Publications by authors named "S H Suliman"
Community engagement is essential for shaping equitable biomedical research priorities, but it is often underutilized, especially for marginalized populations. To integrate community feedback from the public into research, herein we describe a collaborative pilot funded by the Chan Zuckerberg Initiative which pairs the University of California San Francisco (UCSF) with the Rafiki Coalition for Health and Wellness. Utilizing focus groups modeled on Research Prioritization by Affected Communities, participants identified themes that included mistrust in healthcare, representation gaps, and the need for culturally responsive research.
View Article and Find Full Text PDFProstate cancer is the most common noncutaneous malignancy among men worldwide, including in Sudan, where it represents a significant public health challenge. CD147, a transmembrane glycoprotein implicated in tumor progression, invasion, and metastasis, has shown potential as a prognostic biomarker in various cancers. This retrospective case-control study aimed to evaluate CD147 expression in prostate adenocarcinoma among Sudanese men and its association with tumor grade.
View Article and Find Full Text PDFBackground Malaria, a persistent public health issue in Nigeria, particularly among children, is often complicated by misdiagnosis, hindering effective treatment and control. The global adoption of rapid diagnostic tests (RDTs) for malaria has significantly improved management. This study, therefore, compares the diagnostic performance of microscopy, RDT, and polymerase chain reaction (PCR) for Plasmodium falciparum detection in children in Kano state, Nigeria, providing crucial insights for effective control and elimination.
View Article and Find Full Text PDFBackground: Mycophenolate mofetil (MMF) dose is commonly reduced after kidney transplantation (KT). This study examined MMF dosing in the first 5 years after KT to determine if a lower MMF dose impacted outcomes.
Methods: We retrospectively studied 432 recipients who underwent KT between February 2012 and February 2015 in 3 centers.
Article Synopsis
- Early identification of risk factors for prolonged mechanical ventilation (PMV) can lead to timely clinical interventions and reduce complications like infections, especially in the context of COVID-19.
- This study utilized ensemble machine learning (ML) to analyze clinical data at the time of intubation to distinguish between patients at high risk for PMV (more than 14 days) and those not at risk (14 days or less).
- The ML approach demonstrated strong predictive performance, highlighting key clinical markers like glucose levels and platelet counts that can inform patient management and optimize hospital resource allocation.