BACKGROUND:Osteoporotic fracture is the most serious complication of osteoporosis.Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture,but the causal relationship between the two is unclear. OBJECTIVE:To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS:The genome-wide association study(GWAS)datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9,respectively.Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,Mendelian randomization analyses with random-effects inverse variance weighted,MR-Egger regression,weighted median,simple model,and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture.Sensitivity analyses were performed to test the reliability and robustness of the results.Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION:The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture.Elevated abundance of Actinomycetales[odds ratio(OR)=1.562,95%confidence interval(CI):1.027-2.375,P=0.037),Actinomycetaceae(OR=1.561,95%CI:1.027-2.374,P=0.037),Actinomyces(OR=1.544,95%CI:1.130-2.110,P=0.006),Butyricicoccus(OR=1.781,95%CI:1.194-2.657,P=0.005),Coprococcus 2(OR=1.550,95%CI:1.068-2.251,P=0.021),Family ⅩⅢ UCG-001(OR=1.473,95%CI:1.001-2.168,P=0.049),Methanobrevibacter(OR=1.274,95%CI:1.001-1.621,P=0.049),and Roseburia(OR=1.429,95%CI:1.015-2.013,P=0.041)would increase the risk of osteoporotic fractures in patients.Elevated abundance of Bacteroidia(OR=0.660,95%CI:0.455-0.959,P=0.029),Bacteroidales(OR=0.660,95%CI:0.455-0.959,P=0.029),Christensenellacea(OR=0.725,95%CI:0.529-0.995,P=0.047),Ruminococcaceae(OR=0.643,95%CI:0.443-0.933,P=0.020),Enterorhabdus(OR=0.558,95%CI:0.395-0.788,P=0.001),Eubacterium rectale group(OR=0.631,95%CI:0.435-0.916,P=0.016),Lachnospiraceae UCG008(OR=0.738,95%CI:0.546-0.998,P=0.048),and Ruminiclostridium 9(OR=0.492,95%CI:0.324-0.746,P=0.001)would reduce the risk of osteoporotic fractures in patients.We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method.That is,using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture.The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice,but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveThe detection of RNA single nucleotide polymorphism (SNP) is of great importance due to their association with protein expression related to various diseases and drug responses. At present, splintR ligase-assisted methods are important approaches for RNA direct detection, but its specificity will be limited when the fidelity of ligases is not ideal. The aim of this study was to create a method to improve the specificity of splintR ligase for RNA detection. MethodsIn this study, a dual-competitive-padlock-probe (DCPLP) assay without the need for additional enzymes or reactions is proposed to improve specificity of splintR ligase ligation. To verify the method, we employed dual competitive padlock probe-mediated rolling circle amplification (DCPLP-RCA) to genotype the CYP2C9 gene. ResultsThe specificity was well improved through the competition and strand displacement of dual padlock probe, with an 83.26% reduction in nonspecific signal. By detecting synthetic RNA samples, the method demonstrated a dynamic detection range of 10 pmol/L-1 nmol/L. Furthermore, clinical samples were applied to the method to evaluate its performance, and the genotyping results were consistent with those obtained using the qPCR method. ConclusionThis study has successfully established a highly specific direct RNA SNP detection method, and provided a novel avenue for accurate identification of various types of RNAs.

Improving the entire outpatient clinic experience is an important measure in the new era to enhance patients'sense of gain from medical treatment.Beijing Tongren Hospital Affiliated to Capital Medical University explores the establishment of a full-process outpatient service management system based on continuous improvement of medical services.Through the concept of forward service,it focuses on the patient's pre-diagnosis experience;the process is simplified and intelligently guided to optimize the patient's in-diagnosis experience;and continuous diagnosis and treatment Model innovation improves patients'post-diagnosis experience and creates a Chinese-style modern outpatient medical service model to continuously meet the people's growing needs for a better life.

Improving the entire outpatient clinic experience is an important measure in the new era to enhance patients'sense of gain from medical treatment.Beijing Tongren Hospital Affiliated to Capital Medical University explores the establishment of a full-process outpatient service management system based on continuous improvement of medical services.Through the concept of forward service,it focuses on the patient's pre-diagnosis experience;the process is simplified and intelligently guided to optimize the patient's in-diagnosis experience;and continuous diagnosis and treatment Model innovation improves patients'post-diagnosis experience and creates a Chinese-style modern outpatient medical service model to continuously meet the people's growing needs for a better life.

Improving the entire outpatient clinic experience is an important measure in the new era to enhance patients'sense of gain from medical treatment.Beijing Tongren Hospital Affiliated to Capital Medical University explores the establishment of a full-process outpatient service management system based on continuous improvement of medical services.Through the concept of forward service,it focuses on the patient's pre-diagnosis experience;the process is simplified and intelligently guided to optimize the patient's in-diagnosis experience;and continuous diagnosis and treatment Model innovation improves patients'post-diagnosis experience and creates a Chinese-style modern outpatient medical service model to continuously meet the people's growing needs for a better life.

Improving the entire outpatient clinic experience is an important measure in the new era to enhance patients'sense of gain from medical treatment.Beijing Tongren Hospital Affiliated to Capital Medical University explores the establishment of a full-process outpatient service management system based on continuous improvement of medical services.Through the concept of forward service,it focuses on the patient's pre-diagnosis experience;the process is simplified and intelligently guided to optimize the patient's in-diagnosis experience;and continuous diagnosis and treatment Model innovation improves patients'post-diagnosis experience and creates a Chinese-style modern outpatient medical service model to continuously meet the people's growing needs for a better life.

Improving the entire outpatient clinic experience is an important measure in the new era to enhance patients'sense of gain from medical treatment.Beijing Tongren Hospital Affiliated to Capital Medical University explores the establishment of a full-process outpatient service management system based on continuous improvement of medical services.Through the concept of forward service,it focuses on the patient's pre-diagnosis experience;the process is simplified and intelligently guided to optimize the patient's in-diagnosis experience;and continuous diagnosis and treatment Model innovation improves patients'post-diagnosis experience and creates a Chinese-style modern outpatient medical service model to continuously meet the people's growing needs for a better life.