Objective:To compare the clinical efficacy of autologous bone grafting or bioceramic bone grafting combined with locking compression plate (LCP) internal fixation in the treatment of Schatzker type II and III tibial plateau fractures.Methods:A retrospective analysis was conducted on 104 patients with Schatzker type II and III tibial plateau fractures who underwent surgical treatment at the Department of Orthopedics, PLA Central Theater Command General Hospital from January 2010 to December 2021. The cohort comprised 55 males and 49 females, with an average age of 49.13±13.80 years (range 18-73 years). All fractures were unilateral: 55 on the left and 49 on the right. According to the Schatzker classification, 59 were Type II and 45 were Type III. Causes of injury included traffic accidents (48 cases), falls from height (3 cases), sprains or falls (45 cases), and other causes (8 cases). During surgery, bioceramic material or allograft bone tissue was implanted into the collapsed tibial plateau region to restore articular surface flatness. Based on graft type, patients were divided into the allograft bone group (63 cases) and the bioceramic group (41 cases). All cases underwent proximal lateral tibial LCP internal fixation. The two groups were compared in terms of operative time, intraoperative blood loss, bone graft volume, length of hospital stay, fracture healing time, and postoperative complications. The Rasmussen radiographic collapse score was used to evaluate fracture reduction, and the Hospital for Special Surgery (HSS) score system was used to assess knee joint function.Results:All patients were followed up for 12(12, 13) months (range 12-16 months). The fracture healing time in the allograft bone group was 13.70±1.36 weeks (range 11-16 weeks), which was significantly shorter than that in the bioceramic group: 14.59±1.73 weeks (range 11-19 weeks) ( t=2.911, P=0.004). The time to full weight-bearing in the allograft group was 15.0(14.0, 17.0) weeks (range 13-23 weeks), which was shorter than the 16.0 (15.5, 18.5) weeks (range 12-24 weeks) in the bioceramic group, showing a statistically significant difference ( Z=-3.019, P=0.002). At 12 months postoperatively, the Rasmussen radiographic collapse score was 1(0, 1) in the bioceramic group, significantly lower than the 1(1, 2) score in the allograft group ( Z=-2.083, P=0.037). No statistically significant differences were observed between the two groups in bone graft volume, surgical duration, intraoperative blood loss, hospitalization duration, or HSS scores at 6 and 12 months postoperatively ( P>0.05). The complication rate was 6.3% (4/63) in the allograft group and 2.4% (1/41) in the bioceramic group, with no statistically significant difference (χ 2=0.830, P=0.362). Conclusions:For Schatzker type II and III tibial plateau fractures, the use of allograft bone or bioceramic combined with LCP internal fixation can achieve good results. Allograft bone has advantages in terms of fracture healing speed and early weight-bearing recovery, while bioceramic is more effective in maintaining joint surface stability.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

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*

Objective:To compare the clinical efficacy of autologous bone grafting or bioceramic bone grafting combined with locking compression plate (LCP) internal fixation in the treatment of Schatzker type II and III tibial plateau fractures.Methods:A retrospective analysis was conducted on 104 patients with Schatzker type II and III tibial plateau fractures who underwent surgical treatment at the Department of Orthopedics, PLA Central Theater Command General Hospital from January 2010 to December 2021. The cohort comprised 55 males and 49 females, with an average age of 49.13±13.80 years (range 18-73 years). All fractures were unilateral: 55 on the left and 49 on the right. According to the Schatzker classification, 59 were Type II and 45 were Type III. Causes of injury included traffic accidents (48 cases), falls from height (3 cases), sprains or falls (45 cases), and other causes (8 cases). During surgery, bioceramic material or allograft bone tissue was implanted into the collapsed tibial plateau region to restore articular surface flatness. Based on graft type, patients were divided into the allograft bone group (63 cases) and the bioceramic group (41 cases). All cases underwent proximal lateral tibial LCP internal fixation. The two groups were compared in terms of operative time, intraoperative blood loss, bone graft volume, length of hospital stay, fracture healing time, and postoperative complications. The Rasmussen radiographic collapse score was used to evaluate fracture reduction, and the Hospital for Special Surgery (HSS) score system was used to assess knee joint function.Results:All patients were followed up for 12(12, 13) months (range 12-16 months). The fracture healing time in the allograft bone group was 13.70±1.36 weeks (range 11-16 weeks), which was significantly shorter than that in the bioceramic group: 14.59±1.73 weeks (range 11-19 weeks) ( t=2.911, P=0.004). The time to full weight-bearing in the allograft group was 15.0(14.0, 17.0) weeks (range 13-23 weeks), which was shorter than the 16.0 (15.5, 18.5) weeks (range 12-24 weeks) in the bioceramic group, showing a statistically significant difference ( Z=-3.019, P=0.002). At 12 months postoperatively, the Rasmussen radiographic collapse score was 1(0, 1) in the bioceramic group, significantly lower than the 1(1, 2) score in the allograft group ( Z=-2.083, P=0.037). No statistically significant differences were observed between the two groups in bone graft volume, surgical duration, intraoperative blood loss, hospitalization duration, or HSS scores at 6 and 12 months postoperatively ( P>0.05). The complication rate was 6.3% (4/63) in the allograft group and 2.4% (1/41) in the bioceramic group, with no statistically significant difference (χ 2=0.830, P=0.362). Conclusions:For Schatzker type II and III tibial plateau fractures, the use of allograft bone or bioceramic combined with LCP internal fixation can achieve good results. Allograft bone has advantages in terms of fracture healing speed and early weight-bearing recovery, while bioceramic is more effective in maintaining joint surface stability.

Objective To develop a predictive model for guiding blood transfusion decisions in pediatric patients with traumatic brain injury(TBI)by identifying and analyzing key factors that influence blood transfusion requirements.Methods A retrospective analysis was conducted on the clinical data of 1,535 pediatric patients with TBI admitted to four medical institutions from January 1,2015,to December 31,2022.Patients were divided into two groups:those who received red blood cell transfusions during hospitalization and those who did not.Comparative analyses were performed on demographic,clinical,and laboratory data between these two groups.Logistic regression analysis was used to identify risk factors associated with in-hospital blood transfusion,and a predictive model was developed using a nomogram.The performance of this model was evaluated using a receiver operating characteristic(ROC)curve.Results Significant differences were observed between the blood transfusion and non-blood transfusion groups in terms of baseline demographics,clinical indicators,and laboratory test results(all P<0.05).Patients in the blood transfusion group exhibited significantly higher in-hospital mortality,compli-cation rates,use of mechanical ventilation,ICU admission rates,and length of stay compared to those in the non-blood transfusion group(all P<0.05).Multivariate logistic regression analysis identified heart rate,presence of other fractures,treatment methods,hemoglobin(Hb),platelet count(Plt),activated partial thromboplastin time(APTT),and D-dimer levels as independent risk factors for blood transfusion in TBI patients.The area under the ROC curve for the blood transfusion prediction model,based on these independent risk factors,was 0.95(95%CI:0.94～0.97),indicating excellent predictive accuracy.Calibration and decision curves further validated the robust-ness and reliability of the model's predictive capacity.Conclusions Heart rate,presence of other fractures,treatment methods,Hb,Plt count,APTT,and D-dimer levels serve as independent risk factors for blood transfusion in TBI patients.The prediction model developed based on these factors demonstrates excellent predictive performance,thereby guiding clinicians in making informed blood transfusion decisions and enhancing the success rate of patient outcomes.

[Abstract] [Objective] To explore the risk factors of blood transfusion in patients with isolated traumatic brain injury (iTBI) based on multiple machine learning methods, so as to establish a predictive model to provide reasonable guidance for blood transfusion in patients with iTBI. [Methods] A total of 2 273 patients with iTBI from the First Affiliated Hospital of Nanchang University from January 1, 2015 to June 30, 2021 were included to compare and analyze the differences in variables such as vital signs, clinical indicators and laboratory testing indicators between transfusion and non transfusion patients. Furthermore, six machine learning models were established to compare the performance of different models through cross validation, accuracy, specificity, recall, f1 value and area under the ROC curve. The SHAP plot was used to explain the influencing factors of blood transfusion in iTBI patients. [Results] This study included 2 273 iTBI patients, with a total of 301 patients receiving blood transfusions. There were significant differences (P<0.05) in gender, age, HR, clinical diagnosis, skull fracture, treatment methods, hemorrhagic shock, GCS, K, Ca, PT, APTT, INR, RBC, Hct, Hb and Plt between transfusion and non transfusion patients; Moreover, the LOS, incidence of complications, mechanical ventilation rate, ICU admission rate, readmission rate within 90 days and in-hospital mortality rate of transfusion patients were all higher than those of the non transfusion group (P<0.05). Six machine learning algorithms were used for model construction, and the validation results on the test set showed that the CatBoost model performed the best with an AUC of 0.911. Furthermore, the SHAP framework was used to explain and visualize the optimal model CatBoost, showing that surgical treatment, lower GCS, higher INR, lower Hct, lower K, lower Ca, age ≥60 years, skull fractures and hemorrhagic shock increase the risk of blood transfusion in patients. [Conclusion] This study established a machine learning model for predicting blood transfusion in iTBI patients, and the CatBoost model performed the best. This model may be useful and beneficial for identifying transfusion risks in this population, making clinical transfusion decisions and monitoring progress.

【Objective】 To investigate the effect of double plasma molecular adsorption system and sequential half-dose plasma exchange (DPMAS+HPE) on the short-term survival rate of patients with hepatitis B associated acute-on-chronic liver failure (HBV-ACLF). 【Methods】 Data on HBV-ACLF cases hospitalized in our hospital from January 1, 2015 to December 31, 2022 were retrospectively collected, and were divided into standard comprehensive medical treatment group and DPMAS+HPE group according to different treatment methods. Propensity score matching (PSM) was used to eliminate inter group confounding bias. The baseline data and improvement of laboratory indicators after treatment between two groups were compared. Death related risk factors in HBV-ACLF patients were screened by logistic regression analysis, and cumulative survival rates at 30 and 90 days between the two groups were compared by Kaplan-Meier survival analysis. 【Results】 A total of 373 cases of HBV-ACLF were included in this study. Among them, 136 cases in the treatment group received DPMAS+HPE once on the basis of comprehensive internal medicine treatment, and 237 cases only received comprehensive internal medicine treatment. After PSM, 136 patients were included as the control group. The decrease in alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total protein (TP) in the treatment group before and after treatment was significantly greater than that in the control group (446.5 vs 159.0, 317.0 vs 92.0,5.2 vs 0.3), with statistically significant difference (P<0.05). DPMAS+HPE treatment is an independent protective factor for mortality in HBV-ACLF patients at 30 and 90 days (30 days: OR=0.497, P<0.05; 90 days: OR= 0.436, P<0.05). The cumulative survival rates at 30 and 90 days in the treatment group were significantly higher than those in the control group (30 days: 50.71% vs 44.12%, P<0.05; 90 days: 30.15% vs 22.79%, P<0.05). 【Conclusion】 DPMAS+HPE improves the short-term prognosis of HBV-ACLF patients and can serve as an effective artificial liver model for the treatment of HBV-ACLF patients.

Background::Although some well-established oncogenes are involved in cancer initiation and progression such as prostate cancer (PCa), the long tail of cancer genes remains to be defined. Goosecoid ( GSC) has been implicated in cancer development. However, the comprehensive biological role of GSC in pan-cancer, specifically in PCa, remains unexplored. The aim of this study was to investigate the role of GSC in PCa development. Methods::We performed a systematic bioinformatics exploration of GSC using datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, German Cancer Research Center, and our in-house cohorts. First, we evaluated the expression of GSC and its association with patient prognosis, and identified GSC-relevant genetic alterations in cancers. Further, we focused on the clinical characterization and prognostic analysis of GSC in PCa. To understand the transcriptional regulation of GSC by E2F transcription factor 1 ( E2F1), we performed chromatin immunoprecipitation quantitative polymerase chain reaction (qPCR). Functional experiments were conducted to validate the effect of GSC on the tumor cellular phenotype and sensitivity to trametinib. Results::GSC expression was elevated in various tumors and significantly correlated with patient prognosis. The alterations of GSC contribute to the progression of various tumors especially in PCa. Patients with PCa and high GSC expression exhibited worse progression-free survival and biochemical recurrence outcomes. Further, GSC upregulation in patients with PCa was mostly accompanied with higher Gleason score, advanced tumor stage, lymph node metastasis, and elevated prostate-specific antigen (PSA) levels. Mechanistically, the transcription factor, E2F1, stimulates GSC by binding to its promoter region. Detailed experiments further demonstrated that GSC acted as an oncogene and influenced the response of PCa cells to trametinib treatment. Conclusions::GSC was highly overexpressed and strongly correlated with patient prognosis in PCa. We found that GSC, regulated by E2F1, acted as an oncogene and impeded the therapeutic efficacy of trametinib in PCa.

Objective:To explore the plasma metabolomic characteristics of children with transfusion-dependent thalassemia(TDT),and reveal the changes of metabolic pattern in children with TDT.Methods:23 children with TDT who received regular blood transfusion in Ganzhou Women and Children's Health Care Hospital in 2021 were selected,and 11 healthy children who underwent physical examination during the same period were selected as the control group.The routine indexes between children with TDT and the control group were compared,and then the metabolic composition of plasma samples from children with TDT and the control group was detected by liquid chromatography-mass spectrometry.An OPLS-DA model was established to perform differential analysis on the detected metabolites,and the differential metabolic pathways between the two groups were analyzed based on the differential metabolites.Results:The results of routine testing showed that the indexes of ferritin,bilirubin,total bile acid,glucose and triglycerides in children with TDT were significantly higher than those in healthy controls,while hemoglobin and total cholesterol were significantly lower(all P＜0.05).However there was no significant difference in lactate dehydrogenase between the two groups(P＞0.05).Compared with the control group,190 differential metabolites(VIP＞1)were identified in TDT children.Among them,168 compounds such as arginine,proline and glycocholic acid were significantly increased,while the other 22 compounds such as myristic acid,eleostearic acid,palmitic acid and linoleic acid were significantly decreased.The metabolic pathway analysis showed that the metabolic impact of TDT on children mainly focused on the upregulation of amino acid metabolism and downregulation of lipid metabolism.Conclusion:The amino acid and lipid metabolism in children with TDT were significantly changed compared with the healthy control group.This finding is helpful to optimize the treatment choice for children with TDT,and provides a new idea for clinical treatment.