Effectiveness of an encecalin standardized extract of Ageratina pichinchensis on the treatment of onychomycosis in patients with diabetes mellitus.
- 作者列表："Romero-Cerecero O","Islas-Garduño AL","Zamilpa A","Tortoriello J
:Ageratina pichinchensis is utilized in traditional medicine for the treatment of dermatomycosis and inflammation. The aim of this study was to evaluate the clinical and mycological effectiveness of the topical administration of an enecalin standardized extract of A. pichinchensis for treating onychomycosis in patients with type 2 diabetes mellitus (DM2). A double blind, randomized, and controlled clinical trial was carried out that included patients with DM2 and who had mild or moderate onychomycosis. Participants were administered topically, for 6 months, a lacquer containing the encecalin standardized extract of A. pichinchensis (experimental group) or 8% ciclopirox (control group). In a large percentage of both, the control group (77.2%) and the experimental group (78.5%), clinical efficacy was detected as a decrease in the number of affected nails and a reduction in the severity of nail involvement. Without exhibiting statistically significant differences between groups, the encecalin standardized extract of A. pichinchensis was clinically and mycologically effective in the treatment of mild and moderate onychomycosis in patients with DM2. The treatment of onychomycosis in patients with DM2 implies a greater challenge, while control of blood glucose levels in these patients, played a very important role in the response of patients to treatment.
: 胡黄连在传统医学中用于治疗皮肤真菌病和炎症。本研究的目的是评价局部给予一种洋地黄标准化提取物治疗 2 型糖尿病 (DM2) 患者甲真菌病的临床和真菌学有效性。进行了一项双盲、随机、对照临床试验，纳入了 DM2 和轻度或中度灰指甲患者。参与者局部给药，持续 6 个月，使用含有 enecalin 标准提取物的漆。pichinchensis (实验组) 或 8% 环吡酮 (对照组)。在两者的很大一部分百分比，对照组 (77.2%) 和实验组 (78.5%), 临床疗效检测为受累指甲数量的减少和指甲受累严重程度的降低。在各组之间没有表现出统计学显著差异的情况下，土茯苓的 enecalin 标准化提取物在治疗 dm2 患者的轻度和中度甲真菌病方面具有临床和真菌学上的有效性。DM2 患者甲真菌病的治疗意味着更大的挑战，而控制这些患者的血糖水平，在患者对治疗的反应中起着非常重要的作用。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.