A retrospective study on 4693 patients with epidemic fever/epidemic haemorrhagic fever during 1990-1999 in Thanh Hãa province to find and identify the epidemic haemorrhagic fever, viral isolation, serological diagnosis in 5 coast line districts, 9 district in the delta region and 1 mountain district. The results have demonstrated that the average morbidity rate of the haemorrhagic fever in Thanh Hãa during 1990-1999 was 24.5/100.000; the average mortality rate of haemorrhagic fever was 0.04/100.000. Some epidemics had high morbidity rate included HËu Léc (1573.7/1in 1991) and SÇm S¬n (1978.7/1in 1998). Both were under the country's spread epidemics. There was significant difference of morbidity and mortality rate from the geographic region to another. The disease occurred all ages
Fever ; epidemiology

There were 104 cases of new emerged den-4 serotype hemorrhagic dengue in 2 communes comprising of 81.7% of grade I and 18.3% of grade II. No cases of shock and death were reported. Under 15 years old children account for 26.9%. Differential diagnosis was established late, but active measures such as the elimination of vectorial larvae by larvivorous fishes were well applicated to prevent the spread of the epidemic.
Dengue ; Disease Outbreaks

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.