Detection and Characterisation of Colistin-Resistant in Broiler Meats.

The irrational use of antimicrobials has led to the emergence of resistance, impacting not only pathogenic bacteria but also commensal bacteria. Resistance against colistin, a last-resort antibiotic, mediated by globally disseminated plasmid-borne mobile colistin resistance () genes, has raised significant global concerns. This cross-sectional study aimed to investigate the antimicrobial resistance patterns of colistin-resistant () and mobilised colistin resistance ( 1-5) genes from broiler meat.

Effects of black soldier fly larvae oil on lipid metabolism, liver fatty acid composition, and plasma metabolite profiles in gilthead seabream juveniles.

The potential of insects as alternative ingredients in animal feeds is well-established. However, limited information is available on the use of insect oils as alternative lipid sources in aquafeeds. To address this, a study was conducted on gilthead seabream (Sparus aurata) juveniles to evaluate the effects of including black soldier fly (Hermetia illucens) larvae oil (HIO).

Hermetia illucens larvae oil as an alternative lipid source: Effects on immune function, antioxidant activity, and inflammatory response in gilthead seabream juveniles.

Hermetia illucens larvae oil (HIO) is a promising new ingredient that can potentially be an alternative lipid source in aquafeeds. To assess its viability in gilthead seabream juvenile diets, a 10-week feeding trial was performed, and the effects on antioxidant, immune, and inflammatory responses were evaluated. Four diets were formulated to include HIO at increasing levels: 0, 4, 7.

One-class classification with confound control for cognitive screening in older adults using gait, fingertapping, cognitive, and dual tasks.

Background And Objectives: Early detection of cognitive impairment is crucial for timely clinical interventions aimed at delaying progression to dementia. However, existing screening tools are not ideal for wide population screening. This study explores the potential of combining machine learning, specifically, one-class classification, with simpler and quicker motor-cognitive tasks to improve the early detection of cognitive impairment.