Intraoperative cell salvage (IOCS) has been widely applied as an important blood conservation measure in surgical operations. However, there is currently a lack of clinical practice guidelines for the implementation of IOCS in patients with malignant tumors. This report aims to provide clinicians with recommendations on the use of IOCS in patients with malignant tumors based on the review and assessment of the existed evidence. Data were derived from databases such as PubMed, Embase, the Cochrane Library and Wanfang. The guideline development team formulated recommendations based on the quality of evidence, balance of benefits and harms, patient preferences, and health economic assessments. This study constructed seven major clinical questions. The main conclusions of this guideline are as follows: 1) Compared with no perioperative allogeneic blood transfusion (NPABT), perioperative allogeneic blood transfusion (PABT) leads to a more unfavorable prognosis in cancer patients (Recommended); 2) Compared with the transfusion of allogeneic blood or no transfusion, IOCS does not lead to a more unfavorable prognosis in cancer patients (Recommended); 3) The implementation of IOCS in cancer patients is economically feasible (Recommended); 4) Leukocyte depletion filters (LDF) should be used when implementing IOCS in cancer patients (Strongly Recommended); 5) Irradiation treatment of autologous blood to be reinfused can be used when implementing IOCS in cancer patients (Recommended); 6) A careful assessment of the condition of cancer patients (meeting indications and excluding contraindications) should be conducted before implementing IOCS (Strongly Recommended); 7) Informed consent from cancer patients should be obtained when implementing IOCS, with a thorough pre-assessment of the patient's condition and the likelihood of blood loss, adherence to standardized internally audited management procedures, meeting corresponding conditions, and obtaining corresponding qualifications (Recommended). In brief, current evidence indicates that IOCS can be implemented for some malignant tumor patients who need allogeneic blood transfusion after physician full evaluation, and LDF or irradiation should be used during the implementation process.

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 evaluate cardiac involvement in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) using echocardiography combined with electrocardiography. Methods A retrospective analysis was performed on the detailed medical records of AAV patients treated in Jining First People’s Hospital between January 2020 and December 2024. Eighty patients were enrolled in the AAV group, and the risk of heart disease was compared between the AAV group and a control group with 80 subjects matched for age, sex, and cardiovascular disease risk factors. Results Electrocardiographic abnormalities were observed in 78.75% of patients in the AAV group, while significant electrocardiographic abnormalities only occurred in symptomatic patients in the control group. There were no differences in left atrial enlargement or interventricular septal thickening between the AAV group and the control group. The overall left ventricular systolic function in the AAV group was lower than that in the control group (8.75% vs. 0). The incidence of reduced diastolic function in the AAV group was significantly higher than that in the control group (37.5% vs. 15%). The incidence rates of tricuspid regurgitation, mitral regurgitation, aortic regurgitation, and pericardial effusion in the AAV group were significantly higher than those in the control group. Pericardial thickening, aortic stenosis, pulmonary hypertension, and rare periaortic granulomas were found in the AAV group, but not in the control group. Conclusion Echocardiography and electrocardiography are important examination methods for evaluating cardiac involvement in AAV. These methods have key roles in disease screening, diagnosis and treatment, follow-up, and prognosis judgment.

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.