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PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

Corneal Epithelial Alterations Associated With Cyclin-Dependent Kinase 4/6 Inhibitor Therapy in Hormone Receptor-Positive Breast Cancer.

IMPORTANCE: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors are standard therapy for hormone receptor-positive (HR+), HER2-negative breast cancer. By blocking the G1/S cell-cycle transition, these agents may impair renewal of the corneal epithelium. No controlled study has systematically evaluated corneal epithelial changes in patients receiving CDK4/6 inhibitors. OBJECTIVE: To determine whether CDK4/6 inhibitor-based therapy is associated with cornealepithelial alterations independent of aromatase inhibitor exposure and tear film dysfunction. DESIGN, SETTING, AND PARTICIPANTS: Retrospective comparative cross-sectional study at a tertiary ophthalmology center. A total of 132 women were enrolled: 45 receiving a CDK4/6 inhibitor plus an aromatase inhibitor (CDKAI group), 44 receiving aromatase inhibitor monotherapy (AI group), and 43 age-matched postmenopausal controls without systemic oncologic therapy. EXPOSURES: CDK4/6 inhibitor (ribociclib, palbociclib, or abemaciclib) combined with an aromatase inhibitor; aromatase inhibitor alone; or no systemic oncologic therapy. MAIN OUTCOMES AND MEASURES: Prevalence and severity of punctate epitheliopathy and vortex keratopathy, assessed by a masked ophthalmologist. Secondary outcomes included Schirmer I test, tear film break-up time, and Ocular Surface Disease Index (OSDI). RESULTS: PE was present in 44.4% of eyes in the CDKAI group vs 4.7% in the AI group and 2.3% in controls (χ² = 34.31; P < .001). All moderate (13.3%) and severe/complicated (8.9%) PE cases occurred exclusively in the CDKAI group. Vortex keratopathy was observed in 13.3% of CDKAI patients and in none of the other groups (P = .025). Schirmer values, tear film break-up time, and OSDI scores did not differ among groups (all P > .05). Within the CDKAI group, PE was not associated with treatment duration (P = .963) or tear film parameters. CONCLUSIONS AND RELEVANCE: In this comparative study, CDK4/6 inhibitor-based therapy was associated with significantly higher prevalence and severity of PE and vortex keratopathy, independent of aromatase inhibitor exposure and in the absence of measurable tear film dysfunction. These findings suggest a direct cytostatic effect on the corneal epithelium. Symptom scores were low, although OSDI interpretation was limited by incomplete responses. Proactive corneal surface evaluation with fluorescein staining may be warranted during CDK4/6 inhibitor treatment.

Humans↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Novel Germline ELP1 Splice-Acceptor Variant in NF1-Negative Optic Pathway Glioma: Expanding the Clinical Spectrum Associated With ELP1 Variation.

We report a 7-year-old boy with NF1-negative optic pathway glioma harboring a novel germline ELP1 splice-acceptor variant (NM_003640.5:c.2205-2A>G) identified by whole-exome sequencing. The variant was likely pathogenic (ACMG/AMP: PVS1, PM2) and inherited from an asymptomatic father, consistent with incomplete penetrance, expanding the limited evidence linking germline ELP1 variation to gliomas.

Humans↗

PubMed2026

Predictive value of anal sphincter electromyography for sacral neuromodulation test-phase outcomes.

BACKGROUND: Sacral neuromodulation (SNM) is an established therapy for refractory pelvic organ dysfunction. Anal sphincter electromyography (EMG) is commonly used preoperatively to assess sacral and peripheral nerve integrity. However, the prognostic significance of chronic neurogenic EMG changes for SNM outcomes remains unclear. OBJECTIVE: To evaluate whether chronic neurogenic changes on preoperative anal sphincter EMG predict the outcome of the SNM test phase. METHODS: We retrospectively analysed 62 consecutive patients with bladder and/or bowel dysfunction or pelvic pain who were candidates for SNM treatment and who underwent preoperative anal sphincter EMG. EMG findings were classified as normal or showing chronic neurogenic changes. SNM test-phase success was defined as a ≥50% improvement of symptoms at 24 days. Outcomes were compared between EMG groups. RESULTS: Of the 62 patients (49 women, 13 men), 30 (48%) had normal EMG findings and 32 (52%) showed chronic neurogenic changes. Overall, the SNM test phase was successful in 47 patients (76%). Success rates were similar in patients with normal EMG (72%) and neurogenic EMG changes (79%), with no statistically significant difference (p = 0.878). Sex-stratified analyses revealed no significant association between EMG findings and test-phase success in women or men. CONCLUSIONS: Chronic neurogenic changes on anal sphincter EMG do not predict SNM test-phase outcomes. These findings suggest that abnormal sphincter EMG results should not be used as a standalone criterion to exclude patients from SNM therapy. SIGNIFICANCE: Signs of neurogenic damage on anal sphincter EMG are not an indicator of reduced neuromodulatory capacity or diminished clinical response to SNM.

Humans↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed2026

Association of lipoprotein-associated phospholipase A2 with recurrence risk and its predictive value in large artery atherosclerotic stroke.

OBJECTIVE: To investigate the association of lipoprotein-associated phospholipase A2 (Lp-PLA2) with large artery atherosclerotic (LAA) stroke and its predictive value for recurrence. METHODS: We consecutively enrolled 412 acute LAA stroke patients. Using a cutoff of 200 ng/mL, patients were divided into high and low Lp-PLA2 groups, and into recurrence and non‑recurrence groups based on 1‑year follow‑up. Baseline characteristics, lipid profiles, National Institutes of Health Stroke Scale (NIHSS) scores, and vascular stenosis degree were compared. Binary logistic regression and Receiver Operating Characteristic (ROC) analysis were used to identify independent risk factors and evaluate predictive value. RESULTS: The high Lp-PLA2 group had significantly higher low-density lipoprotein cholesterol (LDL-C), small dense low-density lipoprotein cholesterol (sdLDL-C), prevalence of severe stenosis (≥70%), and proportion of NIHSS > 15 (all P < 0.05). The recurrence group showed elevated Lp-PLA2, higher LDL‑C and sdLDL-C, more severe neurological deficits, and more severe stenosis (all P < 0.001). Multivariable regression identified elevated Lp-PLA2 (per 10 ng/mL: OR = 1.139, 95% CI: 1.089-1.191), moderate (OR = 3.145) and severe (OR = 11.663) neurological deficits, and severe stenosis (OR = 9.390) as independent risk factors for recurrence (all P < 0.05). The Area Under the Curve (AUC) of Lp-PLA2 was 0.75 (95% CI: 0.69-0.82), with an optimal cutoff of 208.95 ng/mL. CONCLUSION: Elevated Lp-PLA2 is associated with adverse lipid profiles, more severe neurological deficits, and greater vascular stenosis in LAA stroke patients, and independently predicts 1‑year recurrence. Lp-PLA2 shows moderate predictive value, supporting its potential for risk stratification.

Humans↗

PubMed2026

Time to subsequent therapy (TTST) as an endpoint in clinical studies: development of standardized documentation of subsequent therapy through systematic literature review, expert interviews, and Delphi survey.

BACKGROUND: The endpoint Time to Subsequent Therapy (TTST) is an intermediate endpoint used in research and regulatory assessments. TTST denotes initiation of subsequent therapy and is a clearly definable, clinically relevant event for healthcare professionals. However, it has not been systematically established to which extent TTST is subjectively meaningful to patients. The objective of this study was to define TTST as a patient-relevant intermediate endpoint. METHODS: The study examined five oncological indications (breast cancer, prostate cancer, melanoma, multiple myeloma, and non-small cell lung cancer) using a systematic literature review, analysis of case report forms used in international randomized controlled trials, review of German Federal Joint Committee (G-BA) documents, semi-structured interviews and a two-stage Delphi survey with healthcare professionals, patients, and relatives. RESULTS: A total of 35 individuals participated in qualitative interviews. Most of them rated TTST as particularly significant. The Delphi Survey included 264 interviewees in round one, and 117 in round two. Patient-relevance of TTST was confirmed by 81% of respondents (95% confidence interval 76%, 85%). Nine treatment scenarios that justify TTST were identified. To capture patient-relevance, prospective collection of reasons for and consequences of therapy change are required. A checklist with standardized response formats plus free-text fields was developed: a comprehensive master checklist for flexible, complete documentation and a short version focused on therapy change-specific items. CONCLUSIONS: TTST is an intermediate endpoint whose systematic documentation of characteristics demonstrating patient-relevance can be standardized in research and clinical practice using the developed checklists.

Humans↗

PubMed2026

A Sentiment-Based Comparison of AI- and Physician-Generated Empathic Statements in Palliative Care.

CONTEXT: Empathic communication promotes trust in patient-provider relationships. As healthcare integrates artificial intelligence (AI) into patient communication, we have yet to understand how these models' communication compares to that of physicians. OBJECTIVES: Our primary objectives were to examine patient preferences for AI-generated vs. palliative care physician-generated empathic statements addressing fear and anxiety around cancer treatment, and to analyze associations between linguistic features and patient preferences. METHODS: We conducted a secondary analysis of the PALL-AI trial, a randomized controlled survey comparing cancer patients' preferences of AI- to physician-generated empathic statements. Physicians and AI were provided the same prompt with a maximum sentence length. Patient preferences for each statement were measured in blinded surveys. We analyzed sentiment of the statements using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and the National Research Council Canada (NRC) Emotion Lexicon. We evaluated associations between sentiment scores and patient preferences using Spearman's correlation coefficients. RESULTS: A total of 105 patients completed blinded surveys, preferring the AI-generated statement 72.4% of the time. VADER sentiment analysis showed all three AI statements displayed positive sentiment, while all three physician statements displayed negative sentiment. Controlling for statement length, AI statements used twice as many positive words as human statements. However, they contained a similar number of negative words. Of the eight NRC emotions, "trust" and "joy" demonstrated the strongest correlations with patient preference. CONCLUSION: Patients preferred AI-generated statements around cancer care over those from palliative care physicians when standardized for prompt and statement length. Analysis shows AI-generated statements contain more positive language which may be the factor driving patient preference toward AI.

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

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societal barriers may limit professional fulfilment and psychological wellbeing of individuals with DD, calling for the development of effective interventions to counteract them. As DD is associated with challenges in both phonological and visuo-attentional domains, different longitudinal training approaches were developed to strengthen them. However, they require a considerable amount of personal, social and economic resources and the outcomes may vary depending on individual differences in behavioural and neurophysiological functionality. Hence, predicting training outcomes might help in developing personalised treatment protocols and optimising the use of resources. In the present work we applied machine learning to resting-state EEG to predict longitudinal training outcomes in adults with DD enrolled in a randomized clinical trial. In particular, one group received a visuo-attentional training combined with transcranial alternating current stimulation (tACS), another group received visuo-attentional training with sham/placebo stimulation, and the third group received a phonological training with sham/placebo stimulation. The improvement in text reading speed was associated with spectral power in low-beta and individual frequencies in the alpha (IAF) and beta (IBF) bands, while the improvement in pseudoword reading was associated with IBF. The findings highlight the potential of capturing neural markers of treatment responsiveness in DD. Future studies should focus on the generalisability of predictive models to real-world settings, while investigating whether specific EEG markers predict responsiveness to distinct remediation protocols, thus supporting the development of personalised interventions.

Humans↗

PubMed2026

Adjuvant alectinib versus chemotherapy in resected ALK-positive non-small-cell lung cancer (ALINA): health-related quality-of-life and safety outcomes from a randomised, open-label, phase 3 trial.

BACKGROUND: For patients with resected, ALK-positive non-small-cell lung cancer (NSCLC), adjuvant alectinib significantly improved disease-free survival versus platinum-based chemotherapy in the global, phase 3, open-label, randomised ALINA trial. We report safety and health-related quality-of-life (HRQoL) outcomes from the ALINA trial. METHODS: Eligible patients aged 18 years or older with resected, ALK-positive, stage IB (≥4 cm)-IIIA NSCLC (per the American Joint Committee on Cancer and the Union for International Cancer Control Cancer Staging Manual 7th edition) and an Eastern Cooperative Oncology Group performance status of 0-1 were randomly assigned (1:1) via a block-stratified randomisation method to receive oral alectinib (600 mg twice daily) for 24 months or intravenous platinum-based chemotherapy for four 3-week cycles. Randomisation was stratified according to disease stage and race. The primary endpoint, previously reported, was disease-free survival. Safety was a secondary endpoint and HRQoL was an exploratory endpoint. Safety was assessed by the investigator as per the National Cancer Institute Common Terminology Criteria for Adverse Events version 5·0 until 28 days after the last alectinib dose or chemotherapy cycle. HRQoL was assessed via the Short-Form 36-item health survey version 2 (SF-36v2) questionnaire at baseline, every 3 weeks to week 12, then every 12 weeks until disease recurrence, consent withdrawal, death, or week 96. Norm-based scoring was applied; clinically meaningful changes were defined using the SF-36v2 manual. Safety was assessed in the safety-evaluable population and HRQoL in the intention-to-treat population. This study is registered with ClinicalTrials.gov (NCT03456076) and is ongoing. FINDINGS: Between Aug 16, 2018, and Dec 8, 2021, 257 patients were assigned to receive alectinib (n=130) or chemotherapy (n=127). 123 (48%) patients were male and 134 (52%) were female; 143 (56%) were Asian. The safety-evaluable population comprised 128 patients who received alectinib and 120 patients who received chemotherapy; median duration of safety follow-up was 24·8 months (IQR 22·0-24·9) in the alectinib group and 3·7 months (IQR 3·7-3·8) in the chemotherapy group. The safety of adjuvant alectinib was generally consistent with its known profile. The most common grade 3-4 adverse events were blood creatine phosphokinase increased (eight [6%] of 128), alanine aminotransferase increased (two [2%] of 128), and blood bilirubin increased (two [2%] of 128) in the alectinib group, and neutrophil count decreased (12 [10%] of 120), neutropenia (ten [8%] of 120), and nausea (five [4%] of 120) in the chemotherapy group. Serious treatment-related adverse events occurred in two (2%; one each with appendicitis and pneumonitis) of 128 patients in the alectinib group and eight (7%) of 120 patients in the chemotherapy group ( most common were gastrointestinal disorders in three [3%] patients). No deaths due to adverse events were reported in either group. There were fewer discontinuations due to adverse events with alectinib (seven [5%]) versus chemotherapy (15 [13%]). A clinically meaningful difference in improvement from baseline was seen at week 12 for bodily pain, role physical, mental health, social functioning, and vitality SF-36v2 domains with alectinib; improvements in physical and mental HRQoL were maintained over 2 years of active treatment (at week 96, mean Mental Component Summary score: 49·9 [SD 10·4]; mean Physical Component Summary score: 48·8 [SD 7·2]) and reached levels similar to the general population (population norm: 50). INTERPRETATION: For patients with resected ALK-positive NSCLC, adjuvant alectinib had a manageable safety profile; HRQoL improved and was maintained over 2 years of active treatment. Together with the disease-free survival benefit seen in ALINA, these data support adjuvant alectinib as an important new standard-of-care for patients with resected ALK-positive NSCLC. FUNDING: F Hoffmann-La Roche.

Adult↗

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

Assessment of the Potential of Different Anthropometric Indices in Predicting the Risk of Diabetes and Associated Co-morbidities.

Diabetes, a chronic disorder, is showing a rapidly increasing trend globally. India holds the second position in the global diabetes epidemic. The present investigation is an assessment of different anthropometric measurements and their association with type 2 diabetes to determine their diagnostic potential for diabetes as well as its co-morbidities. In this cross-sectional study, we have measured anthropometric parameters and blood biomarkers in subjects with diabetes. We have presented the comparisons of cost- and time-effective anthropometric variable with costly and time-dependent biochemical variables in control and diabetic groups (n = 233/group). Correlations between anthropometric variables and biochemical measurements, as well as the diagnostic utility of anthropometric variables for diabetes, were evaluated. The diagnostic utility of anthropometric variables for diabetes was assessed through receiver operating characteristic (ROC) curves. Neck circumference, sagittal abdominal diameter (SAD), skinfold thickness, and body roundness index (BRI) displayed high specificity and diagnostic utility for diabetes, emphasizing their potential in predicting diabetes and the further development of metabolic syndrome. The study highlights the importance of cost- and time-effective anthropometric assessments in diabetes risk evaluation and calls for further research to elucidate this intricate relationship and develop personalized management strategies.

Humans↗

PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗