儿童和青少年 1 型糖尿病胰岛素泵的空气闭塞。
- 作者列表："Knoll MM","Vazifedan T","Gyuricsko E
:Background Insulin pumps are a frequently used technology among youth with type 1 diabetes. Air bubbles within insulin pump tubing are common, preventing insulin delivery and increasing the risk of large glycemic excursions and diabetic ketoacidosis (DKA). We sought to determine the prevalence of air bubbles in insulin pump tubing and identify factors associated with clinically significant air bubbles. Methods Fifty-three subjects were recruited over 65 office visits. The insulin pump tubing was visualized, and any air bubbles were measured by length. The length of air bubbles was then converted to time without insulin at the lowest basal rate. Generalized linear model (GLM) was used to determine the associations between air bubble size and other variables. Results Of the 65 encounters, 45 had air bubbles in the tubing. Five (5/65 = 7.7%) encounters had a time without insulin of more than 60 min. Air bubble size was inversely correlated with time since infusion set change (p < 0.001), and directly correlated with age of the subject (p = 0.049). Conclusions Significantly more air bubbles were found in the tubing of insulin pumps soon after infusion set change and with older subjects, suggesting a relationship with the technique of filling the insulin cartridge and priming the tubing.
: 背景胰岛素泵是青年 1 型糖尿病患者经常使用的技术。胰岛素泵管路内的气泡很常见，可防止胰岛素输送，增加血糖大幅波动和糖尿病酮症酸中毒 (DKA) 的风险。我们试图确定胰岛素泵管路中气泡的患病率，并确定与临床显著气泡相关的因素。方法在 65 名门诊就诊的受试者中，招募 53 名。显示胰岛素泵管道，并通过长度测量任何气泡。然后以最低的基础速率将气泡的长度转换为无胰岛素的时间。广义线性模型 (GLM) 被用来确定气泡大小和其他变量之间的关联。结果 65 次相遇中，45 次油管中有气泡。5 次 (5/65 = 7.7%) 相遇，无胰岛素时间超过 60 min。气泡大小与输液器变化后的时间呈负相关 (p <0.001)，与受试者年龄直接相关 (p = 0.049)。结论在输液器更换后不久以及年龄较大的受试者中，胰岛素泵的管路中发现明显更多的气泡，提示与填充胰岛素筒和灌注管路的技术有关。
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.