Vibriosis in South Asia: a systematic review and meta-analysis
Despite efforts to control vibriosis, reported cases are on the rise [1, 2]. Vibriosis is a bacterial ailment caused by pathogenic strains of non-cholera Vibrio species [2, 3]. The species most known to cause vibriosis include non-O1/non-O139 V. cholerae, V. vulnificus, V. parahaemolyticus, V. fluvialis, V. mimicus, and V. alginolyticus [3, 4]. Of these, V. vulnificus and V. parahaemolyticus are the most common Vibrio species that are known to cause seafood-related food poisoning [5]. Research has demonstrated that the majority of vibriosis cases are associated with tropical or subtropical locations [6] and have spread wor...
Source: International Journal of Infectious Diseases - February 1, 2024 Category: Infectious Diseases Authors: Basilua Andre Muzembo, Kei Kitahara, Ayumu Ohno, Januka Khatiwada, Shanta Dutta, Shin-Ichi Miyoshi Source Type: research
Assessment of the in-vitro probiotic efficacy and safety of Pediococcus pentosaceus L1 and Streptococcus thermophilus L3 isolated from Laban, a popular fermented milk product
This study assessed the probiotic efficacy and safety of LAB strains isolated from Laban, a traditional fermented milk product. Seven primarily selected Gram-positive, catalase-negative, non-spore-forming isolates were examined for their antimicrobial activity against the bacterial pathogens Bacillus cereus, Salmonella typhi, Staphylococcus aureus, and Vibrio cholera, and the fungal pathogen Candida albicans. Two isolates, identified as Pediococcus pentosaceus L1 and Streptococcus thermophilus L3, which showed antimicrobial activity against all pathogens, were further evaluated for their probiotic competence. The selected ...
Source: Archives of Microbiology - January 31, 2024 Category: Microbiology Authors: Shanta Paul Tanim Jabid Hossain Ferdausi Ali Md Elias Hossain Tasneem Chowdhury Ibrahim Khalil Faisal Jannatul Ferdouse Source Type: research
How can machine learning predict cholera: insights from experiments and design science for action research
In this study, we developed a CORP model using design science perspectives and machine learning to detect cholera outbreaks in Nigeria. Nonnegative matrix factorization (NMF) was used for dimensionality reduction, and synthetic minority oversampling technique (SMOTE) was used for data balancing. Outliers were detected using density-based spatial clustering of applications with noise (DBSCAN) were removed improving the overall performance of the model, and the extreme-gradient boost algorithm was used for prediction. The findings revealed that the CORP model outcomes resulted in the best accuracy of 99.62%, Matthews's corre...
Source: Journal of Water and Health - January 31, 2024 Category: Environmental Health Authors: Hauwa Ahmad Amshi Rajesh Prasad Birendra Kumar Sharma Saratu Ilu Yusuf Zaharaddeen Sani Source Type: research
Resurgence of cholera in Lebanon
East Mediterr Health J. 2023 Nov 30;26(11):837-838. doi: 10.26719/emhj.23.111.NO ABSTRACTPMID:38279878 | DOI:10.26719/emhj.23.111 (Source: Eastern Mediterranean Health Journal)
Source: Eastern Mediterranean Health Journal - January 27, 2024 Category: Middle East Health Authors: Zeina Bayram Abdul Rahman Bizri Umayya Musharrafieh Source Type: research
