The armadillo repeat containing 5 (ARMC5) gene is part of a family of protein-coding genes that are rich in armadillo repeat sequences, are ubiquitously present in eukaryotes, and mediate interactions between proteins, playing roles in various cellular processes. Current research has demonstrated that reduced expression or absence of the ARMC5 gene in various tumor tissues can lead to uncontrolled cell proliferation, thereby inducing a range of diseases. The ARMC5 gene was initially extensively studied in the context of bilateral macronodular adrenocortical disease (BMAD), with harmful pathogenic variants in ARMC5 identified in approximately 50% of BMAD patients. With advancing research, scientists have discovered that ARMC5 pathogenic variants may also have potential effects on other diseases and could be associated with increased susceptibility to certain cancers. This review aims to present the latest research progress on how the ARMC5 gene plays its role in tumors. It outlines the basic structure of ARMC5 and the regions where it functions, as well as the diseases currently proven to be associated with ARMC5. Moreover, some evidence suggests its relation to embryonic development and the regulation of immune system activity. In conclusion, the ARMC5 gene is a crucial focal point in genetic and medical research. Understanding its function and regulation is of great importance for the development of new therapeutic strategies related to diseases associated with its pathogenic variants.
Humans ; Neoplasms/genetics* ; Armadillo Domain Proteins/genetics* ; Animals ; Genetic Predisposition to Disease ; Cytoskeletal Proteins/genetics* ; Tumor Suppressor Proteins/genetics*

Objective: To analyze the results of national external quality assessment (EQA) for transfusion compatibility test from 2018 to 2023, with the aim of providing references for improving laboratory testing quality and ensuring the safety of clinical blood transfusion. Methods: Three EQA programs were conducted annually, each distributing 22 quality assessment samples. Participating transfusion laboratories were required to complete testing within specified deadlines and to submit results along with documentation of testing methodologies, reagents, and equipment used. National Center for Clinical Laboratories (NCCL) conducted statistical analysis of laboratory results, evaluated testing outcomes and related circumstances, and provided feedback to participating laboratories. EQA data from transfusion laboratories across China from 2018 to 2023 were collected and systematically analyzed. Results: From 2018 to 2023, the qualification rates for all five items (ABO forward typing, ABO reverse typing, Rh blood group typing, antibody screening, and cross-matching) were 67.59%, 77.11%, 77.38%, 72.78%, 79.96%, and 85.16%, respectively. The mean qualification rates for ABO forward typing, ABO reverse typing, RhD blood group typing, antibody screening, and cross-matching over the past six years were 96.25%±0.59%, 90.45%±4.52%, 96.05%±0.71%, 90.88%±2.86%, and 88.34%±3.48%, respectively. The qualification rates in 2019, 2020, 2022, and 2023 all showed a stable trend of "blood stations>tertiary hospitals>secondary hospitals". The mean qualification rate of laboratories in secondary hospitals from 2018 to 2023 was significantly lower than those of laboratories in tertiary hospitals and blood stations (P<0.05), while no significant difference was observed between laboratories in tertiary hospitals and blood stations (P>0.05). The micro column agglutination method was the most widely used in all five tests. In the four test items, namely ABO forward typing, ABO reverse typing, antibody screening, and cross-matching, there was a statistically significant difference in the qualification rate of micro column agglutination method compared to other methods (P<0.05). There was a statistical difference in the qualification rate between manual and automated detection using micro column agglutination method in the cross-matching tests (P<0.05), whereas no significant difference was noted for the other test items (P>0.05). Conclusion: From 2018 to 2023, the number of laboratories participating in EQA activities has been increasing year by year, and the qualification rate has shown an overall upward trend. The type of laboratory is a key factor affecting the qualification rate, and the testing capabilities of some laboratories still need to be improved. The micro column agglutination method is widely used in transfusion compatibility tests. The established EQA program effectively monitors quality issues in laboratories, drives continuous improvement, and ensures sustained enhancement of testing standards to safeguard clinical blood safety.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Objective:To screen the risk factors for non-complete procedural success of transvenous lead extraction(TLE),and to establish a prediction model based on the results and evaluate its predictive efficacy.Methods:A total of 1 029 patients who underwent TLE in Peking University People's Hospital from January 2014 to December 2020 were enrolled and divided into training set(n=720)and validation set(n=309)using the random number method.There were no statistically significant differences among the variables in the training set and the validation set.The training set was divided into the complete procedural success(CPS)group(n=664)and the non-CPS group(n=56).Univariate analysis was employed to screen the relevant indicators of non-CPS,followed by binary logistic regression analysis to identify the independent risk factors of non-CPS.Subsequently,a predictive model and nomogram were constructed.The receiver operating characteristic(ROC)curve analysis was applied to evaluate the ability of the model to distinguish non-CPS from TLE patients in the training set and validation set.The Hosmer-Lemeshow goodness-of-fit test was used to assess the consistency between the predicted risk and the actual risk of the model.Results:Univariate analysis showed that the relevant variables with P<0.1 including the age at the first implantation of the lead,the number of leads extracted,the oldest dwell time of lead extracted,the presence of abandoned leads,non-manual traction for lead extracted,the number of extracted leads>3,bilateral lead implantation,and the indications for TLE.The binary logistic regression analysis revealed that the presence of abandoned leads(OR=2.252,95%CI:1.111-4.564,P=0.024),the oldest dwell time of the extracted leads(OR=1.009,95%CI:1.005-1.012,P<0.001),and the number of extracted leads>3(OR=3.177,95%CI:1.306-7.733,P=0.011)were independent risk factors for non-CPS of TLE.ROC curve analysis revealed that the area under the ROC curve(AUC)of the training set was 0.80(95%CI:0.75-0.85,P<0.001).The AUC of the validation set was 0.81(95%CI:0.72-0.90,P<0.001).The Hosmer-Lemeshow goodness-of-fit test indicated that the P values of both the training set(P=0.089)and the validation set(P=0.136)were greater than 0.05.Conclusions:The presence of abandoned leads,the oldest dwell time of lead extracted,and the number of extracted leads>3 are independent risk factors for non-CPS in patients undergoing TLE.The nomogram model based on the above factors has satisfactory predictive ability.

Objective To analyze the risk factors for catheter-related thrombosis(CRT)in the upper arm infusion port(UAP)and to construct a machine-learning prediction model.Methods A total of 6028 patients,who received UAP implantation at Shanghai Renji Hospital of China from February 2014 to February 2023,were enrolled in this study.The patients were divided into training set(n=4 219)and validation set(n=1 809).Six machine-learning prediction models,including Least Absolute Shrinkage and Selection Operator(LASSO)regression,random forest,decision tree,neural network,XGBoost and logistic,were constructed,and the model having best performance was selected as the optimal model.SHapely Additive exPlanations(SHAP)analysis was used to explain the neural network model,and DALEXtra package was used to explain the continuous variables.Results The neural network model was chosen as the final model.The variables,in order of the degree of importance from high to low,included sex,the diameter of catheter,catheter tip confirmation method,the length of catheter,inpatient or outpatient status,history of central venous catheter implantation,the length of subcutaneous tunnel,age,body mass index(BMI),primary tip displacement,and left or right venous approach.The learning curve,i.e.the area under curve(AUC)of the receiver operating characteristic(ROC)curve,for the training set was＞0.6,and the Delong testing and Bootstrap Methods Test showed that the neural network model performed well(P＜0.05).The Kolmogorov-Smirnov plot(KS plot)value was 0.313 5,indicating that the model had the good ability of discrimination.The clinical impact curve(CIC)assessment revealed that the model had good clinical value.Conclusion The machine-learning prediction model of upper arm infusion port with CRT has been successfully constructed.For minimizing the risk of CRT,it is recommended to prioritize the use of 5 F diameter catheters,adopt left-sided venous approach and positioning the tip of the catheter based on anatomical measurements,besides,the catheter length should be not shorter than 36.56 cm,and the subcutaneous tunnel length should not be less than 5 cm.The basic features associated with higher CRT risk include age of 50-65 years,BMI being between 18.69 kg/m2 and 20.81 kg/m2 or between 23.68 kg/m2 and 23.94 kg/m2 and male.

Objective To explore the association between walking exercise guided by 6 minute walking test(6MWT)and the incidences of 3-year major adverse cardiovascular event(MACE)in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 628 elderly ACS patients who undergoing PCI and obtaining success-ful coronary revascularization in our department from November 2018 to April 2019 were enrolled,and divided into 6MWT group(n=147)and control group(n=481)based on participa-ting in walking exercise guided by 6MWT or not.All of them were followed up for 3 years.The incidences of MACE[including coronary target vascular restenosis,acute myocardial infarction,heart failure,ischemic or hemorrhagic stroke]and all-cause death were observed.Univariate and multivariate Cox proportional analyses and Kaplan-Meier survival curve analysis were employed for data statistical analyses.Results At the end of follow-up,the incidences of target vascular restenosis(6.9％vs 2.0％,P=0.028),heart failure(3.7％vs 0％,P=0.036),stroke(3.7％vs 0％,P=0.036),and total MACE incidence(15.0％vs 4.1％,P=0.000)were statistically higher in the control group than the 6MWT group.Kaplan-Meier survival curve analysis showed that the cumulative incidence of MACE was significantly lower in the 6MWT group than the control group(Plog rank=0.001).Multivariate Cox regression analysis showed that not participating in walking exercise guided by 6MWT was an independent risk factor for occurrence of 3-year MACE(HR=3.102,95％CI:1.327-7.250,P=0.009).Conclusion Walking exercise guided by 6MWT reduces the incidence of 3-year MACE and improves the long-term prognosis of elderly ACS patients after PCI.

Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.

Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.