Mini Invasive Floating Metatarsal Osteotomy for Diabetic Foot Ulcers Under the First Metatarsal Head: A Case Series.
- 作者列表："Tamir E","Smorgick Y","Ron GZ","Gilat R","Agar G","Finestone AS
:Diabetic foot ulcers under the first metatarsal head are difficult to treat and prevent recurrence. The aim of this study is to summarize the results of a distal first metatarsal minimally invasive floating osteotomy for ulcers under the first metatarsal head in patients with diabetic neuropathy. We reviewed files of patients with diabetic neuropathy undergoing a floating first metatarsal osteotomy. Demographic and clinical data were collected and analyzed to determine success and complications. We found records for 21 patients (mean age 64) with University of Texas 1A ulcers. The ulcer's mean age was 11.2 months. Following surgery, the ulcer completely resolved after a mean of 3.7 (2 to 11) weeks in 19 patients. During the first year, there were 4 complications related to the surgery (including 3 infections). At latest follow-up, 17/21 (81%) patients had healed with satisfactory results. Minimal invasive floating distal osteotomy of the first metatarsal can cure and prevent recurrence of diabetic foot ulcers under the first metatarsal head in 80% of the patients, but the ability to provide close follow-up and prompt response are prerequisites.
: 第一跖骨头下的糖尿病足溃疡难以治疗，预防复发。本研究的目的是总结远端第一跖骨微创浮动截骨术治疗糖尿病神经病变患者第一跖骨头下溃疡的结果。我们回顾了接受浮动第一跖骨截骨术的糖尿病神经病变患者的档案。收集人口统计学和临床数据并进行分析，以确定成功与否和并发症。我们发现了 21 例德克萨斯大学 1A 型溃疡患者 (平均年龄 64 岁) 的记录。溃疡的平均年龄为 11.2 个月。手术后，19 例患者平均 3.7 (2 ~ 11) 周后溃疡完全消退。在第一年，有 4 例手术相关的并发症 (包括 3 例感染)。在最近的随访中，17/21 例 (81%) 患者愈合，结果满意。第一跖骨微创浮动远端截骨术可治愈和预防 80% 患者第一跖骨头下糖尿病足溃疡复发,但是提供密切随访和迅速反应的能力是先决条件。
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.