Supplementation of lactobacillus-fermented rapeseed meal in broiler diet reduces Campylobacter jejuni cecal colonization and limits the l-tryptophan and l-histidine biosynthesis pathways.

BACKGROUND: Campylobacter jejuni (C. jejuni), a widely distributed global foodborne pathogen, primarily linked with contaminated chicken meat, poses a significant health risk. Reducing the abundance of this pathogen in poultry meat is challenging but essential. This study assessed the impact of Lactobacillus-fermented rapeseed meal (LFRM) on broilers exposed to C. jejuni-contaminated litter, evaluating growth performance, Campylobacter levels, and metagenomic profile. RESULTS: By day 35, the litter contamination successfully colonized broilers with Campylobacter spp., particularly C. jejuni. In the grower phase, LFRM improved (P < 0.05) body weight and daily weight gain, resulting in a 9.2% better feed conversion ratio during the pre-challenge period (the period before artificial infection; days 13-20). The LFRM also reduced the C. jejuni concentration in the ceca (P < 0.05), without altering alpha and beta diversity. However, metagenomic data analysis revealed LFRM targeted a reduction in the abundance of C. jejuni biosynthetic pathways of l-tryptophan and l-histidine and gene families associated with transcription and virulence factors while also possibly leading to selected stress-induced resistance mechanisms. CONCLUSION: The study demonstrated that LFRM inclusion improved growth and decreased cecal Campylobacter spp. concentration and the relative abundance of pivotal C. jejuni genes. Performance benefits likely resulted from LFRM metabolites. At the molecular level, LFRM may have reduced C. jejuni colonization, likely by decreasing the abundance of energy transduction and l-histidine and l-tryptophan biosynthesis genes otherwise required for bacterial survival and increased virulence. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Heterologous Expression of the Core Genes in the Complex Fusarubin Gene Cluster of Fusarium Solani.

Through stepwise recreation of the biosynthetic gene cluster containing PKS3 from Fusarium solani, it was possible to produce the core scaffold compound of bostrycoidin, a red aza-anthraquinone pigment in Saccharomyces cerevisiae. This was achieved through sequential transformation associated recombination (TAR) cloning of FvPPT, fsr1, fsr2, and fsr3 into the pESC-vector system, utilizing the inducible bidirectional galactose promoter for heterologous expression in S. cerevisiae. The production of the core metabolite bostrycoidin was investigated through triplicate growth cultures for 1-4 days, where the maximum titer of bostrycoidin was achieved after 2 days of induction, yielding 2.2 mg/L.

Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty.

INTRODUCTION: In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS. METHODS: A systematic search of publications between 2010-2023 was conducted following PRISMA guidelines. Considering the inclusion and exclusion criteria, 28 out of 722 gathered papers from PubMed, Web of Science, and manual search were included in the study. Descriptive statistics was used to analyze the extracted data regarding data preprocessing, model development, and model performance assessment. RESULTS: Most of the papers work on LOS as a binary variable. Patient's age was identified as the most frequently used and reported as important variable for predicting DOS and LOS. Investigations also illustrated that within the resulting 28 papers, more than 71% of models reached good to perfect performance based on the area under the receiver operating characteristic curve (AUC), where artificial neural networks and ensemble learning models had the biggest share among the best-performing models. CONCLUSION: The utilization of ML models is increasing in the literature. The current performance level indicates that ML can potentially turn to powerful tools in predicting DOS and LOS for different purposes. Meanwhile, the literature is not matured yet in reporting real-life application. Future studies can focus on model specification and validation by considering empirical application.