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.

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.

Objective: To explore the correlation between parent-child separation experience and non suicidal self injury (NSSI) behavior among junior and senior high school students, so as to provide a reference for the prevention and improvement of NSSI behavior among junior and senior high school students. Methods: Using a stratified cluster random sampling method, 3 114 junior and senior high school students aged 11-19 years were recruited from Chenzhou and Jishou cities in Hunan Province between November 2024 and September 2025. A self designed questionnaire and the Functional Self injury Behavior Assessment Scale were used to collect information on parental labor migration and adolescents NSSI behavior status. Binary Logistic regression model was used to analyse the association between parent-child separation experiences and NSSI behavior among junior and senior high school students, with further stratified analysis by gender. Results: The prevalence rate of NSSI behavior among junior and senior high school students was 9.9%, and the overall incidence of parent-child separation was 48.9%, comprising 17.1% separated from their father only, 5.5% separated from their mother only, and 26.3% separated from both parents. After adjusting for variables such as age, gender, and ethnicity, the results of binary Logistic regression analyses indicated that adolescents with parent-child separation experiences had a higher risk of NSSI behavior ( OR =1.31, 95% CI =1.01-1.69, P <0.05). Compared with those without parent-child separation experiences, adolescents who were separated from their mother only ( OR =1.68, 95% CI =1.03-2.74) or from both parents ( OR =1.41, 95% CI =1.04-1.92) showed significantly increased risks of NSSI behavior (both P <0.05). Conclusions Parent-child separation experiences are associated with an increased risk of NSSI behavior among junior and senior high school students. Strengthened attention should be paid to left behind senior high school students to reduce the occurrence of NSSI in adolescents.

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.

ObjectiveTo investigate the mechanism of ferroptosis mediated by long-chain acyl-CoA synthetase 4 （ACSL4） signalling pathway in rats with coronary heart disease with blood stasis syndrome and the intervention effect of Xuefu Zhuyutang. MethodsSPF male SD rats were randomly divided into normal group， sham-operation group， model group， trimetazidine group （5.4 mg·kg^-1）， low-， medium-， and high-dose group （3.51， 7.02，14.04 g·kg^-1） of Xuefu Zhuyutang. The coronary artery left anterior descending ligation method was used to prepare a model of coronary heart disease with blood stasis syndrome， and continuous treatment for 7 d was conducted， while the sham-operation group was only threaded and not ligated. The general macroscopic symptoms of the rats were observed， and indicators such as electrocardiogram， echocardiography， and blood rheology were detected. The pathological morphology of myocardial tissue was observed by hematoxylin-eosin （HE） staining， and the changes in mitochondria in myocardial tissue were observed by transmission electron microscopy. The level of iron deposition in myocardial tissue was observed by Prussian blue staining. The levels of 12-hydroxyeicosatetraenoic acid （12-HETE） and 15-HETE were detected in serum by enzyme-linked immunosorbent assay. A biochemical colourimetric assay was used to detect the levels of Fe²⁺， lipid peroxidation （LPO）， glutathione （GSH）， and T-GSH/glutathione disulfide （GSSG） in myocardial tissue. DCFH-DA fluorescence quantitative assay was employed to detect the levels of reactive oxygen species （ROS）. Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was adopted to detect the protein and mRNA expressions of glutathione peroxidase 4 （GPX4）， ferritin heavy chain 1 （FTH1）， ACSL4， and ly-sophosphatidylcholine acyltransferase3 （LPCAT3） in myocardial tissue. ResultsCompared with those in the normal group， the rats in the model group were poor in general macroscopic symptoms. The electrocardiogram showed widened QRS wave amplitude and increased voltage， bow-back elevation of the ST segments， elevated T waves， J-point elevation， and accelerated heart rate. Echocardiography showed a significant reduction in left ventricular ejection fraction （LVEF） and left ventricular fraction shortening （LVFS） （P<0.01）. Blood rheology showed that the viscosity of the whole blood （low， medium， and high rate of shear） was significantly increased （P<0.01）. HE staining showed an abnormal structure of myocardial tissue. There was a large area of myocardial necrosis and inflammatory cell infiltration and a large number of connective tissue between myocardial fibers. Transmission electron microscopy showed that the mitochondria were severely atrophy or swelling. The cristae were reduced or even broken， and the matrix was flocculent or even vacuolated. Prussian blue staining showed that there were a large number of iron-containing particles， and the iron deposition was obvious. The content of 12-HETE and 15-HETE in the serum was significantly increased （P<0.01）. The content of Fe²⁺， LPO， and ROS in myocardial tissue was significantly increased （P<0.01）. The content of GSH was significantly decreased （P<0.01）， and T-GSH/GSSG was decreased （P<0.01）. The protein and mRNA expressions of GPX4 and FTH1 in myocardial tissue were both significantly decreased （P<0.05， P<0.01）， while those of ACSL4 and LPCAT3 increased significantly （P<0.01）. Compared with the model group， the general macroscopic symptoms and electrocardiogram results of rats in low-， medium- and high-dose groups of Xuefu Zhuyutang were alleviated， and the differences in LVEF/LVFS ratios were all significantly increased （P<0.05， P<0.01）. The differences in whole-blood viscosity （low， medium， and high rate of shear） were all significantly decreased （P<0.01）. The results of HE staining and transmission electron microscopy showed that the morphology， structure， and mitochondria of cardiomyocytes were improved. The content of 12-HETE and 15-HETE in serum was reduced to different degrees in low-， medium-， and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）. The content of Fe²⁺， LPO， and ROS was significantly reduced in the medium- and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）， and the content of GSH and T-GSH/GSSG was significantly increased （P<0.05， P<0.01）. The protein and mRNA expressions of GPX4 and FTH1 were significantly increased to varying degrees in the medium- and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）， and ACSL4 and LPCAT3 were decreased to different degrees in the low-， medium-， and high-dose groups of Xuefu Zhuyutang （P<0.05， P<0.01）. ConclusionXuefu Zhuyutang can regulate iron metabolism and anti-lipid oxidation reaction to mediate ferroptosis through the ACSL4 signalling pathway， thus exerting a protective effect on rats with coronary heart disease with blood stasis syndrome.

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.

ObjectiveTo investigate the influence of histone deacetylase 3 (HDAC3) on the occurrence, development of psoriasis-like inflammation in mice, and the relative immune mechanisms. MethodsHealthy C57BL/6 mice aged 6-8 weeks were selected and randomly divided into 3 groups: control group (Control), psoriasis model group (IMQ), and HDAC3 inhibitor RGFP966-treated psoriasis model group (IMQ+RGFP966). One day prior to the experiment, the back hair of the mice was shaved. After a one-day stabilization period, the mice in Control group was treated with an equal amount of vaseline, while the mice in IMQ group was treated with imiquimod (62.5 mg/d) applied topically on the back to establish a psoriasis-like inflammation model. The mice in IMQ+RGFP966 group received intervention with a high dose of the HDAC3-selective inhibitor RGFP966 (30 mg/kg) based on the psoriasis-like model. All groups were treated continuously for 5 d, during which psoriasis-like inflammation symptoms (scaling, erythema, skin thickness), body weight, and mental status were observed and recorded, with photographs taken for documentation. After euthanasia, hematoxylin-eosin (HE) staining was used to assess the effect of RGFP966 on the skin tissue structure of the mice, and skin thickness was measured. The mRNA and protein expression levels of HDAC3 in skin tissues were detected using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot (WB), respectively. Flow cytometry was employed to analyze neutrophils in peripheral blood and lymph nodes, CD4⁺ T lymphocytes, CD8⁺ T lymphocytes in peripheral blood, and IL-17A secretion by peripheral blood CD4⁺ T lymphocytes. Additionally, spleen CD4⁺ T lymphocyte expression of HDAC3, CCR6, CCR8, and IL-17A secretion levels were analyzed. Immunohistochemistry was used to detect the localization and expression levels of HDAC3, IL-17A, and IL-10 in skin tissues. ResultsCompared with the Control group, the IMQ group exhibited significant psoriasis-like inflammation, characterized by erythema, scaling, and skin wrinkling. Compared with the IMQ group, RGFP966 exacerbated psoriasis-like inflammatory symptoms, leading to increased hyperkeratosis. The psoriasis area and severity index (PASI) skin symptom scores were higher in the IMQ group than those in the Control group, and the scores were further elevated in the IMQ+RGFP966 group compared to the IMQ group. Skin thickness measurements showed a trend of IMQ+RGFP966>IMQ>Control. The numbers of neutrophils in the blood and lymph nodes increased sequentially in the Control, IMQ, and IMQ+RGFP966 groups, with a similar trend observed for CD4⁺ and CD8⁺ T lymphocytes in the blood. In skin tissues, compared with the Control group, the mRNA and protein levels of HDAC3 decreased in the IMQ group, but RGFP966 did not further reduce these expressions. HDAC3 was primarily located in the nucleus. Compared with the Control group, the nuclear HDAC3 content decreased in the skin tissues of the IMQ group, and RGFP966 further reduced nuclear HDAC3. Compared with the Control and IMQ groups, RGFP966 treatment decreased HDAC3 expression in splenic CD4⁺ and CD8⁺ T cells. RGFP966 treatment increased the expression of CCR6 and CCR8 in splenic CD4⁺ T cells and enhanced IL-17A secretion by peripheral blood and splenic CD4⁺ T lymphocytes. Additionally, compared with the IMQ group, RGFP966 reduced IL-10 protein levels and upregulated IL-17A expression in skin tissues. ConclusionRGFP966 exacerbates psoriatic-like inflammatory responses by inhibiting HDAC3, increasing the secretion of the cytokine IL-17A, and upregulating the expression of chemokines CCR8 and CCR6.

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.