BACKGROUND:Osteoporosis is a significant contributor to the global burden of disease and disability.Plasma proteins are involved in complex biological processes and play a crucial role in uncovering disease mechanisms and identifying potential therapeutic targets.Although existing studies have suggested an association between plasma proteins and osteoporosis,the causal nature of these associations is not fully clarified.Therefore,it is imperative to identify the causal proteins associated with osteoporosis and potential therapeutic targets for the amelioration and management of this condition using large-scale plasma protein data.OBJECTIVE:To evaluate the causal relationship between plasma proteins and osteoporosis based on the UK Biobank database as source information using the two-sample Mendelian randomization.METHODS:A total of 1 001 plasma protein-related genome-wide significant quantitative trait loci(P<5×10-8)were obtained from the UK Biobank database and used as instrumental variables,with linkage disequilibrium excluded.Summary data on osteoporosis were collected from the FinnGen database,which included 438 872 individuals of European descent.The study was analyzed using inverse variance weighting,MR-Egger regression,weighted median,and several sensitivity analyses to ensure the robustness of the results.Further,a protein-protein interaction network was constructed,and Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted to explore the functional relevance and potential mechanisms of plasma proteins.RESULTS AND CONCLUSION:(1)The Mendelian randomization analysis using the inverse variance weighted method identified 50 plasma proteins that have causal associations with osteoporosis(P<0.05).Among them,20 plasma proteins,including chromosome 19 open reading frame 12(odds ratio[OR]=0.610;95%confidence interval[CI]:0.483-0.769,P=2.967×10-5)and epidermal growth factor(EGF;OR=0.877;95%CI:0.770-0.999,P=0.049),might be associated with a reduced risk of osteoporosis.In contrast,30 plasma proteins,such as C-C motif chemokine ligand(CCL)18(OR=1.091;95%CI:1.037-1.147,P=0.001)and CD209(OR=1.036;95%CI:1.003-1.070,P=0.034),might be associated with an increased risk of osteoporosis.After Bonferroni correction,only chromosome 19 open reading frame 12 showed a significant causal association with osteoporosis.(2)Multiple sensitivity analyses revealed no evidence of pleiotropy or heterogeneity,indicating the robustness of the results.(3)The construction of the PPI network identified core proteins such as EGF,CCL5,C-X-C motif chemokine ligand(CXCL)13,CXCL5,vascular endothelial growth factor C,CCL17,CCL18,TEK receptor tyrosine kinase,tyrosine kinase with immunoglobulin like and EGF like domains 1,and CCL23.(4)Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that these plasma proteins play essential roles in the immune system,influencing osteoporosis through processes such as signal transduction,cell migration,and chemotaxis.(5)This study reveals the potential causal associations between 1 001 plasma proteins and osteoporosis,highlighting the utility of a large-scale,data-driven approach to identify new biomarkers and drug targets in diverse populations.Additionally,our findings suggest that processes such as immune signaling,cell migration,and chemotaxis play significant roles in the pathogenesis of osteoporosis,offering new directions for research under specific genetic backgrounds and environmental factors.Finally,the core proteins identified in this study(e.g.,EGF,CCL5,and CXCL13)may serve as novel biomarkers or therapeutic targets,providing a new basis for the precise prevention and treatment of osteoporosis.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

BACKGROUND:Osteoporosis is a significant contributor to the global burden of disease and disability.Plasma proteins are involved in complex biological processes and play a crucial role in uncovering disease mechanisms and identifying potential therapeutic targets.Although existing studies have suggested an association between plasma proteins and osteoporosis,the causal nature of these associations is not fully clarified.Therefore,it is imperative to identify the causal proteins associated with osteoporosis and potential therapeutic targets for the amelioration and management of this condition using large-scale plasma protein data.OBJECTIVE:To evaluate the causal relationship between plasma proteins and osteoporosis based on the UK Biobank database as source information using the two-sample Mendelian randomization.METHODS:A total of 1 001 plasma protein-related genome-wide significant quantitative trait loci(P<5×10-8)were obtained from the UK Biobank database and used as instrumental variables,with linkage disequilibrium excluded.Summary data on osteoporosis were collected from the FinnGen database,which included 438 872 individuals of European descent.The study was analyzed using inverse variance weighting,MR-Egger regression,weighted median,and several sensitivity analyses to ensure the robustness of the results.Further,a protein-protein interaction network was constructed,and Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted to explore the functional relevance and potential mechanisms of plasma proteins.RESULTS AND CONCLUSION:(1)The Mendelian randomization analysis using the inverse variance weighted method identified 50 plasma proteins that have causal associations with osteoporosis(P<0.05).Among them,20 plasma proteins,including chromosome 19 open reading frame 12(odds ratio[OR]=0.610;95%confidence interval[CI]:0.483-0.769,P=2.967×10-5)and epidermal growth factor(EGF;OR=0.877;95%CI:0.770-0.999,P=0.049),might be associated with a reduced risk of osteoporosis.In contrast,30 plasma proteins,such as C-C motif chemokine ligand(CCL)18(OR=1.091;95%CI:1.037-1.147,P=0.001)and CD209(OR=1.036;95%CI:1.003-1.070,P=0.034),might be associated with an increased risk of osteoporosis.After Bonferroni correction,only chromosome 19 open reading frame 12 showed a significant causal association with osteoporosis.(2)Multiple sensitivity analyses revealed no evidence of pleiotropy or heterogeneity,indicating the robustness of the results.(3)The construction of the PPI network identified core proteins such as EGF,CCL5,C-X-C motif chemokine ligand(CXCL)13,CXCL5,vascular endothelial growth factor C,CCL17,CCL18,TEK receptor tyrosine kinase,tyrosine kinase with immunoglobulin like and EGF like domains 1,and CCL23.(4)Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that these plasma proteins play essential roles in the immune system,influencing osteoporosis through processes such as signal transduction,cell migration,and chemotaxis.(5)This study reveals the potential causal associations between 1 001 plasma proteins and osteoporosis,highlighting the utility of a large-scale,data-driven approach to identify new biomarkers and drug targets in diverse populations.Additionally,our findings suggest that processes such as immune signaling,cell migration,and chemotaxis play significant roles in the pathogenesis of osteoporosis,offering new directions for research under specific genetic backgrounds and environmental factors.Finally,the core proteins identified in this study(e.g.,EGF,CCL5,and CXCL13)may serve as novel biomarkers or therapeutic targets,providing a new basis for the precise prevention and treatment of osteoporosis.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Objective To construct and appraise a new model for predicting the prognosis of rectal cancer patients using the Lasso-Cox strategy.Methods The clinical pathological data of 599 rectal cancer patients who underwent radical resection were analyzed.Comparison between groups,Lasso and Cox regression were used to select varia-bles and construct a model,and its discrimination,consistency,and clinical benefits were appraised by the receiv-er operating characteristic(ROC),calibration curve,and decision curve analysis.Results Comparison between groups showed that age,body mass index(BMI),preoperational nutrition status,carbohydrate antigen199(CA199),preoperative chemotherapy,intraoperative blood transfusion,vascular or nerve invasion,cancer nod-ules,pathologic T,N,and TNM stages,tumor recurrence or metastasis,radiotherapy and postoperative survival time were associated with grouping of death or survival in rectal cancer patients.Among them,8 variables were se-lected by lasso and contained into the Cox regression model.Age(HR=1.04,P＜0.05),BMI(HR=0.89,P＜0.05),blood transfusion(HR=2.29,P＜0.05),postoperative chemotherapy(HR=0.16,P＜0.01),recur-rence(HR=43.67,P＜0.01),and metastasis(HR=2.75,P＜0.05)were identified as independent prognostic factors,which were used to construct a nomogram model.The area under the curve(AUC)and the 95％confi-dence interval of the receiver operating characteristic(ROC)curve of the predictive model was 0.95(0.91-0.99),P＜0.01.The predicted probability of 1-year and 3-year survival was close to the actual probability.The DCA curve of the model was far away from a decision line parallel to the X-axis and another line with a negative slope.Conclusion The newly established nomogram has good discrimination,consistency and clinical benefits,which help predict the prognosis of rectal cancer after surgery.

Objective: To identify the associated factors of peripheral blood lymphocyte counts (PBLC) in gastric cancer patients Methods: In this cross⁃sectional study , data were collected from patients underwent gastric cancer surgery. Univariate analysis , multiple linear regression and variable importance metrics were used to assess factors associating PBLC in pre⁃operative patients. The findings were further investigated by fitting locally weighted regression(loess) and robust linear model (rlm) . Results: Patients with pre⁃operative PBLC < 1 . 1 × 109/L( group A) were 138(20. 72% ) , whereas patients with PBLC ≥ 1. 1 × 109/L ( group B) were 528(79. 28% ) . Compared to group B , patients in group A were older[(64. 61 ± 10. 42 years) vs (62. 18 ± 10. 41 years) , P < 0. 05 ] and showed lower neutrophils [(3. 21 ± 1. 41) × 109/L vs (3. 59 ± 1. 31) × 109/L , P < 0. 01] . Lymphopenia was associated with later stage of gastric cancer, P < 0. 01 . A multiple linear regression model with randomly distributed residuals found that age ( β = - 0. 01 , t = - 3. 70 , P < 0. 01) and tumor stage [ β ( Ⅱ vs Ⅰ ) = - 0. 16 , t =- 2. 79 , P < 0. 01 ; β ( Ⅲ vs Ⅰ ) = - 0. 18 , t = - 3. 86 , P < 0. 01 ; β ( Ⅳ vs Ⅰ ) = - 0. 21 , t = - 2. 16 , P <0. 05] were to be statistically significant factors for lymphopenia. Neutrophil increase (β = 0. 05 , t = 3. 61 , P < 0. 01) was associated with a higher PBLC. The relative importance analysis for continuous independent variables showed that relative importance metric LMGs in variable age , neutrophil and CEA were 55. 55% , 44. 14% and 0. 31% respectively. Loess and rlm models showed that age was the negatively influencing factor of PBLC. Conclusion PBLC in gastric cancer patients were positively associated with neutrophils , and negatively associated with age and tumor stages.

Objective:To evaluate the post-marketing safety and immunogenicity of a 23-valent pneumococcal polysaccharide vaccine (PPV23).Methods:From September 2020 to June 2021, a clinical trial of single-dose PPV23 was conducted in people ≥3 years old in Centers for Disease Control and Prevention of Guizhou, Hunan and Fujian provinces. Blood samples were collects from the subjects before and 30 d after vaccination. ELISA was used to quantitatively detect IgG antibodies against capsular polysaccharides of 23 Streptococcus pneumoniae serotypes in serum samples. The adverse events (AEs) were monitored within 7 d after vaccination. Results:A total of 409 subjects were enrolled and included in safety analysis. Except for one with antibody level inversion, the other 408 participants were included in immunogenicity analysis. The levels of antibodies against the 23 Streptococcus pneumoniae serotypes were all increased after vaccination by an average of 4.24 folds. The two-fold growth rates of the antibodies ranged from 51.72% to 96.81% with a total two-fold growth rate of 78.59%. The overall rate of AEs was 27.14% (111/409). Local AEs were mainly pain, induration, redness and swollen. No serious adverse events related to vaccination occurred. Conclusions:This study preliminarily demonstrated the good immunogenicity and safety of PPV23 vaccine.

Objective: To evaluate the efficacy of articular compression molding techniques in the surgery for acetabular posterior wall fracture. Methods: A retrospective study was conducted of the 28 patients (28 hips) with fracture of acetabular posterior wall plus comminuted compression of articular surface who had been treated using the articular compression molding techniques at Department of Orthopedics, The Hospital of 81 Group Army of PLA from January 2014 to January 2018. They were 25 males and 3 females, aged from 26 to 63 years (average, 49.3 years). The time from injury to surgery ranged from 4 to 12 days (average, 7.8 days). According to the Letournel-Judet classification, all were single element (simple) posterior wall fractures combined with posterior dislocation of the femoral head and articular compression of different degrees; transient sciatic nerve injury was complicated in 12 cases. The posterior hip dislocations were timely and successful reset at emergency treatment. The compression fractures of the posterior wall and hip joint were treated by open reduction and internal fixation via the acetabular posterior (K-L) approach. The articular compression was treated by 4 stabilization techniques: absorbable screwing and blocking technique in 10 cases, indwelling screwing in 12 cases, indwelling Kirschner wiring in 2 cases and die push and squeeze tamping in 4 cases. The operation time, intraoperative bleeding and complications were recorded. The therapeutic efficacy was evaluated by the modified Merle d’Aubigne & Postel scoring criteria at the final follow-up. Results: The operation time lasted from 76 to 118 minutes (average, 94.2 minutes); the intraoperative bleeding ranged from 100 to 320 mL (average,220.8 mL). No incision liquefaction, infection or other complications occurred. The 28 patients were followed up for 10 to 36 months (average, 17.6 months). According to the Matta scoring criteria, 24 cases achieved anatomical reduction but 4 dissatisfactory reduction. By the modified Merle d’Aubigne & Postel scoring criteria at the final follow-up, the efficacy was evaluated as excellent in 26 cases and as good in 2. Heterotopic ossification was observed in 3 cases and microscopic free dense shadow in the joint cavity (about 2 mm in the round ligament) in 3 cases. There were no cases of obvious traumatic arthritis or osteoarthritis, femoral head necrosis, walking pain, lameness or hip abduction weakness. The 12 patients complicated with preoperative transient sciatic nerve injury recovered within 3 months after surgery. Conclusion The articular compression molding techniques can effectively treat severely comminuted articular compression in the acetabular posterior wall fracture and improve reduction of articular surface and hip joint matching, leading to stability and good joint function.

Background and objective National Comprehensive Cancer Network (NCCN) guidelines recom-mend video-assisted thoracoscopic surgery (VATS) anatomical lobectomy as the ifrst choice for the treatment of resectable lung cancer. However, sublobar resection offers signiifcantly better functional preservation compared with lobectomy for stage I lung cancer. At present, the inferiority of sublobar resection to lobectomy is still uncertain. Herein, we compared the prognoses of these two types of surgical treatment for stage I lung adenocarcinoma. Methods Retrospective research was conducted on 258 patients with stage I lung adenocarcinomas who underwent VATS lobectomy and sublobar resection at the First Affliated Hospital of Guangzhou Medical University between January 2009 and December 2011. VATS lobectomy was performed on 222 patients, and VATS sublobe resection was performed on 36 patients. Propensity score matching analyses were conducted on the two groups. Results A total of 70 patients were matched in the two groups. No signiifcant difference was observed between the lobectomy and sublobar resection groups atfer matching (P=0.137). hTe disease-free survival (DFS) of the two groups were 49.3 and 42.7 months, and their overall survival (OS) were 50.3 and 49.0 months (P=0.122). Further, stratiifed analysis showed no signiifcant differences in DFS and OS between the two groups with stage Ia lung adenocarcinoma. Nev-ertheless, the DFS and OS of the two groups signiifcantly differed in matched patients with stage Ib lung adenocarcinomas. Conclusion Sublobar resection could achieve a similar prognosis to VATS lobectomy for stage Ia lung adenocarcinoma. Lo-bectomy should still be the ifrst choice for the treatment of stage Ib lung adenocarcinoma.