Establishment of a corneal ulcer prognostic model based on machine learning.

Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model's performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63-1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63-0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92-1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94-1.00) for visual impairment.

Excess Salt Intake Activates IL-21-Dominant Autoimmune Diabetogenesis via a Salt-Regulated Ste20-Related Proline/Alanine-Rich Kinase in CD4 T Cells.

The fundamental mechanisms by which a diet affects susceptibility to or modifies autoimmune diseases are poorly understood. Excess dietary salt intake acts as a risk factor for autoimmune diseases; however, little information exists on the impact of salt intake on type 1 diabetes. To elucidate the potential effect of high salt intake on autoimmune diabetes, nonobese diabetic (NOD) mice were fed a high-salt diet (HSD) or a normal-salt diet (NSD) from 6 to 12 weeks of age and monitored for diabetes development. Our results revealed that the HSD accelerated diabetes progression with more severe insulitis in NOD mice in a CD4+ T-cell-autonomous manner when compared with the NSD group. Moreover, expression of IL-21 and SPAK in splenic CD4+ T cells from HSD-fed mice was significantly upregulated. Accordingly, we generated T-cell-specific SPAK knockout (CKO) NOD mice and demonstrated that SPAK deficiency in T cells significantly attenuated diabetes development in NOD mice by downregulating IL-21 expression in CD4+ T cells. Furthermore, HSD-triggered diabetes acceleration was abolished in HSD-fed SPAK CKO mice when compared with HSD-fed NOD mice, suggesting an essential role of SPAK in salt-exacerbated T-cell pathogenicity. Finally, pharmacological inhibition of SPAK activity using a specific SPAK inhibitor (closantel) in NOD mice ameliorated diabetogenesis, further illuminating the potential of a SPAK-targeting immunotherapeutic approach for autoimmune diabetes. Here, we illustrate that a substantial association between salt sensitivity and the functional impact of SPAK on T-cell pathogenicity is a central player linking high-salt-intake influences to immunopathophysiology of diabetogenesis in NOD mice.

Revealing potential Rab proteins participate in regulation of secretory autophagy machinery.

Autophagy can be classified as degradative and secretory based on distinct functions. The small GTPase proteins Rab8a and Rab37 are responsible for secretory autophagy-mediated exocytosis of IL-1ß, insulin, and TIMP1 (tissue inhibitor of 54 metalloproteinase 1). Other Rab family members participating in secretory autophagy are poorly understood. Herein, we identified 26 overlapped Rab proteins in purified autophagosomes of mouse pancreatic ß-cell "Min-6" and human lung cancer cell "CL1-5-Q89L" with high secretory autophagy tendency by LC-MS/MS proteomics analysis. Six Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, Rab37, and Rab7a) were detected in autophagosomes of four cell lines, associating them with autophagy-related vesicle trafficking. We used CL1-5-Q89L cell line model to evaluate the levels of Rab proteins colocalization with autophagy LC3 proteins and presence in purified autophagosomes. We found five Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, and Rab37) are highly expressed in the autophagosome compared to the normal control by immunoblotting under active secretion conditions. However, only Rab8a, Rab35, and Rab37 showing high colocalization with LC3 protein by cofocal microscopy. Despite the discrepancy between the image and immunoblotting analysis, our data sustains the speculation that Rab8a, Rab11b, Rab27a, Rab35, and Rab37 are possibly associated with the secretory autophagy machinery. In contrast, Rab7a shows low colocalization with LC3 puncta and low level in the autophagosome, suggesting it regulates different vesicle trafficking machineries. Our findings open a new direction toward exploring the role of Rab proteins in secretory autophagy-related cargo exocytosis and identifying the cargoes and effectors regulated by specific Rab proteins.