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  1. Shao Y, Dang M, Lin Y, Xue F
    Life Sci, 2019 Aug 15;231:116422.
    PMID: 31059689 DOI: 10.1016/j.lfs.2019.04.048
    This study was performed to evaluate the antidiabetic and wound healing activity of plumbagin in diabetic rats by macroscopical, biochemical, histological, immunohistochemical and molecular methods. Percentage of wound closure and contraction was delayed in diabetic rats when compared to non-diabetic group. There was significant reduction in period of epithelialization, collagen and protein content. Serum insulin level was significantly lowered together with increase in glucose level in diabetic rats. Lipid levels were increased significantly with concomitant decrease in HDL level. The mRNA levels of Nrf2, collagen-1, TGF-β and α-SMA were significantly lowered whereas Keap-1 levels were increased in diabetic rats. The level of lipid peroxides was increased while the levels of antioxidants were lowered significantly. ELISA results reveal upregulated levels of inflammatory markers. Western blot result shows upregulated levels of CD68 and CD163 proteins in wound area of diabetic rats. Histopathological observation revealed increased inflammatory cells infiltration in diabetic control. Immunofluorescent staining and immunohistochemical analysis also displayed delayed wound healing in diabetic groups. Diabetic rats treated with 10% and 20% plumbagin showed increased epithelialization, collagen deposition, increased serum insulin level and increased antioxidant status. Lipid peroxides and lipid levels were lowered significantly with increase in HDL level. Inflammatory markers were lowered, and growth factors expressions were increased markedly. Thus, the results of the study indicated that plumbagin administration could improve wound healing activity and could serve as a potent antidiabetic and anti-inflammatory agent.
  2. Xue F, Wei N, Wu X
    Front Psychol, 2023;14:1202408.
    PMID: 38655498 DOI: 10.3389/fpsyg.2023.1202408
    INTRODUCTION: The risk of college students facing psychological problems, such as stress, anxiety, and depression, has increased, which may have a negative impact ontheir academic performance and overall well-being, especially after the outbreakof the pandemic.

    METHODS: This paper summarizes the potential psychological issues thatuniversity students may face and the corresponding coping measures. Basedon this, a theoretical model of ideological and political education to enhancepsychological education was established.

    RESULTS: There was a total of 446 participantsin the study, with a mean age of 21.4 years and 44.6 per cent male. With 406 valid survey responses, the theoretical model was examined using the structuralequation modeling method. The results showed that education and teaching, practical activities, counseling services, prevention and intervention, and multilevelplatforms are effective measures to protect the psychological health ofuniversity students.

    DISCUSSION: Based on the insights gained from this study, policies canbe implemented to help university students improve their mental health andinspire higher education institutions to prioritize psychological education.

  3. Zhang J, Xue F, Liu SD, Liu D, Wu YH, Zhao D, et al.
    World J Gastrointest Surg, 2023 Mar 27;15(3):387-397.
    PMID: 37032800 DOI: 10.4240/wjgs.v15.i3.387
    BACKGROUND: Surgical site infections (SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.

    AIM: To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.

    METHODS: We retrospectively analysed the inpatient records of Shaanxi Provincial People's Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002 (NRS 2002) scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance (NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.

    RESULTS: A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus (42.2%), the liver (27.6%), the gastrointestinal tract (19.1%), the appendix (5.9%), the kidney (3.7%), and the groin area (1.4%). SSI occurred in 5% of the patients (n = 150). The risk factors associated with SSI were as follows: Age; gender; marital status; place of residence; history of diabetes; surgical season; surgical site; NRS 2002 score; preoperative white blood cell, procalcitonin (PCT), albumin, and low-density lipoprotein cholesterol (LDL) levels; preoperative antibiotic use; anaesthesia method; incision grade; NNIS score; intraoperative blood loss; intraoperative drainage tube placement; surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio (OR) = 5.698, 95% confidence interval (CI): 3.305-9.825, P = 0.001], antibiotic use (OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3 (OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia (OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2 (OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L (OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L (OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL (OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season (P < 0.05), surgical site (P < 0.05), and incision grade I or III (P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score (0.662).

    CONCLUSION: The patient's condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

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