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PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗

PubMed2026

Fasting-refeeding regimes induce compensatory growth and muscle transcriptomic remodeling in juvenile Qihe gibel carp (Carassius gibelio var. Qihe).

Compensatory growth, an important adaptive response in fish, holds considerable potential for improving feeding efficiency in aquaculture. To identify an optimal fasting-refeeding strategy for juvenile Qihe gibel carp (Carassius gibelio var. Qihe) and to clarify the mechanisms underlying the compensatory growth, we divided two-month-old fish into four groups, namely S0 group (continuous feeding for 28 days), S2 group (4 cycles of 2-day fasting followed by 5-day refeeding), S4 group (fasting for 4 days followed by refeeding for 24 days), and S8 group (fasting for 8 days followed by refeeding for 20 days), then growth performance, muscle tissue morphology, biochemical responses, and muscle transcriptomic profiles under different feeding regimes were investigated. After a 28-day aquaculture experiment, fish in the S4 group exhibited significantly greater body length and weight than those in the S0, S2, and S8 groups, indicating over-compensatory growth. Histological analysis further showed that muscle growth in the S4 group was mainly associated with myofiber hyperplasia. Different feeding regimes also induced distinct changes in hepatic antioxidant and metabolic enzyme activities, as well as intestinal digestive enzyme activities. Transcriptome analysis revealed that the forkhead box O (FoxO) signaling pathway was significantly enriched during compensatory growth. Key genes, including serum/glucocorticoid regulated kinase 1 (sgk1) and insulin receptor substrate 1 (irs1), were predicted to play important roles in this process. Overall, these results indicate that fasting for 4 days followed by refeeding for 24 days (the S4 regime) is the optimal strategy for inducing compensatory growth in juvenile Qihe gibel carp. This study provides new insights into the morphological, physiological, and molecular basis of compensatory growth and offers a scientific foundation for developing efficient and sustainable feeding strategies for this species.

Animals↗

PubMed2026

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum β2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans↗

PubMed2026

Could the preoperative urethral curve be used to predict immediate urinary continence following Retzius-sparing robot-assisted radical prostatectomy? A retrospective multi-center study.

PURPOSE: Immediate urinary continence (UC) recovery following Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains highly variable, highlighting the need for reliable preoperative prediction. We aimed to develop and validate models to identify patients likely to achieve immediate UC recovery following RS-RARP. MATERIALS AND METHODS: A total of 580 prostate cancer patients who underwent RS-RARP from four medical centers were assigned to a training set (n=348), an internal validation set (n=103) and an external validation set (n=129). Independent predictors were identified through univariate analysis and LASSO regression. A nomogram was constructed using multivariate logistic regression. Its performance was evaluated with receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS: Immediate UC recovery was observed in 84.5% (294/348) of patients in the training cohort, 80.6% (83/103) in the internal validation cohort, and 81.4% (105/129) in the external validation cohort, respectively. Multivariate analysis identified membranous urethral length (MUL) (OR=1.23, P=0.029) and urethral curvature (OR=2.84, P<0.001) as independent predictors, while prostate volume (PV) (OR=0.84, P <0.001) as a protective factor. The nomogram integrating MUL, PV, and urethral curvature demonstrated superior predictive accuracy, with an AUC of 0.87 (95% CI, 0.83-0.91) in the training cohort. The bootstrap-corrected calibration slope was 0.96, and the Brier score was 0.08. Calibration curves and decision curve analysis confirmed the predictive accuracy and clinical utility of the nomogram. CONCLUSIONS: Our study introduces a novel quantitative method for assessing urethral curvature. The mpMRI-based model, integrating urethral curvature and prostate spatial configuration, offers enhanced predictive accuracy for postoperative immediate UC recovery.

Humans↗

PubMed2026

Integrated miRNA-mRNA profiling reveals candidate regulatory relationships associated with high-fat diet-induced muscle lipid deposition in black seabream (Acanthopagrus schlegelii).

High-fat diets are increasingly used in aquaculture due to their protein-sparing effects; however, the post-transcriptional regulatory mechanisms of fish muscle in response to high-fat diets (HFD) remain unclear. In this study, juvenile black seabream were fed either a normal-fat diet (NFD) or a HFD to investigate the miRNA-mRNA regulatory network associated with diet-induced muscle lipid deposition. Oil Red O staining and biochemical analysis showed that high-fat diet feeding markedly increased lipid droplet accumulation and crude lipid content in muscle, indicating significant induction of muscle lipid deposition. Integrated mRNA and miRNA expression profiling revealed substantial transcriptomic and post-transcriptional responses to high-fat diet challenge. A total of 271 differentially expressed genes were identified, including 120 upregulated and 151 downregulated genes. Through combined target prediction and expression correlation analysis, thirteen candidate inverse miRNA-mRNA relationships were subsequently identified, and RT-qPCR supported the expression patterns of selected miRNAs and mRNAs. These pairs included miR-499-x-dmgdh, miR-499-y-gatm, miR-727-y-ass1, miR-4649-x-foxo4, miR-9129-z-myl7, and several novel miRNA-mediated interactions involving adk, chst11, lypla2, frem2, kcnc4, wars1, bag2, and capn2. Functional analysis suggested that these regulatory pairs were mainly associated with metabolic adaptation, structural remodeling, and cellular stress responses. In particular, gatm, dmgdh, ass1, and adk were associated with energy metabolism-related processes, including pathways previously linked to Ampk regulation, whereas myl7, frem2, and kcnc4 may contribute to muscle structural maintenance and excitability regulation. Overall, this study provides candidate miRNA-mRNA regulatory relationships potentially involved in high-fat diet-induced muscle lipid deposition and adaptive remodeling in black seabream, offering a basis for future functional studies on muscle metabolism and quality regulation in marine fish.

Animals↗

PubMed2026

Comparison of the predictive performance of systemic immune-inflammation index and neutrophil-to-lymphocyte ratio for three-month poor functional outcome in ischemic stroke: a systematic review and meta-analysis.

INTRODUCTION: Ischemic stroke (IS) is a leading cause of global mortality and disability. Early and accurate prognosis is crucial for patient management. The neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) are emerging inflammatory biomarkers; however, their relative predictive value for three-month poor functional outcome (modified Rankin Scale [mRS] > 2) remains uncertain. METHODS: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library up to 20 July 2025, adhering to PRISMA guidelines. Observational studies reporting the association of SII or NLR with three-month poor outcome were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Area under the curve (AUC), odds ratios (OR), and standardized mean differences (SMD) were pooled using random-effects models in Stata 16.0. RESULTS: Twenty-one studies involving 7520 IS patients were analysed. NLR demonstrated marginally superior discriminative ability compared to SII (AUC 0.71, 95% CI: 0.67-0.76 vs. 0.68, 95% CI: 0.64-0.71), though this difference was not statistically significant. Elevated NLR was significantly associated with poor outcome (OR = 1.26, 95% CI: 1.17-1.37, p < .001), whereas SII was not (OR = 1.00, 95% CI: 1.00-1.00, p = .384). Both markers showed moderate effect sizes (SMD: NLR = 0.69, SII = 0.72; p < .001). NLR performed better in non-intervention and Chinese subgroups, while SII exhibited consistent AUC values across treatment and ethnic subgroups. CONCLUSION: NLR and SII are accessible prognostic markers in IS. NLR demonstrates superior accuracy and a significant association with poor outcome, while SII shows greater stability across patient subgroups. Both may assist in risk stratification, in resource-limited settings.

Humans↗

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

Genetic overlap between estimated glomerular filtration rate and cardiovascular disease identifies potential targets for cardiorenal syndrome.

Heart and kidney diseases frequently coexist, but the genetic basis of this relationship remains unclear. We analyzed genetic data from large-scale studies to investigate how kidney function (estimated glomerular filtration rate, eGFR) and six common cardiovascular diseases share genetic risk factors. Using MiXeR method, and conjunctional false discovery rate (conjFDR) to identify overlapping genetic regions, we found 478 shared genomic loci between eGFR and cardiovascular diseases. These shared genes are involved in tissue development and structure. We also identified 29 genes that could be targeted by existing medications approved by the US Food and Drug Administration, such as PRKAG2, PDE1A, and IGF1R. Among these, genetically predicted higher level of IGF1R expression is associated with a higher eGFR, which reflects good kidney function and is protective against cardiorenal diseases, such as atrial fibrillation, and myocardial infarction. These findings reveal genetic overlap between kidney function and cardiovascular diseases, highlighting potential targets for understanding and treating cardiorenal syndrome.

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

O'nyong-nyong virus adaptive mutations in non-structural protein 1 and 3 enhance RNA replication and overcome FHL1 requirement.

Arthritogenic alphaviruses, like o'nyong-nyong virus (ONNV), cause debilitating musculoskeletal diseases and are geographically expanding. To predict their emergence, we seek to better understand evolutionary mechanisms that enable changes in virus tropism. Here, we identify adaptive mutations in the ONNV non-structural proteins (nsPs) that arose during cellular serial passaging and enabled ONNV to infect non-permissive Lunet cells. Using shotgun proteomics, we show that this human hepatoma cell line lacks the four-and-a-half-LIM domain protein 1 (FHL1), an essential host factor in ONNV RNA replication. Individual single nucleotide mutations in the nsP1 ring-aperture membrane-binding and oligomerization domain, the nsP3 macrodomain, and the nsP3 opal stop codon overcome FHL1 deficiency in Lunet cells by enhanced RNA replication. These findings demonstrate how subtle genomic changes in nsPs can profoundly influence alphavirus replication and tropism.

LIM Domain Proteins↗

PubMed2026

Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.

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

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

A novel peptide encoded by circTLL1 drives osimertinib resistance in lung cancer by modulating the NT5C2/Ras/PI3K axis.

BACKGROUND: Acquired resistance to osimertinib, a third-generation EGFR tyrosine kinase inhibitor, remains a major clinical challenge in the treatment of non-small cell lung cancer (NSCLC). Although circular RNAs (circRNAs) have been increasingly implicated in drug resistance, most studies have focused on their canonical role as microRNA sponges, while their capacity to encode functional micropeptides remains largely unexplored. This study aimed to identify novel circRNAs involved in osimertinib resistance and to characterize their regulatory functions at the protein level. METHODS: Osimertinib-resistant (OR) NSCLC cell lines were established and validated. High-throughput RNA sequencing was performed to compare the circRNA expression profiles between parental and OR cells. The function of the candidate circRNA was assessed through a series of in vitro and in vivo experiments, including cell viability assays, apoptosis analysis, and xenograft mouse models. Mechanistic investigations involved mass spectrometry, co-immunoprecipitation and western blotting to explore its protein-coding potential and downstream signaling pathways. RESULTS: We identified a novel circRNA, termed circTLL1, that was stably and significantly upregulated in OR-NSCLC cells. Functionally, overexpression of circTLL1 promoted osimertinib resistance, whereas its knockdown restored drug sensitivity both in vitro and in vivo. Mechanistically, we discovered that circTLL1 harbors an open reading frame (ORF) that is translated into a novel 90-amino-acid protein, which we designated circTLL1-90aa. Further investigation revealed that circTLL1-90aa directly interacts with and promotes the degradation of 5'-nucleotidase, cytosolic II (NT5C2), thereby uncoupling nucleotide metabolism from its normal regulatory constraints. The consequent downregulation of NT5C2 leads to elevated GTP levels and leading to the sustained activation of the downstream Ras/PI3K/AKT signaling pathway. CONCLUSION: Our findings unveil a previously unrecognized circRNA/micropeptide/metabolism cascade underlying osimertinib resistance. The identification of the circTLL1-90aa/NT5C2/Ras/PI3K axis not only expands the functional repertoire of the non-coding genome but also provides new insights into the complexity of drug resistance. Given its selective upregulation in resistant cells, circTLL1-90aa holds promise both as a predictive biomarker for treatment stratification and as an actionable therapeutic target, offering a novel strategy to overcome osimertinib resistance in NSCLC patients.

Pyrimidines↗

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

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗