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PubMed2026

Blood Metabolomic Signatures of 1-Hour Glucose Predict Cardiometabolic Risk.

BACKGROUND: Elevated 1-hour glucose levels during an oral glucose tolerance test strongly predict type 2 diabetes (T2D) and cardiovascular disease. We investigated whether the fasting blood metabolome predicting 1-hour glucose could be a target for improving β-cell function, long-term glycemic trajectories, and reducing the risks of T2D and coronary heart disease. We also investigated whether plasma microRNAs derived from key metabolic organs regulate changes in a metabolomic risk score (MRS) for predicting 1-hour glucose. METHODS: Untargeted blood metabolomics and a frequently sampled 75-g oral glucose tolerance test were performed in participants from the OmniCarb trial (n=162). In an independent weight-loss dietary intervention trial (POUNDS Lost [Preventing Overweight Using Novel Dietary Strategies]), temporal changes in MRS and plasma microRNAs measured by genome-wide sequencing were analyzed. In addition, associations of MRS at baseline and its 10-year changes with long-term risk of incident T2D and coronary heart disease were prospectively investigated in the NHS (Nurses' Health Study). RESULTS: We created a fasting blood MRS for predicting 1-hour glucose (Pearson r=0.8) and found significant associations with half-day (diurnal) postprandial glucose excursions and insulin secretion after 5-week controlled feeding interventions varying in carbohydrate amount and glycemic index. In the POUNDS Lost trial, diet-induced changes in MRSs were related to 2-year trajectories of glucose metabolism; circulating microRNAs regulating cardiometabolic abnormalities were pivotal factors influencing these changes. In the NHS, women in the top 20% of MRS had a multivariate-adjusted relative risk of 3.80 (95% CI, 2.22-6.51) for T2D and 1.48 (95% CI, 1.04-2.12) for coronary heart disease compared with those in the lowest 20%. In addition, 10-year increases in plasma metabolites related to 1-hour glucose were linearly associated with a higher risk of T2D. CONCLUSIONS: Our findings indicate that fasting blood metabolomic signatures predicting elevated 1-hour glucose reflect disease pathophysiology and could be targets for preventing T2D and coronary heart disease.

blood glucose↗

PubMed2026

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

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

Humans↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

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

Serum Copeptin Rises After Tolvaptan for Hyponatraemia, but Does Not Predict Risk of Rapid Sodium Rise: Pre-Specified Secondary Analysis of the TVFR Trial.

OBJECTIVE: Hyponatraemia is a common electrolyte disorder often driven by excess arginine vasopressin (AVP). Copeptin is a stable surrogate marker co-secreted with AVP. It is unclear whether treatment of hyponatraemia with tolvaptan, an AVP-V2 receptor antagonist, impacts copeptin. We aimed to assess the effects of tolvaptan on serum copeptin, compared to fluid restriction. DESIGN: Pre-specified secondary analysis of an open-label randomised trial comparing tolvaptan or fluid restriction for 3 days. PATIENTS: Hospitalised patients with plasma sodium (pNa) 115-130 mmol/L at a single-centre tertiary hospital in Melbourne, Australia. MEASUREMENTS: Copeptin measured at baseline and completion (Day 4, or discharge if sooner). RESULTS: Copeptin results were available in 45/54 participants, randomised to tolvaptan (n = 25) or FR (n = 20). Mean baseline copeptin was 10.4 pmol/L. pNa increased in both groups, significantly more with tolvaptan as previously reported. Copeptin remained stable after FR, but significantly increased after tolvaptan (mean adjusted difference between groups over 3 days 8.4 pmol/L, 95% CI 2.1-14.6, p = 0.01). Baseline copeptin did not predict rapid sodium rise. The rise in copeptin after tolvaptan may represent an exaggerated response to osmolality rise in these patients ('reset osmostat'), or feedback mechanisms from AVP blockade. CONCLUSION: Tolvaptan increased serum copeptin compared to fluid restriction. Further research is required to determine if there is clinical utility for measuring copeptin in hyponatraemia before it is adopted into practice. TRIAL REGISTRATION: ACTRN12619001683123.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

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

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

Diagnostic utility of high-risk HPV polymerase chain reaction-based testing in head and neck FNA specimens with indeterminate cytomorphology.

BACKGROUND: Fine-needle aspiration (FNA) is critical in the initial diagnosis of many high-risk human papillomavirus (HR-HPV)-associated, metastatic oropharyngeal squamous cell carcinomas. Updated guidelines recommend HR-HPV-specific polymerase chain reaction (PCR) analysis over p16 immunohistochemistry on FNA specimens because p16 performs poorly on cytology material. PCR-based assays on liquid cytology material have demonstrated excellent analytic performance; however, the diagnostic utility of a positive HR-HPV PCR result in specimens with indeterminate cytomorphology remains uncharacterized. METHODS: The authors retrospectively identified 279 head and neck FNA specimens that had paired HR-HPV PCR testing on residual liquid cytology material over a 5-year period. The positive predictive value for histopathologically confirmed squamous cell carcinoma on surgical follow-up was calculated within each cytologic interpretive category. RESULTS: The HR-HPV PCR results were positive in 50.2% of specimens, negative in 40.9%, and indeterminate in 9.0%. The HR-HPV positivity rate ranged from 0% in specimens categorized as negative for malignancy to 57.3% in cytologically positive specimens, with 19.0%, 41.2%, and 50.0% positivity in the atypical, suspicious, and nondiagnostic categories, respectively. Among cytologically indeterminate specimens with positive HR-HPV PCR results (n = 14), the positive predictive value was 100% (95% confidence interval, 78.5%-100.0%). Blinded slide review additionally identified 15 cytologically positive specimens in which the definitive malignant interpretation depended substantially on HR-HPV positivity; all 15 were confirmed as squamous cell carcinoma. CONCLUSIONS: A positive HR-HPV PCR result on liquid cytology material carries a positive predictive value of 100% for malignancy in cytologically indeterminate head and neck FNA specimens. These findings support integrating HR-HPV PCR analysis into routine cytologic interpretation with the potential to upgrade some indeterminate specimens to malignant when HR-HPV is detected, expediting definitive treatment and sparing patients additional, invasive sampling.

Humans↗

PubMed2026

Novel non-contrast computed tomography parameters for predicting spontaneous stone passage and surgical requirement in ureteral stones: The role of ureteral wall thickness and dilatation ratio.

We investigated the predictive value of standard non-contrast computed tomography (NCCT) measurements, the ureteral dilatation ratio (DDR) and intraluminal urine stasis markers, for spontaneous stone passage (SSP) versus surgical intervention in patients with ureteral stones. We also evaluated ureteral wall thickness (UWT) as a practical clinical marker. This retrospective study included 461 patients diagnosed with ureteral stones via NCCT. Patients were categorised into two groups based on clinical outcomes: the spontaneous passage group (MET; n = 229) and the endoscopic surgery group (URS; n = 232). Stone volume, stone density (HU), UWT, DDR and intraluminal urine attenuation values were measured for all patients. Independent risk factors were identified using a multivariate logistic regression model and clinical cut-off values were determined via ROC curve analysis. Stone volume, density, UWT and hydronephrosis grade were all significantly higher in the URS group. Multivariate regression analysis revealed that increased UWT (OR: 5.03, 95% CI: 3.66-6.90; p < 0.001) was the strongest independent predictor of surgery. Higher DDR (OR: 1.88; p = 0.003), advanced hydronephrosis, stone volume, and density also increased surgical risk. A UWT cut-off ≥ 2.97 mm predicted surgery with 84.8% sensitivity and 84.3% specificity (AUC: 0.872). A DDR cut-off > 1.79 yielded 81.7% specificity and 40.4% sensitivity. UWT weakly correlated with stone volume (r = 0.145), indicating wall thickening reflects an inflammatory response rather than a mere mechanical consequence. UWT is a superior predictor of SSP failure, supported by increased DDR as a highly specific complementary risk factor. These parameters could help clinicians to identify patients who would benefit from early surgical counselling and intervention rather than prolonged conservative management.

Humans↗

PubMed2026

Prognostic Value of Frailty in Aortic Surgery: A Systematic Review and Meta-Analysis Comparing Frailty Assessment Tools.

BACKGROUND: Frailty is increasingly recognized as an important determinant of outcomes after aortic vascular surgery, but assessment methods vary substantially and the optimal tool for risk stratification remains uncertain. This systematic review and meta-analysis evaluated the prognostic value of preoperative frailty and compared the predictive performance of different frailty instruments in aortic surgery. METHODS: PubMed, Embase, and Cochrane Library were searched from inception to April 27, 2026. Eligible studies included patients undergoing open, endovascular, or hybrid aortic procedures involving abdominal, thoracic, thoracoabdominal, arch, and proximal aortic diseases, including aneurysms and dissections, assessed frailty preoperatively, and reported postoperative outcomes. RESULTS: Thirty studies comprising 419,459 patients were included. Frailty was associated with higher early mortality (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.54-3.14) and late mortality (hazard ratio 2.18; 95% CI 1.64-2.90). Frail patients also had increased risks of major complications (OR 2.52; 95% CI 1.22-5.19), acute kidney injury (OR 1.64; 95% CI 1.34-2.02), and nonhome discharge (OR 5.50; 95% CI 3.05-9.92). Associations were consistent across surgical approaches and aortic segments. Judgment-based or phenotype-like tools yielded higher effect estimates than deficit-accumulation indices, although differences were not statistically significant; among index-based tools, Modified Frailty Index (mFI)-11 outperformed mFI-5. CONCLUSION: Preoperative frailty strongly predicts mortality, morbidity, and loss of functional independence after open, endovascular, and hybrid aortic surgery across different aortic segments and pathologies, including aneurysmal and dissecting aortic disease. Routine frailty assessment may improve risk stratification and perioperative decision-making.

Humans↗

PubMed2026

Exploring China's Clean Air Act and associated cardiovascular disease risk: a prospective, quasi-experimental, and causal inference modelling study.

BACKGROUND: Substantial improvements in air quality have been recorded following the implementation of China's Clean Air Act (CCAA) in 2013. However, the association between CCAA implementation and individual-level cardiovascular disease (CVD) risk remains unclear. We aimed to examine the long-term association between CCAA implementation and individual-level predicted CVD risk. METHODS: In this prospective, quasi-experimental study, we used data from the China Kadoorie Biobank, a prospective cohort study that recruited participants from five urban and five rural areas across China between 2004 and 2008, with three resurveys conducted after the baseline survey (in 2008, 2013-14, and 2020-21). We included 34 862 individuals (mean age 51·3 years) who participated in at least one resurvey and had no history of CVD at baseline. Participants were classified into intervention (n=25 497) and control (n=9365) groups based on the local government's targets for particulate matter reduction. We estimated the 10-year risk of incident CVD morbidity or mortality using a validated risk prediction model. We used a difference-in-difference model to assess the long-term association between CCAA implementation and predicted risk, with adjustments made for regional confounders and individual-level characteristics, including demographics, lifestyle factors, medical history, and indoor air pollution exposure. The relationship between changes in long-term exposure to PM2·5, PM10, and O3 and predicted risk after CCAA implementation was analysed using a linear model. The estimated risk differences associated with air pollutant changes were estimated based on the magnitude of changes and their corresponding effect sizes. FINDINGS: After the CCAA was implemented, PM2·5 and PM10 concentrations declined in both groups, but O3 concentrations increased. The intervention group showed a 3·95% (95% CI 3·18-4·72%) lower increase in predicted risk than the control group, with larger estimated differences under stricter enforcement. Between 2013 and 2021, each 10 μg/m3 change in PM2·5 concentration was positively associated with a 1·80 (1·34-2·27) percentage point change in predicted CVD risk, whereas each 10 μg/m3 change in PM10 concentration was associated with a 1·24 (0·84-1·63) percentage point change and each 10 μg/m3 change in O3 concentration with a 0·58 (0·33-0·83) percentage point change. Overall, the observed changes in air pollutants during the study period were associated with an average 6·6 percentage point reduction in predicted CVD risk. INTERPRETATION: The CCAA and improved air quality were associated with a slower increase in predicted CVD risk, supporting the necessity for stricter, multipollutant air quality policies to maximise public health benefits. FUNDING: National Natural Science Foundation of China, Kadoorie Charitable Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key R&D Program of China, Chinese Ministry of Science and Technology, and UK Wellcome Trust.

Journal Article↗

PubMed2026

MicroRNAs in Oral Bio-Fluids as Predictive Biomarkers of Orthodontic Tooth Movement: A Systematic Review.

This systematic review was designed to assess scientific evidence of the association of microRNA expression during orthodontic tooth movement through various time points. A systematic review was performed in accordance with the PRISMA checklist. A search strategy was developed in electronic databases including Med Line, Scopus, EBSCO Host and ProQuest Dissertations & Theses Global until June 2025. Eligibility criteria included studies that investigated microRNA expression in saliva/GCF during orthodontic treatment. The risk of bias of the included studies was analysed using the QUADAS-2 and RoB-2 tools. The search retrieved 2800 records, of which nine studies were selected. Minor variations in GCF collection were noted, while stimulated saliva was collected in one study. RT-PCR and the Fluro meter accounted for the majority of miRNA estimation. Thirteen miRNAs were identified as target biomarkers for OTM regulation. Despite the high risk of bias, the evidence from the current systematic review indicates that microRNAs can be considered as potential biomarkers of orthodontic tooth movement in oral biofluids. Trial Registration: Prospero ID-CRD420251153064.

Humans↗

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

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2026

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

Robotic Surgical Procedures↗

PubMed2026

Lateral Femoral Condyle Angle Is an Anatomic Risk Factor for Anterior Cruciate Ligament Primary Injury and Secondary Graft Rerupture.

PURPOSE: To define the lateral femoral condylar angle (LFCA)-a parameter measuring condylar inclination-and assess its predictive capacity for primary and recurrent anterior cruciate ligament (ACL) injuries. METHODS: Patients aged 18 to 50 years who underwent primary ACL reconstruction between 2017 and 2022 were screened. Those with graft rerupture were matched 1:1:1 by sex, age, body mass index, and meniscal status to ACL-intact controls and patients without graft failure during a minimum 48-month follow-up. LFCA was measured as the angle between the long axis of the distal femur and the axis of the lateral femoral condyle, alongside 38 established parameters using preoperative 3.0-T magnetic resonance imaging scans. Intergroup differences were assessed with one-way analysis of variance; multivariate regression and receiver operating characteristic analyses identified independent predictors and evaluated predictive performance. RESULTS: The study included 540 patients (180 per group: primary ACL injury, rerupture, controls). One-way analysis of variance revealed a significant gradient in LFCA across controls (81.3°, 95% CI: 80.8-81.8°), primary injuries (81.9°, 95% CI: 81.5-82.3°), and reruptures (84.2°, 95% CI: 83.7-84.6°) (P < .001). In multivariate models adjusted for baseline factors, LFCA independently predicted primary injury (odds ratio [OR] = 1.27, 95% CI: 1.13-1.42, P < .001; area under the curve [AUC] = .576) and rerupture (OR = 1.46, 95% CI: 1.21-1.76, P < .001; AUC = .704). Combined models incorporating LFCA with tibial slopes, notch angle, and notch height achieved an AUC of .847 for primary injury prediction, whereas models with LFCA, lateral condylar flexion curvature radius, slope asymmetry, and tibial AP distance reached an AUC of .817 for rerupture. LFCA alone with lateral condylar flexion curvature radius yielded an AUC of .777 for rerupture prediction. Sex-stratified analyses confirmed LFCA predicted rerupture in both male patients (OR = 1.44, 95% CI: 1.19-1.75) and female patients (OR = 1.49, 95% CI: 1.16-1.92), and primary injury in male patients (OR = 1.30, 95% CI: 1.15-1.48). CONCLUSIONS: LFCA is an independent predictor of ACL injury, showing particular strength in identifying Asian patients at high risk for graft rerupture. LEVEL OF EVIDENCE: Level III, retrospective case-control study.

Humans↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

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