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.

ObjectiveCerebral ischemic injury triggers a complex pathological cascade characterized by excessive reactive oxygen species (ROS) accumulation, persistent oxidative stress, and sustained neuroinflammation in the injured brain microenvironment. These events collectively drive mitochondrial dysfunction, microglial overactivation, pro-inflammatory cytokine release, and progressive neuronal apoptosis, ultimately leading to severe and irreversible neurological deficits. However, conventional therapeutic strategies face critical limitations, including poor blood-brain barrier penetration, insufficient local drug concentration, uncontrolled drug release, and off-target systemic side effects. To address this pathological process, we rationally designed and fabricated an injectable ROS-responsive hydrogel loaded with polydopamine nanoparticles (PDA NPs) for spatiotemporally controlled antioxidation, anti-inflammation, and neuroprotection in the ischemic injury microenvironment. The present study aimed to systematically characterize the physicochemical properties, ROS-responsive drug release behavior, biocompatibility, and neuroprotective efficacy of this composite hydrogel system in vitro. MethodsPDA NPs were fabricated via oxidative self-polymerization. The ROS-responsive hydrogel was cross-linked using N1-(4-boronobenzyl)-N3-(4-boronophenyl)-N1,N1,N3,N3-tetramethylpropane-1, 3-diaminium (TSPBA) and polyvinyl alcohol (PVA). Morphology, particle size, Zeta potential, and structure of PDA NPs were characterized by dynamic light scattering (DLS), Zeta potential analysis, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Microstructure, rheological properties, shear-thinning behavior, and ROS-triggered release profiles of the hydrogel were examined by SEM and rheometry. Biocompatibility was evaluated using HT22 mouse hippocampal neurons with CCK-8 and live/dead staining. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established to simulate ischemic injury in vitro. ROS levels and neuronal apoptosis were detected by DHE staining and TUNEL assay. Microglial polarization and pro-inflammatory cytokine expression were analyzed using immunofluorescence and RT-qPCR in BV-2 microglia. Transwell co-culture was used to verify the indirect neuroprotection mediated by modulated microglia. ResultsCharacterization results confirmed that the as-prepared PDA NPs were monodispersed spherical nanoparticles with uniform diameter and negative surface potential, demonstrating favorable dispersibility and robust ROS-scavenging activity. The TSPBA-PVA hydrogel exhibited a highly porous interconnected network, suitable mechanical strength, and obvious shear-thinning behavior, supporting its application as an injectable implant. More importantly, the hydrogel displayed typical ROS-responsive degradation and on-demand PDA NP release in a ROS-concentration-dependent manner. In vitro cellular experiments demonstrated that the PDA NP-loaded hydrogel possessed excellent biocompatibility with HT22 cells. In the OGD/R model, the hydrogel significantly reduced intracellular ROS accumulation and markedly suppressed neuronal apoptosis. Furthermore, the composite hydrogel effectively redirected BV-2 microglia from the pro-inflammatory M1 toward the anti-inflammatory M2 phenotypes, downregulated the expression of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6, and reduced inflammatory damage. Transwell co-culture assays further validated that M2-polarized microglia mediated by the hydrogel significantly enhanced the survival of OGD/R-injured HT22 neurons and attenuated apoptosis. ConclusionIn this study, we successfully developed a novel injectable ROS-responsive hydrogel loaded with PDA NPs for synergistic antioxidative and anti-inflammatory neuroprotection. This intelligent hydrogel system enables ROS-triggered on-demand release of PDA NPs, efficiently scavenges excessive ROS, inhibits oxidative stress injury, modulates microglial polarization, and suppresses neuroinflammation, thereby exerting robust neuroprotective effects in vitro. This biomaterial platform provides a promising strategy for the targeted and controlled delivery of bioactive nanomaterials in the central nervous system diseases and establishes a solid experimental foundation for the development of in situ injectable therapies for ischemic brain injury.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective To explore the correlation between thyroid hormones and massive proteinuria in patients with type 2 diabetes mellitus (T2DM). Methods A total of 308 patients with T2DM admitted to 3201 Hospital between June 2020 and June 2025 were selected as the observation group, and another 250 healthy individuals who underwent physical examinations during the same period served as the control group. The thyroid hormones levels [free triiodothyronine (FT₃), thyroid stimulating hormone (TSH), and free thyroxine (FT₄)] were measured and compared between the two groups. Based on whether 24-hour urinary protein quantification was ≥ 3.5 g, the patients in the observation group were divided into a massive proteinuria group and a non-massive proteinuria group. Multivariate logistic regression analysis was used to analyze the factors influencing the occurrence of massive proteinuria in T2DM patients. Pearson method was applied to analyze the relationship of thyroid hormone indicators with severity indicators of massive proteinuria. Results Compared with the control group and the non-massive proteinuria group, FT₃ and FT₄ in the massive proteinuria group were lower, while TSH was higher (P<0.05). Multivariate logistic regression analysis suggested that glycosylated hemoglobin (OR=1.113, 95%CI: 1.045-1.185), TSH (OR=1.123, 95%CI: 1.006-1.253), eGFR (OR=0.894, 95%CI: 0.822-0.973), and FT₃ (OR=0.898, 95%CI: 0.828-0.973) were related factors for the occurrence of massive proteinuria in T2DM patients (P<0.05). Pearson correlation analysis revealed that FT3 and FT4 levels in T2DM patients were significantly negatively correlated with 24-hour urinary protein quantification (r=-0.514, -0.502), while the level of TSH was positively correlated with 24-hour urinary protein quantification (r=0.486) (P<0.001). Conclusion In T2DM patients, low FT3 and eGFR levels and high TSH and glycosylated hemoglobin levels are related factors for the occurrence of massive proteinuria, and thyroid hormone levels are significantly associated with the severity of massive proteinuria.

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.

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:To explore the correlation between serum ferritin(SF)levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Methods:Patients with malignant hematological diseases who received repeated transfusions of apheresis platelets in Department of Hematology of Aerospace Center Hospital in 2023 were selected.The platelet corrected count increment(CCI)was used to evaluate the efficacy of platelet transfusion.The correlations between sex,age,disease type,transplantation history,red blood cell transfusion history,and SF level and the efficacy of platelet transfusion were analyzed.Results:A total of 87 patients were included,with a cumulative 326 person-times platelet transfusions.As suggested by one-way analysis of variance,compared with the patients in the age groups of 24-45 years old and 46-66 years old,the patients in the age group of 2-23 years old had a better efficacy of platelet transfusion(P=0.004,P=0.004).There was no significant difference in the efficacy of platelet transfusion between the patients in the age group of 24-45 years old and those in the age group of 46-66 years old(P=0.876).Compared with the patients who had a history of red blood cell transfusion within 3 days,the patients without a history of red blood cell transfusion within 3 days had a better efficacy of platelet transfusion(P＜0.001).Compared with the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L,the group with SF levels＜1.44 ng/L had a better efficacy of platelet transfusion(P=0.028,P＜0.001).Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).After adjusting for age and the history of red blood cell transfusion,the transfusion efficacy of the group with SF levels＜1.44 ng/L was better than that of the groups with SF levels of 1.44-2.78 ng/L and＞2.78 ng/L(P=0.021,P＜0.001);Compared with the group with SF levels＞2.78 ng/L,the group with SF levels of 1.44-2.78 ng/L had a better efficacy of platelet transfusion(P=0.001).Both univariate and multivariate linear regression models showed that SF levels were negatively correlated with the efficacy of platelet transfusion(P＜0.001).Conclusion:There is a negative correlation between SF levels and the efficacy of platelet transfusion in patients with malignant hematological diseases.Detection of SF levels may provide guidance for predicting the efficacy of platelet transfusion.

Aim To explore the effect of hydroxysafflor yellow A(HSYA)on lipocalin 2(LCN2)-induced fer-roptosis in HT22 cells and the related mechanism.Methods Thirty male Sprague-Dawley(SD)rats were used to establish the middle cerebral artery occlu-sion/reperfusion(MCAO/R)model by the suture method.The rats were randomly divided into the Sham group,the MCAO/R group,and the MCAO/R+HSYA group.The infarct area was measured by TTC staining,and the degree of neurological deficit was evaluated by the Z-Longa scoring method.The expressions of LCN2 and 24P3R in brain tissues were detected by Western blot.LCN2 protein was added to HT-22 cells,and the cells were divided into the normal group,the LCN2 group,and the LCN2+HSYA group.The optimal con-centration of LCN2-induced neuronal ferroptosis was screened by LDH assay and Western blot,and the ex-pression levels of ferritin,FPN1,GPX4,SLC7A11,COX2,and 24P3R were detected.LCN2 was knocked down by siRNA transfection,and the expressions of GPX4 and ferritin were detected.The contents of glu-tathione(GSH),malondialdehyde(MDA),GPX4,and Fe2+were determined by colorimetry,and the expres-sion of GPX4 was detected by immunofluorescence.The binding force between HSYA and LCN2 was ana-lyzed by molecular docking technology.Results Ani-mal experiments showed that HSYA could reduce the cerebral infarction area and decrease the neurological function score of MCAO/R rats.Compared with the sham group,the levels of LCN2 and 24P3R increased in the MCAO/R group,while HSYA inhibited their ex-pressions.Cell experiments showed that the optimal concentration of LCN2 to induce ferroptosis in HT22 cells was 2 μmol·L-1.After knocking down LCN2 by siRNA transfection,compared with the LCN2 group,the expression levels of GPX4 and ferritin in the siLCN2 group increased significantly.Compared with the nor-mal group,the expressions of SLC7A11,GPX4,FPN1,ferritin,and GSH in the LCN2 group decreased signifi-cantly,while the concentration of Fe2+,and the expres-sions of MDA,COX2,and 24P3R increased.HSYA could increase the expressions of SLC7A11,GPX4,FPN1,ferritin,and GSH,reduce the contents of Fe2+and MDA,and inhibit the expressions of COX2 and 24P3R.Molecular docking showed that the binding en-ergy between HSYA and LCN2 was-8.0 kJ·mol-1.Conclusion HSYA can inhibit LCN2-induced ferrop-tosis in HT22 cells through the SLC7A11/GPX4 signa-ling pathway.