Deep learning assisted fluid volume calculation for assessing anti-vascular endothelial growth factor effect in diabetic macular edema.

Objective: To develop an algorithm using deep learning methods to calculate the volume of intraretinal and subretinal fluid in optical coherence tomography (OCT) images for assessing diabetic macular edema (DME) patients' condition changes. Design: Cross-sectional study. Participants: Treatment-naive patients diagnosed with DME recruited from April 2020 to November 2021. Methods: The deep learning network, which was built for autonomous segmentation utilizing an encoder-decoder network based on the U-Net architecture, was used to calculate the volume of intraretinal fluid (IRF) and subretinal fluid (SRF). The alterations of retinal vessel density and thickness, and the correlation between best-corrected visual acuity (BCVA) and OCT parameters were analyzed. Results: 2,955 OCT images of fourteen eyes from DME patients with IRF and SRF who received anti-vascular endothelial growth factor (VEGF) agents were obtained. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the algorithm was 0.993 for IRF and 0.998 for SRF. The volumes of IRF and SRF were significantly decreased from 1.93 ± 0.58 /1.14 ± 0.25 mm3 (baseline) to 0.26 ± 0.13 /0.26 ± 0.18 mm3 (post-injection), respectively (p = 0.0170 for IRF, and p = 0.0004 for SRF). The Spearman correlation demonstrated that the reduction of IRF volume was negatively correlated with age (coefficient = -0.698, p = 0.006). Conclusion: We developed a deep learning assisted fluid volume calculation algorithm with high sensitivity and specificity for assessing the volume of IRF and SRF in DME patients. Key words: deep learning; diabetic macular edema; optical coherence tomography.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

Empirical mode decomposition using deep learning model for financial market forecasting.

Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

Transthyretin Regulated by linc00657/miR-205-5p Promoted Cholesterol Metabolism by Inducing SREBP2-HMGCR and Inhibiting LXRα-CYP7A1.

BACKGROUND: Transthyretin functions as a serum transport protein for retinol. Transthyretin has been found associated with amyloid diseases and it is an important nutrition indicator. For this study, we aimed to investigate the up and down stream molecular mechanisms of Transthyretin in cholesterol metabolism. METHODS: We have recruited 237 fatty liver patients to evaluate the serum Transthyretin and its association with cholesterol and other clinical characteristics. And then Transthyretin was up and down regulated by plasmids to investigate its downstream mechanisms in vitro. RESULTS: Linc00657 (NORAD) and miR-205-5p were further confirmed as upstream mechanisms to regulate Transthyretin. High level Transthyretin patients tended to have higher cholesterol, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) level than low level Transthyretin patients. Moreover, Transthyretin expressed higher in LO2 than that in QSG7701. Furthermore, Transthyretin negatively regulated CYP7A1, LXRα and ABCG 5/8 and positively regulated HMGCR and SREBP2. Linc00657 expressed lower in LO2 than that in QSG7701 and miR-205-5p expressed higher in LO2 than that in QSG7701. Furthermore, we found that linc00657 negatively regulated miR-205-5p and Transthyretin in vitro. And, up regulation of miR-205-5p in linc00657-LO2 cell line could reverse the inhibitory effects of linc00657 on Transthyretin. CONCLUSION: Transthyretin regulated cholesterol metabolism mainly through inhibiting LXRα-CYP7A1 and promoting SREBP2-HMGCR. And linc00657 could negatively regulate Transthyretin by inhibiting miR-205-5p, providing novel therapeutic targets for decreasing serum cholesterol level. Besides, Transthyretin could be a potential novel biomarker for predicting liver function along with AST and ALT.