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Fermented liquid feed (FLF) can improve dietary nutrient absorption levels, degrade anti-nutrient factors in diets, and increase beneficial bacteria abundance in animal guts. However, few systematic studies have been conducted on WFLF in pigs. The present study evaluates the effects of WFLF on the growth performance, nutrient digestibility, gastric volume, intestinal morphology, intestinal health, intestinal barrier function, serum biochemical immunity, gut microbiota, and intestinal microbial diversity of grower-finisher pigs. In total, 80 weaned pigs were randomly allocated to two treatment groups based on their initial body weight: a basal diet with pellet dry feeding (CON) and a basal diet with wheat-based fermented liquid feed (WFLF), with four replicate pens per group. The experiment lasted 82 d. Compared with CON pigs, those fed WFLF were heavier significantly at 60-82 d and had significantly higher average daily feed intake, average daily gain, and gain: feed ratio at 60-82 d and 1-82 d. WFLF pigs had significantly greater jejunum, total tract, and ileal digestibility for all nutrients and amino acids, excluding arginine, than CON pigs. WFLF intake influenced villus height, villus height: crypt depth ratio of the anterior segment of the jejunum(A-jejunum), crypt depth, and redox potential of the posterior segment of the jejunum (P-jejunum) while significantly affecting body weight. Additionally, FLF improved gastric capacity significantly. Furthermore, mRNA expression of occludin and claudin-1 in the mucosa of the ileum and jejunum was significantly higher in WFLF pigs than in CON pigs. WFLF increased serum concentrations of alanine transaminase and reduced low-density lipoprotein cholesterol, total cholesterol, and total bile acid content. The alpha diversity (Shannon and Simpson indices) in the stomachs of WFLF pigs was significantly higher than in CON pigs. Microbial diversity in the stomach, ileum, and cecum, as well as the abundance of lactic acid bacteria, were increased in WFLF pigs compared to CON pigs. In conclusion, WFLF intake may positively influence intestinal ecology by improving digestive tract structure, upregulating intestinal barrier-related genes, and improving intestinal morphology to enhance intestinal digestive function and health. Collectively, the present study shows that WFLF intake can increase growth performance while maintaining beneficial nutrient digestibility in grower-finisher pigs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators., Methods: Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits., Results: Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models., Conclusion: Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Weng, Ding, Hu, Chen, Liu, Liu, Chen, Guo, Cao, Yi, Yang and Peng.)