of treatment outcomes and overall survival for EOC patients.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2004

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse.

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.

Adenylyl Cyclases↗

PubMed2026

Effect of Time to Start of Biologic Therapy on Treatment Response in Childhood Arthritis: Results From the UCAN CAN-DU Cohort.

OBJECTIVE: To estimate the effect of time from symptom onset to start of biologic treatment on achieving inactive arthritis within six months in a cohort of patients with juvenile idiopathic arthritis (JIA). METHODS: The international UCAN CAN-DU study prospectively enrolled patients with JIA across Canada and the Netherlands. A nested cohort study was performed and biologic-naive patients with nonsystemic JIA were included at the start of biologic therapy. The primary outcome was inactive arthritis at six months. Demographics, disease-related parameters, and treatment response were compared using (non)parametric tests among early (time symptom onset to biologic start: 0-6 months), intermediate (7-12 months), and late (13-24 months) treatment groups. A logistic regression model analyzed the effect of time to biologic start on the response at six months, adjusting for active joint count and physician global assessment. A graphical representation of the model was created. RESULTS: One hundred and thirty children with JIA were included (early: n = 35; intermediate: n = 46; late: n = 49), 66% were female, and the median age at symptom onset was 11.0 years. The proportion of patients that reach inactive arthritis in the early starters (83%) was significantly higher than in late starters (57%). For each month of delay to the start of biologic treatment, the adjusted odds of having active arthritis after six months of therapy was 1.09 (interquartile range: 1.02-1.17, P = 0.009). CONCLUSION: Early start of biologic therapies in patients with JIA was associated with a higher proportion of patients reaching inactive arthritis within six months, suggesting a window of opportunity to control disease activity.

Humans↗

PubMed2026

Analysis of the Relationship between Early Clinical Factors and Glasgow Outcome Scale in Patients With Traumatic Brain Injury.

OBJECTIVE: This study aimed to evaluate the association between early clinical factors and the Glasgow outcome scale (GOS) in patients with traumatic brain injury (TBI). METHODS: We conducted a retrospective analysis of 98 TBI patients who underwent emergency surgery between January 2021 and January 2024. Based on GOS scores at 6 months post-surgery, patients were classified into a favorable outcome group (GOS ≥ 4, defined as moderate disability or good recovery, n = 58) and an unfavorable outcome group (GOS < 4, i.e., death, persistent vegetative state, or severe disability, n = 40). Baseline and early clinical parameters were compared between groups. Statistically significant variables from univariate analysis were entered into a multivariate logistic regression model to identify independent prognostic factors. RESULTS: Significant intergroup differences were observed in age, time from injury to surgery, bleeding site, midline shift, Glasgow coma scale (GCS) score at admission, blood glucose level, and D-dimer level (all p < 0.05). Multivariate analysis confirmed that age, time from injury to surgery, GCS score, blood glucose, and D-dimer level were independent predictors of GOS (all p < 0.05). CONCLUSION: Early clinical factors, including age, time to surgery, GCS score, blood glucose, and D-dimer level, independently influence GOS in TBI patients. Time from injury to surgery emerged as a potentially modifiable factor in this cohort, suggesting that minimizing delays may improve outcomes.

Humans↗

PubMed2025

Pica in Childhood: Concurrent and Sequential Psychiatric Comorbidity.

OBJECTIVE: Pica is the persistent eating of nonnutritive, nonfood substances, and is associated with serious medical consequences. There has been a lack of research into the psychiatric comorbidities of pica, despite being important for informing clinical care. The current study examines psychiatric comorbidities of pica in childhood and the longitudinal relationship between childhood pica and adolescent eating disorders. METHOD: We analyzed data from the Avon Longitudinal Study of Parents and Children study. Pica and psychopathology, assessed with the Development and Well-Being Assessment and the Strengths and Difficulties Questionnaire, were assessed at about 7- and 10-years of age, and reported eating disorders (EDs) at 14-, 16-, and 18-years of age. We conducted linear and logistic regression models, adjusting for covariates, to identify concurrent psychiatric comorbidities, as well as risk for later EDs. We conducted the Benjamini-Hochberg correction procedure to correct for multiple testing. RESULTS: Pica (prevalence ranged from 0.33% to 2.33% dependent on age) was associated with increased odds of any psychiatric disorder and behavioral disorders in early childhood (OR = 7.30, q < 0.001, and OR = 5.65, q < 0.001, respectively) and mid-childhood (OR = 5.75, q < 0.001, and OR = 10.66, q < 0.001, respectively), and greater concurrent hyperactivity, conduct problems, peer problems, prosocial difficulties, and emotional difficulties (q < 0.01 across analyses). We did not find evidence that pica presence increased odds for concurrent emotional disorders nor for later ED risk. DISCUSSION: The association between pica and psychiatric and behavioral disorders indicates a likely shared etiology. Our findings provide insight into the psychiatric characteristics of children with pica and highlight they may require complex behavioral support beyond their eating difficulties.

Humans↗

PubMed2026

Sex Hormone Receptors, HBV Integrations and Their Prognostic Predictive Value Among Hepatocellular Carcinoma Patients.

Hepatocellular carcinoma (HCC) related to hepatitis B virus (HBV) infection predominantly affects males, yet few studies have investigated the association between sex hormones and HBV integrations, and their involvement in HCC prognosis. We assessed estrogen receptor alpha (ERα) and androgen receptor (AR) expression via immunohistochemistry on tissue microarrays constructed from 426 HBV-related HCC samples. HBV integration features were determined using HBV-captured sequencing data. Logistic regression models were utilized to evaluate the association between sex hormone receptor expression level and HBV integration features. Cox regression models, combined with machine learning (ML) methods, were implemented to investigate the prognostic value of sex hormone receptors and HBV integrations concerning overall survival. We found high AR expression level was significantly associated with higher HBV integration levels (adjusted odds ratio [aOR] = 1.84, 95% confidence interval [CI]: 1.09-3.11, P for trend = 0.012), TERT integration (aOR = 2.34, 95% CI: 1.16-4.74, P for trend = 0.047), intergenic integration (aOR = 2.25, 95% CI: 1.20-4.24, P for trend = 0.021), and promoter integration (aOR = 1.81, 95% CI: 1.00-3.31, P for trend = 0.034). The inclusion of sex hormone receptors and HBV integrations in the predictive models led to improvements across all performance metrics in the Cox regression analyses (AUC improvement: 0.014 [Training], 0.026 [Validation]) and the ML (AUC improvement: 0.022 [Training]), although a slight deterioration in performance was noted in the ML validation set. The results suggested a relationship between AR expression level and HBV integration events, as well as the potential utility of HBV integration biomarkers and sex hormone receptor profiles in assessing post-surgical prognosis among HCC patients.

Humans↗