Weill Cornell Medical College in Qatar

Background Surgical options for neck of femur fracture (NFF) include open reduction and internal fixation (ORIF) or arthroplasty (hemiarthroplasty, HA or total hip arthroplasty, THA). The choice is based on several variables with each modality having distinct advantages and disadvantages. Previous studies have shown that ORIF may be a protective factor in the development of postoperative deep vein thrombosis (DVT) compared to arthroplasty. However, there is significant variation in methodology, results and limited follow up. Objective This study aimed to investigate whether ORIF is a protective factor for postoperative DVT compared to arthroplasty with follow up of patients for up to 3 months. Methods Retrospective chart review study of patients who had NFF, were 60 years or older and underwent surgical intervention between January 2020 and May 2023. Clinical and demographic characteristics were recorded and analyzed. DVT rates were compared between ORIF and arthroplasty. This was repeated for ORIF vs HA vs THA. Results Among 161 NFF patients, 1 (3.7%) of ORIF patients developed DVT versus 2 (1.44%) of arthroplasty. This difference was not significant, no patients in the THA (n=5) group developed DVT. Conclusion The number of postop DVT observed was likely underestimated as only symptomatic DVT was assessed, population size was less than expected and the vast majority of patients underwent arthroplasty. Larger, multicenter studies with 3-month follow-up are required to determine whether ORIF may be a protective factor in developing DVT.

Background:Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, are known to influence infant sleep trajectories. While previous research has examined their individual and combined associations, the predictive utility of these MMH symptoms for the early identification of infant sleep problems through machine learning (ML) remains understudied. Objective:This study aimed to examine whether postpartum MMH measures can predict infant sleep outcomes during the first year of life. The analysis focused on 2 clinically relevant sleep indicators: (1) nocturnal sleep duration and (2) night awakening frequency. Methods:A total of 409 mother-infant dyads were included in the study. Predictor variables comprised postpartum MMH symptoms assessed between 3 and 12 months postpartum, along with sociodemographic characteristics of mothers and infants. MMH symptoms were measured using 3 validated instruments: the Edinburgh Postnatal Depression Scale, the Hospital Anxiety and Depression Scale, and the City Birth Trauma Scale. Infant sleep outcomes were assessed using the Brief Infant Sleep Questionnaire. Six supervised ML algorithms were evaluated: logistic regression, random forest, support vector classifier, extreme gradient boosting, Light Gradient Boosting Machine, and multilayer perceptron. Post hoc feature importance analyses were conducted to identify the most influential predictors associated with each infant sleep outcome. Results:All models demonstrated high predictive performance. The best model achieved a precision-recall area under the curve of 0.92, F1-score of 0.84, and accuracy of 0.88 for predicting short nocturnal sleep duration. For frequent night awakenings, the top precision-recall area under the curve was 0.91, with an F1-score of 0.78 and accuracy of 0.85. Key predictors included maternal age and total scores from the Edinburgh Postnatal Depression Scale, Hospital Anxiety and Depression-Anxiety subscale, and City Birth Trauma Scale, with individual symptom items offering additional discriminative value. Conclusions:ML models can accurately predict which infants are at risk for suboptimal sleep based on MMH measures, enabling personalized, responsive, and developmentally informed postpartum care that promotes long-term maternal and infant well-being.

Abstract BackgroundAdult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is essential for maximizing survival prospects. Molecular classification, particularly the detection of isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions, has become crucial for accurate diagnosis and prognosis. Artificial intelligence (AI) has emerged as a promising adjunct in enhancing diagnostic accuracy using histopathological images. Existing reviews mostly focused on radiology rather than histopathology, and no comprehensive systematic review has specifically evaluated AI performance exclusively from histopathological images for detecting these two molecular markers. ObjectiveThis study aims to systematically evaluate the performance of AI models in detecting and classifying IDH mutation status and 1p/19q gene codeletion in adult-type gliomas using histopathological images. MethodsA systematic review was conducted in accordance with PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses–Extension for Diagnostic Test Accuracy) guidelines. Seven databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) were searched for studies published between 2015 and 2025. Eligible studies used AI models on histopathological images for molecular classification of adult-type gliomas and reported performance metrics. Study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool were conducted independently by two reviewers. Extracted data were synthesized narratively. ResultsA total of 2453 reports were identified, with 22 studies meeting the inclusion criteria. The pooled average accuracy, sensitivity, specificity, and area under the curve (AUC) across studies were 85.46%, 84.55%, 86.03%, and 86.53%, respectively. Hybrid models demonstrated the highest diagnostic performance (accuracy 92.80% and sensitivity 89.62%). In general, AI models that used multimodal data outperformed those that used unimodal data in terms of sensitivity (90.15% vs 84.31%) and AUC (88.93% vs 86.29%). Furthermore, models had a better overall performance in identifying IDH mutations than 1p/19q codeletions, with higher accuracy (86.13% vs 81.63%), specificity (86.61% vs 78.11%), and AUC (86.74% vs 85.15%). Unexpectedly, AI models designed for binary classification exhibited lower performance than those for multiclass classification in terms of both accuracy (91.98% vs 84.02%) and sensitivity (93.41% vs 80.18%). However, these differences should be interpreted as descriptive trends rather than statistically validated superiority, as formal between-group comparisons were not feasible. ConclusionsAI models show strong potential as complementary tools for the molecular classification of adult-type gliomas using histopathology images, particularly for IDH mutation detection. However, these findings are constrained by the limited number of studies, the focus on adult-type gliomas, lack of meta-analysis, and restriction to English-language publications. While AI offers valuable diagnostic support, it must be integrated with expert clinical judgment. Future research should prioritize larger, more diverse datasets and multimodal AI frameworks and extend to other brain tumor types for broader applicability.

Multiparametric ultrasound (MPUS) integrates B-mode, Doppler techniques and microvascular imaging, contrast-enhanced ultrasound (CEUS) and elastography, enhancing diagnostic precision across a wide spectrum of scrotal diseases. Developed under the auspices of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB), these guidelines provide evidence-based recommendations for the clinical use of MPUS in scrotal imaging. Based on the Oxford Centre for Evidence-Based Medicine framework, this document outlines the diagnostic value of MPUS in acute scrotal pain, trauma, infertility, focal and extratesticular lesions, cryptorchidism, and testicular incidentalomas. The recommendations highlight CEUS as the reference method for vascular assessment and elastography as a complementary tool for tissue characterization. These guidelines aim to standardize MPUS practice and promote its integration into routine scrotal imaging.