Effects of different dietary starch sources on growth and glucose metabolism of geese

High Starch Induces Hematological Variations, Metabolic Changes, Oxidative Stress, Inflammatory Responses, and Histopathological Lesions in Largemouth Bass (Micropterus salmoides)

This study evaluated effects of high starch (20%) on hematological variations, glucose and lipid metabolism, antioxidant ability, inflammatory responses, and histopathological lesions in largemouth bass. Results showed hepatic crude lipid and triacylglycerol (TAG) contents were notably increased in fish fed high starch. High starch could increase counts of neutrophils, lymphocytes, monocytes, eosinophils, and basophils and serum contents of TAG, TBA, BUN, and LEP (p < 0.05). There were increasing trends in levels of GLUT2, glycolysis, gluconeogenesis, and LDH in fish fed high starch through the AKT/PI3K signal pathway. Meanwhile, high starch not only triggered TAG and cholesterol synthesis, but mediated cholesterol accumulation by reducing ABCG5, ABCG8, and NPC1L1. Significant increases in lipid droplets and vacuolization were also shown in hepatocytes of D3-D7 groups fed high starch. In addition, high starch could decrease levels of mitochondrial Trx2, TrxR2, and Prx3, while increasing ROS contents. Moreover, high starch could notably increase amounts of inflammatory factors (IL-1β, TNF-α, etc.) by activating NLRP3 inflammasome key molecules (GSDME, caspase 1, etc.). In conclusion, high starch could not only induce metabolic disorders via gluconeogenesis and accumulation of glycogen, TAG, and cholesterol, but could disturb redox homeostasis and cause inflammatory responses by activating the NLRP3 inflammasome in largemouth bass.

Comparison of growth performance, meat quality, and blood biochemical indexes of Yangzhou goose under different feeding patterns

The East China region is the main market for the breeding and consumption of meat geese in China, in order to provide data reference for small and medium-sized farms and farmers to choose breeding methods and growth performance. This study selected 300 Yangzhou geese as materials and determined the number of geese in each group according to different modes. The meat quality, blood biochemical indicators, and economic benefits of 4 common feeding methods (Group I: full concentrate feeding; Group II: concentrate feeding in the first stage + 3% fat addition in the later stage; Group III: concentrate feeding + pasture supplementation; Group IV: grazing feeding + concentrate) in East China were analyzed. The results are as follows: The average daily weight gain of Yangzhou geese in Group IV at 5 to 8 wk old was the highest, with the highest feed utilization rate. The body weight at 8 wk old was significantly higher than that of the group III (P < 0.05). The total mortality rate of Group I and II remained at a relatively low level, while the mortality rates of Group III and IV exceeded 17%. The SR, FECR, and FECW of female geese in Groups II, III, and IV were significantly higher than those in Control group I (P < 0.05). Different feeding methods have little effect on the quality of goose breast muscles, while in terms of leg muscles, Group II has the highest binding force, significantly higher than Group I (P < 0.05). The rate of chest muscle loss in group III was significantly higher than that in groups I and II (P < 0.05). However, the pH of leg muscles in groups I, II and III was significantly higher than that in group IV (P < 0.05). Group II has the highest protein and collagen content, and Group I has the highest fat content. Except for the significantly higher histidine content in Groups I And II compared to those in Groups III and IV (P < 0.05), there was almost no significant difference in amino acid content among the groups (P > 0.05). There was no significant difference in ALB/GLO content among the 3 groups of Groups II to IV, but they were all significantly higher than those of Group I (P < 0.05). There was no statistically significant difference in other indicators among the groups (P > 0.05). There was no significant difference in the content of Ca, Cu, Fe, P, Zn, and other elements in the muscles between the groups (P > 0.05). This study solved the problems of slow growth, poor meat performance, and low economic benefits in meat goose breeding, providing theoretical basis and data support for meat goose breeding enterprises and farmers to choose appropriate breeding modes.

Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection

Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.