Diabetes during Pregnancy: A Maternal Disease Complicating the Course of Pregnancy with Long-Term Deleterious Effects on the Offspring. A Clinical Review

In spite of the huge progress in the treatment of diabetes mellitus, we are still in the situation that both pregestational (PGDM) and gestational diabetes (GDM) impose an additional risk to the embryo, fetus, and course of pregnancy. PGDM may increase the rate of congenital malformations, especially cardiac, nervous system, musculoskeletal system, and limbs. PGDM may interfere with fetal growth, often causing macrosomia, but in the presence of severe maternal complications, especially nephropathy, it may inhibit fetal growth. PGDM may also induce a variety of perinatal complications such as stillbirth and perinatal death, cardiomyopathy, respiratory morbidity, and perinatal asphyxia. GDM that generally develops in the second half of pregnancy induces similar but generally less severe complications. Their severity is higher with earlier onset of GDM and inversely correlated with the degree of glycemic control. Early initiation of GDM might even cause some increase in the rate of congenital malformations. Both PGDM and GDM may cause various motor and behavioral neurodevelopmental problems, including an increased incidence of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Most complications are reduced in incidence and severity with the improvement in diabetic control. Mechanisms of diabetic-induced damage in pregnancy are related to maternal and fetal hyperglycemia, enhanced oxidative stress, epigenetic changes, and other, less defined, pathogenic mechanisms.     

一种改进的视网膜图像中微小动脉瘤的检测算法

基于糖尿病性视网膜病变中最早出现的微小动脉瘤病症进行了研究,提出一种有效的微小动脉瘤检测算法。首先在传统模板匹配算法的基础上提出了一种动态多参数模板匹配算法,并且使用相对误差和与相关系数来共同制约匹配度,从而实现了更为精确的匹配提取;其次提出了基于分布特性的计分策略和自适应加权的汇总策略,避免了单纯采用各个特征量作为独立约束指标进行筛选时忽视各个特征量的约束力大小的弊端。实验结果表明,该检测算法能够有效地提高微小动脉瘤的检测真阳性率。     

基于CTCNet对球结膜进行糖尿病视网膜病变分类

糖尿病视网膜病变(diabetic retinopathy, DR)是一种糖尿病性微血管病变,会在球结膜微血管上有所体现,球结膜血管图像的获取比眼底图像更加便捷,但微血管的特征变化微小且难以量化。为了能够对患者进行早期辅助诊断,本文依据球结膜微血管形态与DR的关联,首先对球结膜图像进行预处理,使用限制对比度自适应直方图均衡(contrast limited adaptive histogram equalization, CLAHE)算法进行图像增强,随机处理使数据增强,然后结合卷积神经网络(convolutional neural network, CNN)和Transformer各自的网络优势构建CTCNet,对处理后的球结膜血管图像进行DR分类,分类准确率达到了97.44%,敏感度97.69%,特异性97.11%,精确度97.69%,通过实验对比CNN和Transformer, CTCNet网络性能优于其他模型,能够有效识别DR。     

Poly(pentahydropyrimidine)-Based Hybrid Hydrogel with Synergistic Antibacterial and Pro-Angiogenic Ability for the Therapy of Diabetic Foot Ulcers

Bacterial infection and impaired angiogenesis make the treatment of diabetic foot ulcers (DFU) extremely challenging. Cationic polymers are expected to treat infected wounds due to their excellent antibacterial properties, but still, it is difficult to meet the therapeutic needs of pro-angiogenesis and anti-infections due to their simple construction units and outmoded synthesis methods. Herein, a cationic poly(pentahydropyrimidine) (PPHP) library with strong modifiability is synthesized to construct a hybrid hydrogel with synergistic therapeutic effects for the treatment of infected DFUs. It is found that the as-synthesized hybrid hydrogel can up-regulate angiogenesis-related gene (HIF-1, VEGF, and bFGFR/bFGF) expression and targeted disruption of bacterial cell membranes, which finally promotes the healing of infected DFU (wound healing rate: 92%) within 10 days. This hydrogel, thus, holds great promise in developing new strategies to significantly enhance the treatment of DFU and other bacterial-infected pathological diagnoses.     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.     

Upregulation of miR145 and miR126 in EVs from Renal Cells Undergoing EMT and Urine of Diabetic Nephropathy Patients

Simple SummaryDiabetic nephropathy is one of the most frequent complications of diabetes, resulting from diffuse damage to different kidney cells. The identification of subjects at risk is mandatory to prevent its development and provide appropriate therapies reducing the unmanageable evolution towards end-stage kidney disease. The aim of this work was to identify urinary-derived extracellular vesicles (EVs) miRNA cargo to be used as biomarker of kidney damage in diabetic patients. The miRNA profile was then correlated with the molecular mechanism associated with the glomerular and tubular damage using a diabetic-like model. In patients, miR145 and miR126 in urinary EVs increased together with albuminuria. MiR145 and miR126 increased in parallel in EVs from renal epithelial cells undergoing transition to a fibrotic mesenchymal phenotype. These data unveiled a role for miR126 and miR145 as the biomarkers of damage progression and proteinuria development in diabetic nephropathy. AbstractDiabetic nephropathy (DN) is a severe kidney-related complication of type 1 and type 2 diabetes and the most frequent cause of end-stage kidney disease. Extracellular vesicles (EVs) present in the urine mainly derive from the cells of the nephron, thus representing an interesting tool mirroring the kidney’s physiological state. In search of the biomarkers of disease progression, we here assessed a panel of urinary EV miRNAs previously related to DN in type 2 diabetic patients stratified based on proteinuria levels. We found that during DN progression, miR145 and miR126 specifically increased in urinary EVs from diabetic patients together with albuminuria. In vitro, miRNA modulation was assessed in a model of TGF-β1-induced glomerular damage within a three-dimensional perfusion system, as well as in a model of tubular damage induced by albumin and glucose overload. Both renal tubular cells and podocytes undergoing epithelial to mesenchymal transition released EVs containing increased miR145 and miR126 levels. At the same time, miR126 levels were reduced in EVs released by glomerular endothelial cells. This work highlights a modulation of miR126 and miR145 during the progression of kidney damage in diabetes as biomarkers of epithelial to mesenchymal transition.     

Immune Modulation by Myeloid-Derived Suppressor Cells in Diabetic Kidney Disease

Diabetic kidney disease (DKD) frequently leads to end-stage renal disease and other life-threatening illnesses. The dysregulation of glomerular cell types, including mesangial cells, endothelial cells, and podocytes, appears to play a vital role in the development of DKD. Myeloid-derived suppressor cells (MDSCs) exhibit immunoregulatory and anti-inflammatory properties through the depletion of L-arginine that is required by T cells, through generation of oxidative stress, interference with T-cell recruitment and viability, proliferation of regulatory T cells, and through the promotion of pro-tumorigenic functions. Under hyperglycemic conditions, mouse mesangial cells reportedly produce higher levels of fibronectin and pro-inflammatory cytokines. Moreover, the number of MDSCs is noticeably decreased, weakening inhibitory immune activities, and creating an inflammatory environment. In diabetic mice, immunotherapy with MDSCs that were induced by a combination of granulocyte-macrophage colony-stimulating factor, interleukin (IL)-1β, and IL-6, reduced kidney to body weight ratio, fibronectin expression, and fibronectin accumulation in renal glomeruli, thus ameliorating DKD. In conclusion, MDSCs exhibit anti-inflammatory activities that help improve renal fibrosis in diabetic mice. The therapeutic targeting of the proliferative or immunomodulatory pathways of MDSCs may represent an alternative immunotherapeutic strategy for DKD.     

Nitro Dihydrocapsaicin,a Non-Pungent Capsaicin Analogue,Inhibits Cellular Senescence of Lens Epithelial Cells via Upregulation of SIRT1

Diabetic cataracts are a common complication that can cause blindness among patients with diabetes mellitus. A novel nitro dihydrocapsaicin (NDHC), a capsaicin analog, was constructed to have a non-pungency effect. The objective of this research was to study the effect of NDHC on human lens epithelial (HLE) cells that lost function from hyperglycemia. HLE cells were pretreated with NDHC before an exposure to high glucose (HG) conditions. The results show that NDHC promoted a deacceleration of cellular senescence in HLE cells. This inhibition of cellular senescence was characterized by a delayed cell growth and lower production of reactive oxygen species (ROS) as well as decreased SA-β-galactosidase activity. Additionally, the expression of Sirt1 protein sharply increased, while the expression of p21 and phospho-p38 proteins decreased. These findings provide evidence that NDHC could exert a pharmacologically protective effect by inhibiting the senescence program of lens cells during diabetic cataracts.     

Peroxisome Proliferator-Activated Receptor-α (PPARα) Expression in a Clinical Population of Pakistani Patients with Type 2 Diabetes and Dyslipidemia

Poor glycemic control and dyslipidemia are hallmarks of type 2 diabetes mellitus (T2DM), which predispose to cardiovascular diseases. Peroxisome proliferator-activated receptor-α (PPARα) has been associated with atherosclerosis, but its role in T2DM is less clear. Previously, we studied PPARα expression levels in diabetics with and without dyslipidemia (DD). In this study we described the association with fasting blood glucose, HbA1c levels and lipid levels of the study population. Patient demography and biochemical data were collected from hospitals in Islamabad, Pakistan, and RT-PCR data of PPARα expression were retrieved from our previous study from the same cohort. We performed t-tests and regression analysis to evaluate the relationships between PPARα expression and demographic and clinical variables. As expected, body mass index and HbA1c were elevated in T2DM and DD patients compared to controls. Blood lipids (total cholesterol, triglycerides, LDL and HDL) were significantly higher in the DD group compared to the other two groups. In the T2DM and DD groups, the PPARα expression was not associated with any of the physical and biochemical parameters measured in this study. Expression of the PPARα gene was independent of blood lipids and glycemic control in this study. Further research is necessary to better understand the biological parameters of PPARα expression.