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This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved utilizing three datasets: Messidor, IDRiD (Indian Diabetic Retinopathy Detection), and APTOS 2019 Blindness Detection, which collectively encompassed 5,379 fundus images. Different types of processed fundus images were fed into the model to determine the optimal pre-processing approach for stage detection in Diabetic Retinopathy. The model was assessed on the original dataset with some augmentation techniques applied. According to the training data, the model achieved a maximum accuracy of 72%. However, converting the dataset to grayscale yielded an improved accuracy of 80%. Similarly, extracting the green, red, and blue channels individually resulted in accuracies of 72%, 76%, and 73% respectively. Notably, when the green channel extracted images underwent histogram equalization, the model achieved its highest accuracy of 83%. Furthermore, the application of a Sobel filter to the red channel images led to a maximum accuracy of 51%. Finally, to determine the effectiveness of each processed image type, sensitivity and specificity measures were compared. Among all the variations, the green channel extracted images with histogram equalization demonstrated superior performance in correctly identifying the respective classes, outperforming the other approaches. [ABSTRACT FROM AUTHOR]

Background: Abdominal obesity, a significant risk factor for the progression of diabetic retinopathy (DR), may lead to improved visual outcomes through early assessment. This study aims to evaluate any potential associations between DR and novel lipid metabolism markers, including the Atherogenic Index of Plasma (AIP), Visceral Adiposity Index (VAI), and Lipid Accumulation Product (LAP). Methods: This study aimed to elucidate the association between various lipid markers and DR by screening the National Health and Nutrition Examination Survey (NHANES) database in the United States from 2005 to 2008. To examine the correlation, multifactor logistic regression analysis, subgroup analysis, threshold effect analysis, interaction test, and smooth curve fitting were used. Results: Among the 2591 participants included, the incidence of DR was 13.6% and the mean age was 59.55 ± 12.26 years. After adjusting for important confounding covariates, logistic regression studies suggested a possible positive association between LAP, VAI, AIP, and DR occurrence (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.002, 1.006; P < 0.0001; [OR] = 1.090; 95% [CI]: 1.037, 1.146; P = 0.0007; [OR] = 1.802; 95% [CI]: 1.240, 2.618; P = 0.0020). The nonlinear association between LAP and DR was further illustrated using an S-shaped curve by smoothing curve fitting, with the inflection point of the curve located at 63.4. Subgroup analyses and interaction tests were performed with full variable adjustment (P > 0.05 for all interactions). Conclusion: Studies have shown that elevated levels of LAP, VAI, and AIP increase the likelihood of DR, suggesting that they have the potential to be predictive markers of DR, emphasizing their potential utility in risk assessment and prevention strategies, and advocating for early intervention to mitigate the likelihood of DR. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Radiography is one of the most widely used x‐ray imaging modalities. In National Council on Radiation Protection and Measurements (NCRP) Report No. 147, transmission data for radiographic systems were evaluated on those installed before 2000. For x‐ray systems (except intraoral dental) manufactured on or after June 10, 2006, the U.S. required minimum half‐value layer (HVL) was increased; for example, 2.9 (2.3) mm Al at 80 kV, where the value in parenthesis denotes the earlier requirement before the above date.To calculate the transmission of the broad x‐ray beam of modern digital radiography (DR) through shielding materials.X‐ray beam HVLs on two DR systems (Agfa DR 600, GE Revolution XR/d) were measured in 10 kV increments between 60 and 120 kV, with a calibrated ionization chamber (Radcal model 10 × 5‐60) and varying thickness of aluminum 1100 plates. Monte Carlo (Geant4) simulation was performed to calculate the transmission of broad x‐ray beams through lead, concrete, gypsum, and steel, with x‐ray HVLs matching those of the DR 600 at two beam filtrations (default, 1 mm Al plus 0.1 mm Cu added filtration). The transmission data were fitted to the Archer equation.HVLs on two DR systems with default beam filtration were consistent (median difference, 2.1%; maximum difference, 5.5%). An additional beam filtration option (1 mm Al plus 0.1 mm Cu) on the DR 600 substantially increased HVLs by 45.2%–61.2%. Transmission fitting parameters were provided for seven tube voltages (60–120 kV) at two beam filtrations.This work presents transmission data for modern DR systems, indicating increased x‐ray beam filtration compared to the primary x‐ray beam in NCRP Report No. 147. The updated transmission data can enhance structural shielding evaluations at individual tube voltages or with a workload distribution. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of artificial intelligence (AI) tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing deep learning (DL) methods designed for the automatic screening of diabetic retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset consists of color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of DR and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7 % utilized detection methods, 46.5 % employed classification techniques, 41.9 % relied on segmentation, and 7 % opted for a combination of classification and segmentation. Metrics calculated from 80 % of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple DL techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification, and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes; however, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR, but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining the high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to develop new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes. [ABSTRACT FROM AUTHOR]

During the past decade, the technology to automate demand response (DR) in buildings and industrial facilities has advanced significantly. Automation allows rapid, repeatable, reliable operation. This study focuses on costs for DR automation in commercial buildings with some discussion on residential buildings and industrial facilities. DR automation technology relies on numerous components, including communication systems, hardware and software gateways, standards-based messaging protocols, controls and integration platforms, and measurement and telemetry systems. This paper discusses the impact factors that contribute to the costs of automated DR systems, with a focus on OpenADR 1.0 and 2.0 systems. In addition, this report compares cost data from several DR automation programs and pilot projects, evaluates trends in the cost per unit of DR and kilowatts (kW) available from automated systems, and applies a standard naming convention and classification or taxonomy for system elements. In summary, median costs for the 56 installed automated DR systems studied here are about $200/kW. The deviation around this median is large with costs in some cases being an order of magnitude greater or less than median. Costs to automate fast DR systems for ancillary services are not fully analyzed in this report because additional research is needed to determine the total such costs.

Abstract Background Abdominal obesity, a significant risk factor for the progression of diabetic retinopathy (DR), may lead to improved visual outcomes through early assessment. This study aims to evaluate any potential associations between DR and novel lipid metabolism markers, including the Atherogenic Index of Plasma (AIP), Visceral Adiposity Index (VAI), and Lipid Accumulation Product (LAP). Methods This study aimed to elucidate the association between various lipid markers and DR by screening the National Health and Nutrition Examination Survey (NHANES) database in the United States from 2005 to 2008. To examine the correlation, multifactor logistic regression analysis, subgroup analysis, threshold effect analysis, interaction test, and smooth curve fitting were used. Results Among the 2591 participants included, the incidence of DR was 13.6% and the mean age was 59.55 ± 12.26 years. After adjusting for important confounding covariates, logistic regression studies suggested a possible positive association between LAP, VAI, AIP, and DR occurrence (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.002, 1.006; P 0.05 for all interactions). Conclusion Studies have shown that elevated levels of LAP, VAI, and AIP increase the likelihood of DR, suggesting that they have the potential to be predictive markers of DR, emphasizing their potential utility in risk assessment and prevention strategies, and advocating for early intervention to mitigate the likelihood of DR.

These days, diabetes is an incurable disease, with millions of people suffering from it worldwide. Several variables namely lack of education, crowded living conditions, obesity and improper diet are among the causes of this recent upsurge in diabetes cases. They are identified by the name of infections induced by bacteria or viruses, harmful compounds in food, autoimmune reactions, obesity, unhealthy lifestyles, and pollution in the environment. Excessive and sight-threatening diabetic retinopathy (DR) is the most common retinal micro-vascular dysfunction that is characterized by the occurrence of a disorder of retinal blood vessels resulting in impaired vision. The IoT-based work is conducted in this work on the machine learning (ML) techniques, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The classification of diabetic retinopathy is a topic that is under research. The range of activities of the processes of downsampling, labelling, image flattening, and format conversion is all within the dataset preparation process. An advanced prognosis model is designed which follows a combination of two machine learning techniques such as SVM and KNN. This approach classifies the images of diabetic retinopathy into five segments (subclasses), thus facilitating in-depth analysis. Our solution proposal in this case is a superior one because of its higher classification accuracy and faster processing speed as the findings showed. The robustness and accuracy that the SVM is known for are ensured by the convergence of the KNN to the SVM. The paper also proves a close linkage of clinical symptoms and blood sugar readings to an algorithmic DM prediction system that is based on IoT and ML approaches. This is another advantage of this method that it outperforms the existing classification methods. Amongst all the classifiers that we used in this project, the KNN ML classifier turned out to be the most accurate one with an accuracy rate of 93%. It was found that the algorithm performed with a 79% accuracy rate after tough testing and training and it was consistently providing number one quality DM predictions. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is characterized by chronic, low‐grade inflammation. This state may be related to the heightened production of neutrophil extracellular traps (NETs) induced by high glucose (HG). Human cathelicidin antimicrobial peptide (LL37) is an endogenous ligand of G protein‐coupled chemoattractant receptor formyl peptide receptor 2 (FPR2), expressed on neutrophils and facilitating the formation and stabilization of the structure of NETs. In this study, we detected neutrophils cultured under different conditions, the retinal tissue of diabetic mice, and fibrovascular epiretinal membranes (FVM) samples of patients with proliferative diabetic retinopathy (PDR) to explore the regulating effect of LL37/FPR2 on neutrophil in the development of NETs during the process of DR. Specifically, HG or NG with LL37 upregulates the expression of FPR2 in neutrophils, induces the opening of mitochondrial permeability transition pore (mPTP), promotes the increase of reactive oxygen species and mitochondrial ROS, and then leads to the rise of NET production, which is mainly manifested by the release of DNA reticular structure and the increased expression of NETs‐related markers. The PI3K/AKT signaling pathway was activated in neutrophils, and the phosphorylation level was enhanced by FPR2 agonists in vitro. In vivo, increased expression of NETs markers was detected in the retina of diabetic mice and in FVM, vitreous fluid, and serum of PDR patients. Transgenic FPR2 deletion led to decreased NETs in the retina of diabetic mice. Furthermore, in vitro, inhibition of the LL37/FPR2/mPTP axis and PI3K/AKT signaling pathway decreased NET production induced by high glucose. These results suggested that FPR2 plays an essential role in regulating the production of NETs induced by HG, thus may be considered as one of the potential therapeutic targets. [ABSTRACT FROM AUTHOR]

A total of 3102 neurons were recorded before and following acute and chronic methylphenidate (MPD) administration. Acute MPD exposure elicits mainly increases in neuronal and behavioral activity in dose–response characteristics. The response to chronic MPD exposure, as compared to acute 0.6, 2.5, or 10.0 mg/kg MPD administration, elicits electrophysiological and behavioral sensitization in some animals and electrophysiological and behavioral tolerance in others when the neuronal recording evaluations were performed based on the animals' behavioral responses, or amount of locomotor activity, to chronic MPD exposure. The majority of neurons recorded from those expressing behavioral sensitization responded to chronic MPD with further increases in firing rate as compared to the initial MPD responses. The majority of neurons recorded from animals expressing behavioral tolerance responded to chronic MPD with decreases in their firing rate as compared to the initial MPD exposures. Each of the six brain areas studied—the ventral tegmental area, locus coeruleus, dorsal raphe, nucleus accumbens, prefrontal cortex, and caudate nucleus (VTA, LC, DR, NAc, PFC, and CN)—responds significantly (p < 0.001) differently to MPD, suggesting that each one of the above brain areas exhibits different roles in the response to MPD. Moreover, this study demonstrates that it is essential to evaluate neuronal activity responses to psychostimulants based on the animals' behavioral responses to acute and chronic effects of the drug from several brain areas simultaneously to obtain accurate information on each area's role in response to the drug. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a severe complication of diabetes, causing damage to retinal blood vessels due to high blood sugar levels. Early detection is crucial but often requires significant time and expertise from ophthalmologists. While artificial intelligence (AI) and image recognition hold promise for DR detection, inconsistent image quality poses a challenge. Our study presents a novel technique that integrates pixel color amplification and EfficientNetV2 to enhance fundus image attributes, aiming to address issues related to image quality and achieving superior performance in DR detection. Leveraging EfficientNetV2, an advanced convolutional neural network (CNN) architecture, we achieve 84% multiclass accuracy and 99% binary accuracy, surpassing various other CNN models, including VGG16-fc1, VGG16-fc2, NASNet, Xception, Inception ResNetV2, EfficientNet, InceptionV3, MobileNet, and ResNet50. Our research tackles the critical challenge of early detection of DR, essential for preventing vision loss. This advancement holds the potential to enhance the efficiency and accuracy of DR classification, potentially alleviating the burden on medical professionals and ultimately improving the quality of life for individuals at risk of vision loss. [ABSTRACT FROM AUTHOR]

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Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the level of accuracy and precision of bone scan (BS), MRI, and digital radiography (DR) to measure long bone tumors to design custom-made prosthesis (CMP)/modular prosthesis (MP) in limb salvage surgery (LSS) with the help of phantom and patient's study. There are two separate groups: one is the phantom study and another one is the patient's study. The phantom study is done with the Jaszack Phantom for the Gamma camera and the indigenous phantom for the MRI and DR. Three independent imaging professionals (nuclear medicine physicians and radiologists) measured the distance between standardized, preselected points on the Jaszack phantom in the Gamma Camera (GC) and indigenous phantom on the coronal and sagittal view of the MRI scan and in digital radiography. The measured values were compared with the known values for phantom measurement. A total of 36 patients, which include 24 males and 12 females, 3 independent imaging professionals measured the patient's long bone in a bone scan, MRI and DR and compared it with histopathological specimen measurement after limb salvage surgery (LSS). Descriptive statistics using appropriate measures of central tendency and dispersion were employed to describe the data. Karl-Pearson correlation coefficient was used to establish the association between continuous covariates. Paired t-test was utilized to test the differences in paired values for statistical significance. A near-perfect positive correlation was evident between all three pairs of bone scan, MRI scan, and digital radiography values, and a positive agreement within 1 mm of the bone scan, MRI scan, and DR values of all three pairs was around 95%. For the phantom study, we conclude that Gamma camera and MRI measurements are equal in physical measurements (MCF-1). DR measurements were found to be near equal physical measurements and multiplication correction factor (MCF)—0.9104 and three observer's measurements values were also near normal. For the patient's study, we conclude that the bone scan, MRI, and DR measurements of 3 independent imaging professionals are near normal, and it was confirmed with pathological specimen after LSS, to confirm reliability, repeatability, reproducibility, and accuracy of the tumor length to do custom-made prosthesis or modular prosthesis for the patients who are affected by osteosarcoma and Ewing's sarcoma. [ABSTRACT FROM AUTHOR]

BackgroundDiabetic retinopathy (DR) is a serious microvascular complication of diabetes mellitus. Research has identified a close relationship between fibroblast growth factor 21 (FGF21) and DR. FGF21 is a member of the FGF subfamily, which is activated by the Klotho coenzyme involved in the occurrence of DR. However, the association between FGF21, Klotho, and DR remains controversial.AimTo assess FGF21 and Klotho levels in patients with DR.MethodsA literature search of the Web of Science, Wiley Online Library, PubMed, China National Knowledge Infrastructure and Wanfang databases was performed. The title or abstract search terms “diabetic retinopathy” and “DR” were used in combination with “fibroblast growth factor 21”, “FGF21”, and “Klotho”. Meta-analysis results are presented as standardized mean difference (SMD) with corresponding 95% confidence interval (CI).ResultsFifteen studies were included in this meta-analysis. FGF21 levels in patients with DR were significantly higher than in non-DR patients with diabetes (SMD: 2.12, 95% CI [1.40, 2.84]). Klotho levels in patients with DR were significantly lower than in non-DR patients with diabetes (SMD: –0.63, 95% CI [–1.22, – 0.04]).ConclusionsThis systematic review is the first to evaluate the relationship between FGF21, Klotho levels, and DR. FGF21 levels were significantly higher in patients with DR. Fully elucidating the role of FGF21 will significantly contribute to the treatment of DR.

IntroductionMultifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.MethodsThe electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study’s design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.ResultsMetabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.ConclusionThis review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Diabetic retinopathy (DR), a major complication of diabetes causing blindness, is characterized by retinal damage due to capillary degeneration and vascular leakage. Current treatments are not fully effective, highlighting the need for searching new therapeutic targets. Hematopoietic cell kinase (HCK), a protein involved in various diseases, has been identified as a potential biomarker in DR, but its role in disease progression requires further investigation. Here we investigated the role of HCK in DR and its potential mechanism. We found the expression of HCK increased under the stimulation of high glucose (HG) in human retinal capillary endothelial cells (HRECs). Knockdown of HCK can improve HREC cell viability and the integrity of the internal blood–retinal barrier. HCK depletion suppressed the AMPK pathway in HG-induced HRECs. In summary, HCK may be a potential target for the treatment of DR, which provides a theoretical basis for the development of new treatment strategies.

This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients' needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a progressive disease which can cause health problem. It has been reported that bone marrow mesenchymal stem cells (BMSCs)‐secreted exosomes could regulate the progression of DR via carrying microRNAs. Meanwhile, miR‐483‐5p was downregulated in DR; however, whether BMSCs‐secreted exosomes can modulate DR progression via carrying miR‐483‐5p remains unclear. To mimic DR in vitro, ARPE‐19 cells were exposed to 30 mM high glucose (HG). Exosomes were isolated from BMSCs and identified by transmission electron microscopy, nanoparticle tracking analysis, and western blot. Cell counting kit‐8 assay was applied for assessing the cell viability. Flow cytometry was applied to test the cell apoptosis. Meanwhile, dual luciferase assay was used to evaluate the association among miR‐483‐5p and downstream target insulin‐like growth factor 1 receptor (IGF‐1R). In addition, quantitative reverse‐transcription polymerase chain reaction and western blot were used for exploring the level of miR‐483‐5p and IGF‐1R. HG significantly induced apoptosis in ARPE‐19 cells, while BMSCs‐derived exosomes reversed this phenomenon. In addition, inhibition of miR‐483‐5p expression of exosomes further aggravated HG‐induced ARPE‐19 cell apoptosis. Meanwhile, IGF‐1R was the downstream messenger RNA of miR‐483‐5p, and IGF‐1R silencing could reverse the effect of exosomes with downregulated miR‐483‐5p on HG‐induced cell injury. Exosomes derived from BMSCs inhibit the progression of DR via carrying miR‐483‐5p. Thus, our study might provide a theoretical basis for discovering new strategies against DR. [ABSTRACT FROM AUTHOR]

The present work has taken a challenge to design a model predictive controller (MPC) for automatic load frequency control (ALFC) of two-area, wind-integrated thermal power system equipped with battery energy storage system (BESS) and demand response (DR) for frequency regulation task. Primarily, the incremental BESS model employs a new state of charge (SOC)-based strategy to regulate the power from battery for saving battery life. Then, DR, along with the SOC-based BESS, is employed in ALFC for frequency regulation. A modified state space model of MPC incorporating all BESS variables is developed and employed in ALFC of the studied power system. The performance of the designed MPC is examined for inertia issues arising from wind in the conventional two-area power system. Furthermore, the capability of BESS for frequency regulation and effect on the life of BESS with the proposed control strategy are measured through MPC-based ALFC and results are compared with system performance when integral controller in ALFC and inertia controller from wind are present. In addition to DR and BESS in ALFC, double-fed induction generator-based proportional derivative (PD) inertia controller also contributes in the power system for frequency support from wind energy section to avoid inertia issues. So, all the controllers of the test power system such as integral controller in ALFC and PD controller in wind are tuned concurrently for smooth frequency control. However, performance of MPC is tested for smooth frequency regulation by tuning PD controller gain of wind only while keeping MPC gain parameters as available in the literature. Particle swarm optimization is used to tune the integral controller gain of ALFC for the studied power system to compare the results with MPC-based ALFC. A transfer function model of wind-integrated two-area thermal power system is taken into consideration in the present study to verify the effectiveness of the battery variable concerning MPC design for ALFC collaborating with DR for smooth frequency control and provide safe battery life. Finally, results confirm the effectiveness of the designed MPC-based ALFC, collaborating with DR- and SOC-based incremental BESS through various case studies. [ABSTRACT FROM AUTHOR]

Introduction and Objectives: Diabetic retinopathy is a serious ocular complication of diabetes and the leading cause of blindness and loss of vision in developed and developing countries. The present study aimed to investigate and compare the possible ascorbic acid (AA)-enhanced therapeutic effect of bone marrow hemopoietic stem cells (BMHSCs) to adipose mesenchymal stem cells (AMSCs) on streptozotocin (STZ)-induced early diabetic retinopathy (DR) in male albino rat model. Methods and Results: 30 adult male albino rats were divided into: Donor group: 2 rats for BMHSCs and AMSCs isolation and culture. Group І (Control Group): 6 rats, Group II (DR Group): 8 rats, 50 mg of STZ were injected intraperitoneally (IP), Diabetes was confirmed 3 days after STZ injection by monitoring blood glucose levels. Group III (DR BMHSCs and AAtreated Group): 7 rats, 1x106 BMHSCs, were injected IP combined with oral administration of AA at a dose of 500 mg/Kg daily and Group IV (DR AMSCs and AA-treated Group): 7 rats, 1x106 AMSCs were injected combined with AA as in group III following confirmation of diabetes. Groups I, II, III and IV were sacrificed 8 weeks following confirmation of diabetes. Electroretinogram (ERG), serological, biochemical, histological, morphometric and statistical studies were performed. In group II, neurodegenerative changes were found in the retina that regressed in groups III more obviously than IV. ERG, blood glucose, MDA, SOD, TNFα and qPCR were confirmative. Conclusions: AA enhanced regenerative effect of BMHSCs was more obvious, suggesting the possibility of its application as a therapeutic modality for DR. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage. [ABSTRACT FROM AUTHOR]

More than 85% of people with long-term diabetes are affected by Diabetic Retinopathy (DR), and it is a foremost reason for blindness in the 20–64 age range for both young and old patients. Non-proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) are two distinct kinds of DR. In the recent years, due to lack of precise diagnosis or timely prediction, 85% of people have lost their vision due to DR. Several techniques using diverse fundamental concepts were offered to diagnose this issue. According to the proposed method, the DR is categorized into five groups with a range of zero to four integers. Deep Convolutional Neural Network (DCNN) technique seems to work well enough, but describing the problem in a unique way to figure out how often the disease happens is still hard. One method proposed to automatically detect DR had 86.17% accuracy. This strategy utilized a CNN but lacked clinical training and validation data for the dataset. Even though CNNs have achieved remarkable results and are acclaimed for their generally high precise results in terms of image processing tasks, there are many hindrances and obstacles which affect the performance of CNN like the complex algorithm in terms of computation and processing time. To solve this problem of CNN, region proposals are identified, that can detect the region of interest based on the context or purpose. The next big problem is that there is no dataset of images of fundus that everyone or most people agree on. This makes it hard to use algorithms to analyze and get correct results. The Diabetic Retinopathy Image Database (DRiDB), for example, aims to get around this problem. So, our approach is to implement a Region Convolutional Neural Network (RCNN) for the detection of features. Usage of RCNN and commonly accepted database will ensure further accurate prediction of DR. [ABSTRACT FROM AUTHOR]

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.

Аim: to study the structural and functional characteristics of albumin in patients with decompensated cirrhosis, their relationship with ascites; to identify the relationship between improvement in albumin characteristics and regression of ascites.Materials and methods. Fifty patients with decompensated liver cirrhosis and ascites were divided into groups. The first group received standard treatment for cirrhosis, the second — standard treatment and replacement therapy with 20 % human albumin solution at a dose of 200 mL per week for 3 months.Results. The value of the native conformation of albumin and the functional parameters of albumin were significantly lower than in the group of healthy individuals (p < 0.001). With the severity of ascites, the native conformation index (DR), which characterizes the structural usefulness of the albumin molecule, decreased. Median DR for ascites stage I (IAC) was –1.69, II grade — –2.28, III grade — –2.42 (p < 0.05). Replacement therapy with albumin allowed to achieve regression of ascites in 48.4 % of patients, compared with 7.1 % in the standard treatment group. Along with clinical improvement, restoration of albumin structural and functional properties was observed in the albumin group. The mean serum albumin level at which ascites remained in remission for 3 months was 42.11 g/L (p < 0.001).Conclusions and discussion. The structural and functional characteristics of albumin were impaired in patients with decompensated cirrhosis and ascites. The severity of changes in the structural and functional properties of albumin depended on the severity of ascites. The regression of ascites was accompanied by the restoration of the functional and structural usefulness of albumin against the backdrop of albumin replacement therapy. The criterion for stopping transfusion therapy with albumin can be the achievement of a serum albumin level of 42.11 ± 7.04 g/L, DR — 1.05, BE — 73.51 %, RTQ — 75.10 %, DTE — 72.71 %.

目的: 在健康体检中通过CT、DR和肿瘤标识物对肺癌进行筛查, 对筛查结果进行分析研究, 评价各 种筛查价值. 方法: 本研究选取2020年1月至2023年11月健康体检中确诊肺腺癌785例为研究对象, 分析肺腺癌检出 线索, 包括CT、DR、肿瘤标志物CEA、Ca211等, 分析各种线索的检出阳性率并将其进行对比. 结果: CT检出率 435.67/10万, DR检出率8.75/10万, 两种对比差异明显, 经统计学计算, P < 0.001, 有显著统计学意义. 单独CEA检 出率1.61/10万, 单独Ca211检出率2.49/10万, 两种对比差异明显, 经统计学计算, P < 0.05, 有统计学意义. 结论: 筛 查早期肺癌胸部CT比DR优势显著, 可以有效提高肺癌检出率, 联合CEA、Ca211筛查可以提高肺腺癌发现率, 尤其 Ca211可以成为肺腺癌筛查的主要肿标. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes is associated with ocular complications including diabetic macular edema (DME). Current therapies are invasive and include repeated intravitreal injections and laser therapy. Photobiomodulation (PBM) is a treatment (Tx) that utilizes selected wavelengths of light to induce cellular benefits including reduction of inflammation and edema. This single-center, open-label, post-hoc analysis explored the utility of multiwavelength PBM in subjects with DME.Methods: Analysis included review of data from patients undergoing standard clinical care with an approved and marketed PBM medical device, the Valeda® Light Delivery System. Subjects with early-stage DME with good vision (Best-corrected visual acuity (BCVA) > 20/25, logMAR > 0.1) were evaluated in clinic and treated with one series of multiwavelength PBM (Tx delivered 3x/week over 3– 4 weeks; total of 9 Tx sessions). Clinical, anatomical, and safety parameters were assessed in addition to subjective quality of life.Results: A total of 30 eyes (19 subjects) were analyzed. Subjects were predominately male (68.4%) with a mean age of 56 ± 14 years. Reductions in central retinal thickness (CRT), resolution of intraretinal fluid (IRF) and improvement in diabetic retinopathy severity scale scores were observed following PBM treatment in select patients. Baseline BCVA remained stable over the follow-up observation period of 3 months post-PBM. Approximately 64% of patients reported subjective improvements in their ocular condition and decreased influence in everyday life. Detailed OCT evaluations confirmed no safety issues related to phototoxicity up to 16 months.Conclusion: Early-stage DME subjects treated with Valeda multiwavelength PBM showed improvements in clinical and anatomical parameters. The Valeda multiwavelength PBM approach demonstrates a favorable safety profile with no signs of phototoxicity following an independent OCT review. PBM therapy may offer an alternative, non-invasive treatment strategy with a unique mechanism and modality for patients with early-stage DME. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is a common complication of diabetes. The adipocytokines are closely associated with the occurrence and development of diabetes and its related complications. Literature confirms that the level of adiponectin in patients with DR is significantly higher; however, the relationship between other adipocytokines (leptin, chemerin, apelin, and omentin-1) and DR remains unclear. Aim: This study aimed to systematically evaluate the association between adipocytokines (leptin, chemerin, apelin, and omentin-1) and DR. Methods: The PubMed, Web of Science, Embase, EBSCO and Willy databases were used to search for potential studies with keywords such as "diabetic retinopathy" or "DR" in combination with the terms "leptin," "chemerin", "apelin" or "omentin-1" in the search titles or abstracts. Standardized mean differences (SMD) with corresponding 95% confidence intervals (CIs) were determined as the results of the meta-analysis. Results: After screening, 18 articles were included in the meta-analysis including 750 DR cases and 993 controls. Leptin and chemerin levels in patients with DR were significantly higher than those in the control group (SMD: 0.68, 95% CI [0.1, 1.26]; SMD: 0.79, 95% CI [0.35, 1.23]). The omentin-1 levels in patientswithDRwere significantly lower than those in the controls (SMD: -0.85, 95% CI [-1.08, -0.62]). Conclusions: To the best of our knowledge, this is the first meta-analysis to evaluate the leptin, chemerin, apelin, and omentin-1 levels in patients with DR. Further high-quality studies are warranted to support the association between these adipocytokines and DR. [ABSTRACT FROM AUTHOR]

A review of OCT as diagnostic tool and its application in ayurved. Optical Coherence Tomography (OCT) is one of the elite diagnostic measures in modern science which not only helps in diagnosing diseases but also in establishing relation between modern science and ancient science. OCT is usually used in diagnosing and managing diseases like Diabetic Macular Edema (DME), Myopia, Diabetic Retinopathy (DR), Central Serous Retinopathy (CSR), Glaucoma, etc. Depending on the disease condition; result of OCT can be co-related with modern aspect as well as ancient aspect. The primary objective of this literary review is concerned with gunas of vataj, pittaj, kaphaj dosha with clinical findings as seen in OCT. Ayurvedic classics state that guna of vata, guna of pitta, guna of pitta, guna of kapha are evident in shrotas and shrotojanya vyadhi. In the present era changes in lifestyle, food habits, uninhibited use of steroids had led to disorders of retina which is visible as changes in normative findings of OCT. Therefore, a proper understanding of doshas and its guna will help in decoding the findings of OCT with ayurvedic perspective. [ABSTRACT FROM AUTHOR]

During the past decade, the technology to automate demand response (DR) in buildings and industrial facilities has advanced significantly. Automation allows rapid, repeatable, reliable operation. This study focuses on costs for DR automation in commercial buildings with some discussion on residential buildings and industrial facilities. DR automation technology relies on numerous components, including communication systems, hardware and software gateways, standards-based messaging protocols, controls and integration platforms, and measurement and telemetry systems. This paper discusses the impact factors that contribute to the costs of automated DR systems, with a focus on OpenADR 1.0 and 2.0 systems. In addition, this report compares cost data from several DR automation programs and pilot projects, evaluates trends in the cost per unit of DR and kilowatts (kW) available from automated systems, and applies a standard naming convention and classification or taxonomy for system elements. In summary, median costs for the 56 installed automated DR systems studied here are about $200/kW. The deviation around this median is large with costs in some cases being an order of magnitude greater or less than median. Costs to automate fast DR systems for ancillary services are not fully analyzed in this report because additional research is needed to determine the total such costs.

[Display omitted] • Preoperative pain facilitates postoperative cognitive function. • A new circuit exists between PAG and DR. • Inhibition of PAG-DR circuit decreases the cognitive function in rats. Postoperative cognitive dysfunction (POCD) is a medically induced, rapidly occurring postoperative disease, which is hard to recover and seriously threatens the quality of life, especially for elderly patients, so it is important to identify the risk factors for POCD and apply early intervention to prevent POCD. As we have known, pain can impair cognition, and many surgery patients experience different preoperative pain, but it is still unknown whether these patients are vulnerable for POCD. Here we found that chronic pain (7 days, but not 1 day acute pain) induced by Complete Freund's Adjuvant (CFA) injected in the hind paw of rats could easily induce spatial cognition and memory impairment after being exposed to sevoflurane anesthesia. Next, for the mechanisms, we focused on the Periaqueductal Gray Matter (PAG), a well-known pivotal nucleus in pain process. It was detected the existence of neural projection from ventrolateral PAG (vlPAG) to adjacent nucleus Dorsal Raphe (DR), the origin of serotonergic projection for the whole cerebrum, through virus tracing and patch clamp recordings. The Immunofluorescence staining and western blot results showed that Tryptophan Hydroxylase 2 (TPH2) for serotonin synthesis in the DR was increased significantly in the rats treated with CFA for 7 days and sevoflurane for 3 hours, while chemo-genetic inhibition of the vlPAG-DR projection induced obvious spatial learning and memory impairment. Our study suggests that preoperative chronic pain may facilitate cognitive function impairment after receiving anesthesia through the PAG-DR neural circuit, and preventative analgesia should be a considerable measure to reduce the incidence of POCD. [ABSTRACT FROM AUTHOR]

To comprehend the etiology of diabetic retinopathy (DR), it is crucial to clarify the genetic susceptibility factors for DR. Previous studies have reported that five single nucleotide polymorphisms (SNPs), including rs9362054 (near the CEP162 gene), rs1990145 (MRPL19), rs10519765 (FMN1), rs237025 (SUMO4) and rs767649 (MIR155HG) were associated with DR. This study was conducted to elucidate the association between the five SNPs and DR in a Chinese Han population A total of 957 individuals with type 2 diabetes mellitus (T2DM) including diabetes mellitus without retinopathy (DNR = 478), nonproliferative DR (NPDR = 384) and proliferative (PDR = 95) were recruited in this study. SNPs were genotyped using the Mass ARRAY MALDI-TOF system. The genotype and allele frequencies were determined using χ2 tests. For genotype and allele risk, odds ratios (OR) and 95% confidence intervals (CI) were calculated. Four genetic models (homozygous, heterozygous, dominant, and recessive) were used to further investigate the link between the five SNPs and DR. There was a statistically significant difference of CEP162 rs9362054 between NPDR and DNR (P =.027, OR = 1.26, 95%CI = 1.03–1.54) and a significant association of SUMO4 rs237025 detected between PDR and DNR (P =.031, OR = 1.45, 95%CI = 1.03–2.02). The association of CEP162 rs9362054 was also observed under the dominant mode (P =.03, OR = 1.35, 95%CI = 1.03–1.77). The association of SUMO4 rs237025 was found under the heterozygous model (P =.03, OR = 1.68, 95%CI = 1.06–2.69) and the dominant model (P =.02, OR = 1.70, 95%CI = 1.08–2.67). No associations of the other three SNPs with NPDR and PDR were detected when compared with DNR under these genetic models. This study showed that rs9362054 and rs237025 were associated with NPDR and PDR when compared with DNR, suggesting that SUMO4 may be involved in the development of PDR, while CEP162 may be associated with NPDR. [ABSTRACT FROM AUTHOR]

Male mammals display different paternal responses to pups, either attacking or killing the young offspring, or contrastingly, caring for them. The neural circuit mechanism underlying the between-individual variation in the pup-directed responsiveness of male mammals remains unclear. Monogamous mandarin voles were used to complete the present study. The male individuals were identified as paternal and infanticidal voles, according their behavioral responses to pups. It was found that the serotonin release in the medial preoptic area (MPOA), as well as the serotonergic neuron activity, significantly increased upon licking the pups, but showed no changes after attacking the pups, as revealed by the in vivo fiber photometry of the fluorescence signal from the 5-HT 1.0 sensor and the calcium imaging indicator, respectively. It was verified that the 5-HTergic neural projections to the MPOA originated mainly from the ventral part of the dorsal raphe (vDR). Furthermore, the chemogenetic inhibition of serotonergic projections from the vDR to the MPOA decreased the paternal behaviors and shortened the latency to attack the pups. In contrast, the activation of serotonergic neurons via optogenetics extended the licking duration and inhibited infanticide. Collectively, these results elucidate that the serotonergic projections from the vDR to the MPOA, a previously unrecognized pathway, regulate the paternal responses of virgin male mandarin voles to pups. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the main leading causes of visual impairment worldwide. The current study elucidates the role of JQ1 in DR. A diabetic model was constructed by STZ injection and a high-fat diet. After establishment of the diabetic model, rats were assigned to treatment groups: 1) control, 2) diabetic model, and 3) diabetic+JQ1 model. In vitro Transwell and wound-healing assays were used to measure BV2 cell viability by stimulation with low glucose and high glucose with or without JQ1 and 740Y-P. Pathological methods were used to analyze DR, and Western blotting was used to analyze protein expression. Identification of enriched pathways in DR was performed by bioinformatics. Histopathological examination demonstrated that JQ1 rescued the loss of retinal cells and increased the thickness of retinal layers in diabetic rats. JQ1 attenuated high glucose-stimulated BV2 microglial motility and migration. The bioinformatics analysis implied that the Pl3K-Akt signaling pathway was enriched in DR. JQ1 decreased the phosphorylation of PI3K and AKT as well as the immunostaining of PI3K in BV2 cells. 740Y-P (a PI3K agonist) significantly reversed the decrease in p-PI3K and p-AK in BV2 cells. Additionally, JQ1 decreased the protein expression of p-PI3K, p-AKT, and MMP2/9 and immunostaining of PI3K in retinal tissues of rats. JQ1 suppresses the PI3K/Akt cascade by targeting MMP expression, thus decreasing the viability and invasion capacity of retinal microglia, suggesting an interesting treatment target for DR.

In this paper, bending analysis of rectangular functionally graded nanoplates under a uniform transverse load has been considered based on the modified couple stress theory. Using Hamilton's principle, governing equations are derived based on a higher-order shear deformation theory. The set of coupled equations are solved using the dynamic relaxation method combined with finite difference discretization technique for clamped and simply supported boundary conditions. Finally, the effects of aspect ratio, thickness-to-length ratio, boundary conditions, transverse load, and length scale parameter are studied in detail. The results showed that by rising the length scale-to-thickness ratio, the influence of the grading index on the deflection decreased. [ABSTRACT FROM AUTHOR]

Studying resilience provides an opportunity to address a range of urban environmental problems. However, existing studies pay little attention to urban ecological resilience (UER), and the system of assessing urban resilience pays little attention to the process attribute of resilience. This study focuses on UER and constructs an evaluation framework based on the pressure _state _response (PSR) framework. The 'pressure' indicator morphological resilience (MR) is evaluated using source _sink landscape theory. The 'state' indicator density resilience (DR) is evaluated using the ratio of ecological carrying capacity to ecological footprint. The 'response' indicator uses indicators of economic structure, vitality, and innovation for evaluation. We found that the MR and DR of the study area in 2020 showed a spatial layout of low in the central area and high in the peripheral areas, while the high-value ER area was in the central part. The average district and county MR was 1.44, DR was between 0.003 and 1.975, and ER was 0.32; overall, ER and MR are better in the study area, but DR is worse. The spatial layout of comprehensive UER was found to be low in the middle and high in the periphery of the study area. Some areas with low MR and DR have high UER, which verifies the compensation effect of ER on urban ecology. This study provides a new method for assessing UER, and the findings can provide useful information for urban planning. [ABSTRACT FROM AUTHOR]

Background: Retinal ischaemia is present to a greater or lesser extent in all eyes with diabetic retinopathy (DR). Nonetheless, our understanding of its pathogenic mechanisms, risk factors, as well as other characteristics of retinal ischaemia in DR is very limited. To date, there is no treatment to revascularise ischaemic retina. Methods: Review of the literature highlighting the current knowledge on the topic of retinal ischaemia in DR, important observations made, and underlying gaps for which research is needed. Results: A very scarce number of clinical studies, mostly cross-sectional, have evaluated specifically retinal ischaemia in DR. Interindividual variability on its natural course and consequences, including the development of its major complications, namely diabetic macular ischaemia and proliferative diabetic retinopathy, have not been investigated. The in situ, surrounding, and distance effect of retinal ischaemia on retinal function and structure and its change over time remains also to be elucidated. Treatments to prevent the development of retinal ischaemia and, importantly, to achieve retinal reperfusion once capillary drop out has ensued, are very much needed and remain to be developed. Conclusion: Research into retinal ischaemia in diabetes should be a priority to save sight. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively.

Abstract Background Diabetic retinopathy (DR) is a micro-vascular consequence of diabetes mellitus (DM) that manifests clinically as retinal ischemia, neovascularization, altered retinal permeability, and macular edema. In the adult population, DR is now considered a leading cause of blindness. Fetuin-A is a multifunctional glycoprotein that, because of its dual role in insulin resistance and angiogenesis, could be an early trigger of DR pathogenesis. Objective This study aimed to evaluate the level of Fetuin-A in diabetic patients with and without retinopathy and demonstrate if it could be used as an early indicator of DR. Patients and methods A case–control study enrolled 45 participants selected from Al-Zahraa University Hospital, between March 2021 and October 2021, they were divided into three groups: Group 1: healthy control group (n=15); Group 2: type 2 diabetic patients without DR (n=15); and Group 3: type 2 diabetic patients with DR (n=15). All groups were age- and sex-matched and were investigated by enzyme-linked immunosorbent assay (ELISA) to evaluate serum level of Fetuin-A. Results There was a highly significant difference of FBG, 2HPP, and HbA1c between the studied groups (p< 0.001), while there was no significant difference between the three studied groups regarding Fetuin-A. There were no significant correlation between Fetuin-A, glycemic parameters, and diabetic duration in Groups 2 and 3. Conclusion Fetuin-A has no significant role in the pathogenesis of diabetic retinopathy.

Abstract Background As a leading cause of vision decline and severe blindness in adults, diabetic retinopathy (DR) is characterized by the aggravation of retinal oxidative stress and apoptosis in the early stage. Emerging studies reveal that mesenchymal stem cells-derived small extracellular vesicles (MSC-sEV) treatment represents a promising cell-free approach to alleviate ocular disorders. However, the repairing effects of MSC-sEV in DR remain largely unclear. This study aimed at exploring the role and the underlying mechanism of MSC-sEV in hyperglycemia-induced retinal degeneration. Methods In vivo, we used streptozotocin (STZ) to establish diabetic rat model, followed by the intravitreal injection of MSC-sEV to determine the curative effect. The cell viability and antioxidant capacity of retinal pigment epithelium (RPE) cells stimulated with high-glucose (HG) medium after MSC-sEV treatment were analyzed in vitro. By detecting the response of cell signaling pathways in MSC-sEV-treated RPE cells, we explored the functional mechanism of MSC-sEV. Mass spectrometry was performed to reveal the bioactive protein which mediated the role of MSC-sEV. Results The intravitreal injection of MSC-sEV elicited antioxidant effects and counteracted retinal apoptosis in STZ-induced DR rat model. MSC-sEV treatment also reduced the oxidative level and enhanced the proliferation ability of RPE cells cultured in HG conditions in vitro. Further studies showed that the increased level of phosphatase and tensin homolog (PTEN) inhibited AKT phosphorylation and nuclear factor erythroid 2-related factor 2 (NRF2) expression in RPE cells stimulated with HG medium, which could be reversed by MSC-sEV intervention. Through mass spectrometry, we illustrated that MSC-sEV-delivered neuronal precursor cell-expressed developmentally downregulated 4 (NEDD4) could cause PTEN ubiquitination and degradation, activate AKT signaling and upregulate NRF2 level to prevent DR progress. Moreover, NEDD4 knockdown impaired MSC-sEV-mediated retinal therapeutic effects. Conclusions Our findings indicated that MSC-sEV ameliorated DR through NEDD4-induced regulation on PTEN/AKT/NRF2 signaling pathway, thus revealing the efficiency and mechanism of MSC-sEV-based retinal protection and providing new insights into the treatment of DR.