In recent years,with the continuous advancement of deep space exploration missions,astronauts have been increasingly exposed to complex and extreme environmental factors in space,such as microgravity,space radiation,circadian rhythm disruption,and confined isolation.These conditions can easily induce brain dysfunction in astronauts,manifesting as cognitive decline,emotional disturbances,sleep disorders,and neuroinflammatory responses.Traditional Chinese Medicine(TCM),characterized by its holistic regulation and syndrome differentiation-based treatment principles,exhibits multi-target regulation and systemic coordination,and has attracted growing attention in the field of brain function protection.This study systematically reviews the protective applications and research progress of TCM and related techniques in both actual spaceflight missions and simulated space environment models against brain functional disorders.The underlying mechanisms and future prospects are discussed,with the aim of providing insights for safeguarding astronauts'brain health and laying a theoretical foundation for the precise intervention and systematic protection strategies of TCM in future deep space missions.

OBJECTIVE：To study in vi tro inhibitory act ivities of 9 kinds of TCM for dredging collaterals and dispelling wind on xanthine oxidase （XO），and to screen TCM with outstanding activity. METHODS ：Using xanthine as substrate and xanthinase as reaction enzyme ，allopurinol as positive control ，with water extract and methanol extract （hereinafter referred to as “ethanol extract”）from the stem and leaves of Hedera nepalensis ，the whole plant of Piper wallichii ，the fruits of Rubus corchorifolius ，the root of Caragana sinica ，the root of Wisteria sinensis ，the root of Rubus crataegifolius ，the bark of Catalpa ovata ，the root of Campsis grandiflora ，the stem of P. hancei （hereinafter referred to by plant name ）and petroleum ether ，dichloromethane，ethyl acetate，n-butanol and water fraction of active extract as the objects ，inhibition rate of each sample to XO was detected by spectrophotometry；IC50values were calculated with Graphpad prism 6.0 software to screen active extract/fraction. Double reciprocal method was used to determine the type of enzyme inhibition. RESULTS ：Among 9 kinds of TCM and 18 kinds of the extracts ，the inhibitory rates to XO of 500 μg/mL extracts from each TCM（except for ethanol extract of P. wallichii ），250 μg/mL water extract and ethanol extract of H. nepalensis ，P. wallichii ，R. corchorifolius and P. hancei ，250 μg/mL water extract of C. ovata ，250 μ g/mL ethanol extract of C. sinica ，R. crataegifolius and C. grandiflora ，125 μ g/mL ethanol extract of C. sinica and R. crataegifolius，62.5 μg/mL ethanol extract of C. sinica were more than 50％. The IC 50 value of the ethanol extract from C. sinica was 43.43 μg/mL，which was lower than the extracts of other TCM ，and which was the active extract. The IC 50 values of petroleum ether，dichloromethane，ethyl acetate ，n-butanol and water fracti on of ethanol extract from C. sinica were ＞200，193.35，7.67， 14.80 and ＞200 μg/mL，respectively. The IC 50 value of ethyl XO was competitive-noncompetitive inhibition ， which was different from competi tive inhibition of positive control. CONCLUSIONS：The ethanol extracts of C. sinica ，R. crataegifolius ，P. wallichii ，R. corchorifolius ，P. hancei ，H. nepalensis and the water extracts of H. nepalensis ，P. wallichii ，C. ovata show certain inhibitory activity in vitro to XO ，especially ethanol extract of C. sinica . The ethyl acetate fraction of the ethanol extract of C. sinica has similar inhibitory activity to allopurinol but their inhibition types are different.

Background: Computer-aided diagnosis based on deep learning technology is a research hotspot in the field of gastroenterology, and computer-aided diagnosis of colorectal polyps has received more and more attention. Aims: To validate a model based on deep learning for the automatic identification of colorectal polyps, and to analyze its auxiliary learning function for helping novice endoscopists. Methods: A total of 1 200 colonoscopy images (600 colorectal polyp images and 600 normal images) in the endoscopy center database of Qingdao Municipal Hospital (East) from January 2019 to January 2020 were retrospectively collected. Deep learning model was used to identify the 1 200 images. The sensitivity, specificity, accuracy and diagnosis time of deep learning model and 5 novice endoscopists for diagnosis of colorectal polyps were compared. Results: The deep learning model showed a sensitivity of 93.2%, specificity of 98.7%, accuracy of 95.9% for detecting colorectal polyps, and the diagnosis time of each image was (0.20±0.03) second. The sensitivity, accuracy, and diagnosis time of the model were superior to 5 novice endoscopists, and the specificity was superior to some novice endoscopists. The accuracies of model for polyps with size ≤5 mm and 6~9 mm were 88.1% and 96.8%, respectively, and were superior to 5 novice endoscopists; the accuracy of model for polyps with size ≥10 mm was 100%, and was similar to 5 novice endoscopists. The accuracy of model for polyps with protrude type was 94.8%, and was superior to some novice endoscopists; the accuracy of model for polyps with flat type was 91.7%, and was superior to 5 novice endoscopists. Missing the polyps with flat type (38.8%), polyps at mucosal folds (32.7%), and mistaking the mucosal folds as polyps (12.2%) were the main causes of false negative or false positive results of the model. Conclusions: The deep learning model has a high accuracy, sensitivity, specificity and shorter diagnosis time for diagnosis of colorectal polyps, and can be used to assist novice endoscopists in diagnosing small polyps and flat polyps.

Objective: To explore the clinical significance of combination preoperative carbohydrate loading and postoperative enteral nutrition in patients with gastric cancer during perioperative period. Methods: 70 patients were randomly divided into group A(preoperative fasting + postoperative EN,n = 23),group B(preoperative carbohydrate loading + postoperative TPN,n = 23) and group C(preoperative carbohydrate loading + postoperative EN,n = 20).After operation,insulin sensitivity,nutritional status,immune function and clinical outcome were compared among three groups. Results: Compared with the other two groups in insulin sensitivity,nutritional status,immune function and clinical outcome,the group C was better and different(P