The university began with only 23 students and 3 teachers. It was the first Sudanese women's college. The current president is Dr. Gasim Badri, Yusuf Badri's son.
Postoperative adverse events (AEs) significantly impact patient outcomes and healthcare resources. The Clavien-Dindo Classification (CDC) is widely used to grade surgical complications, but manual grading is labor-intensive and subject to inter-observer variability. Automated algorithms, including rule-based, machine learning (ML), and large language model (LLM)-based natural language processing (NLP) tools, offer scalable solutions for consistent complication grading. A systematic review was conducted following PRISMA 2020 guidelines. Databases searched included PubMed, Embase, Scopus, and Cochrane Library. Studies reporting automated grading of surgery-related AEs using the CDC as a reference, with human validation, were included. Data extraction covered algorithm type, sample size, surgical population, comparator, data source, performance metrics, and outcomes. Three studies met the inclusion criteria, encompassing a total of 1,661 surgical cases. Automated algorithms for Clavien-Dindo Classification (CDC) grading including rule-based systems, machine-learning (ML) models, and large language model (LLM)/natural language processing (NLP) approaches demonstrate high agreement with expert reviewers, with rule-based algorithms achieving Cohen's κ up to 0.89, ML prediction models reporting discrimination up to an AUC of 0.863 for severe (CDC ≥ III) complications, and LLM/NLP approaches reaching accuracy of approximately 97% and Cohen's κ up to 0.92. Together, these methods show potential for scalable and, in some settings, near-real-time postoperative complication monitoring. These tools may support clinical decision-making, research, and quality improvement with promising but preliminary applicability across surgical domains. However, conclusions are limited by the small number of available studies and heterogeneity in surgical settings.
Melkersson–Rosenthal Syndrome (MRS) is a rare idiopathic neuro-mucocutaneous disorder characterized by the classic triad of recurrent facial palsy, orofacial edema, and fissured tongue. The exact etiology remains uncertain, with potential genetic, inflammatory, and infectious components contributing to its development. This case series aims to analyze the clinical presentation, treatment response, and possible genetic factors in MRS. We report three affected siblings of Sudanese African ethnicity diagnosed with MRS: Case 1 (male, current age 32 years, presented at age 20 years), Case 2 (female, current age 31 years, presented at age 22 years), and Case 3 (female, current age 27 years, presented at age 23 years), highlighting varying treatment responses. While Case 1 showed rapid resolution with a short corticosteroid course (prednisone 60 mg/day × 5 days) and complete recovery, Cases 2 and 3 required prolonged corticosteroid therapy (> 8 weeks) with incomplete recovery and side effects (steroid-induced acne, fatigue). Family pedigree analysis suggests possible genetic predisposition, though no genetic testing was performed. This case series highlights the heterogeneity of MRS in terms of clinical presentation and steroid response. The differences observed suggest a potential genetic component that warrants further investigation. Additionally, the observed gender-based variations in treatment response suggest that hormonal or immunological factors may affect disease progression and therapeutic outcomes.
Malaria remains a major public health challenge in Sudan, primarily driven by Plasmodium falciparum. Timely and effective case management is critical, necessitating high adherence to the newly issued Sudan Malaria Case Management Protocol 2023. This study aimed to assess the knowledge, attitudes, and current practices of Sudanese medical professionals regarding this 2023 protocol. This was a cross-sectional analytical study conducted in 2024–2025. A convenience sample of 371 currently practicing Sudanese medical professionals was recruited and surveyed. Data were analyzed to identify levels of awareness and adherence, as well as the major barriers to protocol implementation. High awareness was reported, with 67.9
Background/Objectives: Distinguishing idiopathic pulmonary fibrosis (IPF) from other interstitial lung diseases is prognostically and therapeutically important. We developed Fibresolve, a machine learning system that analyzes chest computed tomography (CT) to support IPF diagnosis. Fibresolve version 1 (v1) was validated in the PUFAIR study, met its co-primary endpoints, and received U.S. FDA authorization. Fibresolve version 2 (v2) was subsequently developed under the FDA’s Predetermined Change Control Plan (PCCP) framework and, here, is evaluated against the authorized v1 model. Methods: v2 augments the original CT-only v1 classifier by incorporating age, sex, and forced vital capacity into an ensemble, while retaining the v1 CT-only model as a fallback when these variables are unavailable. Both models were evaluated in the same 300-patient PUFAIR cohort (83 IPF, 217 non-IPF) from two U.S. centers. Reference-standard diagnoses were established by institutional multidisciplinary discussion and supported by surgical pathology in 94.7% of cases. McNemar test, Cohen κ, and bootstrap noninferiority analyses were used for paired model comparisons. Results: In the key thin-slice diagnostic CT subgroup (N = 137), v2 achieved a sensitivity of 57.5% (95% CI, 42.2–71.5) and specificity of 84.5% (95% CI, 76.0–90.4), compared with 55.0% (95% CI, 39.8–69.3) and 82.5% (95% CI, 73.7–88.8) for v1. Performance improvements were directionally consistent across most subgroups, with no evidence of inferior performance. Conclusions: Fibresolve v2 preserved the performance of the FDA-authorized v1 model while providing modest, directionally favorable improvements. These findings support v2 as the primary model, with v1 retained as a robust fallback when clinical variables are unavailable.
Psychosocial stress has been recognized as a potential risk factor for stroke; however, the strength and consistency of its association with first-ever stroke remain uncertain. This systematic review and meta-analysis aimed to determine the overall association between psychosocial stress and the risk of first stroke in a stroke-free population. We conducted a systematic review and meta-analysis in accordance with the PRISMA and MOOSE guidelines. PubMed, Scopus, Web of Science, and Ovid were searched from inception to 1 January 2026 for cohort or case–control studies. Eligible studies enrolled participants without a prior history of stroke and reported adjusted risk estimates with 95
