Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues.Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

OBJECTIVE: To compare clinical effects of external fixation and minimally invasive percutaneous plate osteosynthesis (MIPPO) after external fixation in treating open fractures of tibial shaft. METHODS: From January 2020 to June 2022, 151 patients with open fracture of tibial shaft treated with external fixation stenting were divided into external fixation group and combined group according to different surgical methods. There were 81 patients in external fixation group, including 48 males and 33 females, aged from 21 to 68 years old with an average of (42.58±7.44) years old;according to Gustilo classification, 49 patients with typeⅡ, 32 patients with type ⅢA;the time from injury to treatment ranged from 2.5 to 10 h with an average of (4.25±0.74) h;external fixed stenting was performed. There were 70 patients in combined group, including 42 males and 28 females, aged from 20 to 69 years old with an average of (41.39±7.02) years old;35 patients with type Ⅱ and 35 patients with type ⅢA according to Gustilo classification;the time from injury to treatment ranged from 3 to 9 h with an average of (4.31±0.85) h;MIPPO treatment was performed after external fixed stenting. The time of callus formation, fracture healing and complications were compared between two groups. Rasmussen score and Hospital for Special Surgery (HSS) score were used to evaluate functional recovery of knee joint at 6 months after operation. RESULTS: Both groups were followed up for 6 to 13 months with an average of (10.17±2.33) months. The time of callus formation and fracture healing were (13.98±4.02) d and (70.26±12.15) d in combined group, and (18.56±4.37) d and (79.87±15.41) d in external fixation group, respectively. Combined group was better than external fixation group in the time of callus formation and fracture healing (P<0.05). At six months after operation, Rasmussen and HSS scores in combined group were (26.79±3.11) and (83.36±9.44), which were higher than those in external fixation group (24.51±4.63) and (79.63±8.46) (P<0.05). In external fixation group, there were 2 patients with incision infection, 2 patients with nail tract infection, 1 patient with stent loosening, fracture displacement, delayed union and malunion, and 1 patient with biocompatibility reaction in combined group, with statistical significance between two groups (P<0.05). CONCLUSION MIPPO could accelerate callus formation and fracture healing, improve knee function, improve clinical effects and reduce complications in patients with open tibial shaft fractures after external and external fixation.
Humans ; Male ; Female ; Middle Aged ; Adult ; Aged ; Tibial Fractures/physiopathology* ; Fracture Fixation, Internal/methods* ; Retrospective Studies ; Bone Plates ; External Fixators ; Fractures, Open/physiopathology* ; Stents ; Young Adult

Objective To establish evaluation of ozagrel sodium by weighted TOPSIS method so as to provide a reference for improving the rational use of ozagrel sodium.Methods Based on the drug instructions,guidelines,relevant literatures and Delphi method,the evaluation criteria for the clinical rationality of ozagrel sodium were formulated.Attribute hierarchical model(AHM)was used to assign weights to the indicators,the weighted TOPSIS method was employed to analyze and evaluate the rationality of 108 patients that discharged from the Third People's Hospital of Henan Province from January 2021 to April 2022.Results The reponse rates of two rounds of expert advice questionnaires were 100％,the authoritative coefficients(Cr)were 0.85,0.83(＞0.70),and the experts fully affirmed the items of the standard.Among the 108 cases evaluated,37 cases(34.26％)were judged to be reasonable,52 cases(48.15％)were judged to be basically reasonable and 19 cases(17.59％)were judged to be unreasonable.The main unreasonable problems were manifested in indications,the timing of administration,drug course and monitoring of efficacy and laboratory parameters.Conclusion The method of DUE of ozagrel sodium based on weighted TOPSIS is more comprehensively and intuitively.The application of ozagrel sodium in the hospital is relatively standardized,but there are problems in the course of medication,indications,and timing of administration.So it is necessary to promote the rational use by strengthening the cooperation between pharmacists and physicians,and improving pharmaceutical intervention.

The purpose of this study was to reveal the resistance mechanism of Mycoplasma bovis from Xizang yaks to macrolide antibiotics and provide theoretical basis for clinical medication.In this study,10 strains of Mycoplasma bovis from Xizang yaks were tested for drug sensitivity to macrolide antibiotics using the micromethod,and sensitive strains were induced to be highly resist-ant in vitro.The results showed that all 10 strains of Mycoplasma bovis exhibited varying degrees of resistance to macrolide antibiotics.However,through methylation enzyme and inactivation en-zyme gene detection and analysis,it was found that there was no methylation enzyme encoded by the methylation enzyme gene and no resistance mechanism mediated by macrolide inactivation en-zymes,indicating that there were no mutations in the target gene loci of the sensitive and resistant strains.However,highly resistant strains in vitro have mutations at the domain Ⅱ,L22,and even L4 target gene loci,indicating that if two or more target gene amino acid loci undergo mutations,highly resistant strains can be produced.After testing with active efflux systems,it was found that there were no active efflux systems using macrolide antibiotics as substrates.It can be seen that the strain of Mycoplasma bovis from Xizang yaks is prone to mutation under the pressure of high con-centrations of macrolides,and there are mutations in two or more target gene amino acid sites,which is prone to produce highly resistant strains.

Objectives:This study was aimed to investigate the risk factors for moderate and severe acute kidney injury(AKI),in-hospital mortality and dialysis dependence after acute Stanford type A aortic dissection(TAAD)surgery. Methods:Complete clinical data of 294 TAAD patients who underwent ascending aorta replacement,total aortic arch replacement combined with frozen elephant trunk between December 2014 and December 2016 with time between symptom onset and diagnosis<14 days were retrospectively and consecutively collected and analyzed.AKI was defined according to the SCr component of the Kidney Disease Improving Global Outcomes(KDIGO)2012 consensus criteria,and classified as moderate-severe AKI(AKI stage 2-3)or non-moderate-severe AKI(no AKI or AKI stage 1).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for moderate to severe AKI,in-hospital mortality or dialysis dependence after TAAD surgery. Results:AKI occurs in 232 out of 294 patients(79.3%),124(42.2%)with AKI stage 1,45(15.3%)with AKI stage 2,63(21.4%)with AKI stage 3,and 27(9.2%)requiring continuous renal replacement therapy(CRRT).The dialysis dependence rate at discharge was 3.1%(n=9),and overall in-hospital mortality rate was 3.7%(n=11).Univariate analysis revealed that patients who developed the moderate-severe AKI were more likely to present with hypertension,preoperative renal hypoperfusion,longer duration of cardiopulmonary bypass(CPB),longer duration of aortic cross-clamp and higher intraoperative ultrafiltration volume(all P<0.05).Multivariate logistic regression analysis showed that renal hypoperfusion(OR=4.95,95%CI:1.97-12.26,P<0.001),and prolonged CPB time(OR=1.01,95%CI:1.00-1.02,P=0.016)were independent risk factors for moderate-severe AKI after TAAD surgery.Further analysis revealed that prolonged CPB time(OR=1.02,95%Cl:1.01-1.03,P=0.007)and moderate-severe AKI(OR=10.49,95%Cl:1.22-90.62,P=0.033)were independent risk factors for in-hospital mortality or dialysis dependence after TAAD surgery. Conclusions:Preoperative renal hypoperfusion and prolonged CPB time are independent risk factors for moderate-severe AKI after TAAD surgery.Prolonged CPB time and occurrence of moderate to severe AKI significantly increase the risk of in-hospital mortality and dialysis dependence,indicating that close clinical follow-up of these patients is required.

Background::Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.Methods::This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.Results::When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.Conclusions::DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.