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Renal unilateral ischemia-reperfusion injury (UIRI) constitutes a significant global health challenge, with poor recovery leading to chronic kidney disease and subsequent renal fibrosis. Extracellular vesicles (EVs) present substantial potential benefits for renal diseases. However, the limited yield and efficacy of EVs produced through traditional methodologies (2D-EVs) severely restrict their widespread application. Moreover, the efficient and effective strategies for using EVs in UIRI treatment and their mechanisms remain largely unexplored. In this study, we propose an innovative approach by integrating bioprinted mesenchymal stem cell microfiber extracellular vesicles production technology (3D-EVs) with a tail vein injection method, introducing a novel treatment strategy for UIRI. Our comparison of the biological functions of 2D-EVs and 3D-EVs, both in vitro and in vivo, reveals that 3D-EVs significantly outperform 2D-EVs. Specifically, in vitro, 3D-EVs demonstrate a superior capacity to enhance the proliferation and migration of NRK-52E cells and mitigate hypoxia/reoxygenation (H/R)-induced injuries by reducing epithelial-mesenchymal transformation, extracellular matrix deposition, and ferroptosis. In vivo, 3D-EVs exhibit enhanced therapeutic effects, as evidenced by improved renal function and decreased collagen deposition in UIRI mouse kidneys. We further elucidate the mechanism by which 3D-EVs derived from KLF15 ameliorate UIRI-induced tubular epithelial cells (TECs) ferroptosis through the modulation of SLC7A11 and GPX4 expression. Our findings suggest that bioprinted mesenchymal stem cells microfiber-derived EVs significantly ameliorate renal UIRI, opening new avenues for effective and efficient EV-based therapies in UIRI treatment.
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BACKGROUND: To assess associations of high academic performance with ametropia prevalence and myopia development in Chinese schoolchildren. METHODS: This multicohort observational study was performed in Guangdong, China. We first performed a cross-sectional cohort analysis of students in grades 1 to 9 from Yangjiang to evaluate the relationship between academic performance and refractive status on a yearly basis. We also performed longitudinal analyses of students in Shenzhen to evaluate the trend of academic performance with refractive changes over a period of 33 months. All refractive statuses were measured using noncycloplegic autorefractors. RESULTS: A total of 32,360 children with or without myopia were recruited in this study (mean age 10.08 years, 18,360 males and 14,000 females). Cross-sectional cohort analyses in Yangjiang showed that the prevalence of hyperopia was associated with lower academic scores in grade one, the year students entered primary school (ß=-0.04, P=0.01), whereas the prevalence of myopia was associated with higher academic scores in grade six and grade eight, the years in which students were about to take entrance examinations for junior high school or senior high school (ß=0.020, P=0.038; ß=0.041, P=0.002). Longitudinal analysis showed that in Shenzhen, faster myopia development was associated with better scores in all grades even after adjustments for BMI, outdoor activity time, screen time, reading time, and parental myopia (grade two at baseline: ß=0.026, P<0.001; grade three at baseline: ß=0.036, P=0.001; grade four at baseline: ß=0.014, P<0.001; grade five at baseline: ß=0.039, P<0.001; grade six at baseline: ß=0.04, P<0.001). CONCLUSIONS: Refractive errors correlated significantly with academic performance among schoolchildren in China. Children with high academic performance were more likely to have faster myopia development.
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BACKGROUND: Strabismus affects approximately 0.8-6.8% of the world's population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice. METHODS: An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020. RESULTS: In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1-99.7%], a specificity of 98.3% (95% CI, 94.6-99.5%), and an AUC of 0.998 (95% CI, 0.993-1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion. CONCLUSIONS: The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.