Objective To explore the value of oral contrast-enhanced ultrasonography (OCEUS) combined with contrast-enhanced CT in predicting preoperative T staging in patients with gastric cancer. Methods A retrospective analysis was conducted on 80 patients with gastric cancer confirmed via endoscopic biopsy or postoperative pathology at the First People’s Hospital of Jining from January 2021 to November 2024. The cohort included 56 males and 24 females, aged 38-79 years, with a median age of 55.9 years. All patients underwent both OCEUS and contrast-enhanced CT within one week prior to surgery. T staging of gastric cancer was determined using OCEUS, contrast-enhanced CT, or their combination. The results were compared with pathological T staging, and statistical differences in accuracy were analyzed. Results Pathological T staging identified T1 in 9 cases, T2 in 16 cases, T3 in 42 cases, and T4 in 13 cases. OCEUS indicated T1 in 6 cases, T2 in 14 cases, T3 in 50 cases, and T4 in 10 cases, with an accuracy rate of 80.0%. Contrast-enhanced CT indicated T1 in 4 cases, T2 in 12 cases, T3 in 52 cases, and T4 in 12 cases, with an accuracy rate of 75.0%. The combination of OCEUS and contrast-enhanced CT indicated T1 in 6 cases, T2 in 15 cases, T3 in 47 cases, and T4 in 12 cases, with an accuracy rate of 87.5%. The combined approach demonstrated significantly higher accuracy in preoperative T staging compared to either method alone (P < 0.05). Conclusion The combination of OCEUS and contrast-enhanced CT improves the accuracy of preoperative T staging in gastric cancer patients, providing valuable support for their diagnosis and treatment.

Objective To investigate the association between lifestyle and risk of heart disease and diabetes in the elderly population in Xi'an City. Methods From January 2021 to January 2024, a staged cluster sampling method was used to investigate the lifestyle and the occurrence of heart disease and diabetes in elderly population aged 60 years and above in the communities of Xi'an. Multivariate logistic regression was used to analyze the relationship between lifestyle and the risk of heart disease and diabetes. Results A total of 413 elderly people were investigated, of which 31.96% had heart disease, 27.12% had diabetes, and 10.90% had diabetes with heart disease. Multivariate logistic regression analysis revealed that age, BMI, family history, sweet food preference, smoking, and sitting and lying for a long time were risk factors for diabetes in the elderly population (P<0.05). Age, BMI, family history, history of diabetes, preference for salted products, smoking, drinking, and sitting and lying for a long time were risk factors for heart disease in the elderly population (P<0.05). Conclusion The incidence rates of heart disease and diabetes are high in the elderly population in Xi'an City. The risk of diabetes is related to unhealthy lifestyles such as sweet food preference, smoking, and sitting and lying for a long time, while heart disease is related to unhealthy lifestyles such as preference for salted products, smoking, drinking, and sitting and lying for a long time.

BACKGROUND: Chronic kidney disease (CKD) is associated with common pathophysiological processes, such as inflammation and fibrosis, in both the heart and the kidney. However, the underlying molecular mechanisms that drive these processes are not yet fully understood. Therefore, this study focused on the molecular mechanism of heart and kidney injury in CKD. METHODS: We generated an microRNA (miR)-26a knockout (KO) mouse model to investigate the role of miR-26a in angiotensin (Ang)-II-induced cardiac and renal injury. We performed Ang-II modeling in wild type (WT) mice and miR-26a KO mice, with six mice in each group. In addition, Ang-II-treated AC16 cells and HK2 cells were used as in vitro models of cardiac and renal injury in the context of CKD. Histological staining, immunohistochemistry, quantitative real-time polymerase chain reaction (PCR), and Western blotting were applied to study the regulation of miR-26a on Ang-II-induced cardiac and renal injury. Immunofluorescence reporter assays were used to detect downstream genes of miR-26a, and immunoprecipitation was employed to identify the interacting protein of LIM and senescent cell antigen-like domain 1 (LIMS1). We also used an adeno-associated virus (AAV) to supplement LIMS1 and explored the specific regulatory mechanism of miR-26a on Ang-II-induced cardiac and renal injury. Dunnett's multiple comparison and t -test were used to analyze the data. RESULTS: Compared with the control mice, miR-26a expression was significantly downregulated in both the kidney and the heart after Ang-II infusion. Our study identified LIMS1 as a novel target gene of miR-26a in both heart and kidney tissues. Downregulation of miR-26a activated the LIMS1/integrin-linked kinase (ILK) signaling pathway in the heart and kidney, which represents a common molecular mechanism underlying inflammation and fibrosis in heart and kidney tissues during CKD. Furthermore, knockout of miR-26a worsened inflammation and fibrosis in the heart and kidney by inhibiting the LIMS1/ILK signaling pathway; on the contrary, supplementation with exogenous miR-26a reversed all these changes. CONCLUSIONS Our findings suggest that miR-26a could be a promising therapeutic target for the treatment of cardiorenal injury in CKD. This is attributed to its ability to regulate the LIMS1/ILK signaling pathway, which represents a common molecular mechanism in both heart and kidney tissues.
Animals ; MicroRNAs/metabolism* ; Angiotensin II/toxicity* ; Mice ; Renal Insufficiency, Chronic/chemically induced* ; Mice, Knockout ; Disease Models, Animal ; Male ; Signal Transduction/genetics* ; LIM Domain Proteins/genetics* ; Mice, Inbred C57BL ; Cell Line ; Humans

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.

Objective:It analyzes the resource allocation efficiency and its changing trend of 17 types of medical institutions in Beijing from 2016 to 2022,discusses the problems existing in resource allocation,and provides reference suggestions for improving the efficiency of health resource allocation.Methods:The Banker-Chames-Cooper(BCC)model in Data Envelopment Analysis(DEA)was used to statically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing in 2022.The Malmquist index model was used to dynamically analyze the health resource allocation efficiency of 17 types of medical institutions in Beijing from 2016 to 2022.Results:In 2022,the overall comprehensive efficiency of medical institutions in Beijing was not high,with an average of 0.804.There were 6 types of medical institutions with effective production efficiency(35.30％),4 types of medical institutions with weak production efficiency(23.50％),and 7 types of medical institutions with ineffective production efficiency(41.20％).From 2016 to 2022,the total factor productivity change index of general hospitals,orthopedic hospitals,other specialized hospitals and nursing homes was greater than 1,and the average total factor productivity change index was 0.907.Conclusion:The allocation efficiency of health resources in various medical institutions in Beijing needs to be improved.It is suggested that the government should strengthen macro-control and allocate health resources in combination with the market;accelerate the promotion of hierarchical diagnosis and treatment system,adjust the structure of medical resources,and improve the utilization rate of resources;make full use of psychiatric hospital health resources,avoid resource waste;improve technical ability and promote technological innovation.