Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

ZNF185-derived peptide induces fertility suppression in mice.

Zinc finger protein 185 (ZNF185) belongs to the ZNF family and is involved in male reproduction. However, it is unclear whether ZNF185 may be a target candidate for contraceptive vaccines. In this study, antigenic peptides derived from ZNF185 were prepared, and their immune contraceptive effects were investigated using mice. Results from enzyme-linked immunosorbent assay (ELISAs) showed that peptide immunization induced an antibody titre increase that reached a peak in week 12. Peptide-3 and peptide-4 were then chosen for subsequent experiments. The results of the fertility assays showed that peptide immunization inhibited the mating and fertility rates of the mice, whereas there were no obvious changes in the number of pups per litter. Subsequently, epididymal sperm was analysed. The results demonstrated that the sperm count and sperm motility were significantly decreased in the peptide group, while the amount of abnormal sperm was significantly increased in the peptide-3 group. The male reproductive organs were also evaluated. There were no obvious differences in testis or epididymal weights, in the diameters of the seminiferous tubules, or in the thicknesses of the seminiferous epithelium between the peptide group and the phosphate buffer saline (PBS) group. In addition, histological analysis indicated that there were no obvious pathologic changes in testis and epididymal histology in the peptide group; however, the number of spermatozoa present in the epididymal lumen of the peptide group was significantly decreased when compared with the PBS group. Our study demonstrates for the first time that peptides derived from ZNF185 may induce fertility suppression in mice without damaging reproductive organs. These peptides have the potential to be used as a male contraceptive vaccine.