Self-healing materials have attracted considerable attention because of their improved safety, lifetime, energy efficiency and environmental impact. Supramolecular interactions have been extensively considered in the field of self-healing materials due to their excellent reversibility and sensitive responsiveness to environmental stimuli. However, development of a polymeric material with good mechanical performance as well as self-healing capacity is very challenging. In this study, we report a robust self-healing polyurethane (PU) elastomer polypropylene glycol-2-amino-5-(2hydroxyethyl)-6-methylpyrimidin-4-ol (PPG-mUPy) by integrating ureidopyrimidone (UPy) motifs with a PPG segment with a well-defined architecture and microphase morphology. To balance the self-healing capacity and mechanical performance, a thermal-triggered switch of H-bonding is introduced. The quadruple H-bonded UPy dimeric moieties in the backbone induce phase separation to form a hard domain as well as enable further aggregation into microcrystals by virtue of the stacking interactions, which are stable in ambient temperature. This feature endows the PU with high mechanical strength. Meanwhile, a high healing efficiency can be realized, when the reversibility of the H-bond was unlocked from the stacking at higher temperature. An optimized sample PPG 1000-mUPy 50% with a good balance of mechanical performance (20.62 MPa of tensile strength) and healing efficiency (93% in tensile strength) was achieved. This strategy will provide a new idea for developing robust self-healing polymers.

Background: In recent years, sodium-glucose co-transporter 2 inhibitors (SGLT2is) have been increasingly used in the treatment of patients with non-alcoholic fatty liver disease (NAFLD). This updated metaanalysis aimed to evaluate the efficacy and safety of SGLT2is for patients with NAFLD. Methods: PubMed, Embase, Cochrane Library, Web of Science, Wan Fang, China National Knowledge Infrastructure and VIP databases were searched for relevant studies from inception to April 30, 2021. Values of weighted mean differences (WMDs) and risk ratios (RRs) were determined for continuous and dichotomous outcomes, respectively. Results: A total of 1,498 patients with NAFLD from 20 studies were included for further analysis. Pooled analyses indicated significant improvements in body mass index [WMD: −0.84 kg/m 2 , 95% CI ( −1.09, −0.60)], alanine aminotransferase [WMD: −4.36 U/L, 95% CI ( −7.17, −1.54)], aspartate aminotransferase [WMD: −2.94 U/L, 95% CI ( −5.33, −0.55)], fasting plasma glucose [WMD: −4.08 mmol/L, 95% CI ( −6.21, −1.95)] and fibrosis-4 index [WMD: −0.08, 95% CI ( −0.11, −0.05)] following SGLT2i treatment ( p < 0.01 for all above parameters). There was no significant difference in the incidence of total adverse events between the SGLT2i group and the control group (RR = 0.78, 95% CI (0.58, 1.06), p = 0.11]. Conclusion: SGLT2is seem to be a promising treatment for patients with NAFLD to improve metabolic and fibrosis indexes without increasing the incidence of adverse events. Most included studies were conducted in NAFLD patients with diabetes. Therefore, the results of this meta-analysis are more applicable to the diabetic population.

ObjectiveWe aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.MethodsClinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.ResultsThe development cohort enrolled 730 patients with a median follow-up time of 87 (40–98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043–1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951–0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678–13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930–0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287–3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p &lt; 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839–0.921) and 0.798 (95% CI = 0.720–0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.ConclusionWe developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

Background: In recent years, many studies have reported the relationship between non-alcoholic fatty liver disease (NAFLD) and sex hormones, especially total testosterone (TT) and sex hormone–binding globulin (SHBG). However, the relationship between sex hormones and the severity of NAFLD is still unclear. Methods: PubMed, Embase, Cochrane Library, Web of Science, WanFang, China National Knowledge Infrastructure and VIP databases were searched for relevant studies from inception to 31 August 2021. Values of weighted mean differences (WMDs) and odds ratios (ORs) with their 95% confidence intervals (CIs) were combined by Stata 12.0 software to evaluate the relationship between TT, SHBG and the severity of NAFLD in males. Results: A total of 2995 patients with NAFLD from 10 published cross-sectional studies were included for further analysis. The meta-analysis indicated that the moderate-severe group had a lower TT than the mild group in males with NAFLD (WMD: −0.35 ng/ml, 95% CI = −0.50 to −0.20). TT and SHBG were important risk factors of moderate-severe NAFLD in males (ORTT = 0.79, 95% CI = 0.73 to 0.86; ORSHBG = 0.22, 95% CI = 0.12 to 0.39; p < 0.001). Moreover, when the analysis was limited to men older than age 50, SHBG levels were lower in those with moderate-severe disease (WMD: −11.32 nmol/l, 95% CI = −14.23 to −8.40); while for men with body mass index (BMI) >27 kg/m2, moderate-severe NAFLD had higher SHBG levels than those with mild disease (WMD: 1.20 nmol/l, 95% CI = −2.01 to 4.42). Conclusion: The present meta-analysis shows that lower TT is associated with the severity of NAFLD in males, while the relationship between SHBG and severity of NAFLD is still to be further verified.

Objective Diabetes is a major cause of the progression of acute kidney injury (AKI). Few prediction models have been developed to predict the renal prognosis in diabetic patients with AKI so far. The aim of this study was to develop and validate a predictive model to identify high-risk individuals with non-recovery of renal function at 90 days in diabetic patients with AKI. Methods Demographic data and related laboratory indicators of diabetic patients with AKI in the First Affiliated Hospital of Guangxi Medical University from January 31, 2012 to January 31, 2022 were retrospectively analysed, and patients were followed up to 90 days after AKI diagnosis. Based on the results of Logistic regression, a model predicting the risk of non-recovery of renal function at 90 days in diabetic patients with AKI was developed and internal validated. Consistency index (C-index), calibration curve, and decision curve analysis were used to evaluate the differentiation, accuracy, and clinical utility of the prediction model, respectively. Results A total of 916 diabetic patients with AKI were enrolled, with a male to female ratio of 2.14:1. The rate of non-recovery of renal function at 90 days was 66.8% (612/916). There were 641 in development cohort and 275 in validation cohort (ration of 7:3). In the development cohort, a prediction model was developed based on the results of Logistic regression analysis. The variables included in the model were: diabetes duration (OR = 1.022, 95% CI  1.012–1.032), hypertension (OR = 1.574, 95% CI 1.043–2.377), chronic kidney disease (OR = 2.241, 95% CI 1.399–3.591), platelet (OR = 0.997, 95% CI 0.995–1.000), 25-hydroxyvitamin D3 (OR = 0.966, 95% CI  0.956–0.976), postprandial blood glucose (OR = 1.104, 95% CI 1.032–1.181), discharged serum creatinine (OR = 1.003, 95% CI 1.001–1.005). The C-indices of the prediction model were 0.807 (95% CI 0.738–0.875) and 0.803 (95% CI 0.713–0.893) in the development and validation cohorts, respectively. The calibration curves were all close to the straight line with slope 1. The decision curve analysis showed that in a wide range of threshold probabilities. Conclusion A prediction model was developed to help predict short-term renal prognosis of diabetic patients with AKI, which has been verified to have good differentiation, calibration degree and clinical practicability.

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