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PubMed2026

Combined Effects of Nicorandil and Enhanced External Counterpulsation on Coronary Microcirculation and Exercise Capacity in Patients With Coronary Slow Flow Phenomenon: A Randomized, Controlled, 3-Arm Trial.

PURPOSE: To evaluate the combined efficacy and safety of combined nicorandil and enhanced external counterpulsation (EECP) therapy compared with respective monotherapies in patients with coronary slow flow phenomenon (CSFP). METHODS: In this prospective, randomized, 3-arm clinical trial, 309 patients with angiographically defined CSFP based on corrected TIMI frame count were assigned (1:1:1) to the Nicorandil group (N group, n = 103), the EECP group (E group, n = 103), or the Combined therapy group (N+E group, n = 103). The trial was prospectively registered at ClinicalTrials.gov (NCT07534410). IMR and CFR were measured to characterize coronary microvascular physiological status and treatment response. The primary endpoint was corrected TFC at 6 months. Key secondary endpoints included invasive physiological indices (IMR and CFR), Seattle Angina Questionnaire scores, 6-minute walk test (6MWT) distance, peak oxygen uptake via cardiopulmonary exercise testing, and the 12-month rate of re-hospitalization due to recurrent angina. FINDINGS: At 6 months, the N+E group demonstrated superior improvement in coronary hemodynamics compared to the N and E monotherapy groups, with significantly lower TFC (30.4 ± 3.5 vs 38.2 ± 3.8 and 37.5 ± 4.0, respectively; P < 0.001) and IMR (21.2 ± 2.8 vs 28.4 ± 3.2 and 27.6 ± 3.5, respectively; P < 0.001). Clinical symptoms and functional capacity showed the most substantial gains in the N+E group, with significantly higher Seattle Angina Questionnaire angina frequency scores (87.5 ± 8.8) and 6MWT distances (506.8 ± 41.8 m) compared to monotherapy groups (all P < 0.001). Furthermore, peak oxygen uptake in the N+E group increased to 23.5 ± 2.6 mL/kg/min, significantly outperforming the N and E groups (P < 0.001). During the 12-month follow-up, the observed rate of re-hospitalization due to recurrent angina was lower in the N+E group (5.8%) than in the N group (17.5%, P = 0.017), although this clinical outcome should be interpreted cautiously because the trial was powered primarily for physiological endpoints. No significant differences were observed in the incidence of adverse reactions among the 3 groups (P = 0.954). IMPLICATIONS: For patients with CSFP, the combination of Nicorandil and EECP improved coronary microvascular function, anginal symptoms, and objective exercise tolerance more effectively than either active monotherapy. The lower observed rate of angina-related re-hospitalization suggests a potential clinical benefit, but this finding should be considered exploratory and requires confirmation in trials adequately powered for clinical outcomes.

Humans↗

PubMed2026

Effectiveness of a digi-physical tool and working method for paediatric obesity treatment in Abu Dhabi: a non-inferiority intervention study using an external historical comparator.

BACKGROUND: Effective paediatric obesity treatment requires high intensity, scalable interventions. A digi-physical tool for paediatric obesity treatment has shown positive results in Stockholm, Sweden. This study evaluates whether the same treatment method is effective in a different cultural setting. METHODS: This non-inferiority intervention study, using an external historical comparator, included 60 consecutively recruited children aged 6-15.9 years with obesity who initiated treatment at Sheikh Shakhbout Medical City in Abu Dhabi between June and December 2023. Patients were treated with Evira, a digi-physical tool and working method enabling high intensity individualized care, real-time monitoring, and interactive patient-clinician communication. The primary outcome was BMI z-score change at 26 weeks. Non-inferiority was assessed using a predefined margin of 0.10 BMI z-score, with outcomes compared to a prior published trial in Stockholm (n = 107). RESULTS: A total of 112 children were included in the analysis (Abu Dhabi cohort, n = 35; Stockholm cohort, n = 77). The adjusted mean change in BMI z-score was - 0.20 (95% CI: - 0.28, - 0.12) in the Abu Dhabi cohort and - 0.20 (- 0.26, - 0.14) in the Stockholm cohort (p = 0.88). Non-inferiority was confirmed, (predefined margin 0.10 was not exceeded). A clinically significant BMI z-score reduction (≥ 0.20 units) was achieved by 45.7% of participants in Abu Dhabi and 36.4% in Stockholm (p = 0.35). Non-retention rates at 26 weeks were 41.7% vs. 28.0%, respectively (p = 0.07). CONCLUSIONS: The findings provide promising evidence that treatment outcomes achieved with the digi-physical treatment tool were comparable in the Abu Dhabi and Stockholm cohorts, supporting its feasibility in a second cultural and healthcare setting.

Humans↗

PubMed2026

Advanced/Novel Stenting for Pediatric Dynamic Airway Collapse.

Pediatric dynamic airway collapse is a complex condition that can impact all levels of the pediatric airway. These conditions can pose life threatening risk to pediatric patients and carry lasting impacts. While traditionally, tracheostomy has been used to address all levels of dynamic collapse, recent advances have allowed for more individualized, anatomy-specific stenting and splinting strategies for treatment. This article covers pathophysiology and the latest evidence on strategies to address nasopharyngeal, oropharyngeal, proximal trachea, and tracheobronchial dynamic collapse.

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

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2025

Online Risk Behavior in Adolescents: A Systematic Review.

Identifying and categorizing online risk behaviors is crucial for assessing their impact on adolescents. Despite extensive research, previous studies have not provided a clear classification of these behaviors. This systematic review synthesizes the quantitative literature on adolescent online risk behaviors from the inception of research to September 2023, aiming to: (a) offer a comprehensive overview of the types of online risk behaviors and the specific actions encompassed within each category among adolescents; (b) summarize the adverse outcomes associated with these behaviors; and (c) discuss the implications and future research directions. Utilizing key terms, this study sourced studies from four electronic databases (Scopus, PubMed, Web of Science, and EMBASE), ultimately including 22 English-language quantitative studies. The review reveals that online risk behaviors are primarily categorized into content risk behaviors, contact risk behaviors, and conduct risk behaviors. Adolescents engaging in these behaviors are at an increased risk of experiencing physical health issues, mental health problems, externalizing behaviors, and even self-harm and suicidal thoughts or actions. Further research is needed to develop and validate an online risk behavior scale and conduct longitudinal and experimental studies to establish causal relationships and examine the long-term effects of these behaviors on adolescent well-being. The review concludes with implications for future research and potential prevention, intervention, and policy strategies to mitigate online risk behaviors in adolescents.

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

Robust optimisation for photon radiotherapy: A scoping review of models, paradigms, and reporting.

BACKGROUND AND PURPOSE: Robust optimisation offers an alternative to conventional margin-based photon radiotherapy planning by explicitly modelling uncertainty, but practice is variable and not standardised. MATERIALS AND METHODS: A scoping review was conducted to map robust optimisation for photon external beam radiotherapy. Electronic searches of Scopus, PubMed and Google Scholar (2000-2025, English language) identified planning studies that incorporated modelled uncertainties into the optimisation process and reported at least one robustness-related outcome. Data were charted on clinical context, uncertainty models, optimisation paradigms, robustness metrics and evidence for clinical implementation. RESULTS: Seventy-one studies were included. Most investigated prostate, breast or lung cancer and used intensity-modulated radiotherapy or volumetric-modulated arc therapy in commercial or research treatment planning systems. Scenario-based worst-case (minimax) optimisation was the dominant paradigm in clinically oriented work, while chance-constrained, conditional value at-risk, distributionally robust and adaptive formulations were confined to small methodological series. Uncertainty modelling focused mainly on rigid set-up error; fewer studies incorporated respiratory motion, inter-fraction anatomical change, dose-calculation uncertainty or biological variation. Robustness was evaluated with diverse scenario-based dose-volume metrics, probabilistic coverage measures, composite robustness indices and, less often, biological endpoints. Direct clinical implementation reports were scarce. CONCLUSION: Robust photon planning is technically feasible and generally maintains or improves target coverage and organ sparing compared with margin-based planning. However, heterogeneity in uncertainty models, optimisation configuration and robustness reporting limits comparison and synthesis. Pragmatic minimum standards are proposed to support future consensus and wider clinical adoption.

Humans↗

PubMed2026

Safe and Stable Germline Transmission of MSTN Mutations in Cattle.

With the global population expected to reach 10 billion by 2050, sustainable livestock production is critical. Gene editing of the myostatin (MSTN) gene represents a promising strategy to enhance muscle growth in cattle. In this study, MSTN-mutated founder (F0) cows were used to generate F1 offspring via ovum pick-up, in vitro fertilization, and embryo transfer. Four F1 calves were born, all confirmed to be heterozygous for the MSTN mutation. Long-term monitoring showed normal growth and no visible health abnormalities. Whole-genome sequencing identified SNPs, INDELs, and structural variants, most with minimal predicted functional effects. Proteomic profiling of Longissimus dorsi muscle quantified 2947 proteins, revealing only subtle expression differences between MSTN-mutated and wild-type cattle. These results demonstrate stable inheritance and confirm that MSTN editing does not disrupt genome integrity or protein expression. Overall, our findings support the safety and utility of MSTN gene editing to improve livestock productivity for future food security.

Animals↗

PubMed2026

Fructophilic lactic acid bacteria as a window into multi-scale convergent evolution.

Fructophilic lactic acid bacteria (FLAB) are a group of lactic acid bacteria with unique growth characteristics, that is, poor growth on glucose. Their growth is enhanced in the presence of fructose or external electron acceptors. These organisms inhabit fructose-rich environments such as flowers, fruits, and pollinating insects, particularly honey bees. Apilactobacillus spp. and Fructobacillus spp. are representatives of FLAB, although they belong to phylogenetically distant clades. These organisms commonly possess markedly small genomes with a low number of coding DNA sequences. Furthermore, their genomes are characterized by a markedly reduced number of genes involved in carbohydrate transport and metabolism. Genome reduction in FLAB reflects convergent adaptation to fructose-rich environments rather than general genome streamlining. The two distinct FLAB genera, Fructobacillus and Apilactobacillus, independently lost more than 100 genes in statistically similar orders. In contrast, genes involved in carbohydrate and amino acid metabolism exhibited reversed orders of loss between the two genera. Furthermore, FLAB genomes lack an intact bifunctional alcohol/aldehyde dehydrogenase gene (adhE), which causes their poor growth on glucose. A comparative genomic study suggested the evolutionary process underlying adhE gene decay during adaptation to the fructose-rich environments, including pollinating insects. In conclusion, FLAB represent a unique example of habitat-driven convergent reductive evolution that can be investigated across multiple biological scales - from individual genes to whole genomes - in the diverse LAB group with a wide range of habitats, and partially share the fructophilic evolution with eukaryotic yeasts found in fructose-rich habitats.

Fructose↗

PubMed2026

Cationic porphyrin covalent organic framework reinforced hydroxypropyl methylcellulose films for photodynamic-photothermal sterilization and food preservation.

Microbial contamination in food necessitates effective antimicrobial packaging. While cellulose-based packaging materials suffer from limited antimicrobial efficacy, lack of active functionality, and susceptibility to inducing microbial resistance. To address these challenges, this study synthesized a cationic porphyrin-based covalent organic framework (Por-ICOF) as a multimodal photosensitizer. Por-ICOF was uniformly dispersed via non-covalent interaction within hydroxypropyl methylcellulose (HPMC), creating an HPMC/Por-ICOF composite film. This integration enhanced mechanical strength (increased by 26%), hydrophobicity (WCA 71°), and gas barrier properties (OP reduced by 42%, WVP reduced by 36%). Under visible light, the HPMC/Por ICOF film superior absorption generated reactive oxygen species (ROS) and photothermal effects, inactivating 99.2% of Escherichia coli and 99.95% of Staphylococcus aureus within 20 min. The composite film exhibited excellent biocompatibility and effectively extended the shelf life of strawberries. This cationic modification strategy for cellulose-based films offers a novel avenue for the design of high-performance antimicrobial food packaging materials.

Food Preservation↗

PubMed2026

Ventriculostomy-Related Infections by Country-Income Level: A Systematic Review and Bayesian Hierarchical Meta-analysis.

Our objective was to perform a systematic review and meta-analysis of published literature on ventriculostomy-related infection (VRI) and evaluate temporal and global trends. We conducted a systematic review and Bayesian hierarchical random-effects meta-analysis of VRI rates in adults, stratified by country-income level (high-income countries [HIC]; low- or middle-income countries [LMIC]), study design, sample size, enrollment period, VRI intervention, and VRI definition. We identified 159 articles published between 1989 and 2025 that included 523,704 patients with 7293 VRIs. The pooled VRI rate was 8.64% [95% CI: 7.44-9.97], with moderate heterogeneity and good model fit. The leave-one-out sensitivity analysis showed a mean absolute change of 0.06% and a maximum change of 0.2%, indicating robust analysis. Five of the 33 represented countries had VRI rates below the global pooled rate of 8.64%. Four were HICs: Singapore (VRI rate 3.3% [0.8-7]), the United States (VRI rate 4.6% [3.4-5.9]), Germany (VRI rate 6.1% [1.1-18.9]), Norway (8.3% [0.3-68.4]), with 1 LMIC: China (8.5% [5.4-12.4]). VRI was significantly higher in studies using definitions beyond CSF culture alone for VRI (+3.16% [0.11- 6.52]) and in those from Europe (+7.29% [4.62-10.10]) and the Western Pacific (+4.09% [1.55-6.98]). No other subgroup demonstrated significant differences. This Bayesian meta-analysis provides global estimates and factors associated with VRI. Standardization of VRI definitions is critical for future benchmarking of VRI rates.

Humans↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

Access to maternity services for women asylum seekers and refugees: A transnational document analysis of international, European regional, and United Kingdom governance.

Women asylum seekers and refugees face persistent barriers to maternity care (antenatal, intrapartum and postnatal care) across high-income countries, yet the upstream governance shaping access remains under-examined. Although legally distinct, both groups share protection-seeking experiences and are addressed jointly in governance documents. This study examined and synthesised how international (macro), European regional (meso), and United Kingdom (UK, micro) governance documents frame and operationalise maternity service access. Sixty-four documents were analysed using the READ framework. Inductive analysis of macro and meso documents identified six access dimensions: universal coverage; cultural and linguistic adaptation; rights-based approaches; multi-agency collaboration; data, monitoring and accountability; and quality of care. These dimensions structured assessment of UK governance, with jurisdictions rated strong, moderate or weak. Alignment was fragmented: Wales, Scotland and Northern Ireland exempted asylum seekers from charging, whereas England retained charging provisions. Multi-agency collaboration was consistently articulated, yet none of the 35 UK government documents focused on maternity access for this population, and none required outcome monitoring disaggregated by asylum or refugee status. UK governance appears coordinated in form but fragmented in substance. UK-wide minimum standards and routine recording of these data, with safeguards against immigration-related use, could strengthen coherence and accountability and improve visibility of inequities.

Refugees↗

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

Hydrodissection-assisted laparoscopic orchiopexy utilizing needle grasper for palpable undescended testes: Clinical efficacy analysis.

OBJECTIVE: Laparoscopic orchiopexy has emerged as a viable alternative for the treatment of palpable undescended testis (UDT). This study aims to evaluate the feasibility and efficacy of needle-grasper hydrodissection-assisted laparoscopic orchiopexy (NHLO) in comparison to conventional laparoscopic orchiopexy (CLO) for palpable UDT. METHODS: A cohort of 96 patients diagnosed with palpable UDT, admitted between January 2020 and April 2024, was included in this study. Among these, 54 patients underwent NHLO, while 42 patients were treated with CLO. In the NHLO procedure, normal saline was injected into the retroperitoneal space to create a hydrodissection barrier, facilitating the separation and protection of the vas deferens and spermatic cord. The vas deferens and spermatic cord were meticulously dissected following the principles of integrity and minimal tissue trauma. Outcome measures included final testicular position, testicular volume growth, testicular atrophy, success rate, and postoperative complications. RESULTS: No significant differences were observed between the NHLO and CLO groups in terms of age, laterality, operative time (NHLO: 38-46 min; CLO: 39-48 min), or complication rates (NHLO: 1.9 %; CLO: 0.0 %). At follow-up, all patients in both groups exhibited palpable testes in satisfactory scrotal positions. Notably, no visible abdominal scarring was observed in the NHLO group, whereas there were two noticeable scars on the abdomen in CLO. CONCLUSION: Needle-grasper hydrodissection-assisted laparoscopic orchiopexy is a safe, effective, and minimally invasive technique that provides optimal protection of the vas deferens and spermatic cord while achieving excellent cosmetic outcomes.

Humans↗

PubMed2026

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

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

Humans↗