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.

Objective To investigate the effect and possible mechanism of microRNA-26a(miR-26a)on the syn-thesis of extracellular matrix(ECM)induced by high glucose(HG)in renal tubular epithelial cells(RTECs).Methods A model of diabetic kidney disease(DKD)was constructed by inducing RTECs with HG.MiR-26a was overexpressed in HG-induced RTECs,and RT-qPCR and Western blot were used to assess the effects of miR-26a on ECM synthesis and ferroptosis-related markers in HG-treated RTECs.Ferrostatin(Fer-1)was used to inhibit ferroptosis in the DKD model,and its impact on ECM synthesis was evaluated.RT-qPCR and Western blot were performed to measure ferroptosis-related markers,and fluorescence microscopy was used to observe the intensity of reactive oxygen species(ROS).Results Compared with the control group,the expression of miR-26a decreased in HG-treated cells,while the expression levels of ECM synthesis-related indexes fibronectin and collagen Ⅰ in-creased.After overexpressing miR-26a,the HG+miR-26a group showed a significant increase in miR-26a expres-sion and a decrease in fibronectin and collagen Ⅰ expression compared to the HG group.In terms of ferroptosis,the protein and mRNA expression of SLC7A11 and GPX4 significantly decreased,the expression of TFR-1 and AC-SL4 significantly increased,and the fluorescence intensity of ROS was significantly enhanced in the HG group com-pared with the control group.Inhibition of ferroptosis in the HG+Fer-1 group resulted in significant changes in fer-roptosis and ECM synthesis-related indicators expression levels compared to the HG group.Furthermore,re-expres-sion of miR-26a in the HG+miR-26a led to significant changes in ferroptosis-related indicators expression levels and decreased ROS fluorescence intensity compared to the HG group.Conclusions In HG-induced RTECs,miR-26a inhibits the occurrence of ferroptosis,thus reducing ECM synthesis.

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.