Evaluation of a type 1 diabetes serum cohort by SELDI-TOF MS protein profiling

Proteomics analysis of serum from patients with type 1 diabetes (T1D) may lead to novel biomarkers for prediction of disease and for patient monitoring. However, the serum proteome is highly sensitive to sample processing and before proteomics biomarker research serum cohorts should preferably be examined for potential bias between sample groups. SELDI‐TOF MS protein profiling was used for preliminary evaluation of a biological‐bank with 766 serum samples from 270 patients with T1D, collected at 18 different paediatric centers representing 15 countries in Europe and Japan over 2 years (2000–2002). Samples collected 1 (n = 270), 6 (n = 248), and 12 (n = 248) months after T1D diagnosis were grouped across centers and compared. The serum protein profiles varied with collection site and day of analysis; however, markers of sample processing were not systematically different between samples collected at different times after diagnosis. Three members of the apolipoprotein family increased with time in patient serum collected 1, 6, and 12 months after diagnosis (ANOVA, p<0.001). These results support the use of this serum cohort for further proteomic studies and illustrate the potential of high‐throughput MALDI/SELDI‐TOF MS protein profiling for evaluation of serum cohorts before proteomics biomarker research.     

花椒精油对链脲佐菌素诱导的糖尿病小鼠糖代谢的影响

目的:研究花椒精油对1型糖尿病机体糖代谢的影响及可能机制.方法:将50只健康雄性昆明小鼠(KM小鼠)分为空白对照,糖尿病模型组,花椒精油高(15?mg/(kg mb·d))、中(9?mg/(kg mb·d))、低(3?mg/(kg mb·d))剂量组,每组10只.采用链脲佐菌素诱导建立1型糖尿病模型.空白组和糖尿病模型...     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

降血糖功能因子及其食物来源

糖尿病严重威胁着人类健康,开发有潜力和有价值的降血糖功能成分原料已成为当今研究的热点。本文综述了降血糖功能因子的种类和功效,并介绍了降血糖功能食品的动植物原料,旨在为降血糖功能食品的开发和研究提供参考。     

黄连素对2型糖尿病大鼠血清、肾脏的保护作用

目的:观察黄连素对实验性2型糖尿病大鼠血清、肾脏中羟自由基(.OH)能力的影响。方法:采用小剂量链脲佐菌素加高糖高脂饲料喂养的方法建立Wistar大鼠2型糖尿病模型,成模后给予黄连素0.1g/(kg.d)灌胃治疗8周,检测血清、肾脏羟自由基能力的变化。结果:与糖尿病组相比,黄连素组肾脏、血清中羟自由基能力下降,各观察值均与糖尿病组存在显著性差异(P<0.05)。结论:黄连素可保护大鼠的血清和肾脏,降低羟自由基能力,减少自由基对机体细胞的损伤。     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

Model-based Decision support in Diabetes Care

The model-based Karlsburg Diabetes Management System (KADIS^®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS^® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS^® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients.     

血浆肾素-血管紧张素-醛固酮系统活性和 2 型糖尿病患者微量白蛋白尿的相关性研究简

目的：探讨血浆肾素-血管紧张素-醛固酮系统（RAAS）活性和2型糖尿病患者微量白蛋白尿之间的关系。方法用放射免疫法检测116例2型糖尿病患者的 RAAS活性,计算血浆醛固酮/肾素活性比值、尿微量白蛋白/肌酐比值和其他生化指标,采用多因素回归分析观察这些指标和微量白蛋白尿之间的关系。结果经过性别、年龄、BMI、病程校正后,单因素分析结果：糖尿病视网膜病变（DR）、血浆醛固酮、血管紧张素Ⅰ、醛固酮肾素活性比值、空腹血糖与糖尿病微量白蛋白尿显著相关（P＜0．05）;多因素分析结果：空腹血糖、血浆醛固酮、DR与糖尿病微量白蛋白尿显著相关（P＜0．05）。结论空腹血糖、血浆醛固酮、DR是微量白蛋白尿的独立相关因子。     

红光联合护理干预及药物治疗糖尿病合并带状疱疹神经痛的疗效观察

目的:探讨红光照射联合护理干预及药物治疗糖尿病合并带状疱疹神经痛的疗效。方法:选取此类患者56例,随机分为观察组和对照组,各28例。对照组用更昔洛韦加泼尼松、甲钴胺片、维生素B1片进行治疗,观察组在对照组治疗的基础上加用红光照射,两组护理干预方法保持一致,2周后评价疗效。结果:对照组治愈率为32.1%,总愈显率为60.7%,观察组上述数据分别为64.3%、89.3%,观察组疗效显著优于对照组。结论:红光照射联合护理干预及药物治疗糖尿病合并带状疱疹神经痛疗效显著,值得临床推广。