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Ester derivatives of Dictyostelium differentiation-inducing factors exhibit antibacterial activity, possibly via a prodrug-like function.
BMC Res Notes
January 2025
Laboratory of Health and Life Science, Graduate School of Health and Sports Science, Juntendo University, Inzai, 270-1695, Japan.
Objective: Dictyostelium differentiation-inducing factors 1 and 3 [DIF-1 (1) and DIF-3 (2), respectively], along with their derivatives, such as Ph-DIF-1 (3) and Bu-DIF-3 (4), demonstrate antibacterial activity in vitro against Gram-positive bacteria, including methicillin-sensitive Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), vancomycin-sensitive Enterococcus faecalis (VSE), and vancomycin-resistant Enterococcus faecium [VRE (VanA)]. This study investigates the therapeutic potential of DIF compounds against these Gram-positive bacteria.
View Article and Find Full Text PDFDesign and maintenance of medical oxygen concentrators in Sub-Saharan Africa: a systematic review.
BMC Health Serv Res
January 2025
Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, Rome, 00128, Italy.
Background: Oxygen therapy is critical and vital treatment for hypoxemia and respiratory distress, however, access to reliable oxygen systems remains limited in SSA. Despite WHO initiatives that distributed over 30,000 OC oxygen concentrators worldwide, SSA faces significant challenges related to their maintenance and use, due to harsh environmental conditions, technical skill shortages and inadequate infrastructure. This review aims to systematically identify and assess the literature on OC design adaptations, maintenance challenges, and knowledge gaps in SSA, providing actionable recommendations to inform innovative and context-sensitive solutions to improve healthcare delivery in the region.
View Article and Find Full Text PDFRisk of residual/recurrent cervical diseases in HPV-positive women post-conization depends on HPV integration status.
Infect Agent Cancer
January 2025
College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, 350001, China.
Background: It is crucial to identify post-operative patients with HPV infection who are at high risk for residual/recurrent disease. This study aimed to evaluate the association between HPV integration and clinical outcomes in HPV-positive women after cervical conization, as well as to identify HPV integration breakpoints.
Methods: This retrospective study analyzed data of 791 women who underwent cervical conization for cervical intraepithelial neoplasia grades 2-3 (CIN2-3) between September 2019 and September 2023, sourced from the Fujian and Hubei cervical lesion screening cohorts.
Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.
World J Surg Oncol
January 2025
Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.
Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.
BMC Med Inform Decis Mak
January 2025
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Heilongjiang, China.
Background: Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications.
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