Objective To assess the current status of microbial and parasitic quality control for laboratory animals in Jiangxi Province by analyzing microbiological and parasitic test results from production facilities between 2020 and 2024, and to provide a basis for enhancing quality control measures. MethodsIn accordance with the current national standards for laboratory animals at the time of testing, the Jiangxi Provincial Laboratory Animal Quality Inspection Station (affiliated to Institute of Occupational Medicine of Jiangxi) conducted microbial and parasitic testing on 451 laboratory animals of 4 species from 6 laboratory animal production units in Jiangxi Province between 2020 and 2024, and analyzed the quality status of laboratory animals in the province. ResultsPasteurella pneumotropica was detected in one mouse sample in 2020, with a detection rate of 5.00%. Pseudomonas aeruginosa was detected in one mouse sample and mouse hepatitis virus antibody was detected in another mouse sample in 2023, with a detection rate of 2.78%, respectively. No microorganisms or parasites that should be excluded from SPF grade mice as specified in the national standards were detected in 2021, 2022, or 2024, with a qualification rate of 100.00%. Pasteurella pneumotropica was detected in four rat samples in 2020, with a detection rate of 20.00%. Pseudomonas aeruginosa was detected in two rat samples in 2021, with a detection rate of 10.00%, and Tyzzer's disease agent antibody was detected in four rat samples in 2024, with a detection rate of 10.00%. No microorganisms or parasites that should be excluded from SPF grade rats as specified in the national standards were detected in 2022 or 2023, with a qualification rate of 100.00%. For rabbits and guinea pigs, no microorganisms or parasites required to be tested for conventional grade rabbits and guinea pigs as specified in the national standards were detected from 2020 to 2024, with the qualification rate of both species reaching 100.00%. ConclusionBased on the microbial and parasitic testing results, the quality of rabbits and guinea pigs in Jiangxi Province is satisfactory. However, some issues persist with rats and mice. It is recommended to enhance the quality of experimental animals in Jiangxi Province by increasing the frequency of random inspections by quality testing units or by improving the self-inspection capabilities of production and user facilities.

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled com-pound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unla-beled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and Bind-ingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 recep-tor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

The therapy of acute promyelocytic leukemia (APL) with all-trans retinoic acid and arsenic trioxide was first discovered in China, which made a great contribution worldwide to APL treatment. However, foreign guidelines did not include the Chinese chemotherapy regimens, and our regimens were inconsistent with foreign guidelines. Therefore, it is necessary to interpret the home and international guidelines and to explore standard treatment of APL by analyzing APL guidelines of the China, Europe and the United States. Owing to several discrepancies between domestic and foreign APL guidelines, unifying the APL's diagnosis and treatment standard is desperately needed at present according to the evidence-based medicine. It is hoped that Chinese chemotherapy regimens will be more acceptable to other countries of the world, and would benefit the diagnosis and treatment of human APL.

Objective To find out the difference between fetal malnutrition (FM) and small for gestational age (SGA) and its clinical significance. Methods Clinical nutritional status was assessed in 548 singleton term babies. Nine superficial, rapidly detected signs of malnutrition were taken for the clinical assessment nutritional status score (CANSCORE). FM was diagnosed if the total score was less than or equal to 24. Results Among 40 SGA, 21(52.5%) were FM, the other 19 (47.5%) were not FM with scores more than 24, whereas 13(2.8%) out of 508 AGA (appropriate for gestational age) were FM. Conclusion SGA and FM are not synonymous and FM can be rapidly determined by the CANSCORE. Biochemical and ultrasonic studies should be done in high risk preg nancy during second trimester to discover FM and intervene by nutritional treatment to prevent the infants with FM.