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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

Executive function in alcohol use disorder with low psychiatric comorbidity: Comparison with a non-clinical sample and predictive value for treatment outcome.

BACKGROUND: Executive functions (EF) encompass abilities such as planning, decision-making, and inhibitory control, critical for learning, establishing and maintaining behavioral change. The association between alcohol use disorder (AUD) and impairments in EF are well established. However, prior research is dominated by studies on convenience samples including individuals with severe AUD with high levels of psychiatric comorbidity, which limits generalizability. The present study therefore aimed to investigate the degree of impairment and predictive ability of EF, on alcohol consumption, among individuals with moderate AUD with low levels of psychiatric comorbidity. METHODS: Adults with moderate AUD (n = 147) were recruited at three specialized addiction outpatient clinics in Stockholm, to a randomized controlled trial investigating the efficacy of two psychological treatments. Participants underwent neuropsychological testing before treatment. Eight tests from the CANTAB® battery were administered at baseline, assessing mental flexibility, sustained attention, visuospatial working memory, response inhibition, and delay discounting. Assessments of alcohol use and related symptoms were conducted at baseline, the 12- and 26-weeks follow-up. A non-clinical reference sample (n = 72) completed corresponding CANTAB® tests. The two groups were compared regarding EF using descriptive statistics and t-tests, and the predictive value of EF for reduction in alcohol consumption, was investigated using multiple regression models. RESULTS: Individuals with AUD did not perform worse on any of the tests on executive function (CANTAB®) as compared to the non-clinical reference sample. Measures of EF were not significant predictors for reduction in alcohol use for the 12-week, or the 26-week follow-up. CONCLUSIONS: EFs were not impaired and were not a clinically relevant predictor of treatment outcomes in this population with AUD. Future research on EF as a predictor in AUD treatment, needs to corroborate the present findings, and include other populations, e.g., with different socio-economic backgrounds and by including other methodologies for measuring EF.

Humans↗

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

Meta-analysis of growth and inactivation kinetics of Legionella.

Quantitative risk assessments intended to inform evidence-based water management plans and public health targets for Legionella in engineered water systems are constrained by fragmented and heterogeneous growth and inactivation kinetics. We conducted a meta-analysis of 25 growth and 39 thermal- and chemical-inactivation studies, fitting microbial persistence models to harmonize parameters. Nonlinear models outperformed first-order formulations, indicating that lag phases and resistant or protected subpopulations are central to Legionella persistence. Random forest analysis identified environmental and methodological drivers of variability based on 226 growth rates and reduction times for thermal (209) and chemical (135) inactivation. Growth was primarily governed by temperature, nutrient availability, and compatible Legionella-host pairings; thermal inactivation by quantification method, temperature, and turbidity; and chemical inactivation by inoculum size, disinfectant type, concentration, and host-associations. Accordingly, temperature-dependent growth parameters and exposure metrics for heat, free-chlorine, and monochloramine, expressed as TT (Temperature×time) and CT (Concentration×time), were derived as condition-specific inputs for predictive models. Growth optima around 37-40 °C, together with lag-time estimates, indicate that hot-water temperature setbacks and energy-saving practices may favor Legionella proliferation under repeated or prolonged lukewarm exposure. Culture- and viability-based TT differences highlight the need to consider viable‑but-non-culturable persistence in monitoring programs. CT comparisons suggest monochloramine may be advantageous because of its lower apparent sensitivity to host-associated protection. Although limited by restricted experimental conditions, the findings show that predictive models should account for microbial ecology, water matrix effects, and quantification endpoints. Future kinetic studies should prioritize realistic multi-host systems, strain pre-adaptation, complementary viability measurements, and standardized protocols and reporting to ensure reproducibility and enable robust system-level predictive modeling.

Legionella↗

PubMed2026

Dosimetric Parameters of the Heart and Its Substructures in Predicting Cardiac Events or Survival in Patients With Lung Cancer After Radiation Therapy: A Systematic Review and Meta-analysis.

The predictive value of radiation dose to the whole heart (WH) and cardiac substructures (CS) for cardiac events (CEs) and survival in patients with lung cancer remains uncertain. The goal of this study was to conduct a systematic review and meta-analysis to provide an evidence-based estimate of the relationship between these associations. A systematic meta-analysis was performed following PRISMA guidelines. Risk of bias was assessed using the JBI Critical Appraisal Checklist for Case Series. Outcomes were classified into major adverse cardiac events (MACE), arrhythmias, pericardial effusion, and survival. Depending on heterogeneity, random- or fixed-effects models were applied to calculate pooled hazard ratios (HRs) for univariable and multivariable analyses. A total of 80 studies, including 21,645 patients, were analyzed. Of these, 25 studies reported CEs, and 69 reported survival outcomes. Among 91 WH and 215 CS parameters evaluated, several showed significant associations. Key findings from our meta-analysis include: (1) left anterior descending (LAD) V15 was significantly associated with MACE. The mean heart dose (MHD), as well as ventricle and LAD doses, were significantly associated with ischemic events. (2) Multiple CS parameters were associated with different arrhythmia subtypes. (3) MHD, heart V5/V35/V55 and pericardial doses were significantly associated with pericardial effusion. (4) MHD was significantly associated with survival; CS parameters also showed predictive value, and especially, heart base dose being the most significant. (5) We also identified several thresholds with potential predictive values, such as LAD V15 <10% for MACE, left pulmonary vein (LPV) V55 <2%, and right pulmonary vein (RPV) V10 <54% for atrial fibrillation (AF), right atrium (RA) V60 <0.03 cc for non-AF supraventricular tachyarrhythmia, and left main artery (LMA) V10 ≥1 cc for bradyarrhythmia. This study identified 130 WH and CS dosimetric parameters associated with CEs and 131 with survival outcomes. These findings enhance our understanding of radiation-induced heart injury mechanisms and provide guidance for potential protective and intervention strategies.

Humans↗

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

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

Early-stage trajectories of social-occupational functioning and long-term functional outcome prediction in early psychosis: A 12-year follow-up of the randomized controlled trial on extended early intervention.

BACKGROUND: Functional impairment in psychosis often persists despite symptomatic remission. There is a paucity of research examining early-course psychosocial functioning trajectories, and none has been conducted to examine relationship between the trajectories and prospective long-term functional outcomes in early psychosis sample. METHODS: We conducted 12-year follow-up of a randomized controlled trial on extended early intervention for first-episode psychosis to identify early-course social-occupational functioning trajectories and their baseline predictors and associations with 12-year outcomes. Participants who completed Social and Occupational Functioning Scale (SOFAS) scores at three or more timepoints between baseline and 3-year follow-up were included in the study. Premorbid adjustment, illness characteristics, symptom severity, functioning, and treatment profiles were assessed. Latent growth mixture modeling was employed to derive early-course social-occupational functioning trajectories based on SOFAS scores over 3-year follow-up. RESULTS: A total of 148 participants were included in this study, with 106 patients having completed the 12-year follow-up. Our results identified four distinct trajectories, including persistently-good class, gradually-improved class, suboptimal-stable class, and persistently-poor class. Patients in persistently-poor class had more severe negative symptoms at baseline compared to patients in persistently-good class. Patients with persistently-poor trajectory had worse long-term outcomes than those with other classes in the majority of functional measures at 12-year follow-up. CONCLUSIONS: The majority of patients were classified in early-stage suboptimal or poor functional trajectories. Above one-fourth of the participants exhibited persistently-poor social-occupational functioning trajectory, which predicted worse functional outcomes at 12-year follow-up. These findings highlighted the importance of tracking functional changes during the initial years of illness.

Humans↗

PubMed2026

Impact of Albuminuria-Lowering Treatments on Cardiovascular Predictive Ceramides in Diabetes: Post Hoc Analysis of the ROTATE Trials.

AIM: Cardiovascular disease (CVD) is the leading cause of mortality in individuals with diabetes. Diabetic kidney disease, closely related to CVD risk, is prevalent in up to 40% of this population. Emerging evidence suggests ceramide lipids as accurate biomarkers for CVD. We assessed the effect of four albuminuria-lowering drugs on CVD-related ceramides in diabetes by post hoc analysis of the ROTATE trials. MATERIALS AND METHODS: Twenty six adults with type 1 (T1D) as well as 37 with type 2 diabetes (T2D) with a urine albumin-creatinine ratio (UACR) of 30-500 mg/g participated in a 4-week 4-time randomized crossover study with periods of telmisartan, empagliflozin, linagliptin and baricitinib treatment, each separated by a 4-week washout period. Blood samples were collected at the beginning and end of each period and ceramide lipids (Cer16, Cer18, Cer20, Cer22, Cer24 and Cer24:1) were measured. The effect of each treatment was evaluated using linear mixed-effect models. RESULTS: At baseline, individuals with T2D had greater levels of Cer22 and Cer24 compared to the individuals with T1D. Among the treatments, linagliptin was the only drug that demonstrated a reduction of Cer22, Cer24 and Cer24:1 from baseline by 22.6% (95% CI: -33.58; -9.79, p = 0.001), 25.7% (95% CI: -38.94; -9.69, p = 0.003) and 19.6% (95% CI: -31.34; -5.95, p = 0.007), respectively. No changes in the ceramides were observed for the other drugs. CONCLUSION: Our exploratory findings suggest that certain albuminuria-lowering drugs may affect ceramide levels as a secondary effect. However, further mechanistic investigations are needed.

Humans↗

PubMed2026

Factors associated with additional intervention requirement following ESWL in pediatric patients with urolithiasis.

OBJECTIVE: To identify predictors of additional intervention following extracorporeal shock wave lithotripsy (ESWL) in pediatric patients and to develop a clinically applicable predictive model. MATERIALS AND METHODS: This retrospective cohort study included 647 pediatric patients who underwent ESWL between 2015 and 2025. Demographic, clinical, and radiological variables were analyzed. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of additional intervention. Model performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Additional intervention was required in 65 patients (10.0%). On multivariable analysis, stone size 10-20 mm (OR: 3.04, p = 0.003), moderate (OR: 2.16, p = 0.049) and severe hydronephrosis (OR: 6.05, p < 0.001), and multiple stones (OR: 3.52, p = 0.030) were identified as independent risk factors. Increasing age (OR: 0.84, p = 0.026), history of urolithiasis (OR: 0.41, p = 0.006), and lower calyx location (OR: 0.14, p = 0.034) were associated with a reduced risk. The model demonstrated good discriminative performance (AUC: 0.794), with a sensitivity of 72% and specificity of 75%. Internal validation using bootstrap resampling demonstrated stable model performance, yielding a corrected AUC of 0.732. CONCLUSION: Stone burden, hydronephrosis severity, and stone multiplicity are key determinants of additional intervention after ESWL in pediatric patients. The proposed model shows good predictive performance and may support individualized risk stratification and clinical decision-making.

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

Evaluation of the clinical and mechanistic role of MCM2 expression in the prediction of meningioma recurrence after radiotherapy.

OBJECTIVE: Postoperative radiotherapy is an effective treatment for meningiomas; however, treatment response varies among patients. In addition, practical methods for predicting tumor recurrence after radiotherapy have not been well established. Minichromosome maintenance protein 2 (MCM2), a key regulator of DNA replication licensing, was recently implicated in highly proliferative molecular subtypes of meningioma. In this study, the authors evaluated whether MCM2 immunohistochemical expression predicts response to radiotherapy in patients with meningiomas. METHODS: The authors retrospectively analyzed the records of patients with WHO grade 1-3 meningiomas treated with resection followed by radiotherapy at a single institution between July 2003 and November 2023. The MCM2 labeling index was assessed immunohistochemically, and patients were stratified into MCM2-high and -low groups using a cutoff of 35%. Progression-free survival (PFS) was defined as the interval from the completion of radiation therapy to postoperative radiological tumor recurrence or regrowth. Patients who showed no progression were censored at their last follow-up. PFS was estimated using Kaplan-Meier analysis and subsequently evaluated with Cox proportional hazards models. To further investigate the biological mechanisms associated with MCM2 expression, comprehensive transcriptomic analyses, including gene set enrichment analysis, was performed to elucidate the molecular processes that occur within MCM2-high tumors. RESULTS: The study population included 15 men (42%) and 21 women (58%), with a mean age of 63 years. Ten tumors (28%) were classified as MCM2-high meningiomas and 26 (72%) as MCM2-low meningiomas. High MCM2 expression was significantly associated with WHO grades 2-3 histology and higher Ki-67 labeling indices. During a median follow-up of 2.52 years, tumor progression after radiotherapy occurred in 47% of the patients. High MCM2 expression (HR 8.34, p = 0.03) was significantly associated with shorter PFS and remained an independent predictor of recurrence after adjustment for WHO grade, tumor size, and Ki-67 labeling index. Transcriptomic analyses of MCM2-high tumors revealed upregulation of cell proliferation-related pathways, accompanied by increased signaling through the E2F8-CHEK1 axis associated with radiation resistance and suppression of the TNF-α signaling pathway implicated in radiosensitivity. CONCLUSIONS: In meningiomas, high MCM2 expression is associated with early recurrence following radiotherapy. The study findings suggest that this association is driven by diverse biological mechanisms related to cell cycle regulation and radioresistance. Immunohistochemical assessment of MCM2 expression may serve as a practical and accessible biomarker for risk stratification and may support the future development of individualized postoperative radiotherapy strategies.

Humans↗

PubMed2026

Avian egg incubation period: Revisiting existing allometric relationships via surface area-to-volume ratio of an egg.

The incubation period (I) for bird eggs varies among species and is used in establishing allometric relationships. Research on variations in I shed light on the evolutionary mechanisms that gave rise to the differentiation of embryonic development in distinct taxa of birds. Here, using a sampling of 444 images from 444 avian species, 89 families and 30 orders, we calculated their major geometric dimensions: volume (V) and surface area (S). An assessment of the relationship between I and the measured and calculated egg parameters demonstrated the closest and most significant correlation (R = -0.760) between I and the S/V ratio that was adopted as a conditional indicator and reflects the embryo's metabolic rate. Approximation of the values of these parameters made it possible to derive a power-law dependence for the prediction of I depending on the S/V value of a particular egg (R2 = 0.757). The prediction accuracy was higher (R2 = 0.783) if the eggs of the family Procellariiformes (petrels), whose I value is characterized by a longer time, were removed from the general sampling computation. We conclude that the value of the S/V ratio can characterize both the metabolism of an embryo and the conditional thermal conductivity of an egg, which aids in ensuring the temperature regime of egg incubation.

Animals↗

PubMed2026

Active Site Assembly by SMG5 as a Mechanism for SMG6 Endonuclease Licencing in Nonsense-mediated mRNA Decay.

Nonsense-mediated mRNA decay (NMD) is a conserved eukaryotic surveillance pathway that eliminates transcripts containing premature termination codons (PTCs). Substantial progress has been made in defining the transcript features that mark aberrant translation termination for NMD activation, yet key mechanistic steps remain incompletely understood - including how recruitment of the central NMD factor UPF1 is coupled to the downstream effector phase in which targeted mRNAs are nucleolytically degraded. In metazoans, NMD employs an endonucleolytic route mediated by SMG6, a PIN-domain nuclease, alongside SMG5 and SMG7, which act downstream of PTC recognition. SMG5 has recently been proposed to licence SMG6 activity, yet the molecular basis of this licencing has remained elusive. Here, we combine AlphaFold structural predictions with biochemical assays to investigate interactions among human SMG5, SMG6, and SMG7. Structural models predict a high-confidence interface between SMG5 and SMG6 PIN domains that forms a composite active site: a conserved SMG5 aspartate (D893) complements the SMG6 acidic triad to reinstate the canonical tetrad required for PIN-domain catalysis. In vitro, SMG6 alone exhibits weak endonucleolytic activity, which is enhanced ∼10-fold by the SMG5 PIN domain. Mutational analyses confirm that conserved residues from both proteins are essential for this composite configuration. Our findings reveal that the SMG5 PIN domain, previously considered catalytically inert, plays a critical role in activating SMG6 by completing its active site. This work provides mechanistic insight into the SMG5-dependent licencing step and uncovers a composite PIN nuclease architecture at the heart of the metazoan NMD effector phase.

Nonsense Mediated mRNA Decay↗

PubMed2026

A risk-need-responsivity (RNR)-informed systematic review of needs during the pretrial period.

OBJECTIVE: Pretrial risk assessments are becoming increasingly popular in the United States. Despite the importance of assessing and intervening around "needs" in the risk-need-responsivity model, few pretrial risk assessments include comprehensive assessment of needs. We aim to provide a systematic review of the prevalence of and predictive utility of needs within the pretrial population. HYPOTHESES: There were no hypotheses given the nature of the study. METHOD: We conducted searches for articles in the EBSCO, ProQuest, and Google Scholar databases using key words related to 11 needs domains: antisocial personality, procriminal attitudes, procriminal associates, substance use, family/marital relationships, school/work, prosocial recreational activities, self-esteem, housing, mental health, and physical health. We identified 215 articles that reported on the prevalence of needs or explored their predictive associations with pretrial misconduct outcomes in adult populations. RESULTS: Overall, we find few comprehensive investigations of needs in the pretrial domain, apart from substance use. Variation in methodology and operationalization contributes to wide variability in prevalence estimates. We found only 15 articles that examined predictive associations between pretrial needs and outcomes, which were limited to investigations of behavioral health, employment, and housing needs. Substance use and housing needs emerged as the only consistent predictors of pretrial misconduct. CONCLUSIONS: Researchers should more directly assess the prevalence and predictive utility of needs within the pretrial period to bolster the evidence base for including these factors in pretrial risk assessments. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Humans↗

PubMed2026

Peripheral immune markers and choroid plexus volumes as predictors of change in depressive symptoms: Insights from the EMBARC study.

Changes in choroid plexus (ChP) volume and peripheral inflammation have been associated with Major Depressive Disorder (MDD), yet their individual and combined impact on depressive symptoms is unclear. This study investigated whether baseline immune markers and ChP volumes predict changes in depressive symptoms during the 8-week treatment period among Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study participants who received either sertraline or placebo. Adults (n = 222) with MDD with peripheral blood samples were included. Circulating chemokines and cytokines were examined using a 40-plex assay. Depressive symptoms were assessed over 8 weeks using the Hamilton Depression Rating Scale (HAMD-17). Principal component analysis (PCA) was used for dimension reduction. Mixed-effects models were used to examine whether immune profiles and ChP volumes, and their interaction predicted HAMD-17, adjusting for demographic/clinical covariates and baseline depression severity. PCA identified three immune profiles. One profile, characterized by higher levels of cytokines and chemokines including IL-6, TNF-α, and IL-1β, was associated with greater depression severity, higher BMI, age, and CRP at baseline. Higher levels of these immune markers were associated with less improvement in depressive symptoms at 8 weeks (estimate = 1.211, p = 0.018) in models adjusting for right and left ChP volume (right ChP model: estimate = 1.034, p = 0.005; left ChP model: estimate = 0.993, p = 0.007). Interactions between immune markers and ChP volumes were not significant. Future investigations are warranted to examine the relationships between immune markers and ChP volume beyond structural changes in the context of depression symptoms.

Adult↗

PubMed2026

Association of the Charlson Comorbidity Index With 1-Year Outcomes in Patients With Macular Edema Secondary to Retinal Vein Occlusion.

OBJECTIVE: To determine the predictive value of the Charlson Comorbidity Index (CCI) for outcomes in patients with macular edema secondary to retinal vein occlusion (RVO). DESIGN: Retrospective clinical cohort study. SUBJECTS: Patients seen between 2013 and 2023 at the Cole Eye Institute, Cleveland Clinic, were included. All patients were >18, diagnosed with RVO (International Classification of Diseases (ICD)-9 and 10 codes), had a complete CCI score, and had at least 1 year of ophthalmic follow-up data after their first intravitreal injection (baseline). Patients with ocular surgery, trauma, or panretinal photocoagulation were excluded. METHODS: Age-adjusted CCI scores were calculated for each patient from chart review. For patients with bilateral RVO, one eye was selected randomly. Patients were stratified into tertiles by CCI distribution: tertile 1 (CCI 0-5; mean 3.4), tertile 2 (age-CCI 4.1-6; mean 4.9), and tertile 3 (CCI ≥ 8; mean 9.6). Multivariable linear regression was performed to determine the predictive value of CCI and other covariates on visual and anatomical outcomes. MAIN OUTCOME MEASURES: Best-corrected visual acuity (BCVA) and central subfield thickness (CST) at 1-year follow-up. RESULTS: A total of 972 patients met all criteria, with an average age-adjusted CCI score of 6.2. Each one-point increase in CCI predicted 0.38 fewer letters in BCVA at follow-up (P < .001). Baseline BCVA was a significant predictor of follow-up BCVA in all tertiles (P < .001). In the third tertile, each one-point increase in CCI was associated with a 0.72 letter reduction in follow-up BCVA (P < .001). For CST, baseline CST was strongly predictive of final CST (P < .001), while CCI was only significant in the first tertile, where each point increase in CCI predicted a 13.7 µm increase in CST (P = .02). In RVO subtype interaction models, the age-adjusted CCI × CRVO interaction was not statistically significant for either 1-year BCVA (P = .269) or 1-year CST (P = .695). CONCLUSIONS: Higher CCI scores are significantly associated with worse visual outcomes in patients with RVO, particularly in the combined population and most comorbid patients (tertile 3). CCI was significantly associated with higher (thicker) CST only among the least comorbid patients (tertile 1).

Humans↗

PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5 concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗