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PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-α-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗

PubMed2026

Family-Wise Error Rate Control in Clinical Trials With Overlapping Populations.

We consider clinical trials with multiple, overlapping patient populations that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probability to reject at least one true null hypothesis. If the joint distribution of the test statistics is known, the FWER level can be exhausted by determining critical values or adjusted-levels. The adjustment is typically done under the common ANOVA assumptions. However, the performed tests are then only valid under the rather strong assumption of homogeneous null effects, that is, when the null hypothesis applies to all subpopulations and their intersections. We show that under cancelling null effects, when heterogeneous effects cancel out in some or all subpopulations, this procedure does not provide FWER control. We also suggest different alternatives and compare them in terms of FWER control and their power.

Humans↗

PubMed2026

An Assessment of Reliability Estimation Methods for Binomial Health Care Quality Measures.

We evaluated the performance of commonly used methods for estimating the reliability of binomial health care quality measures using simulated datasets spanning a range of performance score means and variances, numbers of entities, and patient sample sizes. For each simulation, reliability was estimated for all selected methods and compared with the known true reliability derived from the simulation parameters, with methods assessed on their accuracy and precision. Logistic regression with reliability estimated on the outcome scale demonstrated the highest accuracy and precision among all methods evaluated. The widely used Adams beta-binomial method performed poorly, although a modification recommended by Nieser and Harris substantially improved its performance. These approaches are applicable only to binomial measures. Among methods that can be applied to both binomial and continuous measures, permutation resampling of the Spearman rank correlation coefficient was the most accurate and precise, outperforming other commonly used approaches. Overall, for binomial quality measures, logistic regression on the outcome scale is the preferred method for reliability estimation, followed closely by the modified beta-binomial approach, while for non-binomial measures, permutation-based Spearman rank correlation appears to be the most suitable method.

Reproducibility of Results↗

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

A systematic review and meta-analysis of OCT-based ophthalmic changes in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease marked by motor decline and respiratory failure. Optical coherence tomography (OCT), a non-invasive imaging technique, has been explored for detecting retinal structural changes that may reflect neurodegeneration in ALS. While some studies report thinning of retinal layers, findings remain inconsistent. Therefore, a meta-analysis is needed to clarify the extent of retinal involvement and the potential of OCT as a biomarker in ALS. METHODS: A systematic literature search was conducted across PubMed, EMBASE, and Cochrane databases for studies published between 2010 and May 2025. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and publication bias was evaluated through funnel plot asymmetry and Egger's test. Pooled effect sizes were calculated using random-effects models to account for between-study heterogeneity, and differences in OCT parameters between ALS patients and healthy controls were expressed as standardized mean differences (SMD) with 95% confidence intervals (CI). Statistical heterogeneity was quantified using the I2 statistic. RESULTS: A total of 17 studies were included in the present meta-analysis. The primary unadjusted global model demonstrated significant reduction of retinal nerve fibre layer (RNFL) thickness in ALS patients compared to controls (unadjusted SMD = -0.295, 95% CI: -0.522, -0.068). Upon applying a Design Effect variance inflation model to address fellow-eye non-independence, the pooled estimate remained robustly significant across a conservative range of intraclass correlations (SMD ranged from -0.256 to -0.249). Subgroup analyses revealed that RNFL thinning was particularly pronounced in spinal-onset ALS (SMD = -0.54, 95% CI: (-0.98, -0.10). When studies were stratified by the region of conduct, RNFL and macular thinning reached statistical significance only within the non-Asian subgroup, though the formal test for subgroup differences was not significant. CONCLUSION: This meta-analysis demonstrates significant bilateral RNFL thinning in ALS, with relative preservation of the Inner Nuclear Layer and Ganglion Cell Layer - Inner Plexiform Layer, supporting retinal neurodegeneration as a feature of this multisystem disorder. SYSTEMATIC REVIEW REGISTRATION: PROSPERO identifier CRD420251076035.

Humans↗

PubMed2026

Three-Dimensional Fracture Mapping of the Terrible Triad of the Elbow: Morphological Characteristics and Clinical Implications.

BACKGROUND: The morphology of fractures in the terrible triad of the elbow (TTE) is complex, and precise management relies on a profound understanding of this morphology. This study aims to systematically analyze, for the first time, the distribution and morphological characteristics of TTE fracture lines using three-dimensional (3D) imaging technology. METHODS: Clinical data and thin-slice CT scans of 112 patients with TTE from January 2021 to December 2024 were retrospectively included. 3D fracture models were reconstructed using Mimics software. Virtual reduction and standardized alignment were performed using 3-matic software. Fracture lines were mapped onto standard ulnar and radial templates, and 3D fracture heat maps were generated using the E-3D software to demonstrate the high-frequency distribution zones of the fracture lines visually. Statistical analysis was performed using SPSS software (version 21.0, IBM Corp., Armonk, NY, USA). Continuous variables were compared using one-way analysis of variance (ANOVA), and categorical variables were compared using the chi-square test (χ2 test). A two-tailed p < 0.05 was considered statistically significant. RESULTS: The study revealed distinct patterns in the distribution of TTE fracture lines. In the coronoid process, the fracture "hot zone" presented as an annular high-density band extending from the lateral middle aspect to the tip. In the radial head, an oblique high-density band was observed in the anterolateral quadrant of the articular surface. The radial neck exhibited a circumferential high-density zone, which was most prominent in the anterolateral aspect. Statistical analysis indicated a significant correlation between age and fracture complexity; the proportion of Regan-Morrey type III coronoid fractures and Mason type III radial head fractures was significantly higher in elderly patients (> 60 years) (p < 0.05), suggesting that advanced age is a significant risk factor for complex fractures. CONCLUSION: This study is the first to visually reveal the Collaborative Distribution Patterns of TTE fracture lines using 3D fracture mapping technology. This model provides morphological evidence for understanding the injury mechanism of TTE and offers an anatomical framework that may assist surgeons in individualizing surgical approaches and fixation strategies.

Humans↗

PubMed2026

Evaluating a coaching intervention for Dementia Care Practice Recommendations in care communities: a cluster randomized controlled trial.

BACKGROUND AND OBJECTIVES: Within care communities, including nursing home and assisted living settings, person-centered dementia care, outlined by the 2018 Alzheimer's Association Dementia Care Practice Recommendations (DCPR), is foundational to quality care and improving staff outcomes. This study evaluates the effectiveness of a 6-month Care Community Coaching Program in enhancing person-centered dementia care and staff outcomes in alignment with the DCPR. RESEARCH DESIGN AND METHODS: A cluster randomized controlled trial was conducted with 77 care communities and 434 staff members-227 from 38 intervention communities and 207 from 39 control communities. Outcomes included employee satisfaction (areas: job satisfaction, team building and communication, scheduling and staffing, training, and management and leadership), person-centered care practices (areas: workplace practices, individualized care and services, caregiver-resident relationships), and dementia care confidence, measured pre- and post-intervention and at 3-month follow-up. A generalized Estimating Equations model was used to estimate intervention effects. RESULTS: Care communities assigned to the coaching intervention showed statistically significant improvements in employee satisfaction and staff perceptions of workplace practices and individualized care. No statistically significant effects on staff perceptions of caregiver-resident relationships or on dementia care confidence were noted. DISCUSSION AND IMPLICATIONS: Findings provide direction for future research and intervention development, including examining coaching's impact on resident quality outcomes, and incorporating skills training into future models. Collectively, findings provide evidence of the effectiveness of a Care Community Coaching Program in improving staff outcomes and person-centered practices, offering a practical path towards improving the lived experience of residents and staff in care communities.

Humans↗

PubMed2026

Positive conversion of latent tuberculosis screening in patients with inflammatory bowel disease on antitumor necrosis factor alpha drugs: a systematic review and meta-analysis.

Inflammatory bowel disease (IBD) patients undergoing antitumor necrosis factor-alpha (anti-TNF) therapy are at increased risk of developing tuberculosis (TB), making screening before anti-TNF initiation mandatory. Repeated screening during treatment is not yet recommended because of a lack of studies to support this practice. We aimed to determine the proportion of patients who develop latent TB during anti-TNF therapy. We systematically searched studies from MEDLINE, Embase, and Lilacs, and performed a single-arm meta-analysis investigating the positive conversion rate in IBD patients under anti-TNF therapy with previous negative TB screening. We calculated the combined proportion with 95% confidence interval, using the random-effects model. A P value less than 0.05 was considered statistically significant for subgroup differences. We included 13 studies from nine countries with 1153 patients. The overall positive conversion rate was 9.20%. Portugal had 18.01% of positive conversion, Spain 4.51%, and the USA 1.11%. Tests for subgroup differences were statistically significant for subgroup analysis by country and consistency of test used (performig same test as baseline). Subgroup analyses by continent, study design, or specific test (tuberculin skin test or interferon-gamma release assay) showed no statistical difference. Meta-regression analysis showed a significant positive association between positive conversion and TB incidence. In conclusion, IBD patients on anti-TNF therapy can have a positive conversion rate of 9.20%. Higher conversion rates were seen in European and Asian studies compared with those in the Americas (particularly the USA). TB prevention strategies should, therefore, be individualized and based on geographic location and risk factors.

Humans↗

PubMed2026

The Statistical Fragility of Saline Nasal Irrigation for Rhinosinusitis: A Systematic Review.

OBJECTIVE: To assess the statistical fragility of randomized controlled trials (RCTs) evaluating high-volume saline nasal irrigation (SNI) for rhinosinusitis using fragility analysis. DATA SOURCES: PubMed, MEDLINE, and Embase were searched for RCTs published between May 1976 and January 2026. REVIEW METHODS: This study was reported as per PRISMA guidelines. RCTs that compared high-volume SNI to non-irrigation standard care for acute, recurrent, or chronic rhinosinusitis, and reported ≥ 1 dichotomous outcome, were included. Fragility index (FI), the minimum number of event reversals needed to alter statistical significance, and fragility quotient (FQ), FI normalized to sample size, were calculated for statistically significant dichotomous outcomes. Reverse FI (rFI) and reverse FQ (rFQ) were calculated for non-significant outcomes. RESULTS: Eight RCTs were included, yielding 38 dichotomous outcomes. Eight outcomes (21.1%) were statistically significant. The overall combined median FI was 5 (FQ 0.062), with similar FI values between significant and non-significant outcomes. In over one-fifth of outcomes, loss to follow-up exceeded FI. Analysis of principal dichotomous outcomes from studies demonstrated a median FI of 6 (FQ 0.092), with five of eight (62.5%) outcomes non-significant. CONCLUSION: RCTs evaluating SNI for rhinosinusitis exhibit moderate-to-high statistical fragility, with small outcome changes capable of reversing study conclusions. Because fragility analysis was limited to dichotomous outcomes while many primary endpoints were continuous, our findings should be interpreted as complementary rather than comprehensive appraisals of RCTs. Future RCTs with larger sample sizes, reduced bias, and pre-specified fragility considerations are needed to better define the clinical role of SNI.

Rhinosinusitis↗

PubMed2026

Effects of short-bout accumulated exercise on postprandial metabolism in adults: A systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate the acute effects of short-bout accumulated exercise (SBAE) interrupting prolonged sedentary behaviour on postprandial glucose, insulin, and triglycerides in adults. METHODS: Systematic searches in PubMed, Web of Science, Embase, Cochrane Library, CINAHL, SPORTDiscus, and CNKI (inception to 10 December 2025) identified randomised crossover trials comparing SBAE (≤10 min/bout, inter-bout interval ≥30 min or adequate recovery) with continuous sedentary behaviour. Outcomes included postprandial glucose, insulin, and triglyceride AUCs. Standardised mean differences (SMD) were pooled using random-effects models. RESULTS: Thirty-one publications reporting data from 29 independent cohorts involving 573 unique participants (mean age 47.8 ± 20.3 years; 47.5% female; mean BMI 29.0 ± 5.1 kg/m²) were included. Compared with continuous sedentary behaviour, SBAE significantly reduced glucose AUC (SMD = -0.53, 95% CI: -0.71 to -0.34, P < 0.001) and insulin AUC (SMD = -0.58, 95% CI: -0.85 to -0.31, P < 0.001), but not triglyceride AUC (SMD = -0.17, 95% CI: -0.48 to 0.15, P = 0.306).Exploratory subgroup analyses showed statistically significant reductions in glucose and insulin for walking and for inter-bout intervals < 60 min, but not for standing alone or intervals ≥ 60 min. A statistically significant insulin-lowering effect was observed in obese individuals. CONCLUSION: SBAE acutely improves postprandial glucose and insulin control. Exploratory subgroup analyses showed statistically significant effects for walking and for inter-bout intervals < 60 min, but these comparisons are observational and no formal interaction test was conducted. These findings provide preliminary evidence for acute SBAE in sedentary populations, though long-term health effects and real-world generalisability require further investigation.

Adult↗

PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗

PubMed2026

Relationships between cannabis and cocaine use in a randomized trial of combined buprenorphine and naltrexone for DSM-IV cocaine dependence.

BACKGROUND: Cannabis is the most commonly used drug in the United States, and among people who use cannabis, polysubstance use is common and understudied. We aimed to examine the association of tetrahydrocannabinol (THC) positive urine drug screen (+UDS) with the odds of submitting a cocaine + UDS during cocaine use disorder treatment. METHODS: We conducted a secondary data analysis of a previously reported double-blind, placebo-controlled clinical trial, CTN0048. Participants meeting criteria for opioid abuse/dependence were assigned to receive extended-release naltrexone and one of three conditions of buprenorphine (placebo, 4 mg/day, 16 mg/day) for 8 weeks. Generalized estimating equations (GEE) were used to analyze urine samples (Liu et al., 2018) collected over time, examining the association between THC + UDS and cocaine + UDS during treatment. RESULTS: Participants (n = 301) averaged 46 (SD = 8.64) years of age, were majority male (78.41 %), non-Hispanic (89.70 %), and African American (66.45 %). GEE results indicated that patients who submitted THC + UDS had significantly higher odds of submitting cocaine + UDS compared to participants who submitted THC-negative UDS across the 25 time points examined (OR = 1.47, 95 % CI = 1.21-1.79, p = 0.00). Time (OR = 0.9998, 95 % CI: 0.9997, 0.9999, p = 0.018) and the covariate of sex assigned at birth (OR = 1.77, 95 % CI = 1.13-2.77, p = 0.013) were also significant in the model, indicating very small decreases in the odds of submitting a cocaine + UDS over time for all patients and 77 % higher odds of submitting cocaine + UDS for females. CONCLUSION: THC + UDS was associated with increased odds of submitting a cocaine + UDS during treatment. Further investigation is needed to discern whether decreasing THC use will result in reduced cocaine use; however, these results suggest that it may be beneficial to counsel patients on cannabis use cessation both before and during treatment for cocaine use, as it is related to cocaine use treatment outcomes. TRIAL REGISTRATION: Secondary data analysis of ClinicalTrials.gov, TRN: NCT01402492 ("A randomized study to test the safety and effectiveness of buprenorphine in the presence of naltrexone for the treatment of cocaine dependence"; National Drug Abuse Treatment Clinical Trials Network (CTN) clinical trial: CTN0048), Registration date: 27 July 2011.

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

Unconfined compressive strength prediction for the ordinary Portland cement-steel slag-silica fume ternary system based on response surface methodology.

This research was undertaken to address environmental concerns associated with industrial solid waste and to reduce cement consumption in geotechnical engineering. It specifically investigates the feasibility of using steel slag (SS) and silica fume (SF) as partial substitutes for ordinary Portland cement (OPC) in soil stabilization. The effects of SS, SF, OPC, and initial moisture content on the unconfined compressive strength (UCS) of stabilized soil were investigated through single-factor experiments and response surface methodology (RSM). The results show that SS and SF can synergistically enhance the strength of stabilized soil, although their interaction effect was not statistically significant within the investigated ranges. Compared with soil stabilized solely with OPC, the addition of 18 % SS and 10 % SF reduced OPC consumption by 3 % without compromising strength. Microstructural and compositional analyses further revealed that SS mainly supplied calcium- and silica-bearing components, while SF provided highly reactive silica and micro-filling effects, jointly promoting hydration reactions and improving the compactness of the stabilized soil matrix. As a result, more hydration products were formed in the OPC/SS/SF-stabilized soil than in the OPC-stabilized soil, which contributed to pore filling and strength enhancement. This study provides useful guidance for the sustainable utilization of industrial solid waste and the low-carbon development of soil stabilization materials.

Construction Materials↗

PubMed2026

Cancer statistics for Asian American, Native Hawaiian, and Pacific Islander people, 2026.

BACKGROUND: Cancer statistics for Asian American and Native Hawaiian and Pacific Islander (NHPI) people are usually aggregated, masking substantial variation within this heterogeneous population. Herein, the American Cancer Society reports cancer incidence and survival for 8 Asian American and 3 NHPI ethnic groups. METHODS: The authors used population-based cancer registry data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program, for Asian American and NHPI ethnic groups from 2000 through 2022. RESULTS: During 2018-2022, overall cancer incidence ranged from 218.3 per 100,000 Kampuchean people to 474.5 per 100,000 Native Hawaiian people, which was 1.5 times higher than the rate for the aggregated Asian American and NHPI population (307.3 per 100,000). High incidence among Native Hawaiian people is largely driven by the highest rates of female breast, colorectal, and prostate cancers, whereas infection-related cancers were highest among Asian American ethnic groups. For example, liver and stomach cancer incidence is highest among Vietnamese (22.2 per 100,000) and Korean people (17.8 per 100,000), respectively, both of which were nearly twice that in Native Hawaiian people (12.9 and 9.6 per 100,000, respectively). Native Hawaiian and Samoan women are twice and 3 times as likely, respectively, to be diagnosed with uterine corpus cancer as aggregated Asian American and NHPI women or White women. Five-year relative survival ranges from 42% in Laotians to 74% in Asian Indians/Pakistanis, with largest differences for colorectal (43% in Laotians to 72% in Asian Indians/Pakistanis) and prostate (63% in Kampucheans to 97% in Japanese) cancers. CONCLUSIONS: Wide variation in cancer risk within the Asian American and NHPI population highlights the critical need for disaggregated data to effectively target cancer prevention and control interventions.

Adolescent↗

PubMed2026

Protective association of the ELMO1 rs741301 variant against diabetes mellitus and diabetic nephropathy: a systematic meta-analysis of case-control studies.

CONTEXT: Diabetes mellitus (DM), an endocrine disorder, is characterised by persistently elevated blood glucose levels due to inadequate insulin production. Diabetic nephropathy (DN), is a critical complication associated with DM, often leading to end-stage renal failure and increased mortality. OBJECTIVE: This meta-analysis aimed to evaluate the association between the ELMO1 rs741301 polymorphism and susceptibility to DN among individuals with diabetes. METHOD: A systematic literature search was conducted for studies published between 2014 and 2024 using Embase, Google Scholar, and PubMed. Eligible case-control studies investigating the association between ELMO1 rs741301 and DN among individuals with DM were selected according to predefined inclusion criteria. Nine case-control studies comprising 880 individuals with DM and 1008 individuals with DN were included in the meta-analysis. RESULTS: The pooled analysis demonstrated a significant protective association between the ELMO1 rs741301 polymorphism and DN under the allelic model (OR = 0.77, 95% CI: 0.67-0.88), recessive model (OR = 0.74, 95% CI: 0.61-0.90), and dominant model (OR = 0.68, 95% CI: 0.53-0.88). In contrast, no statistically significant association was observed under the over-dominant model. CONCLUSION: The findings suggest that the ELMO1 rs741301 polymorphism may be associated with a reduced susceptibility to DN among individuals with DM. These findings provide evidence for a potential genetic contribution of ELMO1 to DN susceptibility and may help improve understanding of the genetic factors underlying diabetic complications. Further well-designed studies in diverse populations are warranted to validate this association.

Humans↗