To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveTo investigate the mechanism of Yangxin Tongmai Formula （养心通脉方， YTF） in trea-ting coronary heart disease with blood stasis syndrome based on DNA methylation. MethodsSeventy-two SD rats were randomly divided into a control group （n=12） and a modeling group （n=60）. The modeling group was subjected to a high-fat diet， intragastric administration of vitamin D3， and subcutaneous injection of isoprenaline to establish the rat model of coronary heart disease with blood stasis syndrome. Forty-one successfully modeled rats were then randomly allocated into model group， YTF low-， medium-， and high-dose groups， and the atorvastatin calcium group， with 8 rats in each group and 1 rat reserved. The YTF low-， medium-， and high-dose groups received YTF at 6， 12， and 18 g/（kg·d） by gavage， respectively. The atorvastatin calcium group received atorvastatin calcium tablets at 1.8 mg/（kg·d） by gavage. The control group and the model group received 0.9% sodium chloride injection at 4 ml/（kg·d） by gavage. All administrations were performed once daily for 3 weeks. Twenty-four hours after the last administration， serum lipid levels including total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C）， myocardial enzymes including cardiac troponin T （cTnT）， creatine kinase MB （CK-MB）， and lactate dehydrogenase （LDH）， and inflammatory factors including interleukin-1β （IL-1β） and interleukin-10 （IL-10） were detected by ELISA. Pathological changes in myocardial tissue were observed via HE staining. Whole blood DNA methylation sequencing was used to analyze differential methylation gene expression among the control group， model group， and YTF high-dose group. Western Blotting was used to verify the protein levels of the key genes and downstream signaling pathways. ResultsCompared to the control group， the model group showed increased levels of TC， TG， LDL-C， cTnT， CK-MB， LDH， and IL-1β， along with decreased levels of HDL-C and IL-10 （P＜0.05 or P＜0.01）. Compared to the model group， all treatment groups exhibited decreased levels of TC， LDL-C， CK-MB， and LDH， along with increased IL-10 levels. Among these， the high-dose YTF group demonstrated superior efficacy in reducing cTnT levels compared to the other TCM groups （P＜0.05 or P＜0.01）. HE staining indicated that the YTF high-dose group ameliorated myocardial cell swelling， disordered arrangement， pyknosis， and disappearance of nuclei， thereby reducing myocardial cell damage. Whole blood DNA methylation sequencing identified 240 differentially methylated genes shared by the control group， model group， and YTF high-dose group， including 109 hypermethylated and 131 hypomethylated genes； eif2ak3 was identified as a key differentially methylated gene. Compared to the control group， the model group exhibited increased protein levels of eukaryotic translation initiation factor 2 alpha kinase 3 （eIf2ak3）， phosphorylated protein kinase RNA-like endoplasmic reticulum kinase （p-PERK）， activating transcription factor 4 （ATF4）， C/EBP homologous protein （CHOP）， and Bax， along with a decreased level of B-cell lymphoma-2 （Bcl-2） protein （P＜0.05 or P＜0.01）. Compared to the model group， the YTF high-dose group showed decreased protein levels of eIf2ak3， p-PERK， ATF4， CHOP， and Bax， and an increased level of Bcl-2 protein （P＜0.05 or P＜0.01）. ConclusionYTF may regulate key differentially methylated genes such as eIf2ak3 and the PERK/ATF4/CHOP signaling pathway， thereby inhibiting endoplasmic reticulum stress， reducing myocardial cell apoptosis， and exerting therapeutic effects in coronary heart disease blood stasis syndrome.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Lactylation (Kla), a protein post-translational modification characterized by the covalent conjugation of lactyl groups to lysine residues in proteins, is widely present in living organisms. Since its discovery in 2019, it has attracted much attention for its role in regulating major pathological processes such as tumorigenesis, neurodegenerative diseases, and cardiovascular diseases. By mediating core biological processes such as signal transduction, epigenetic regulation, and metabolic homeostasis, lactylation contributes to disease progression. However, the lactylation donor lactyl-CoA has a low intracellular concentration, and the specific enzyme catalyzing lactylation is not yet clear, which has become an urgent issue in lactate research. A groundbreaking study in 2024 found that alanyl-transfer t-RNA synthetase 1/2 (AARS1/2), members of the aminoacyl-tRNA synthetase (aaRS) family, can act as protein lysine lactate transferases, modifying histones and metabolic enzymes directly with lactate as a substrate, without relying on the classical substrate lactyl-CoA, promoting a new stage in lactate research. Although exercise significantly increases lactate levels in the body and can induce changes in lactylation in multiple tissues and cells, the regulation of lactylation by exercise is not entirely consistent with lactate levels. Research has found that high-intensity exercise can induce upregulation of lactate at 37 lysine sites in 25 proteins of adipose tissue, while leading to downregulation of lactate at 27 lysine sites in 22 proteins. The level of lactate is not the only factor regulating lactylation through exercise. We speculate that the lactate transferase AARS1/2 play an important role in the process of lactylation regulated by exercise, and AARS1/2 should also be regulated by exercise. This review introduces the molecular biology characteristics, subcellular localization, and multifaceted biological functions of AARS, including its canonical roles in alanylation and editing, as well as its newly identified lactate transferase activity. We detail the discovery of AARS1/2 as lactylation catalysts and the specific process of them as lactate transferases catalyzing protein lactylation. Furthermore, we discuss the pathophysiological significance of AARS in tumorigenesis, immune dysregulation, and neuropathy, with a focus on exploring the expression regulation and possible mechanisms of AARS through exercise. The expression of AARS in skeletal muscle regulated by exercise is related to exercise time and muscle fiber type; the skeletal muscle AARS2 upregulated by long-term and high-intensity exercise catalyzes the lactylation of key metabolic enzymes such as pyruvate dehydrogenase E1 alpha subunit (PDHA1) and carnitine palmitoyltransferase 2 (CPT2), reducing exercise capacity and providing exercise protection; physiological hypoxia caused by exercise significantly reduces the ubiquitination degradation of AARS2 by inhibiting its hydroxylation, thereby maintaining high levels of AARS2 protein and exerting lactate transferase function; exercise induced lactate production can promote the translocation of AARS1 cytoplasm to the nucleus, exert lactate transferase function upon nuclear entry, regulate histone lactylation, and participate in gene expression regulation; exercise induced lactate production promotes direct interactions between AARS and star molecules such as p53 and cGAS, and is widely involved in the occurrence and development of tumors and immune diseases. Elucidating the regulatory mechanism of exercise on AARS can provide new ideas for improving metabolic diseases and promote health through exercise.