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Single-cell sequencing in diabetic retinopathy: progress and prospects.

J Transl Med

January 2025

Department of Ophthalmology, Renmin Hospital of Wuhan University, Jiefang Road, Wuhan, Hubei, 430060, China.

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications.

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Background: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.

Results: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations.

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Cannabis trichome development progresses in distinct phases that underpin the dynamic biosynthesis of cannabinoids and terpenes. This study investigates the molecular mechanisms underlying cannabinoid and terpenoid biosynthesis in glandular trichomes of Cannabis sativa (CsGTs) throughout their development. Female Cannabis sativa c.

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Purpose: To report the occurrence of AMN (Acute Macular Neuroretinopathy) in a Behçet Disease (BD) patient during an active systemic inflammatory relapse and to describe the SD-OCT features of this entity.

Patients And Methods: Retrospective observational case report of a patient who presented with an AMN during a BD associated ocular inflammation (Saint Pierre Hospital, Brussels, Belgium). Clinical record and imaging, including infrared reflectance image (IR) and spectral domain optical coherence tomography (SD-OCT), were analyzed.

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Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.

Med Biol Eng Comput

January 2025

Pathology Department, Beijing Youan Hospital, Capital Medical University, Beijing, 100000, China.

In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information.

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