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.

OBJECTIVE To compare the efficacy and safety of evolocumab and probucol in patients with ultra-high-risk atherosclerotic cardiovascular disease （ASCVD） following percutaneous coronary intervention （PCI）. METHODS A retrospective analysis was conducted on 156 ultra-high-risk ASCVD patients who underwent PCI in our institution between January 1， 2023 and December 31， 2024. According to the lipid-lowering regimen， the patients were categorized into evolocumab group （ n ＝86） and probucol group （ n ＝70）. Changes in lipid parameters [total cholesterol （TC）， low-density lipoprot ein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， triglycerides， lipoprotein （a）， and lipid goal achievement rate ] ， inflammatory markers [interleukin-6 （IL-6） and C-reactive protein （CRP） ] ， and cardiac function indices （left ventricular ejection fraction， left ventricular end-systolic diameter， left ventricular end-diastolic diameter， and N-terminal pro-B-type natriuretic peptide） were compared between two groups at baseline and after 6 months of treatment. The incidence of adverse clinical events during treatment， including acute myocardial infarction， in-stent restenosis， acute heart failure， cerebral hemorrhage， and stroke， was also evaluated. RESULTS No statistically significant differences were observed between the two groups at baseline （ P ＞0.05）. After 6 months of treatment， both groups demonstrated significant improvements in lipid profiles （except HDL-C） and inflammatory markers compared to those at baseline （ P ＜0.05）. The evolocumab group exhibited greater reductions in TC， LDL-C， IL-6， and CRP， along with a higher lipid target achievement rate， compared with the probucol group （ P ＜0.05）. There were no statistically significant differences in the cardiac function-related indicators before and after treatment between the two groups， nor in the incidence of adverse events during the treatment （ P ＞0.05）. CONCLUSIONS For ultra-high-risk ASCVD patients after PCI， both of the above treatment options are associated with improvements in blood lipid and inflammatory response， with good safety during short-term follow-up. Evolocumab shows superior efficacy in TC， LDL-C and inflammatory markers reduction and lipid target achievement， compared to probucol.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate whether the effect of Danggui Shaoyaosan （DSS） on cognitive impairment is related to its promotion of ketone body metabolism in the brain and regulation of glutamate/α-amino-3-hydroxy-5-methyl-4-isox-azolepropionic acid receptor （AMPAR） pathway. MethodsMale APP/PS1 mice were randomized into model， low-dose （3.2 g·kg^-1） DSS， medium-dose （6.4 g·kg^-1） DSS， high-dose （12.8 g·kg^-1） DSS， ketogenic diet， and inhibitor （talampanel， 5 mg·kg^-1） groups. C57BL/6J wild-type mice were selected as the normal group. The talampanel group was intragastrically administered for 2 consecutive weeks from the 6th week， and the other groups were continuously treated for 8 weeks before behavioral testing. The ultrastructure of synapses in hippocampal CA1 region was observed by transmission electron microscopy， and calcium/calmodulin-dependent kinase Ⅱ （CaMKⅡ） expression was detected by immunohistochemistry （IHC）. The levels of β-hydroxybutyrate （BHB） and lactic acid （LAC） were measured by biochemical kits， and those of glutamate （Glu）， glutamine （Gln）， γ-aminobutyric acid （GABA）， and synaptophysin （SYP） by enzyme-linked immunosorbent assay （ELISA）. The mRNA levels of glutamine synthetase （GS）， glutaminase （GLS）， glutamate decarboxylase （GAD）， CaMKⅡ， and AMPAR subunits GluA1 and GluA2 were quantified by Real-time quantitative polymerase chain reaction （Real-time PCR）， and the protein levels of GS， GLS， GAD， GluA1， and GluA2 by Western blot. ResultsCompared with the normal group， the model group exhibited decreased recognition index， spontaneous alternation rate， and residence time in the target quadrant （P<0.01）， increased escape latency and ratio of the time in the opposite side of the target quadrant to the time in the target quadrant （P<0.05， P<0.01）， damaged synaptic ultrastructure of hippocampal CA1 region， increased positive expression of CaMKⅡ， no significant difference in BHB level， elevated LAC and Glu levels （P<0.01）， declined SYP， Gln， and GABA levels （P<0.01）， downregulated mRNA and protein levels of GS， GAD， and GluA2 （P<0.05， P<0.01）， and upregulated mRNA and protein levels of GLS and GluA1 （P<0.05， P<0.01） and mRNA level of CaMKⅡ （P<0.01）. Compared with the model group， DSS intervention improved the behavioral indexes of mice （P<0.05， P<0.01）， enhanced the synaptic ultrastructure plasticity in hippocampal CA1 region， decreased the positive expression of CaMKⅡ， raised the BHB level （P<0.01）， reduced the LAC and Glu levels （P<0.01）， elevated the SYP， Gln， and GABA levels （P<0.01）， upregulated the mRNA and protein levels of GS， GAD， and GluA2， and downregulated the mRNA and protein levels of GLS and GluA1 （P<0.05， P<0.01） and the mRNA level of CaMKⅡ （P<0.01）. ConclusionDSS can improve the learning and memory and enhance the synaptic plasticity of APP/PS1 mice. Its neuroprotective effect is coupled with the promotion of ketone body formation and remodeling of brain energy metabolism， and is related to the regulation of glutamate/AMPAR pathway.

Objective: To evaluate the feasibility of exempting Asian-type DEL pregnant women from anti-D monitoring and RhD immunoglobulin prophylaxis injections by comparing and analyzing the clinical incidence of anti-D alloimmunization between Asian-type DEL pregnant women and true RhD-negative pregnant women. Methods: A total of 165 pregnant women who were initially screened as RhD negative by the saline method and received medical treatment in our hospital from December 2022 to August 2024 were collected as the research subjects. Absorption and elution tests, DEL genotyping, and gene sequencing were used to divide the pregnant women into the Asian-type DEL group and the true negative group. After obtaining informed consent, the following clinical management plan was implemented for pregnant women with Asian-type DEL: exemption from routine anti-D antibody detection, exemption from RhD immunoglobulin prophylaxis, and transfusion of RhD-positive red blood cells. Blood samples of newborns were sent for examination of hemolytic disease of the fetus and newborn (HDFN). The routine management plan was implemented for true negative pregnant women. The incidence of alloimmunization and HDFN was comparatively analyzed between the two groups. Results: Among 165 initially screened RhD negative pregnant women, serological testing and genotyping confirmed 42 as Asian-type DEL, 9 as D variant, and 114 as true negative. Among 42 pregnant women with Asian-type DEL, 3 cases tested positive for HDFN due to receiving RhD immunoglobulin prophylaxis injection. The remaining 39 cases were exempted from anti-D testing after being fully informed of the risk, and did not receive RhD immunoglobulin prophylaxis. The HDFN tests were all negative. In the true negative group, anti-D antibodies were detected in 20 cases, of which 6 cases tested positive for HDFN. A pregnant woman with Asian -type DEL did not show RhD homologous immune response after receiving 2 units of RhD positive red blood cells. Statistical analysis revealed a significantly lower risk of anti-D alloimmunization in Asian-type DEL carriers compared to true D-negative pregnant women (P<0.05). Conclusion: Pregnant women with Asian-type DEL can be exempted from routine anti-D antibody testing and do not require routine RhD immunoglobulin prophylaxis injections.

Triple-negative breast cancer is therapeutically challenging due to the low expression of tumor markers and 'cold' tumor immunosuppressive microenvironment. Here, we present a dual-targeting peptide-drug conjugate (PDC) for tumor inhibition. Our PDC efficiently and selectively delivers cytotoxic Monomethyl Auristatin E (MMAE) into tumor cells via C-X-C chemokine receptor type 4 (CXCR4) and folate receptor 1 (FOLR1) for synergistic inhibition of growth and metastasis. Our results show that the dual-targeting PDC has potent antitumor activity in cultured human cells and several murine transplanted tumor models without apparent toxicity. The combination of dual-targeting PDC and radiotherapy modulates the tumor immunosuppressive microenvironment by increasing CD8⁺ T cell infiltration and attenuating the proportion of myeloid-derived suppressor and regulatory T cells. Therefore, our dual-targeting PDC represents a promising new strategy for cancer therapy that rebalances the immune system and promotes tumor regression.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male