Diabetes is a very complex endocrine disease whose common feature is the increase in blood glucose concentration. Persistent hyperglycemia can lead to blindness, kidney and heart disease, neurodegeneration, and many other serious complications that have a significant impact on human health and quality of life. The number of people with diabetes is increasing yearly. The global diabetes prevalence in 20-79 year olds in 2021 was estimated to be 10.5% (536.6 million), and it will rise to 12.2% (783.2 million) in 2045. The main modes of intervention for diabetes include medication, dietary management, and exercise conditioning. Medication is the mainstay of treatment. Marketed diabetes drugs such as metformin and insulin, as well as GLP-1 receptor agonists, are effective in controlling blood sugar levels to some extent, but the preventive and therapeutic effects are still unsatisfactory. Peptide drugs have many advantages such as low toxicity, high target specificity, and good biocompatibility, which opens up new avenues for the treatment of diabetes and other diseases. Currently, insulin and its analogs are by far the main life-saving drugs in clinical diabetes treatment, enabling effective control of blood glucose levels, but the risk of hypoglycemia is relatively high and treatment is limited by the route of delivery. New and oral anti-diabetic drugs have always been a market demand and research hotspot. Inhibitor cystine knot (ICK) peptides are a class of multifunctional cyclic peptides. In structure, they contain three conserved disulfide bonds (C3-C20, C7-C22, and C15-C32) form a compact “knot” structure, which can resist degradation of digestive protease. Recent studies have shown that ICK peptides derived from legume, such as PA1b, Aglycin, Vglycin, Iglycin, Dglycin, and aM₁, exhibit excellent regulatory activities on glucose and lipid metabolism at the cellular and animal levels. Mechanistically, ICK peptides promote glucose utilization by muscle and liver through activation of IR/AKT signaling pathway, which also improves insulin resistance. They can repair the damaged pancrease through activation of PI3K/AKT/Erk signaling pathway, thus lowering blood glucose. The biostability and hypoglycemic efficacy of the ICK peptides meet the requirements for commercialization of oral drugs, and in theory, they can be developed into natural oral anti-diabetes peptide drugs. In this review, the structural properties, activity and mechanism of ICK pattern peptides in regulating glucose and lipid metabolism were summaried, which provided a reference for the development of new oral peptides for diabetes.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To translate the Eating Behaviors Assessment for Obesity(EBA-O)and test its reliability and validity for providing an effective tool for brief and rapid clinical assessment of dietary behavior in obese patients.Methods The Chinese version of EBA-O was developed by Brislin's translation model for translation,back translation,cultural adaptation,and pilot investigation.Using convenience sampling method,200 obese patients who were admitted to the inpatient ward of the Weight Loss Center of a tertiary hospital in Beijing from April to June 2023 and planned to undergo weight loss metabolic surgery were selected as the research participants to test the reliability and validity of the Chinese version of EBA-O.Results The Chinese version of EBA-O includes 4 dimensions,with a total of 17 items.The Cronbach's α coefficient of the scale is 0.886;the Cronbach's αcoefficients of the dimensions are 0.770～0.866;the test-retest reliability of the scale is 0.881.The content validity index of the scale level is 0.980,and the content validity indexes of item level were 0.889～1.000.Totally 4 common factors were extracted through exploratory factor analysis,with a cumulative variance of 66.363％.Conclusion The Chinese version of EBA-O has good reliability and validity,and it can be used as a tool to evaluate the dietary behavior of obese patients undergoing weight loss metabolic surgery.

Objective: To identify the maximum tolerated dose (MTD) of docetaxel combined with a fixed dose of cisplatin (75 mg/m 2 ) delivered as hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with ovarian cancer. Methods: In this phase I trial, a time-to-event Bayesian optimal interval design was used.Docetaxel was given at a starting dose of 60 mg/m2 and was increased in 5 mg/m2 increments until the MTD was determined or the maximum dose level of 75 mg/m2 was reached. The doselimiting toxicity (DLT) rate was set at 25%, with a total sample size of 30 patients. HIPEC was delivered immediately following debulking surgery at a target temperature of 43°C for 90 minutes. Results: From August 2022 to November 2022, 30 patients were enrolled. Among the patients who received a dose of docetaxel ≤65 mg/m2 , no DLT was reported. DLTs were observed in one patient who received 70 mg/m2 docetaxel (grade 3 anaemia) and in three patients who received 75 mg/m2 docetaxel (one case of grade 3 anaemia, one case of grade 3 hepatic impairment and one case of grade 4 thrombocytopenia). Patients treated with docetaxel 75 mg/m2 in combination with cisplatin 75 mg/m2 had an estimated DLT rate of 25%, which was the closest to the target DLT rate and was therefore chosen as the MTD. Conclusion Docetaxel, in combination with a fixed dose of cisplatin (75 mg/m2), can be used safely at intraperitoneal doses of 75 mg/m2 in ovarian cancer patients who received HIPEC (43°C, 90 minutes) following debulking surgery.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To explore the application effect of theory of inventive problem solving(TRIZ)in the management of loaner instruments in central sterile supply department(CSSD).Methods:TRIZ management team was set up to analyze problems in cleaning,disinfection and sterilization of loaner instruments.The invention principles of TRIZ were compared to determine targeted solutions to the corresponding problems.A total of 1,000 pieces of loaner instruments received by The Third Affiliated Hospital of Air Force Medical University were selected from January and December 2023 were selected,the 500 pieces received from January to June were managed by routine standard management mode,and the 500 pieces received from July to December were managed by the TRIZ management mode.The qualification rates of instruments cleaning,disinfection,packaging and sterilization,the incidence of adverse events,the satisfaction scores of clinical departments and assessment results of newly hired nurses of CSSD were compared between the two management modes.Results:The qualification rates of instruments cleaning,disinfection,packaging and sterilization of TRIZ management mode were 98.00%(490/500),97.20%(486/500),96.40%(482/500)and 96.00%(480/500),respectively,which were higher than those of routine standard management mode,the difference was statistically significant(x2=12.029,11.685,8.859,8.322,P<0.05).The incidence of adverse events of TRIZ management mode was 0%,the routine standard management mode was 1.20%,the difference was statistically significant(x2=6.036,P<0.05).The average scores of CSSD newly hired nurses in of theoretical knowledge,treatment process,cleaning,disinfection and sterilization and packaging of TRIZ management mode were(89.20±6.69)points,(88.47±3.48)points,(92.47±5.37)points and(92.00±5.83)points,respectively,which were higher than those of routine standard management mode,the difference was statistically significant(t=3.993,4.402,3.926,3.332,P<0.05).The satisfaction scores of clinical department personnel with instruments quality,distribution,handover,information traceability,service attitude and overall satisfaction of TRIZ management mode were(18.65±0.81)points,(18.85±1.04)points,(18.95±1.05)points,(18.40±0.75)points,(18.35±0.93)points and(93.20±1.91)points,respectively,which were higher than those of the routine standard management mode,the difference was statistically significant(t=3.599,5.889,4.851,4.865,2.075,8.723,P<0.05).Conclusion:The application of TRIZ in the management of loaner instruments in CSSD can significantly improve theoretical knowledge and practical skills of newly hired nurses in CSSD,thereby improving the qualification rates of instruments cleaning,disinfection,packaging and sterilization of loaner instruments,reducing the occurrence of instrument-related adverse events and improving satisfaction of department personnel with instruments use.

Objective It screens the main factors influencing the choice of the governance structure of the medical alliances,and discusses the matching relationship between the type of governance structure and the performance of the medical alliances,which provides a reference for further promoting the medical alliances.Methods The text coding analysis of interview data from hospital administrators of 54 medical institutions was analyzed using the rooting theory,and 150 medical alliances were selected for questionnaire surveys using convenience sampling to analyze the performance of the medical alliances and the relationship with the choice of the type of governance structure using descriptive statistics,nonparametric tests,factor analysis,and multivariate linear regression.Results The results show that the following factors affect the selection of the governance structure of medical alliances:asset specificity,transaction frequency and uncertainty;regardless of the type of governance structure of the medical alliances,asset specificity and uncertainty both significantly and positively influence the performance of medical alliances.Under the governance structure of the property-type alliance,transaction frequency has no statistically significant effect on the performance of the medical alliances.Conclusion Enhancing the positive impact of asset exclusivity and uncertainty on the performance of medical alliances.

Objective It screens the main factors influencing the choice of the governance structure of the medical alliances,and discusses the matching relationship between the type of governance structure and the performance of the medical alliances,which provides a reference for further promoting the medical alliances.Methods The text coding analysis of interview data from hospital administrators of 54 medical institutions was analyzed using the rooting theory,and 150 medical alliances were selected for questionnaire surveys using convenience sampling to analyze the performance of the medical alliances and the relationship with the choice of the type of governance structure using descriptive statistics,nonparametric tests,factor analysis,and multivariate linear regression.Results The results show that the following factors affect the selection of the governance structure of medical alliances:asset specificity,transaction frequency and uncertainty;regardless of the type of governance structure of the medical alliances,asset specificity and uncertainty both significantly and positively influence the performance of medical alliances.Under the governance structure of the property-type alliance,transaction frequency has no statistically significant effect on the performance of the medical alliances.Conclusion Enhancing the positive impact of asset exclusivity and uncertainty on the performance of medical alliances.

Objective It screens the main factors influencing the choice of the governance structure of the medical alliances,and discusses the matching relationship between the type of governance structure and the performance of the medical alliances,which provides a reference for further promoting the medical alliances.Methods The text coding analysis of interview data from hospital administrators of 54 medical institutions was analyzed using the rooting theory,and 150 medical alliances were selected for questionnaire surveys using convenience sampling to analyze the performance of the medical alliances and the relationship with the choice of the type of governance structure using descriptive statistics,nonparametric tests,factor analysis,and multivariate linear regression.Results The results show that the following factors affect the selection of the governance structure of medical alliances:asset specificity,transaction frequency and uncertainty;regardless of the type of governance structure of the medical alliances,asset specificity and uncertainty both significantly and positively influence the performance of medical alliances.Under the governance structure of the property-type alliance,transaction frequency has no statistically significant effect on the performance of the medical alliances.Conclusion Enhancing the positive impact of asset exclusivity and uncertainty on the performance of medical alliances.