Temporal analysis of histopathology and cytokine expression in the rat cerebral cortex after insulin-induced hypoglycemia.
- 作者列表："Tomita N","Nakamura T","Sunden Y","Miyata H","Morita T
:Hypoglycemic coma causes neuronal death in the cerebral neocortex; however, its unclear pathogenesis prevents the establishment of preventive measures. Inflammation plays a pivotal role in neuronal damage in the hypoglycemic state; however, the dynamics of glial cell activation or cytokine expression remain unknown. Here, we aimed to elucidate the spatiotemporal morphological changes of microglia and time-course cytokine expression profiles in the rat cerebral cortex after hypoglycemic coma. We performed histopathological and immunohistochemical (Iba1, neuronal nuclei, glial fibrillary acidic protein) analyses in the cingulate cortex and four areas of the neocortex: hindlimb area (HL), parietal cortex area 1 (Par1), parietal cortex area 2 (Par2), and perirhinal cortex (PRh). We measured tumor necrosis factor alpha (TNFα) and interleukin-6 messenger RNA (mRNA) expression by real-time reverse transcriptase-polymerase chain reaction. Necrotic neurons appeared in the neocortex as early as 3 h after hypoglycemic coma, while they were absent in the cingulate cortex. Neuronal nuclei-immunopositive neurons in the HL, Par2, and PRh were significantly less abundant than in the control at day 1. In Iba1 immunostaining, large rod-shaped cells were detected at 3-6 h after hypoglycemia, and commonly observed in the HL, Par2, and PRh. After 6 h, rod-shaped cells were rarely observed; instead, there was a prominent infiltration of hypertrophic and ameboid-shaped cells until day 7. The mRNA expression of TNFα was significantly higher than the control at 3-6 h after hypoglycemia in the neocortex, while it was significantly higher only at 3 h in the cingulate cortex. Our results indicate that early and transient appearance of rod-shaped microglia and persisting high TNFα expression levels characterize inflammatory responses to hypoglycemic neuronal damage in the cerebral neocortex, which might contribute to neuronal necrosis in response to transient hypoglycemic coma.
: 低血糖昏迷导致大脑新皮层神经元死亡; 然而，其发病机制尚不清楚，阻碍了预防措施的建立。在低血糖状态下，炎症在神经元损伤中起关键作用; 然而，胶质细胞活化或细胞因子表达的动力学仍然未知。在此，我们旨在阐明低血糖昏迷后大鼠大脑皮层小胶质细胞的时空形态变化和时程细胞因子表达谱。我们在扣带回皮层和新皮层的四个区域进行组织病理学和免疫组织化学 (Iba1，神经元核，胶质纤维酸性蛋白) 分析: 后肢区 (HL), 顶叶皮质区 1 (Par1) 、顶叶皮质区 2 (Par2) 和嗅周皮质 (PRh)。我们通过实时逆转录酶-聚合酶链反应测量了肿瘤坏死因子 α (tnf α) interleukin-6-6 信使 RNA (mRNA) 的表达。低血糖昏迷后 3 h，新皮质内出现坏死神经元，而扣带回皮质内无坏死神经元。第 1 天 HL 、 Par2 和 PRh 的神经元核-免疫阳性神经元数量明显少于对照。在 Iba1 免疫染色中，低血糖后 3-6 h 检测到大杆状细胞，常见于 HL 、 Par2 和 PRh。6 h 后，很少观察到杆状细胞; 相反，直到第 7 天，有明显的肥大和阿米巴状细胞浸润。低血糖后 3-6 h，新皮质 tnf α mRNA 表达显著高于对照组，而扣带回皮质仅 3 h，tnf α mRNA 表达显著高于对照组。我们的结果表明，杆状小胶质细胞的早期和短暂出现以及持续的高 tnf α 表达水平表征了大脑新皮质低血糖神经元损伤的炎症反应, 这可能导致一过性低血糖昏迷的神经元坏死。
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.