Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images

Role of Interleukin-21 in retinal ischemia-reperfusion injury: Unveiling the impact on retinal ganglion cell apoptosis

BACKGROUND

Retinal ischemia-reperfusion (I/R) serves as a significant contributor to ocular diseases, triggering a cascade of pathological processes. The interplay between neuroinflammation and the apoptosis of retinal ganglion cell (RGC) is a well-explored aspect of retinal I/R-induced tissue damage. Within this intricate landscape, the inflammatory cytokine Interleukin-21 (IL21) emerges as a potent mediator of neuroinflammation with known detrimental effects on neuronal integrity. However, its specific impact on RGC apoptosis in the context of retinal I/R has remains to be uncovered. This study aims to unravel the potential anti-apoptotic effects of IL21 siRNA on RGC, shedding light on the neuroprotection of retinal I/R.

METHODS

Sprague-Dawley (SD) rats underwent a controlled elevation of intraocular pressure (IOP) to 110 mmHg for 60 min to simulate retinal I/R conditions. To explore the influence of IL21 on RGC apoptosis and its underlying molecular mechanisms, a comprehensive array of techniques such immunohistochemistry, immunofluorescence, TUNEL, Hematoxylin-eosin (H&E), immunoblotting, and qRT-PCR were carried out.

RESULTS

The landscape of retinal I/R injury revealed an increase in the expression of IL21, reaching its peak at 72 h. Notably, IL21 markedly induced RGC apoptosis within the retinal I/R milieu. The introduction of IL21 siRNA showed promising outcomes, manifesting as an amelioration of neurological function deficits, a reduction in RGC loss, and an increase in the thickness of the inner retinal layer at the 72-hour reperfusion. Additionally, IL21 siRNA demonstrated its ability to hinder the release of proteins associated with apoptosis via the JAK/STAT signaling pathway. In the in vitro setting, IL21 siRNA efficiently reduced R28 cell apoptosis by suppressing the production of proteins associated with apoptosis by regulating the JAK/STAT signaling pathway.

CONCLUSIONS

This study provides evidence for the pathogenic role of IL21 in retinal I/R. The findings underscore IL21 siRNA as a promising therapeutic target for ischemic retinal injury. Its efficacy lies in its ability to mitigate RGC apoptosis by suppressing the JAK/STAT signaling pathway. These findings not only enhance our comprehension of retinal I/R pathology but also suggests IL21 siRNA as a potential transformative factor in the development of targeted therapies for ischemic retinal injuries.

Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model

Traditional laboratory microscopy for identifying bovine milk somatic cells is subjective, time-consuming, and labor-intensive. The accuracy of the recognition directly through a single classifier is low. In this paper, a novel algorithm that combined the feature extraction algorithm and fusion classification model was proposed to identify the somatic cells. First, 392 cell images from four types of bovine milk somatic cells dataset were trained and tested. Secondly, filtering and the K-means method were used to preprocess and segment the images. Thirdly, the color, morphological, and texture features of the four types of cells were extracted, totaling 100 features. Finally, the gradient boosting decision tree (GBDT)-AdaBoost fusion model was proposed. For the GBDT classifier, the light gradient boosting machine (LightGBM) was used as the weak classifier. The decision tree (DT) was used as the weak classifier of the AdaBoost classifier. The results showed that the average recognition accuracy of the GBDT-AdaBoost reached 98.0%. At the same time, that of random forest (RF), extremely randomized tree (ET), DT, and LightGBM was 79.9, 71.1, 67.3 and 77.2%, respectively. The recall rate of the GBDT-AdaBoost model was the best performance on all types of cells. The F1-Score of the GBDT-AdaBoost model was also better than the results of any single classifiers. The proposed algorithm can effectively recognize the image of bovine milk somatic cells. Moreover, it may provide a reference for recognizing bovine milk somatic cells with similar shape size characteristics and is difficult to distinguish.