OBJECTIVE To explore the potential mechanism by which drug-containing serum of Dianxianqing granules （DXQ） inhibits microglial ferroptosis. METHODS Male SD rats were given normal saline and Dianxianqing granules solution via intragastric administration to prepare normal serum and DXQ， respectively. Mice microglia BV2 cells were collected and successfully transfected with a negative control small interfering RNA （si-NC）， and then they were included in the si-NC group and cultured under normal conditions. Cells successfully transfected with small interfering RNA targeting glutathione peroxidase 4 （GPX4） （si-GPX4） were divided into the si-GPX4 group， the CsA group （treated with 1 μmol/L cyclosporine A）， and the DXQ- L， DXQ-M and DXQ-H groups （treated with 5%， 7% and 10% DXQ， respectively）. These groups were subsequently treated with their corresponding drug solutions and ferroptosis inducer Erastin （10 μmol/L）. The intracellular levels of total iron ions， glutathione （GSH）， reactive oxygen species （ROS）， and the expression of mitochondrial superoxide were determined in each group after 48 h of treatment. Additionally， mitochondrial membrane potential， the opening degree of mitochondrial permeability transition pore （MPTP）， and mRNA expressions of GPX4 and cyclophilin D （CypD） were detected. Furthermore， the expressions of ferroptosis-related proteins[GPX4， transferrin receptor 1 （TfR1） and ferritin heavy chain 1 （FTH1）]， as well as MPTP-related proteins [adenine nucleotide translocator （ANT）， cytochrome C （CytC）， mitochondrial calcium uniporter （MCU） and CypD] were assessed. RESULTS Compared with si-NC group， the levels of total iron ions and ROS， the expression level of mitochondrial superoxide， the opening degree of MPTP， protein and its mRNA expressions of CypD as well as protein expressions of TfR1 and MCU were increased or up-regulated significantly （P＜0.01）； however， GSH content， mitochondrial membrane potential， protein and mRNA expressions of GPX4， and protein expressions of FTH1， ANT and CytC were decreased or down-regulated significantly （P＜0.01）. Compared with the si-GPX4 group， the cells in the DXQ-M， DXQ-H groups showed a general improvement in the above quantitative indicators （P＜0.01 or P＜0.05）. CONCLUSIONS DXQ can enhance antioxidant capacity by activating the GSH/GPX4 pathway， regulate the expressions of TfR1 and FTH1 protein to correct iron ion homeostasis， inhibit excessive opening of MPTP to improve mitochondrial function， and ultimately suppress microglial ferroptosis.

A 51-year-old male presented with nasal obstruction, followed by progressive hearing loss and blurred vision. Imaging identified space-occupying lesions in the paranasal sinuses, orbits, and paraspinal regions, while laboratory tests confirmed positive anti-proteinase 3 anti-neutrophil cytoplasmic antibody(PR3- ANCA) immunoglobulin G (IgG)and markedly elevated serum IgG4. Despite treatment with corticosteroids, immunosuppressants, and radiotherapy, the patient exhibited steroid dependency with relentless disease progression. Following multidisciplinary consultation, a diagnosis of inflammatory myofibroblastic tumor (IMT) coexisting with ANCA- associated vasculitis (AAV) was favored, though IgG4-related disease remained a critical differential. Ultimately, profound immunosuppression precipitated a severe herpesvirus infection, leading to disseminated intravascular coagulation and multiple organ dysfunction syndrome. This case underscores the rarity and diagnostic complexity of concurrent IMT and AAV, highlights the therapeutic dilemma of balancing primary disease control against fatal opportunistic infections, and emphasizes the critical role of multidisciplinary collaboration in the diagnosis and treatment of complex diseases.

Objective: To investigate the current status of screening myopia and anxiety symptoms and associated factors among junior and senior high school students in Beijing, so as to provide evidence for myopia prevention and control and the improvement of mental health among adolescents. Methods: From September to November 2024, a total of 17 245 junior high schools, general senior high schools and vocational high schools from 16 districts in Beijing were enrolled by stratified cluster sampling method. Questionnaire surveys and vision screening were conducted to collect data on anxiety symptom and screening diagnosed myopia. The Chi square test was used to analyze the co-occurrence of myopia and anxiety symptoms, and binary Logistic regression analysis was adopted to explore the related factors of the co-occurrence. Results: The overall detection rate of cooccurrence screening myopia and anxiety symptoms among Beijing junior and senior high school students was 6.00%. The detection rate was higher in females ( 7.15 %) than in males (4.90%), higher in urban areas (6.65%) than in suburban areas (5.41%), and higher in general senior high school students (7.61%) than in vocational high school students (6.46%) and junior high school students (4.65%). All differences were statistically significant ( χ 2=38.49, 11.66, 54.88, all P <0.01). Binary Logistic regression analysis showed that female gender ( OR =1.43), general senior high school ( OR =1.60), vocational high school ( OR =1.59), daily sugar sweetened beverage intake ( OR =1.66), participation in academic extracurricular classes in preschool ( OR =1.30), electronic screen use for more than 2 hours per day ( OR =1.21), and insufficient sleep ( OR =2.41) were associated with an increased risk of co-occurring screening diagnosed myopia and anxiety symptoms (all P <0.05). Conclusions The co-occurrence of screening diagnosed of myopia and anxiety symptoms among junior and senior high school students in Beijing is common. Female gender, senior high school students, and unhealthy lifestyle behaviors are all risk factors for the co-occurrence of myopia and anxiety symptom. Comprehensive intervention measures can be adopted to simultaneously promote vision protection and mental health among junior and senior high school students.

Objective To analyze the influencing factors of respiratory failure in patients with sepsis, and to construct and validate a risk prediction model. Methods A total of 308 patients with sepsis in Ganzi Tibetan Autonomous Prefecture People's Hospital from August 2021 to September 2024 were enrolled and divided into a modeling group and a validation group. According to whether respiratory failure occurred during hospitalization, the patients were divided into a respiratory failure group and a non-respiratory failure group. Single-factor and binary logistic regression analysis were used to construct a risk prediction model, and the ROC, calibration, and DCA curves were used to assess the model's predictive performance. Results The incidence of respiratory failure was 20.83% (45/216) in the modeling group and 20.65% (19/92) in the validation group. Albumin, D-dimer, serum calcium, procalcitonin at admission, and sequential organ failure assessment were independent influencing factors of respiratory failure in patients with sepsis. The ROC curve analysis nomogram model predicted that the AUC of respiratory failure in the modeling group was 0.994 (95%CI: 0.985-1.000), and the AUC of the validation group was 0.897 (95%CI: 0.826-0.968). The calibration curve analysis showed that the calibration curve fit well with the ideal curve. DCA curve analysis showed that the net benefit was greater than 0 across all threshold probability ranges. Conclusion The risk prediction model constructed in this study is highly effective and can provide reference for medical staff to evaluate respiratory failure in patients with sepsis.

AIM: To investigate the prevalence of pterygium in the southeast coastal area, analyze associated risk factors, and develop a multimodal machine learning predictive model.METHODS:A cross-sectional epidemiological study. A random sampling of the resident population of the Zhoushan Islands was performed. Data were collected through on-site ophthalmic examinations and epidemiological surveys. Univariate and multivariate analyses were performed to identify risk factors associated with pterygium. A multimodal artificial intelligence machine learning model was constructed to integrate and analyze both structured and unstructured data for predictive modeling. RESULTS:A total of 1 842 participants were enrolled in this study, including 926 males(50.27%)and 916 females(49.73%), with a mean age of 48.62±10.35 y. A total of 644 patients(34.96%), including 323 males(50.15%)and 321 females(49.84%)were diagnosed with pterygium, including 502 cases(77.95%)were unilateral, and 142 cases(22.05%)were bilateral. There were 1 198 cases without pterygium, including 603 males(50.33%)and 595 females(49.67%). Occupation, daily outdoor sunlight exposure duration, meibomian gland orifice obstruction, age, smoking, and alcohol consumption were identified as significant risk factors for pterygium. Occupation and daily outdoor sunlight exposure duration were the most potent risk factors(OR=6.125, P<0.001; OR=5.348, P<0.001, respectively), followed by age >40 y(OR=5.295, P<0.001)and meibomian gland orifice obstruction(OR=5.248, P<0.001). The multimodal machine learning model demonstrated good predictive performance, as evidenced by the receiver operating characteristic(ROC)curve, precision-recall(PR)curve, and confusion matrix, all indicating high predictive value, AUC=0.86, AP=0.77.CONCLUSION: Fishermen and farmers aged over 40 y with more than 4 h of daily sunlight exposure are at high risk for pterygium in the southeast coastal area. Meibomian gland dysfunction is correlated with corneal invasion of pterygium and may facilitate its progression; therefore, active health education and targeted intervention are recommended. The multimodal machine learning model provides accurate and reliable predictive capability for pterygium risk, which may facilitate risk stratification and early warning for pterygium in southeast coastal populations.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

The chemical constituents from the n-butanol fraction of ethanol extract of safflower (Carthamus tinctorius L.) were isolated and purified using chromatographic process including MCI Gel CHP-20, ODS, Sephadex LH-20 column chromatography, and semi-preparative HPLC method, and one alkyne and two phenylpropanoid derivants were obtained. Their structures were identified as (5R,E)-tetradecane-12-ene-8,10-diyne-1,5,14-triol (1), (E)-8-O-β-D-glucopyranosyl n-butyl cinnamate (2), and (7S,8S)-7-(4-hydroxy-3-methoxybenzene)-7-butyl-8,9-diol (3) by modern spectroscopy methods (1D NMR, 2D NMR, UV, IR and MS). 1-3 are all new compounds. Compounds 1-3 were screened for their anti-renal fibrosis activities in vitro, and none of them showed obvious effect.

Six compounds were isolated from the ethyl acetate fraction of Citri Sarcodactylis Fructus by using various column chromatographic methods, such as MCI Gel CHP-20, ODS, Sephadex LH-20, silica gel and semipreparative HPLC. Their structures were identified to be citrusin G (1), citrusin H (2), citrusin E (3), syringin (4), coniferin (5), methylconiferin (6) by NMR, HR-ESI-MS, UV, IR spectra. Compounds 1 and 2 were new secondary metabolism products and 3-6 were obtained from the titled material for the first time. Compound 1 showed anti-renal fibrosis activity in TGF-β1-induced kidney proximal tubular cells.

AIM: To investigate the influence of relevant inflammatory markers in peripheral blood on the progression of neovascular glaucoma(NVG)secondary to diabetic retinopathy(DR)patients.METHODS: Retrospective case-control study. Patients were categorized into two groups based on the presence or absence of NVG: those with proliferative diabetic retinopathy(PDR)alone(PDR group, n=148)and those with NVG secondary to PDR(NVG secondary to PDR group, n=142). Peripheral blood inflammatory markers were evaluated, including white blood cell-related indices, neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), monocyte-to-lymphocyte ratio(MLR), and systemic immune-inflammation index(SII). The distinctions in peripheral blood inflammatory markers between the two groups of patients and their relationships with NVG secondary to PDR were analyzed.RESULTS:No statistically significant differences were observed in basic characteristics between the two groups, confirming their comparability. However, significant differences were found in eosinophil percentage and MLR between the PDR group and the NVG secondary to PDR group(all P<0.05), with both values being significantly higher in the NVG secondary to PDR group. Multivariate Logistic regression analysis revealed that the eosinophil percentage and the MLR were factors influencing the development of patients with NVG secondary to PDR.CONCLUSION: Eosinophil percentage and MLR may be associated with the progression of PDR to NVG, and could serve as potential predictive markers for NVG development in PDR patients.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.