Effects of rhamnolipid replacement of chlortetracycline on growth performance, slaughtering traits, meat quality and antioxidant function in broilers.

BACKGROUND: Rhamnolipids (RLS) are surfactants that improve the growth performance of poultry by improving the absorption of nutrients. This study aims to investigate the effects of RLS replacement of chlortetracycline (CTC) on growth performance, slaughtering traits, meat quality, antioxidant function and nuclear-factor-E2-related factor 2 (Nrf2) signaling pathway in broilers. A total of 600 one-day-old Arbor Acres chicks were randomly assigned to five groups with eight replicates in each group, raised for 42 days. Broilers were respectively fed a basal diet with no CTC or RLS, 75 mg kg-1 CTC, and 250, 500, 1000 mg kg-1 RLS. RESULTS: Dietary supplementation with RLS linearly increased the average daily gain, average daily feed intake, carcass yield, eviscerated yield, ether extract, enhanced total superoxide and glutathione peroxidase (GPx) activities, overexpressed the relative expressions of Nrf2, heme oxygenase 1, Copper/zinc superoxide dismutase, Manganese superoxide dismutase, GPx and catalase and decreased the lightness value at 24 h, drip loss and malondialdehyde contents of broilers (P < 0.05). Compared with the control group, broilers fed 1000 mg kg-1 RLS reduced the drip loss and broilers fed 500 mg kg-1 RLS increased muscle crude fat content (P < 0.05). Compared with the CTC group, dietary supplementation with 1000 mg kg-1 RLS increased eviscerated yield (P < 0.05). CONCLUSION: RLS could improve growth performance, crude fat content, meat quality and antioxidant capacity and activate relative expression of genes in the Nrf2 signaling pathway in broilers. It could be used as an antibiotic substitute in diets and the recommended supplemental dose of RLS in feed of broilers is 1000 mg kg-1. © 2024 Society of Chemical Industry.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.