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ABCA4 Deep Intronic Variants Contributed to Nearly Half of Unsolved Stargardt Cases With a Milder Phenotype.

Invest Ophthalmol Vis Sci

January 2025

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.

Purpose: The purpose of this study was to investigate the contribution and natural progression of ABCA4 deep intronic variants (DIVs) among a Chinese Stargardt disease (STGD) cohort.

Methods: For unsolved STGD probands, DIVs in ABCA4 were detected by next-generation sequencing, and splicing effects were evaluated by in silico tools and validated through minigene experiments. Comprehensive ocular examinations, especially fundus changes, were carried out and analyzed.

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Background: The prevalence of brain metastases (BM) in lung cancer patients is notably high and is associated with poor prognoses. The efficacy of standard treatment regimens in improving intracranial progression-free survival (IPFS) for lung cancer BM is markedly limited. While traditional Chinese medicine (TCM) has been effective in enhancing the quality of life and prognosis of lung cancer patients, its efficacy in treating BM remains unreported.

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Purpose: Cysticercosis, caused by Taenia solium larvae, can affect various ocular and extraocular structures, leading to significant morbidity. Ultrasound B-scan imaging plays a pivotal role in diagnosing and classifying cysticercosis lesions. The aim of the study was to describe the ultrasound B-scan characteristics of ocular and extraocular cysticercosis, proposing a classification system based on anatomical localization to enhance understanding and management.

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Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYE (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR).

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Objective: This study examined the relationship between diffusion tensor imaging indicators and brain network characteristics in patients with cerebral small vessel disease (CSVD) with (CSVD + S) and without (CSVD-S) sleep disturbance. We explored the feasibility of using these imaging biomarkers to investigate the pathophysiological mechanisms underlying sleep disturbance in patients with CSVD.

Methods: A total of 146 patients with CSVD and 84 healthy controls were included.

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