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PubMed2026

Exploring the dose-response relationship between prenatal exercise and postpartum depression: A systematic review and meta-analysis of randomized controlled trials.

IMPORTANCE: Postpartum depression (PPD) is a hidden and widespread global public health crisis affecting millions of mothers and infants annually. Prenatal exercise is a potentially accessible nonpharmacological strategy for PPD prevention, but its optimal dose remains uncertain. OBJECTIVE: To explore the dose-response relationship between prenatal exercise and the incidence of PPD through meta-analysis of randomized controlled trials (RCTs). DATA SOURCES: Systematic searches were conducted in PubMed, Embase, Web of Science, and Cochrane Library using MeSH terms and keywords related to "pregnant women," "prenatal exercise," and "postpartum depression," up to June 23, 2025. STUDY SELECTION: RCTs included examined prenatal exercise interventions in pregnant women without a history of depression, with PPD incidence reported using validated depression scales (such as EPDS, CES-D). Non-RCT studies, duplicate publications, and studies with insufficient data were excluded. DATA EXTRACTION AND SYNTHESIS: Two researchers independently extracted data according to the PRISMA guidelines. A random-effects model was used to pool odds ratios (OR) and their 95% confidence intervals (CI). Linear and nonlinear dose-response models were employed to analyze and evaluate the relationship between exercise dose (measured in METs-min/week) and the incidence of PPD. MAIN OUTCOME(S) AND MEASURE(S): The primary outcome is the incidence of PPD, analyzing its relationship with prenatal exercise dose. RESULTS: Eight RCTs involving 2231 pregnant women were included. The pooled analysis showed that prenatal exercise was associated with a potential reduction in PPD incidence, although the overall effect did not reach statistical significance (OR=0.58, 95% CI [0.33, 1.02]). In dose-stratified analysis, exercise doses ≥500 METs-min/week were associated with significantly lower PPD incidence (OR=0.44, 95% CI [0.24, 0.78]). Subgroup analyses suggested trends toward greater benefits among women aged ≥30 years and those initiating exercise between 14 and 28 weeks of gestation; however, subgroup differences did not reach statistical significance. The linear dose-response trend did not reach statistical significance (p = 0.0533), and neither the overall spline association (p = 0.1704) nor the test for nonlinearity (p = 0.6361) was statistically significant. CONCLUSIONS AND RELEVANCE: Prenatal exercise may be associated with a lower risk of PPD, but the overall pooled effect did not reach statistical significance. Findings concerning ≥500 METs-min/week and the apparent flattening of the dose-response curve should be considered exploratory and require confirmation in larger trials.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

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↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Efficacy, tolerability, and threshold effect of atropine eye drops for myopia control: A systematic review and dose-response meta-analysis.

Atropine is an emerging therapy for myopia, yet the optimal concentration for prescription remains uncertain. We searched PubMed, Embase, Web of Science, Cochrane Library, World Health Organization International Clinical Trials, and ClinicalTrials.gov registry platforms. We included the randomized clinical trials (RCTs) that compared any dose of atropine against a placebo in myopic children. Among 3566 studies assessed, we identified 33 eligible RCTs involving 6301 children aged 4-18 years, with 10 different concentrations and a mean follow-up time of 19.5 ± 12.3 months. A nonlinear relationship was observed between atropine dosage and treatment efficacy (P < 0.001). Compared to placebo groups, the mean differences in reducing annual spherical equivalent refraction progression for atropine concentrations of 0.01%, 0.02%, 0.03%, 0.04%, and 0.05% were 0.21 diopters (D) (95% CI, 0.13-0.28), 0.35 D (95% CI, 0.23-0.46), 0.42 D (95% CI, 0.28-0.56), 0.45 D (95% CI, 0.30-0.60), and 0.46 D (95% CI, 0.32-0.61) respectively For higher concentrations, the estimates were 0.49 D (95% CI, 0.34-0.63) for 0.1% and 0.99 D (95% CI, 0.66-1.31) for 1%, although these were based on fewer and smaller trials. Higher doses of atropine were associated with decreased amplitude of accommodation (P = 0.02), increased pupil diameters (P = 0.01) and a higher frequency of photophobia (P = 0.02). Our findings suggest that the increase in treatment efficacy with higher concentrations may plateau beyond a certain range, and that the current practice of increasing atropine concentrations for children who show inadequate responses to lower doses should be confined to a specific concentration range. This analysis is limited by the number, design heterogeneity, and sample sizes of available trials for higher concentrations, and by the frequent lack of pre-intervention refractive history in included studies. Therefore, estimates-particularly for doses exceeding 0.1%-should be interpreted with caution.

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

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P=.003), injunctive norms (β=0.078; P=.009), perceived susceptibility (β=0.051; P=.03), perceived benefits (β=0.253; P<.001), and trust (β=0.33; P<.001), and negatively associated with perceived severity (β=-0.047; P=.049) and privacy concerns (β=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans↗

PubMed2026

Mealtime satisfaction in public nursing homes: Associations with sensory, foodservice, and dining-room environment factors.

Satisfaction with meals is commonly used to assess how meals are experienced in nursing homes (NH), although limited evidence compares breakfast, lunch, and dinner within a unified analytical framework. This study examined the sensory and contextual factors associated with satisfaction across meals in public NH. A cross-sectional observational study was conducted using structured interviews with 290 residents aged ≥60 years (median 85 years; Q1-Q3: 81-88; 63.1% women) from 19 facilities in Galicia, Spain. Overall satisfaction and 12 factors related to sensory attributes of the food, foodservice characteristics, and dining-room environment were assessed using a 5-point Likert scale. Descriptive analyses used medians and quartiles, and group comparisons were performed using nonparametric tests. Three multivariable linear regression models, one per meal, were estimated including all factors simultaneously. In adjusted models, the largest standardized coefficients were observed for taste (lunch: β = 0.366; P < 0.001), food temperature at serving (dinner: β = 0.319; P < 0.001), and menu variety (breakfast: β = 0.301; P < 0.001). Taste, food temperature at serving, menu variety, and meal schedule showed statistically significant coefficients in all models. Overall satisfaction was lower at dinner (29.0% ≥ 4) than at breakfast (34.8%) and lunch (34.5%) (P = 0.003). Selected dining-room environment factors showed significant coefficients in meal-specific models. Mealtime satisfaction was mainly associated with sensory and contextual factors related to how meals are perceived. Lower satisfaction at dinner suggests this mealtime as a relevant context for understanding variations in meal perception in NH residents.

Humans↗

PubMed2026

Multiplexed CRISPR/Cas9 mediated knockdown of BCH gene in potato enhances beta-carotene to combat vitamin A deficiency.

The inadequate amounts of provitamin A carotenoids in crops contribute to the widespread vitamin A deficiency, leading to malnutrition and blindness in humans. Suppression of the β-carotene hydroxylase (BCH) increases β-carotene levels. In the current study, we utilized the multiplexed CRISPR/Cas9 approach by designing three targets against the BCH gene in a local potato cultivar. Transformation efficiency was recorded as 15%, the successful integration of the CRISPR/Cas9-BCH multiplex construct in potatoes was confirmed through PCR. When analysed using TIDE software, Sanger sequencing revealed the highest indel efficacy of 92.1% in plant 7 and 26.6% in plant 1. qRT-PCR (quantitative real-time PCR) analysis indicated a significant 89-fold reduction in BCH transcript levels in genome-edited potato lines compared to control plants. Spectrophotometry demonstrated a notable increase in beta-carotene levels in genome-edited potato plants, ranging from 0.831 µg/mL FW to 4.236 µg/mL FW, compared to the control plant with the lowest beta-carotene concentration (0.344 µg/mL FW). HPLC analysis further confirmed increased beta-carotene levels in genome-edited potato plants, ranging from 0.11 mg/mL FW to 0.36 mg/mL FW, compared to the unmodified control plant with a minimum beta-carotene value of 0.09 mg/mL. Our results revealed that the multiplexed CRISPR-Cas9 approach targeting the BCH gene results in enhanced beta-carotene contents in potato tubers.

Solanum tuberosum↗

PubMed2026

Public health, public protest: The role of health burdens and healthcare access in protest mobilisation.

Health and politics are intertwined, yet few studies have examined the association between health and protest. This study examined whether population health burdens were associated with protest incidence and whether healthcare access modified these associations. Analysis was based on an unbalanced 2004-2023 country-year panel, combining protest counts from ACLED with rates for 22 GBD causes. Mixed-effects negative-binomial models estimated incidence-rate ratios (IRRs) with interactions for healthcare access (±1 SD). Two-way fixed-effects Poisson models were estimated as a benchmark to distinguish cross-national associations from within-country dynamics. Health burdens were systematically, but heterogeneously, associated with protest. Rates for several non-communicable burdens were associated with protest, notably musculoskeletal disorders (IRR 1.72, 95% CI 1.37-2.15), neoplasms (1.24, 1.06-1.44), substance-use disorders (1.32, 1.12-1.56) and HIV/AIDS and other STIs (1.24, 1.12-1.38). Higher healthcare access generally attenuated health-protest associations. Fixed-effects models confirmed several associations (e.g. HIV/AIDS, neoplasms) but revealed that others (e.g. maternal/neonatal disorders, enteric infections) were driven primarily by cross-national differences. Population health burdens were associated with cross-national variation in protest mobilisation. Chronic, non-communicable burdens were associated with heightened protest, whereas poverty-linked and early-life burdens were associated with lower mobilisation. Healthcare access was associated with attenuation of these relationships.

Humans↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

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

Optimising Exercise Prescription: A Meta-Analysis Examining the Dose Response of Exercise Duration on Cardiorespiratory Fitness Following HIIT and MICT.

BACKGROUND: High-intensity interval training (HIIT) is often promoted as a time-efficient alternative to moderate-intensity continuous training (MICT) for improving cardiorespiratory fitness, yet the duration of HIIT sessions varies considerably across studies. OBJECTIVE: We aimed to characterise the dose-response relationship between exercise session duration and the improvement in cardiorespiratory fitness for HIIT and MICT. METHODS: A dose-response meta-analysis of randomised controlled trials comparing exercise duration in HIIT and MICT, following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and registered in PROSPERO (CRD42022335590). Effect sizes were calculated using a random-effects meta-analysis. The primary outcome was maximal oxygen uptake (VO2max). Secondary outcomes included blood pressure, lipid profiles, glucose metabolism markers and body composition measures. A one-stage random-effects dose-response meta-analysis was performed to examine the relationship between exercise duration and adaptations. We searched PubMed and Google Scholar; eligibility criteria for selecting studies were randomised controlled trials in humans, published in English and exercise interventions lasting at least 4 weeks. RESULTS: We identified 69 randomised controlled trials (2387 participants). High-intensity interval training elicited greater improvements in VO2max than MICT (d = 0.38, 95% confidence interval 0.27-0.49, p < 0.001). High-intensity interval training demonstrated a non-linear dose-response relationship between exercise session duration and VO2max, with 80% of maximal effect (changes in VO2max = 3.45 mL/kg/min) achieved with only ~11 min/session (95% confidence interval 9.5-40.2). Moderate-intensity interval training showed a linear dose-response relationship between exercise session duration and VO2max, requiring ~52 min/session to achieve 80% of the maximal observed effect (95% confidence interval 30.4-55.8). The dose-response relationship was consistent across populations. High-intensity interval training and MICT had comparable effects in improving cardiometabolic risk factors. CONCLUSIONS: High-intensity interval training demonstrated a non-linear dose response, with 80% of maximal effect on VO2max in ~11 min/session, whilst MICT required four to five times longer to reach similar responses. The different types of training had comparable effects on cardiometabolic risk factors.

Journal Article↗

PubMed2026

Prevalence of unruptured intracranial aneurysms according to comorbidities, risk factors, country, and time period: a systematic review and meta-analysis.

BACKGROUND: The incidence of aneurysmal subarachnoid haemorrhage declined between 1980 and 2010, which coincided with a decline in smoking and prevalence of hypertension. We aimed to investigate whether the decrease in subarachnoid haemorrhage incidence is paralleled by declines in unruptured intracranial aneurysm (UIA) prevalence. METHODS: For this systematic review and meta-analysis, we searched Embase, PubMed, and Web of Science for articles published in any language from Jan 1, 2011 to Dec 31, 2025, and reassessed 68 articles published before March 1, 2011 from a 2011 systematic review and meta-analysis. Articles were eligible for inclusion if they used a cross-sectional or case-control design and provided the crude number of participants and those with UIA. We only included studies reporting numbers of UIA separately from ruptured aneurysms and with ten or more patients. Summary data were independently extracted by JD with AZ or CB and conflicts were resolved by GJER. The primary outcome was proportion of participants with UIA. Relative to a hypothetical reference population (mean age 50 years, 50% women, and no comorbidities), age and/or sex-adjusted prevalence ratios (PRs) for regions, comorbidities, and risk ratios (RRs) for female sex, smoking, and hypertension were estimated using generalised linear mixed models. A time trend analysis was done by binomial meta regression using the mid-year of data acquisition. We assessed the certainty of evidence using GRADE. The study was registered with PROSPERO, number CRD420261296728. FINDINGS: Our search screened 4708 studies. 67 reassessed and 95 newly identified articles, reporting on 316 131 participants and 11 822 people with UIAs, were included in our meta-analysis. In the reference population, the estimated prevalence of UIAs was 3·9% (95% CI 3·0-5·1). The prevalence of UIAs in individuals with atherosclerosis was 5·5% (4·7-6·4; 2229 of 40970 participants) and the adjusted PR was 1·3 (95% CI 0·8-2·0) compared with the reference population. For positive family history of aneurysmal subarachnoid haemorrhage (aSAH) or UIA, the UIA prevalence was 7·9% (5·6-11·1; 412 of 4252 participants) and the adjusted PR was 2·4 (0·5-11·2). For connective-tissue disorder, the UIA prevalence was 10·3% (6·5-16·0; 94 of 879 participants) and the adjusted PR was 3·9 (2·0-7·6). For autosomal dominant polycystic kidney disease (ADPKD), the UIA prevalence was 12·8% (9·2-17·6; 293 of 1990 participants) and the adjusted PR was 4·4 (1·5-12·6). RRs were for current smoking 1·4 (1·2-1·6; 798 of 27911 participants), for having hypertension 1·6 (1·5-1·7, 4043 of 83053 participants), and for female sex 1·9 (1·8-2·0; 3415 of 65020 women and 2122 of 76130 men). In studies on healthy individuals with MR angiography or CT angiography as imaging modality, the prevalence in 2016-2022 was 6·6% (6·3-6·8; 2904 of 41191 participants). The adjusted PR was 1·8 (1·1-2·8) for 2016-2022 versus 2002-2015. Prevalence of UIAs of 5 mm or larger was 0·7% (0·6-0·8) in 2002-2015 and 1·4% (1·0-1·9) in 2016-2022. The UIA prevalence did not differ between countries. τ2 showed significant heterogeneity between studies. The certainty of the evidence ranged from very low to moderate. INTERPRETATION: Prevalence of UIA is increasing, particularly over the past two decades. This increase is only in part explained by improved detection of small UIAs and an ageing population, and other factors-such as environmental-are likely involved. Alongside patients with ADPKD and a positive family history of aSAH, patients with connective-tissue disorders had a higher prevalence of UIA than the reference population. Our findings warrant further investigation into the potential benefit of personalised screening and management strategies in groups at high risk for having UIAs. FUNDING: None.

Humans↗

PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (≥54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

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

Extended Long-Term Effects of Cognitive and Aerobic Training on Cognitive Function in Patients With Stress-Related Exhaustion Disorder: A 4.5-Year Follow-Up of a Randomized Controlled Trial.

Stress-related conditions like clinical burnout and exhaustion disorder (ED) are associated with enduring cognitive problems. We have previously demonstrated that the addition of computerised cognitive training (CCT) and aerobic training (AT) to a multimodal rehabilitation programme (MMR) yielded greater improvements in cognitive function compared to MMR alone in patients with ED. These effects were maintained at the 1-year follow-up for CCT, but not for AT. Building on these findings, the present study examined the extended long-term effects of CCT and AT on cognitive function, psychological health, and work ability, 4.5 years after the interventions. Participants were recruited from a stress-rehabilitation clinic and 56 of the initial 132 participants returned for the 4.5-year follow-up. Assessments were conducted before (T1), immediately after (T2), 1-year after (T3) and 4.5-years after (T4) the interventions. Mixed model analyses assessed changes in the intervention groups relative to the control group from T1 to T4, with follow-up comparisons examining within-group stability of outcomes between T3 to T4. The primary outcome was cognitive performance on a global cognitive score. Secondary outcomes included domain-specific cognitive functioning, self-reported cognitive function, psychological health (burnout, depression, anxiety, and fatigue), and work ability. The analysis revealed sustained long-term effects on the global cognitive score, a trained updating task and episodic memory in the CCT group, with stable performance between one- and 4.5-year follow-up. The addition of AT did not yield any extended long-term effects on cognitive performance. Both groups showed extended long-term improvements in self-reported memory problems, although findings were mixed. Extended long-term improvements were observed on burnout for both groups, with additional effects on anxiety and work ability in the AT group. Notably, these effects were not present at the 1-year follow-up and are more plausibly explained by selective attrition rather than a delayed intervention effect. In conclusion, the result indicates that cognitive interventions such as CCT can have lasting positive effects on cognitive function in patients with ED, whereas the long-term psychological effects should be interpreted with caution. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0073772.

Adult↗