Objective To explore scientific and accurate prediction methods for the incidence of hand, foot, and mouth disease in the post-pandemic era, and to address modeling challenges caused by abnormal fluctuations in case numbers from 2020 to 2023. Methods The seasonal index was used to pre-process the data. The traditional seasonal autoregressive integrated moving average (SARIMA) model, singular spectrum analysis (SSA)-ARIMA model, ARIMA-Long short-term memory (LSTM) model, and SSA-ARIMA-LSTM model were used to fit the incidence from 2013 to 2023, and the incidence of hand, foot and mouth disease in 2024 was predicted. The real data collected in 2024 were used as the test set to compare the prediction performance of the models. Results The fitting performance of the constructed models was as follows: the ARIMA model had MAE=107.50 and RMSE=144.53, the SSA-ARIMA model showed MAE=2.84 and RMSE=4.33, the ARIMA-LSTM model achieved MAE=99.46 and RMSE=131.59, and the SSA-ARIMA-LSTM model had MAE=96.35 and RMSE=132.13. In terms of prediction performance, the ARIMA model resulted in MAE=151.64 and RMSE=146.70, the SSA-ARIMA model demonstrated MAE=41.22 and RMSE=57.01, the ARIMA-LSTM model yielded MAE=220.75 and RMSE=257.89, and the SSA-ARIMA-LSTM model recorded MAE=58.83 and RMSE=72.06. Conclusion The SSA-ARIMA model has the best fitting degree and the highest prediction accuracy, and is suitable for predicting the incidence trend of hand, foot and mouth disease.

In order to further standardize the vaccination of kidney transplant candidates and recipients in China, the Branch of Organ Transplantation of Chinese Medical Association has organized experts in kidney transplantation and infectious diseases. Based on the "Vaccination of Solid Organ Transplant Candidates and Recipients: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice", and in combination with the clinical reality of infectious diseases and vaccination after organ transplantation in China, as well as referring to relevant recommendations from home and abroad in recent years, these guidelines are formulated from aspects such as epidemiology, types of vaccines, vaccination principles, target population, and specific vaccine administration. The "Guidelines for Vaccination of Kidney Transplant Candidates and Recipients in China" aims to provide theoretical reference for medical workers in the field of kidney transplantation in China, regarding the vaccination of kidney transplant candidates and recipients. It is expected to better guide the vaccination of kidney transplant candidates and recipients, reduce the risk of postoperative infection, and improve survival outcomes.

Objective: To analyze the status and trends of the disease burden of periodontal disease among women of reproductive age (15-49 years) in China from 1990 to 2021, and to provide a reference for the development of periodontal disease prevention and control strategies for women of reproductive age. Methods: Using the global burden of disease (GBD) data from 1990 to 2021, this study investigated the periodontal disease burden among women of reproductive age, including prevalence, incidence, disability-adjusted life years (DALYs), DALY rates, and their corresponding standardized indicators. Joinpoint 5.2.0.0 software was used for time trend analysis of DALYs, age-specific DALY rates, and annual average percentage change (AAPC) values. A log-linear regression model was used to test trends for DALYs and DALY rates. Results: Compared with 1990, the prevalence and incidence of periodontal disease among Chinese women in 2021 increased by 45.67% (per 100,000 people) and 29.29% (per 100,000 people), respectively. The distribution of periodontal disease among women (15-49 years) showed a continuous and rapid upward trend, with the growth rate increasing rapidly with age. The number of cases increased the fastest in the 45-49 age group, and the prevalence increased the fastest in the 35-44 age group. The incidence of periodontal disease continued to rise with age, with the fastest increase in the 35-44 age group among women of reproductive age. The Joinpoint regression model results showed that periodontal disease led to an expanding trend in the disease burden among women of reproductive age in China, with an AAPC of DALYs = 1.20% and an AAPC of DALY rate = 1.25% (P<0.001). Conclusion The periodontal disease burden among Chinese women aged 15-49 years showed a gradually increasing trend from 1990 to 2021.

ObjectiveTo explore whether fluoroethylnormemantine （FENM）， an NMDA receptor antagonist， could improve compulsive-like behaviors and to investigate its underlying mechanisms in the RU24969-induced obsessive-compulsive disorder （OCD） mouse model. MethodsThirty-two mice were randomly assigned to four groups： Saline （n=8）， RU24969 （n=8）， RU+FENM （n=8）， and FENM （n=8）. Mice received FENM or an equivalent volume of saline for pre-treatment， followed by RU24969 or saline for model induction 30 minutes later. Behavioral tests were performed 1 hour after modeling， and serum samples were collected to measure the level of brain-derived neurotrophic factor （BDNF）. Evans Blue dye was intravenously injected to assess dye content in brain tissue， thereby evaluating potential blood-brain barrier damage. ResultsFENM treatment significantly improved repetitive stereotyped circling behavior （F=39.850， P＜0.001） and alleviated persistent motor activity （F=50.200， P＜0.001） in RU24969 model mice. Additionally， FENM treatment significantly increased serum BDNF level in RU24969-induced OCD mice （F=18.930， P＜0.001）. ConclusionsFENM ， an NMDA receptor antagonist， may alleviate compulsive behaviors in OCD mice by modulating BDNF levels ， thereby exerting anti-compulsive effects. Neither the RU24969 model nor FENM treatment significantly affectes blood-brain barrier integrity.

Background Non-optimal temperatures pose significant threats to public health. Analyzing the association between temperature exposure and the number of emergency cases of acute alcohol intoxication can provide evidence for optimizing emergency resource allocation and response strategies. Objective To analyze the overall impact and lag effects of non-optimal temperatures on the number of 120 emergency calls for acute alcohol intoxication in Wuxi, and to assess the attributable risk, in order to provide empirical evidence for formulating climate-adaptive public health strategies. Methods Call records of acute alcohol intoxication from Wuxi's 120 emergency service, concurrent air pollutant data, and meteorological data (including daily mean temperature) were collected from January 1, 2014 to December 31, 2020. Distributed lag nonlinear modeling was used for time-series analysis, with cross-basis functions to capture the nonlinear relationship and lag effects between temperature and emergency volume. Confounding factors such as long-term trends, humidity, pollutants [ultimately including ozone (O₃) and fine particulate matter (PM_2.5)], day of the week, and holidays were controlled. The maximum lag period was set to 14 days. Single-day lag and cumulative lag effects of extreme temperatures were analyzed, followed by sensitivity analysis. Effects were quantified using relative risk (RR) and 95% confidence intervals (95%CI), and attributable fractions and numbers for different temperature ranges were calculated. Results A total of 10705 emergency cases of acute alcohol intoxication were included during the study period, with males accounting for 89.5% and individuals aged 18–50 years accounting for 83.4%. A non-linear "U-shaped" association was observed between daily mean temperature and the number of emergency calls, with an optimal temperature of 13.3 ℃. The effect of the extreme low temperature (1.88 ℃) was statistically significant from lag1 to lag3 after exposure (P<0.05), with the maximum effect at lag1 (RR=1.063, 95%CI: 1.002, 1.129). The extreme high temperature (32.3 ℃) showed an immediate effect, with maximum effect at lag0 (RR=1.374, 95%CI: 1.248, 1.512). The cumulative effect analysis showed that the risk associated with high temperature was the highest at lag0–3 (RR=2.000, 95%CI: 1.667, 2.399), while the risk associated with low temperature was the highest at lag0–14 (RR=1.462, 95%CI: 1.042, 2.051). The attribution analysis indicated that the mild heat (>24.7–30.8 ℃) and heat (>30.8–33.5 ℃) contributed the largest numbers of attributable cases, with 591 cases (95%CI: 56, 1032) and 190 cases (95%CI: 51, 309), respectively. The corresponding attributable fractions were 5.81% (95%CI: 0.80%, 9.94%) and 1.87% (95%CI: 0.49%, 2.96%), respectively. Conclusion Both extreme high and low temperatures significantly increase the number of 120 emergency calls for acute alcohol intoxication in Wuxi. High temperature exhibit an acute effect, while low temperature show a delayed effect. Mild heat have a significant impact on the emergency burden. It is recommended that emergency departments optimize resource allocation according to climatic characteristics and strengthen emergency responses during extreme weather to reduce alcohol-related health burden.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Objective To investigate the prevalence of scoliosis and congenital heart disease(CHD)and their correlation among children and adolescents of Drung nationality in Yunnan Province.Methods A cross-sectional survey was conducted in November 2022 among all Drung school-aged children and adolescents aged 5-18 years in Gongshan Drung and Nu Autonomous County,Yunnan Province.Visual inspection,Adams for-ward flexion test,and trunk rotation angle(ATR)measurement were comprehensively used for school prelim-inary screening of scoliosis.Individuals who tested positive in the school preliminary screening underwent fur-ther X-ray examination for auxiliary diagnosis.Cardiac auscultation and echocardiography were used for school preliminary screening of CHD.The personal information of the screening subjects,the screening results,etc.were recorded.The prevalence of scoliosis and CHD among children and adolescents of the Drung nationality and the relationship between the two diseases were statistically analyzed,and the positive predictive value of school-based scoliosis screening and its influencing factors were also analyzed.Results A total of 1 036 chil-dren and adolescents of Drung nationality were enrolled,with a mean age of(10.72±3.75)years,icluding 542 males and 494 females.A total of 45 subjects tested positive for scoliosis in the school preliminary screening,with a preliminary positive rate of 4.34%.A total of 22 cases were finally diagnosed with scoliosis,with a prevalence rate of 2.12%.Among them,21 cases were idiopathic scoliosis(accounting for 95.45%),and 1 case was congenital scoliosis(accounting for 4.55%).The prevalence rate was higher in females(2.83%)than that in males(1.48%),higher in the 10 to 18-year-old group(2.30%)than that in the 5 to<10-year-old group(1.87%),and higher in the secondary school group(2.78%)than that in the primary school group(1.78%),hut there were no statistically significant differences(P>0.05).Most idiopathic scoliosis cases were mild(Cobb angle 10° to<20°,90.48%)and classified as Lenke type Ⅴ(57.14%).Two cases of CHD were confirmed,both of which were atrial septal defects,with a prevalence rate of 0.19%.The co-occurrence rate of idiopathic scoliosis and CHD was 4.76%(1/21).The positive predictive value of school-based scoliosis pre-liminary screening was only 48.89%.When the BMI was<18.5 kg/m2,the positive predictive value was sig-nificantly higher than that for BMI≥18.5 kg/m2(P<0.05).Conclusion The prevalence rate of scoliosis a-mong adolescents of the Drung ethnic group in Yunnan Province is 2.12%,predominantly idiopathic scoliosis,with Lenke type V being the most common classification.The prevalence rate of congenital heart disease is 0.19%.BMI is a significant influencing factor for the positive predictive value of school-based scoliosis prelimi-nary screening.

Objective:To observe the therapeutic effect of vibrating the abdomen on anorexia model rats,as well as its effects on cholecystokinin octapeptide(CCK-8)and motilin(MTL)in the peripheral blood. Methods:Forty young rats were randomly divided into a normal group(n=10)and a modeling group(n=30).Rats in the normal group were fed common feed.The anorexia model was established by the etiological simulation method in the modeling group,and these rats were further randomly divided into a drug group,a vibrating abdomen group,and a model group 3 weeks after the anorexia model was induced,with 10 rats in each group.The drug group was given Jian Wei Xiao Shi Pian by intragastric administration at a dose of 0.72 g/(kg·bw)(0.72 g drug was dissolved in 10 mL purified water).The normal group and the model group were given purified water once a day in the morning.The vibrating abdomen group was treated with vibrating the abdomen once a day for 21 times.The body mass,food intake,serum CCK-8,MTL,gastrin(GAS),neurotensin(NT)levels,and the intestinal propulsion rate of rats in each group were measured. Results:Compared with the model group,the body mass,food intake,serum MTL and GAS levels,and the small intestine propulsion rate increased significantly,and the serum CCK-8 and NT levels,the gastric residual rate decreased significantly in the vibrating abdomen group and the drug group(P<0.05).There were no significant differences between the vibrating abdomen group and the drug group(P>0.05). Conclusion:Vibrating the abdomen increases the food intake and body mass of anorexia model rats,reduces the residue of gastric contents,improves the small intestine propulsion rate,and therefore has a good therapeutic effect on anorexia.The mechanism may be related to inhibiting the secretion of CCK-8 and NT in plasma and promoting the release of MTL and GAS in serum.

The microcosmic syndrome differentiation is expounded from the the trinity life view of the body， qi， and spirit. This article analysed the relationship between micro-indicators and body， qi and spirit， considering that the abnormalities of micro-indicators in pathological states involve three different levels in terms of body， qi and spirit， and may reflect the degree of malfunction of body， qi and spirit and the dynamic changes of the focus during different pathological processes.Accordingly， based on the syndrome differentiation and treatment of traditional Chinese medicine， it is proposed that the macroscopic and the microscopic， the local and the whole， as well as the imbalance of body， qi and spirit reflected by microscopic and macroscopic indicators at different stages of disease should be combined to determine the corresponding treatment， thereby restoring the “harmony of body and spirit” of the human body.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.