Endoureteral ablation: A novel technique to occlude the ureter in patients with urinary tract fistulae.

Urinary fistulae are abnormal connections between the urinary system and adjacent body parts, often resulting from factors such as infection, radiation, malignancy, protracted labor, gynecologic surgery, and gastrointestinal tract surgery. These fistulae can lead to persistent urinary incontinence, skin breakdown, social humiliation, psychosocial trauma, and severe infections. Current treatments include surgical repair, urinary diversion, and ureteral embolization, though these can have complications and are not always successful or feasible for all patients.

Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.

Background: The lack of effective pharmacological measures during the early phase of the COVID-19 pandemic prompted the implementation of non-pharmaceutical interventions (NPIs) as initial mitigation strategies. The impact of these NPIs on COVID-19 in Nigeria is not well-documented. This study sought to assess the effectiveness of NPIs to support future epidemic responses.

Undetected circulation of major arboviruses in West Sudan: urging for institutionalizing multisectoral one health strategy for the preparedness, prevention, and control of zoonotic arboviral diseases.

Objectives: Arboviruses pose a significant global health challenge. This study investigated the seroprevalence of major human arboviral infections, including yellow fever (YFV), dengue (DENV), Crimean-Congo hemorrhagic fever (CCHF), Rift Valley fever (RVF), West Nile virus (WNV), and chikungunya (CHIK), in Darfur region from September to December 2018. ELISA-IgM was used to detect antibodies.

Diagnostic accuracy of case-identification algorithms for heart failure in the general population using routinely collected health data: a systematic review.

Background: Heart failure (HF), affecting 1-4% of adults in industrialized countries, is a major public health priority. Several algorithms based on administrative health data (HAD) have been developed to detect patients with HF in a timely and inexpensive manner, in order to perform real-world studies at the population level. However, their reported diagnostic accuracy is highly variable.