Dataset Information

Occurrence of Oxidative Stress and Premature Senescence in the Anterior Segment of Acute Primary Angle-Closure Eyes.

ABSTRACT:

Purpose

To explore whether oxidative stress and premature senescence occur in the anterior segment of acute primary angle-closure (APAC) eyes after increased intraocular pressure.

Methods

The eye samples of 21 APAC patients, 22 age-related cataract patients, and 10 healthy donors were included. Aqueous humor (AqH), iris, and anterior lens capsule samples were collected. The levels of oxidative stress markers and senescence-associated secretory phenotype (SASP)-related cytokines in AqH were estimated using relevant reagent kits and multiplex bead immunoassay technique. The intensity of relevant markers in anterior segment tissues was examined by immunofluorescence- and senescence-associated β-galactosidase (SA-β-gal) staining.

Results

Oxidative stress marker levels elevated significantly in the AqH of APAC eyes. Reactive oxygen species (ROS) and 8-hydroxydeoxyguanosine levels were positively correlated with preoperative peak intraocular pressure and age, whereas reduced glutathione/oxidized glutathione (GSH/GSSH) ratio was negatively correlated with both parameters. The levels of several SASP-related cytokines were markedly increased. ROS and malondialdehyde levels were positively correlated with the levels of some SASP-related cytokines, whereas superoxide dismutase level and GSH/GSSH ratio showed an opposite trend. The number of cells positive for oxidative mitochondrial DNA damage and apoptosis-related markers increased in the iris and anterior lens capsule of the APAC group. Senescence-associated markers (p16, p21, and p53) and SA-β-gal activity were increased in the iris of the APAC group.

Conclusions

Oxidative stress and premature senescence occurred in the anterior segment of APAC patients, suggesting that they may be involved in the development of pathological changes in the anterior segment of APAC eyes.

SUBMITTER: Ye D

PROVIDER: S-EPMC8802011 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PURPOSE:To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images. METHODS:Subjects were recruited as part of the Chinese-American Eye Study (CHES), a population-based study of Chinese Americans in Los Angeles, California, USA. Each subject underwent a complete ocular examination including gonioscopy and AS-OCT imaging in each quadrant of the anterior chamber angle (ACA). Deep learning methods were used to develop 3 competing multi-class convolutional neural network (CNN) classifiers for modified Shaffer grades 0, 1, 2, 3, and 4. Binary probabilities for closed (grades 0 and 1) and open (grades 2, 3, and 4) angles were calculated by summing over the corresponding grades. Classifier performance was evaluated by 5-fold cross-validation and on an independent test dataset. Outcome measures included area under the receiver operating characteristic curve (AUC) for detecting gonioscopic angle closure and PACD, defined as either 2 or 3 quadrants of gonioscopic angle closure per eye. RESULTS:A total of 4036 AS-OCT images with corresponding gonioscopy grades (1943 open, 2093 closed) were obtained from 791 CHES subjects. Three competing CNN classifiers were developed with a cross-validation dataset of 3396 images (1632 open, 1764 closed) from 664 subjects. The remaining 640 images (311 open, 329 closed) from 127 subjects were segregated into a test dataset. The best-performing classifier was developed by applying transfer learning to the ResNet-18 architecture. For detecting gonioscopic angle closure, this classifier achieved an AUC of 0.933 (95% confidence interval, 0.925-0.941) on the cross-validation dataset and 0.928 on the test dataset. For detecting PACD based on 2- and 3-quadrant definitions, the ResNet-18 classifier achieved AUCs of 0.964 and 0.952, respectively, on the test dataset. CONCLUSION:Deep learning classifiers effectively detect gonioscopic angle closure and PACD based on automated analysis of AS-OCT images. These methods could be used to automate clinical evaluations of the ACA and improve access to eye care in high-risk populations.