Childhood simple obesity has become a significant public health issue in China. Modern medicine primarily relies on lifestyle interventions and often suffers from poor long-term compliance， while pharmacological options are limited and associated with potential adverse effects. Traditional Chinese Medicine （TCM） has a long history in the prevention and management of this condition， demonstrating eight distinct advantages， including systematic theoretical foundation， diversified therapeutic approaches， definite therapeutic efficacy， high safety profile， good patient compliance， comprehensive intervention strategies， emphasis on prevention， and stepwise treatment protocols. Additionally， TCM is characterized by six distinctive features： the use of natural medicinal substances， non-invasive external therapies， integration of medicinal dietetics， simple exercise regimens， precise syndrome differentiation， and diverse dosage forms. By combining internal and external treatments， TCM facilitates individualized regimen adjustment and holistic regulation， demonstrating remarkable effects in improving obesity-related metabolic indicators， regulating constitutional imbalance， and promoting healthy behaviors. However， challenges remain， such as inconsistent operational standards， insufficient high-quality clinical evidence， and a gap between basic research and clinical application. Future efforts should focus on accelerating the standardization of TCM diagnosis and treatment， conducting multicenter randomized controlled trials， and fostering interdisciplinary integration， so as to enhance the scientific validity and international recognition of TCM in the prevention and treatment of childhood obesity.

Acute respiratory distress syndrome(ARDS)is a common respiratory emergency,but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures.Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS,but the application of hydrogen has flammable and explosive safety concerns.Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance,thus improving ARDS in patients and animal models.Coral calcium hydrogenation(CCH)is a new solid molecular hydrogen carrier prepared from coral calcium(CC).Whether and how CCH affects acute lung injury in ARDS re-mains unstudied.In this study,we observed the therapeutic effect of CCH on lipopolysaccharide(LPS)induced acute lung injury in ARDS mice.The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable,demonstrating a significant improvement compared to the untreated ARDS model group.CCH treatment significantly reduced pulmonary hemorrhage and edema,and improved pulmonary function and local microcirculation in ARDS mice.CCH promoted mitochon-drial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2(Trx2),improved lung mitochondrial dysfunction induced by LPS,and reduced oxidative stress damage.The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.

Guidelines for Digital Ancient Books of TCM Indexing(T/CIATCM 119-2024)is based on the theoretical knowledge,disciplinary methods,and practical applications of TCM classical cataloging.Taking digital ancient books of TCM as the object,it systematically reveals the content of TCM knowledge,which is an essential indexing processing standard for building an intelligent retrieval system for TCM ancient books,and can provide support for the deep development and innovative utilization of TCM knowledge.It can not only promote the co-construction and sharing of ancient book resources in the TCM industry,but also promote the standardization construction and application of TCM information.This standard specifies the principles,methods,and examples of free indexing of digital ancient books of TCM based on their original content.It is applicable to the indexing and processing of digital ancient books of TCM for TCM professional libraries and related institutions,and to the data processing and construction of various types of TCM ancient book databases.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To evaluate the safety and efficacy of valve-in-valve transcatheter mitral valve replacement(ViV-TMVR)in patients with bioprosthetic valve degeneration who are at high surgical risk.Methods This study is a multi-center,retrospective cohort analysis of 20 consecutive patients who underwent transseptal ViV-TMVR using the Edwards SAPIEN 3 transcatheter heart valve(THV).The primary endpoints include technical success and procedural success,both defined according to the Mitral Valve Academic Research Consortium(MVARC)criteria,as well as mortality and functional change assessed based on New York Heart Association(NYHA)classification at 30-days and six months post-procedure.Clinical follow-up assessments are conducted at 30-days and six months.Results From February 2021 to October 2022,a total of 20 patients with symptoms of bioprosthetic valve degeneration were enrolled across nine sites in China.The patients had a mean age of(73.5±5.5)years,with 85.0％being females and 70.0％classified as NYHA class Ⅲ/Ⅳ.The study achieved a 100.0％technical success rate and a 90.0％procedural success rate finally.All patients remained alive during the 30-day follow-up period.However,six months post-intervention,two patients(10.0％)were re-hospitalized due to heart failure,and sadly,one of them(5.0％)died.None of the patients reported any adverse events related to ViV-TMVR during the follow-up period.Notably,there was a significant improvement in NYHA class compared to baseline(P=0.0004)at six-month follow-ups.Conclusions The transseptal ViV-TMVR technique proved to be highly successful and was associated with significant improvement in NYHA class function.These findings strongly suggest that it serves as a safe and efficient treatment alternative for high-risk patients suffering from bioprosthetic valve degeneration.

ObjectiveTo analyze the reported cases of infectious diseases across different tiers of public medical and healthcare institutions in Minhang District, Shanghai from 2013 to 2023, to investigate the status and changes in reported infectious diseases in this district from a temporal, etiological, and demographic perspectives, so as to provide a scientific basis for the construction of a hierarchica early-warning surveillance system under the grading diagnosis and treatment model in medical institutions, as well as for optimizing sentinel surveillance at facilities of different levels. MethodsA retrospective analysis was performed using surveillance data from the China Disease Prevention and Control Information System in Minhang District from 2013 to 2023. Reported infectious diseases were categorized into three categories based on transmission routes: respiratory infectious diseases, intestinal infectious diseases, and sexually transmitted and blood borne infectious diseases. According to the implementation phase of the grading diagnosis and treatment policy, the research time was divided into four time periods: 2013‒2016, 2017‒2019, 2020‒2022, and 2023. The distribution and temporal changes of reported cases of infectious diseases were compared across community health service centers (CHCs), secondary hospitals, tertiary grade-A hospitals and tertiary grade-B hospitals. Chi-square test was used for univariate analysis of differences in the number of reported cases. Quantitative data with normal distribution were analyzed using parametric tests, otherwise, Kruskal⁃Wallis H tests were used. ResultsThe proportions of total reported cases of infectious diseases in medical institutions at all levels in Minhang District, Shanghai from 2013 to 2023 were 10.66% in CHCs, 9.10% in secondary hospitals, 64.95% in tertiary grade-B hospitals, and 15.29% in tertiary grade-A hospitals, with an overall decline and then rebound trend in the reported cases. After the implementation of grading diagnosis and treatment policy, the number of reported cases in CHCs and secondary hospitals showed a trend of first decreasing and then increasing, while that in tertiary grade-B hospitals showed a steady decreasing trend and that in tertiary grade-A hospitals showed an increasing trend. In terms of the research periods divided above, a total of 10 392 cases were reported in 2013‒2016 (70.34% from tertiary grade-B hospitals and 12.59% from CHCs), including 2 922 cases of respiratory infectious diseases, 1 241 cases of intestinal infectious diseases, and 6 229 cases of sexually transmitted and blood-borne infectious diseases. Between 2017 and 2019, a total of 6 967 cases were reported (73.49% from tertiary grade-B hospitals and 11.84% from tertiary grade-A hospitals), including 2 983 cases of respiratory infectious diseases, 279 cases of intestinal infectious diseases, and 3 705 cases of sexually transmitted and blood-borne infectious diseases. Between 2020 and 2022, a total of 4 599 cases were reported (69.92% from tertiary grade-B hospitals and 24.57% from tertiary grade-A hospitals), including 1 627 cases of respiratory infectious diseases, 123 cases of intestinal infectious diseases, and 2 849 cases of sexually transmitted and blood-borne infectious diseases. In 2023, a total of 4 648 cases were reported (35.20% from tertiary grade-B hospitals and 27.50% from tertiary grade-A hospitals), including 3 165 cases of respiratory infectious diseases, 69 cases of intestinal infectious diseases, and 1 414 cases of sexually transmitted and blood-borne infectious diseases. The proportion of reported cases from tertiary grade-B hospitals was the highest in all the four research periods, but exhibited an obvious decrease in 2023. The differences in the reported cases of infectious diseases with different transmission routes among medical institutions at all levels were statistically significant (χ²=3 225.628, P<0.05). The differences in the mean age of patients among medical institutions at all levels were statistically significant (H=1 325.927, P<0.05). ConclusionThere are significant differences in the number of reported cases of infectious disease in the medical institutions at different levels. Tertiary grade-B hospitals have historically dominated the number of reported cases, but its share has declined recently. Whereas, CHCs and tertiary grade-A hospitals have played an increasingly important role in the surveillance and early warning of respiratory and intestinal infectious diseases. Therefore, it is recommended to leverage the strengths of grading diagnosis and treatment to establish targeted sentinel sites and deploy specialized teams tailored to the epidemiological characteristics of specific disease categories.

Objective To investigate the protective effects of Qiji Special Dietary Food on exercise-induced myocardial injury in mice.Methods An exercise-induced myocardial injury model was established using a treadmill running protocol,and at the same time,the modeled mice were administered Qiji Special Dietary Food via oral gavage,followed by a 4-week treadmill exhaustion test.Serum levels of cardiac troponin T(cTnT),creatine kinase(CK),and lactate dehydrogenase(LDH)were measured.Myocardial tissues were analyzed for superoxide dismutase(SOD)activity and malondialdehyde(MDA)content.Histopathological alterations and ultrastructural changes in myocardial tissue were evaluated using light microscopy and transmission electron microscopy(TEM).Results After 4 weeks of exhaustive training,compared to the control group,the model group exhibited significantly elevated serum cTnT,CK,and myocardial MDA levels(P<0.01),along with reduced myocardial SOD activity(P<0.01).Compared to the model group,high-,medium-,and low-dose treatment significantly attenuated the exhaustive exercise-induced increases in serum cTnT level and myocardial MDA content(P<0.01),restoring these indicators to the levels comparable to those of the normal control group(P<0.05).Additionally,all treatment groups had markedly increased myocardial SOD activity,with no significant difference from the normal group.Histopathological and ultrastructural analyses revealed markedly alleviated myocardial damage in the treatment groups,with the medium-dose group exhibiting the most pronounced protective effects.Conclusion Qiji Special Dietary Food demonstrates significant protective effects against exercise-induced myocardial injury in mice,which maybe associated with its antioxidant activity and mitigation of oxidative stress.

Organophosphorus nerve agents are the most threatening chemical warfare agents and terrorist agents.The number of nerve agents and their related chemicals involved in the verification of Chemical Weapon Convention(CWC)exceeds ten million,with the majority being isomers.Accurate structural identification of these chemicals has always been one of the challenges in CWC related verification analysis.In this work,a total of 17 kinds of alkyl phosphonate isomers and structural analogs from 5 groups were designed and synthesized,and then analyzed by gas chromatography-mass spectrometry(GC-MS)and gas chromatography-infrared spectroscopy(GC-FTIR).The spectra of isomers or structural analogs obtained from two techniques as well as the structural information provided therein were compared and analyzed.The results showed that for isomers or structural analogs with similar MS spectra,FTIR spectra could provided more structural fingerprint information of compounds and had advantages in confirming structures.Combined with the excellent separation ability of GC,GC-FTIR can be used to assist GC-MS in the structural confirmation of alkyl phosphates,achieving rapid and accurate identification of isomers or structural analogues.