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.

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.

Osteoporotic fractures(OPF) refer to the fractures caused by minor violence in the state of osteoporosis, seriously threatening the life and health of elderly patients. Drug and surgical therapies have limitations such as single targets, diverse adverse reactions, and poor prognosis. Kidney-tonifying traditional Chinese medicine(TCM) has good potential in the treatment of OPF. TCM can promote the healing of OPF by promoting angiogenesis in the early stage of bone healing, promoting osteogenic differentiation of bone marrow mesenchymal stem cells in the stage of bone repair, maintaining the balance of osteogenic and osteoclastic system in the stage of bone remodeling, and regulating the oxidative stress responses throughout the process of OPF healing. TCM can alleviate the pathological state of osteoporosis and promote fracture healing in OPF patients via multiple pathways and targets, demonstrating the advantages and potential of biphasic regulation.
Humans ; Drugs, Chinese Herbal/therapeutic use* ; Osteoporotic Fractures/metabolism* ; Animals ; Fracture Healing/drug effects* ; Medicine, Chinese Traditional ; Kidney/metabolism* ; Osteogenesis/drug effects*

Objective:This study aimed to examine the association between malnutrition diagnosed by the Global Leadership Initiative on Malnutrition(GLIM)criteria and clinical outcomes in surgical patients,as well as to assess its prognostic impact on postoperative adverse clinical outcomes.Methods:Electronic databases,including PubMed,Embase,Web of Science,CINAHL,Scopus,The Cochrane Library,Clinical Trials,CNKI,Wanfang Data Knowledge Service Platform,and the Chinese Biomedical Literature Database,were systematically searched.Relevant cohort studies utilizing GLIM criteria to preoperatively diagnose malnutrition in surgical inpatients were included.The exposed group comprised surgical patients diagnosed with preoperative malnutrition using GLIM criteria,while the control group consisted of surgically treated patients without malnutrition as per GLIM criteria.Literature quality was evaluated using the Newcastle-Ottawa Scale(NOS),and meta-analysis was performed using Review Manager 5.4 software.Results:Fourteen literatures were included,with a total sample size of 10,045 patients.Meta-analysis revealed that the malnourished group had a higher incidence of postoperative complications compared to the non-malnourished group[risk ratio(RR)=1.81,95%CI:1.66～1.98),P<0.00001].Additionally,the incidence of severe complications was significantly higher in GLIM-diagnosed malnourished patients.The malnourished group exhibited poorer overall survival[hazard ratio(HR)=1.90,95%CI:1.55～2.34,P<0.00001]and disease-free survival[HR=2.25,95%CI:1.02～4.93,P=0.04]compared to the non-malnourished group.Conclusion:GLIM-diagnosed malnutrition is significantly associated with adverse clinical outcomes in surgical patients,increasing postoperative complication rates and reducing overall and disease-free survival.The GLIM criteria demonstrate value in predicting adverse clinical outcomes in this population.Further high-quality studies are warranted to validate these findings.

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.

The quality of traditional Chinese medicine (TCM) prescriptions (TCMPs) is critical to clinical efficacy; however, evaluating their consistency and identifying sources of variability remain challenging. This study proposes an integrated strategy to assess the quality of 100 widely sold TCMPs. A "one-for-all" chromatographic method was employed to analyze 645 sample batches. This large-scale data collection enabled statistical evaluations, such as hierarchical cluster analysis (HCA) and similarity heatmap, to identify quality inconsistencies. The introduction of a TCM-specific mass spectrometry (MS) database allowed for rapid, automated annotation of chemicals across 100 prescriptions and facilitated the tracing of raw material sources. Results indicate that 19% of prescriptions exhibited chemical inconsistencies, which are associated with high market value, low pricing, and substantial price disparities. The MS database allowed rapid annotation of 761 and 673 compounds in positive and negative modes, respectively, in 100 TCMPs, with 73 prescriptions reported for the first time. The tracing efforts succeeded in identifying >40% of the raw material sources for 51 prescriptions. P93 (Yinianjin (YNJ)) is a case in which the chromatographic profiles from three manufacturers displayed inconsistencies. Analysis using the database traced divergent peaks to Rhei Radix et R hizoma (RRER). Verification with self-prepared samples confirmed that manufacturers utilized three distinct botanical sources. This integrated strategy provides a scalable framework for quality control in TCMPs.