Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

Objective: To analyze the one year follow up changes and influencing factors of anisometropia among primary school students in Beihai, so as to provide data support for formulating targeted prevention and control strategies. Methods: In 2023 and 2024, visual acuity and refractive screening were conducted on primary school students in Beihai. A cohort matching method was used based on unique identifiers to link data from 2023 (baseline) Grades one to five with those from 2024 (follow up) Grades two to six, obtaining a total of 59 743 complete datasets. McNemar test and generalized estimating equations(GEE) model were employed to analyze the changing patterns of anisometropia. Results: The detection rate of anisometropia among primary school students in Beihai increased from 10.88% in 2023 to 12.30% in 2024 ( χ 2=97.12, P <0.01). Among them, the detection rate in Grade 1 decreased from 8.82% in 2023 to 7.50% in 2024, Grade 3 increased from 10.15% to 11.52%, Grade 4 increased from 12.10 % to 15.22%, Grade 5 increased from 14.61% to 17.88% ( χ 2=16.51,18.03,95.52,95.95,all P <0.05). The GEE results showed that the risk of anisometropia development was higher in girls than in boys ( OR =1.15), the risk of anisometropia increased by 16% with each 1 year increment in age ( OR =1.16) among primary school students, the risk of anisometropia development in Grade 5 was 2.20 times higher than that in Grade 1 ( OR =2.20)(all P <0.05). In lower grades, only the baseline anisometropia status in lower grades had a statistically significant effect on anisometropia among primary school students( OR =59.09), while the positive effects of gender and age difference gradually emerged and strengthened in middle and higher grades (all P ＜0.05). Conclusions The detection rate of anisometropia among primary school students in Beihai shows dynamic changes and influencing factors vary by grade level. It is necessary to develop stratified prevention and control strategies tailored to different grades.

Objective: To evaluate the causal effects and population heterogeneity of Guangzhou′s Voluntary Blood Donation Privilege Certificate Policy on blood donation behavior, and to provide empirical evidence for optimizing blood management policies. Methods: Using an interrupted time series (ITS) design, we analyzed 30 quarters of blood collection data from Guangzhou Blood Center (from July 2018 to December 2025). Taking the third quarter of 2021 as the intervention node, we constructed ordinary least squares (OLS) regression models incorporating level and trend effects, and conducted stratified analyses by gender, age group, previous donation frequency, and per-donation volume. Results: The tiered incentive policy demonstrated significant heterogeneous effects. Apheresis donations showed sustained growth, with 2 units of component blood exhibiting a significant trend effect (β=304.58, P<0.001); high-frequency donors (11+ times) demonstrated sustained growth trends in apheresis donations. For whole blood, immediate effects were significant for donors with 1-2 previous donations (β=4 537.55, P<0.001) and 3-10 previous donations (β=2 159.69, P<0.05); high-frequency donors (11-30 times) showed sustained growth trends (P<0.01). Stratified by per-donation volume, 400 mL whole blood demonstrated a significant immediate effect (β=4 391.01, P<0.1), while 200 mL whole blood showed a significant declining trend (β=-893.24, P<0.01). Conclusion: The tiered incentive policy effectively enhanced blood donation participation while maintaining the donors’ altruistic motivations. Young and middle-aged adults primarily drove apheresis donations. The policy demonstrated immediate incentive effects on low-frequency whole blood donors and long-term retention effects on high-frequency donors. Per-donation volume showed an "upward migration" trend, optimizing the blood collection structure. Differentiated outreach, conversion and retention strategies are recommended to further strengthen blood supply security.

AIM: To compare the diagnostic efficiency of binocular uncorrected distance visual acuity(UCDVA), pre-cycloplegia refraction spherical equivalent(PR-SE), axial length(AL)difference, and their different combinations in the screening of anisometropia in children and adolescents, and to evaluate the practical value of different indicator combinations in simplifying the screening process when taking cycloplegic retinoscopy results as the gold standard for diagnosing anisometropia.METHODS: This was a retrospective study. A total of 500 consecutive cases of children and adolescents aged 6-18 years with known refractive status were included. Taking cycloplegic retinoscopy results as the gold standard for anisometropia diagnosis, the binocular UCDVA, PR-SE, and AL difference were incorporated into ROC curve analysis to assess the diagnostic efficacy of each indicator. Furthermore, predictive models were constructed and reliability analysis was performed.RESULTS: The average age of the included cases was 10.75±2.24 years, including 239 males and 261 females. The AUC of the interocular PR-SE difference(0.972, 95%CI: 0.960-0.984)was significantly higher than that of other indicators. The Youden index was the largest when the bincular UCDVA difference was 0.25, the PR-SE difference was 0.743, and the AL difference was 0.31. When the interocular PR-SE difference used 0.743 and 1.00 D as screening cutoffs, the former had a higher AUC(AUC=0.924, 95%CI: 0.895-0.953). Comparison of different constructed predictive models showed that when the binocular PR-SE difference was ≥0.743 D, the negative predictive value reached 98.89%, making it suitable for initial screening. The combination of UCDVA+PR-SE+AL had the highest specificity and positive predictive value, while the PR-SE+AL combination had the highest consistency rate.CONCLUSION: The binocular PR-SE difference is the best choice for single-indicator screening. Combining UCDVA and AL can increase the specificity to 98.00% and the positive predictive value to 88.24%. The PR-SE+AL combination can achieve the highest consistency rate.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

As the application of immune checkpoint inhibitors(ICIs)in the perioperative treatment of melanoma is increasingly introduced at earlier stages,it presents a critical opportunity for the development and clinical translation of neoadjuvant therapy.The results of phaseⅠ/Ⅱ clinical trials on neoadjuvant ICI therapy for melanoma demonstrate that neoadjuvant ICIs effectively improve the pathologic re-sponse rate in melanoma patients.Recent studies have shown that combining ICIs with other treatment modalities,including radiotherapy,chemotherapy,and targeted therapies,can enhance antitumor efficacy of neoadjuvant treatment for patients with melanoma.Optimizing treatment regimens,managing adverse events,identifying and addressing pseudoprogression,and handling cases of oligoprogression have become key areas of research in incorporating ICI regimens into neoadjuvant treatment for patients with melanoma.The search for bio-markers to monitor immunotherapy efficacy is expected to become a major focus of future research.This article provides a review of the re-search progress,controversies,and challenges in the application of ICIs in the neoadjuvant treatment of melanoma,and discusses future re-search directions,aiming to offer insights into the clinical application and development of ICIs in melanoma neoadjuvant therapy.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.