Clinical application of a 3D-printed scaffold in chronic wound treatment: a case series.

OBJECTIVE: This case series evaluates the safety and effectiveness of 3D-printed scaffold in chronic wounds. The scaffold is a composite of natural and synthetic materials, and can be prepared in the form of powder or membrane. METHOD: We recruited patients with pressure ulcera (PU) and/or a diabetic foot ulcers (DFU). We used two methods: 3D-printed scaffolds alone, or 3D-printing powder mixed with platelet-rich fibrinogen (PRF). Clinicians and patients were asked to rate the scaffold's ease of application and comfort during use. RESULTS: A total of five patients were recruited; four with a PU and one with a DFU. For the patient treated with the 3D-printed scaffold membrane (n=1), their PU healed in 28 days, and for patients treated with the 3D-printed scaffold powder (n=2), their PUs healed in 54 days. For the patients treated with the 3D-printing powder mixed with PRF (n=2), the patient with a PU healed in 11 days, and the patient with the DFU healed in 14 days. All clinicians rated the 3D-printed scaffold as 'easy' or 'very easy' to use, and patients rated their comfort during wear and at dressing change as 'good' or 'very good'. CONCLUSION: This study demonstrated that 3D-printed scaffold was convenient to use, have the potential to improve wound healing rates, and provided a safe and effective way for treating chronic wounds.

PSTPIP2 alleviates obesity associated adipose tissue inflammation and insulin resistance in diabetes mice through promoting M2 macrophage polarization via activation of PPARγ.

BACKGROUND: Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2) plays a role in inflammatory disease. In diabetes, very little is known about PSTPIP2 until now. Hence, this study aimed to determine PSTPIP2 functional role in diabetes. METHODS: Diabetes mouse model was constructed by feeding high fat diet (HFD). Intraperitoneal glucose tolerance test and intraperitoneal insulin tolerance test were examined the glucose and insulin tolerance. The expression of genes and proteins was detected by quantitative real time PCR, immunohistochemistry and western blotting. The pathological changes of epididymal adipose tissues were examined by hematoxylin-eosin staining. RAW264.7 macrophages were treated with GW9662 (PPARγ antagonist). Flow cytometry examined the proportion of M1/M2 macrophages. RESULTS: HFD enhanced the body weight, glucose and insulin tolerance, and inhibited PSTPIP2 expression in mice. PSTPIP2 overexpression alleviated glucose and insulin tolerance, reduced inflammation and macrophage accumulation in the epididymal adipose tissues of diabetic mice. The expression of iNOS and TNF-α was increased, the expression of IL-10 and Arg-1 was decreased in diabetic mice, which was abrogated by PSTPIP2 overexpression. In vitro, PSTPIP2 overexpression reduced the proportions of iNOS-positive cells and enhanced the proportions of CD206-positive cells in RAW264.7 cells. PPARγ and p-STAT6 were up-regulated, STAT6 was down-regulated in RAW264.7 cells. GW9662 impaired PSTPIP2 overexpression-mediated up-regulation of Arg-1, YM-1 and FIZZ1 in RAW264.7 cells. CONCLUSION: PSTPIP2 alleviates obesity associated adipose tissue inflammation and insulin resistance in diabetic mice through promoting M2 macrophage polarization via activation of PPARγ, suggesting that PSTPIP2 is a prospective target for diabetes treatment.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations.

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.