Objective To analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three-dimensional (3D) reconstruction for thoracoscopic anatomical pulmonary segmentectomy. Methods A retrospective analysis was conducted on patients who underwent thoracoscopic pulmonary segmentectomy at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI 3D reconstruction were included in the AI group, those who underwent Mimics 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. Results A total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistical differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, or postoperative complications among the three groups (P>0.05). There were statistical differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistical differences in operation time, extubation time, bleeding volume, or postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistical difference in drainage volume between the AI group and the control group (P=0.494), while there were statistical differences in operation time, drainage tube retention time, bleeding volume, and postoperative hospital stay (P<0.05). Conclusion For patients requiring thoracoscopic anatomical pulmonary segmentectomy, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation time and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.

Objective: To explore the association between negative life events and smartphone addiction among middle school students, so as to provide theoretical support and practical guidance for prevention and intervention of smartphone addiction among middle school students. Methods: Using cluster sampling, 8 890 students were selected to survey from 27 junior high schools and 3 senior high schools in a district of Shenzhen in 2022 (baseline) and 2023 (followup). Data were collected through selfresigned questionnaires on basic information, the Smartphone Addiction Scale-Short Version, and the Adolescent Selfrating Life Events Checklist. Mixedeffects models were employed to analyze the association. Results: Compared to 2022, the punishment scores of middle school students in 2023 [1.00 (0.00, 6.00) and 1.00 (0.00, 6.00)] decreased (Z=4.27), while the scores of interpersonal stress, learning stress and adaptation [4.00(0.00, 8.00), 4.00(0.00, 8.00); 4.00(1.00, 8.00), 5.00(2.00, 9.00); 2.00 (0.00, 6.00), 3.00 (0.00, 7.00)] increased (Z=-3.04, -8.36, -6.80) (P<0.01). Mixedeffects models revealed a positive doseresponse relationship between negative life events and smartphone addiction (OR=1.08-1.17, P<0.01). Stepwise regression showed independent positive effects of interpersonal stress (OR=1.05), academic stress (OR=1.03), and adaptation stress (OR=1.11) on smartphone addiction (P<0.01). Subgroup analysis of nonaddicted students in 2022 confirmed persistent associations for academic stress (OR=1.03) and adaptation (OR=1.07) (P<0.01). Conclusion Negative life events exhibit a positive doseresponse relationship with smartphone addiction, particularly interpersonal stress, academic stress, and adaptationrelated events.

Objective: To explore the potential causal association between adolescent compulsive behaviour and smartphone addiction based on longitudinal data, so as to provide reference for the establishment of adolescent smartphone addiction interventions. Methods: A preliminary survey and follow-up were conducted on 8 907 middle and high school students in a district of Shenzhen in 2022 and 2023, respectively. Compulsive behaviours were measured by using the Mental Health Inventory for Middle School Students-60 Items (MMHI-60), smartphone addiction was assessed by using the Smartphone Addiction Scale-Short Version ( SAS- SV), and the associations between compulsive behaviours and smartphone addiction were analysed by using multilevel mixed-effects models and subgroup analyses. Results: Smartphone addiction detection rates among middle school students were significantly associated with genders, father s education level, mother s education level, study load subgroups, and whether or not they were single-parent families, and there were statistical differences ( χ 2=17.21-175.34, P <0.05). Students with compulsive behaviours were 2.98 times more likely to develop smartphone addiction than those without compulsive behaviours ( OR=2.98, 95%CI=2.77-3.22, P <0.05). Subgroup analysis of middle school students without smartphone addiction in the first year found that compulsive behaviours significantly predicted smartphone addiction ( OR= 1.76 , 95%CI=1.54-2.01, P <0.05). Conclusion There is a potential causal association between obsessive-compulsive behaviours and smartphone addiction in middle school students, and obsessive-compulsive behaviours in middle school students could significantly predicted the occurrence of smartphone addiction.

ObjectiveThis study aims to investigate the molecular mechanism by which Guizhi Fulingwan （GFW） inhibits ferroptosis in endometriosis （EMT） through the regulation of the hypoxia inducible factor-1α/heme oxygenase 1 （HIF-1α/HO-1） signaling pathway. MethodsMachine learning was employed to identify ferroptosis-related biomarkers associated with EMT. Network pharmacology was utilized to identify the active components of GFW and its potential therapeutic targets against EMT， including core targets. Functional enrichment analysis was conducted to explore the biological processes， molecular functions， cellular components， and signaling pathways associated with the potential targets. An EMT rat model was established via autologous transplantation. Thirty female Sprague-Dawley （SD） rats were randomly divided into five groups： sham-operated， model， positive control （dienogest at 0.2 mg·kg^-1）， low-dose GFW （2.5 g·kg^-1）， and high-dose GFW （5 g·kg^-1）. After modeling， the rats received their respective treatment by oral gavage for 28 consecutive days， while the sham and model groups received equal volumes of distilled water. Serum and ectopic endometrial tissues were collected. Hematoxylin and eosin （HE） staining was employed to evaluate morphological alterations in ectopic lesions. Quantitative real-time polymerase chain reaction （Real-time PCR） and Western blot were conducted to assess mRNA and protein expression of HIF-1α， HO-1， glutathione peroxidase 4 （GPX4）， spermidine/spermine N1-acetyltransferase （SAT1）， and prostaglandin-endoperoxide synthase 2 （PTGS2）. Tissue levels of malondialdehyde （MDA）， glutathione （GSH）， and ferrous iron （Fe²⁺） were quantified using commercial assay kits. Serum levels of interleukin-6 （IL-6） and transforming growth factor-β₁ （TGF-β₁） were measured via enzyme-linked immunosorbent assay （ELISA）. ResultsFive ferroptosis-related biomarkers in EMT were identified： ALOX12， CHAC1， SAT1， AST1， and HO-1. Network pharmacology analysis revealed 42 active components of GFW and 192 potential therapeutic target genes related to EMT treatment， with FOS， JUN， HO-1 identified as core targets. Functional enrichment analysis indicated that the potential targets were primarily involved in oxidative stress response and reactive oxygen species metabolism and were enriched in the HIF-1 signaling pathway. Compared to the sham-operated group， the model group exhibited significant increases in both mRNA and protein expression of HIF-1α， HO-1， and PTGS2， as well as elevated tissue levels of Fe²⁺ and MDA. Conversely， GSH levels and the expression of GPX4 and SAT1 were markedly reduced， and serum levels of IL-6 and TGF-β₁ levels were significantly higher （P<0.01）. Compared with the model group， all GFW-treated groups showed significant downregulation of HIF-1α and HO-1， reduced Fe²⁺ levels， and downregulated expression of MDA， PTGS2， IL-6， and TGF-β₁. Meanwhile， GSH， GPX4， and SAT1 expression levels were significantly increased （P<0.05， P<0.01）， effectively ameliorating iron overload and oxidative stress， thereby demonstrating therapeutic efficacy in EMT， with the high-dose GFW demonstrating the most pronounced therapeutic effects. ConclusionGFW exerts therapeutic effects on endometriosis by regulating the HIF-1α/HO-1 signaling pathway to rectify iron metabolism disorders and attenuate free iron-induced oxidative damage. It upregulates the antioxidative defense system to inhibit lipid peroxidation cascades and modulates inflammatory cytokine networks. These effects collectively disrupt the pathological interaction between ferroptosis and chronic inflammation， providing a novel theoretical foundation for the clinical application of GFW in EMT treatment.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK _a values. The high reliability and interpretability of GraFpK _a ensure accurate pK _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK _a values, making it a valuable tool in the field of pK _a prediction.

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

To study the carriage status of drug susceptibility, clonal complex groups, serotypes, surface proteins and virulence genes of Streptococcus agalactiae from respiratory specimen sources. A total of 35 strains of S.agalactiae meeting the criteria were collected from 3 hospitals in 2 locations, Tangshan and Jinan. The age span of the patients was 3 days-92 years, and the percentage of elderly patients≥60 years was 71.5%.The susceptibility to 9 antimicrobial drugs was measured and analyzed using the micro broth dilution method. The strains were 100.0% sensitive to penicillin, linezolid, vancomycin, and ceftriaxone; However, it exhibits high resistance rates to erythromycin, clindamycin and levofloxacin, at 97.1%, 85.7% and 82.9% respectively; and the resistance rates to tetracycline and chloramphenicol were 34.3% and 14.2%, respectively. Genome sequence determination and analysis showed that 16 resistance genes were detected in 35 strains, among which: macrolide and lincosamide resistance genes were mainly ermB, with a carrying rate of 74.2%; tetracycline resistance genes were mainly tetM, with a carrying rate of 25.7%; in addition, the mutation rates of the quinolone resistance determinants gyrA and parC were 88.5% and 85.7%, respectively. 35 strains belonged to 6 ST types and 4 clonal groups, with CC10/ST10 as the main one, accounting for 62.8%; they contained 4 serotypes of Ⅰb, Ⅱ, Ⅲ, and Ⅴ, as well as 1 untyped strain, with serotype Ⅰb as the main one, accounting for 65.7%. The strains carried three pilus types, PI1+PI2a, PI2a and PI2b types, respectively, and detected five surface proteins, alpha, alp1, rib, srr, and r df_0594, and seven virulence factors, cba, cfb, cylE, fbsA, hylB, l mb, and pavA. Overall, S.agalactiae isolated from respiratory tract specimens is predominantly sourced from elderly patients, with CC10 strains being most prevalent. These strains harbor multiple drug-resistant and virulence genes, demonstrating elevated resistance rates to macrolides, lincosamides, and quinolones. This emphasizes the necessity for vigilant attention to the health threat posed by S. agalactiae from respiratory tract speciments of elderly patients.