中国血吸虫病防治杂志最新文献

There are multiple bioactive substances in the mosquito saliva, most of which are allergic to humans. Previous studies have demonstrated that mosquito bites may induce allergic reactions mediated by B and T lymphocytes, resulting in a reduction in the quality of life and even death among patients. To date, 11 salivary allergens and 8 non-salivary allergens have been characterized in mosquitoes. Nevertheless, there is still lack of highly sensitive, highly specific and safe tools for diagnosis of mosquito bites-induced allergy, and the difficulty in obtaining natural mosquito salivary allergens results in failure in widespread applications of immunotherapy for mosquito bites-induced allergy. This review provides an overview of the allergic symptoms of mosquito bites and underlying mechanisms, and mosquito salivary allergens that have been characterized and registered in the systematic allergen nomenclature website.

The rapid development of artificial intelligence poses a huge impact on health and has become a core driving force for the new generation of the scientific and technological revolution in the field of healthcare. Recently, artificial intelligence has been gradually applied in the field of parasitic diseases and parasitology, including disease diagnosis, prognosis prediction, prediction of transmission risk, intelligent identification of vectors and intermediate hosts, and disease prevention and control, which facilitates the progress towards elimination of parasitic diseases. In addition, artificial intelligence provides highly efficient tools and approaches for healthcare workers and researchers. This comment mainly reviews the application of artificial intelligence in the fields of parasitic diseases and parasitology.

Objective: To predict the areas of Oncomelania hupensis snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of O. hupensis snail spread, so as to provide insights into investigating the trends in areas of O. hupensis snail spread.

Methods: Data pertaining to O. hupensis snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R²). Following model training, the areas of O. hupensis snail spread were predicted in Anhui Province from 2024 to 2030.

Results: The cumulative areas of O. hupensis snail spread were 40 241.32 hm² in Anhui Province from 1977 to 2023, and the area of O. hupensis snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of O. hupensis snail spread in Anhui Province. The trends in areas of O. hupensis snail spread in Anhui Province from 2024 to 2030 appeared approximately "M"-shaped curves by SVR and NAR neural network models, approximately "W"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of O. hupensis snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and R² of 0.978 9, respectively.

Conclusions: Among the five models, The LSTM neural network model has a high efficiency for predicting areas of O. hupensis snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of O. hupensis snail spread.

Scorpion venom is a highly complicated cocktail of bioactive components including mucoproteins, enzymes, lipids, bioactive peptides, and other organic or inorganic molecules. Scorpion venom antimicrobial peptides are a class of small-molecule bioactive peptides extracted from scorpion venoms, which have shown a variety of biological activities, including antiviral, antibacterial, antifungal and antitumor actions. This review describes the progress of researches on the antiparasitic activities of scorpion venoms and their antimicrobial peptides, so as to provide insights into the research and development of novel antiparasitic agents.