Ferrous ascorbate as a potential biomarker for diabetic retinopathy: a vitreous humour metabolomics study.

BACKGROUND: This study aimed to explore differences in vitreous humour metabolites and metabolic pathways between patients with and without diabetic retinopathy (DR) and identify potential metabolite biomarkers. METHODS: Clinical data and vitreous fluid samples were collected from 125 patients (40 without diabetes, 85 with DR). The metabolite profiles of the vitreous fluid samples were analysed using ultra-high performance liquid chromatography, Q-Exactive mass spectrometry, and multivariate statistical analysis. A machine learning model based on Least Absolute Shrinkage and Selection Operator Regularized logistic regression was used to build a risk scoring model based on selected metabolite levels. Candidate metabolites were regressed to glycated haemoglobin levels by a logistic regression model. RESULTS: Twenty differential metabolites were identified between the DR and control groups and were significantly enriched in five Kyoto Encyclopedia of Genes and Genomes pathways (arginine biosynthesis; tricarboxylic acid cycle; alanine, aspartate, and glutamate metabolism; tyrosine metabolism; and D-glutamate metabolism). Ferrous ascorbate significantly contributes to poorer glycaemic control outcomes, offering insights into potential new pathogenic pathways in DR. CONCLUSIONS: Disorders in the metabolic pathways of arginine biosynthesis, tricarboxylic acid cycle, alanine, aspartate, glutamate metabolism, tyrosine metabolism, and D-glutamate metabolism were associated with DR. Risk scores based on vitreous fluid metabolites can be used for the diagnosis and management of DR. Ferrous ascorbate can provide insights into potential new pathogenic pathways for DR.

Dyadic effects of financial toxicity and social support on the fear of cancer recurrence in breast cancer patients and caregivers: an actor-partner interdependence mediation model.

PURPOSE: In this study, the actor-partner interdependence mediation model (APIMeM) was applied to breast cancer patients and their caregivers to assess the factors that affect the fear of cancer recurrence. In particular, the purpose of this study was to evaluate the mediating effect of social support on financial toxicity and the fear of cancer recurrence, providing an effective basis for developing plans to reduce the level of fear of cancer recurrence. METHODS: This study employed a cross-sectional design, and 405 dyads of breast cancer patients and their caregivers were enrolled. Financial toxicity, social support, and fear of cancer recurrence were assessed by computing comprehensive scores for financial toxicity based on patient-reported outcome measures, the Social Support Rating Scale, and the Fear of Cancer Recurrence Inventory Short Form, respectively. The data were analysed using SPSS 24.0 and AMOS 23.0. RESULTS: The results showed that the fear of cancer recurrence of breast cancer patients and their caregivers was significantly related to dyadic financial toxicity and social support. In addition, the financial toxicity of breast cancer patients and their caregivers had significant actor effects and partner effects on the fear of cancer recurrence through dyadic social support. CONCLUSIONS: The financial toxicity of breast cancer patients and their caregivers could produce actor and partner effects on the fear of cancer recurrence through the mediation of social support, which provided empirical support for improving reducing the level of fear of cancer recurrence among patients and caregivers at the dyadic level.

Applications of nanomaterials in anti-VEGF treatment for ophthalmic diseases.

Angiogenesis has been determined to be essential in the occurrence and metastasis of diabetic retinopathy (DR), age-related macular degeneration (AMD), retinal vein occlusion (RVO), choroidal neovascularization (CNV), retinopathy of prematurity (ROP), tumor, etc. However, the clinical use of anti-vascular endothelial growth factors (VEGF) drugs is currently limited due to its high cost, potential side effects, and need for repeated injections. In recent years, nanotechnology has shown promising results in inhibiting neovascularization and reducing reactive oxygen species (ROS) or inflammatory factors. Some nanomaterials can also act as vehicles for drug delivery, such as lipid nanoparticles and PLGA. The process of angiogenesis and its molecular mechanism are discussed in this article. At the same time, this study aims to systematically review the research progress of nanotechnology and offer more treatment options for neovascularization-related diseases in clinical ophthalmology.

MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.

BACKGROUND AND OBJECTIVE: The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification. METHODS: In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification. RESULTS: Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods. CONCLUSIONS: This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet.