The objective of the present study was to evaluate the potential of the dietary addition of neem () leaf powder (NLP) when compared to monensin (MON) on ruminal fermentation, feed intake, digestibility, and performance of growing lambs. Eighteen Omani lambs (22.8 ± 2.18 kg of body weight (BW)) were equally divided into three groups ( = 6 lambs/group) for 90 days. Animals were fed an basal diet consisting of Rhodes grass () hay (600 g/kg) and a concentrated mixture (400 g/kg) offered twice daily. Experimental treatments were control (basal diet without supplements); MON (control plus 35 mg/kg DM as a positive control); and NLP (control plus 40 g/kg DM). Lambs fed NLP had reduced ruminal ammonia nitrogen concentrations, protozoal counts, total volatile fatty acid, and blood urea nitrogen concentrations compared to the control. Compared to MON, lambs fed NLP had increased ruminal acetate and decreased propionate proportions. Inclusion of NLP in the diet increased blood total protein, globulin, and liver enzyme concentrations in comparison with the control, which was similar to MON. The lamb's final BW and average BW gain were also increased with the NLP relative to the control. Further, adding NLP to the diet increased the digestibility of crude protein compared to the control diet. In conclusion, adding NLP to the diet with 40 g/kg DM could be used as a promising phytogenic supplement for growing lambs with no detrimental effects on the ruminal fermentation profile, nutrient intake, or digestibility.
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http://dx.doi.org/10.3390/ani12223146 | DOI Listing |
J Nutr
December 2024
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, Kazakhstan. Electronic address:
Background: Although large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific sociocultural nuances and regional cuisines, such as those in Central Asia (for example, Kazakhstan), still requires further investigation.
Objectives: To evaluate and compare the effectiveness of the ChatGPT-4 system in providing personalized, evidence-based nutritional recommendations in English, Kazakh, and Russian in Central Asia.
Methods: This study was conducted from 15 May to 31 August, 2023.
Nutrients
February 2024
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: "Traditional and Staple", "Communal and Festive", and "Westernized and Convenience-Oriented". These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders.
View Article and Find Full Text PDFFront Psychiatry
November 2023
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
J Med Internet Res
November 2023
School of Life Science, Beijing University of Chinese Medicine, Beijing, China.
Background: Nutritional management for patients with diabetes in China is a significant challenge due to the low supply of registered clinical dietitians. To address this, an artificial intelligence (AI)-based nutritionist program that uses advanced language and image recognition models was created. This program can identify ingredients from images of a patient's meal and offer nutritional guidance and dietary recommendations.
View Article and Find Full Text PDFJMIR Form Res
May 2023
Department of Biomedical Engineering, Duke University, Durham, NC, United States.
Background: Effective monitoring of dietary habits is critical for promoting healthy lifestyles and preventing or delaying the onset and progression of diet-related diseases, such as type 2 diabetes. Recent advances in speech recognition technologies and natural language processing present new possibilities for automated diet capture; however, further exploration is necessary to assess the usability and acceptability of such technologies for diet logging.
Objective: This study explores the usability and acceptability of speech recognition technologies and natural language processing for automated diet logging.
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