Serum β-carotene may have a protective effect on DM and α-carotene may be a protective factor for DR in non-smokers.

CT decreased significantly in the NDR and mild/moderate NPDR eyes compared with the control eyes. Age is significantly associated with SFCT in the diabetic patients. Diabetic choroidopathy may be present before clinical retinopathy.

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Background Diabetic retinopathy (DR) is related to oxidative stress and insufficient intake of dietary antioxidants may be associated with the onset and progression of DR. This study aimed to detect the association between main dietary antioxidants intake and the risk for DR. Methods This is a cross-sectional study of a Chinese urban population. Four hundred and fifty-five subjects with type 2 diabetes were recruited and divided into diabetic patients without retinopathy (DWR) group and DR group based on their retinal status. CSMO (clinically significant macular oedema) was diagnosed by stereoscopic photography. Demographic and lifestyle characteristics were ascertained by questionnaire. General physical and ophthalmic examinations were completed for all subjects. Dietary antioxidants were assessed by 3-day food records. Subjects who have taken any type of vitamin supplements were excluded from the study. The association of dietary antioxidants with the risk for DR was analysed by logistic regression with adjustment of other factors. The dietary antioxidants levels of the CSMO subjects and non-CSMO subjects were compared using the Wilcoxon rank sum test. Results One hundred and nineteen subjects in DR group and 336 subjects in DWR group participated in the study. Only ten DR subjects had CSMO. The results showed that higher vitamin E (OR (95% CI):0.97 (0.95, 1.00), P = 0.036) and selenium (OR (95% CI):0.98 (0.96, 1.00), P = 0.017) intake appear to be the protective factors of DR. The dietary antioxidants levels of CSMO and non-CSMO subjects had no statistical differences (P > 0.05). Conclusions Dietary antioxidants intake, particularly vitamin E and selenium, were observed to have protective effects on DR.

IntroductionThe retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments.MethodsThis study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases.ResultsNotably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models.DiscussionWith the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.

Lipid metabolic disorders, oxidative stress and inflammation in the liver are key steps in the progression of non-alcoholic fatty liver disease (NAFLD). Ophiopogonin D (OP-D), the main active ingredient of Ophiopogon japonicus , exhibits several pharmacological activities such as antioxidant and anti-inflammatory activities. Therefore, the current study aimed to explore the role of OP-D in NAFLD in a high-fat diet (HFD)-induced obesity mouse model. To investigate the effect of OP-D on NAFLD in vivo , a NAFLD mouse model was established following feeding mice with HFD, then the mice were randomly treated with HFD or HFD + OP-D for 4 weeks. Subsequently, primary mouse hepatocytes were isolated, and enzyme-linked immunosorbent assay, reverse transcription-quantitative PCR western blotting and immunofluorescence analysis were used for assessment to explore the direct effect of OP-D in vitro . The results of the present study indicated that OP-D could ameliorate NAFLD in HFD-induced obese mice by regulating lipid metabolism and antioxidant and anti-inflammatory responses. Additionally, OP-D treatment decreased lipogenesis and inflammation levels in vitro , suggesting that the NF-κB signaling pathway may be involved in the beneficial effects of OP-D on NAFLD.

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