No author has a financial or proprietary interest in any material or method mentioned.

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Objective: Tofacitinib (TOF) is a Janus kinase (JAK) inhibitor used in the treatment of rheumatoid arthritis (RA), but the mechanism of its action remains unclear. In this study, we investigated the influence of TOF on gamma delta regulatory T-cell (γδTreg)/γδT17 cell balance in RA and the role of the nucleotide-binding domain (NOD)-like receptor protein 3 (NLRP3) inflammasome in this process. Methods: We detected levels of inflammatory factors in the serum of RA patients before and after administration of TOF using an enzyme-linked immunosorbent assay (ELISA). A collagen-induced arthritis (CIA) model was constructed to investigate the effect of TOF on arthritis symptoms, γδTreg/γδT17 cell balance and the NLRP3 inflammasome. We used bone marrow-derived macrophages (BMDMs) to study the effect of TOF on NLRP3 inflammasome activation. Nlrp 3 -/- mice were introduced to assess the influence of NLRP3 on γδT17 cell activation in RA. Results: TOF treatment decreased levels of γδT17 cell-related cytokine interleukin-17 (IL-17) in RA patients. In addition, TOF intervention in the CIA model reduced joint inflammation and damage, rebalanced the γδTreg/γδT17 cell ratio and inhibited excessive NLRP3 inflammasome activation in draining lymph nodes and arthritic joints. BMDM intervention experiments demonstrated that TOF decreased the level of secreted IL-1β via downregulation of NLRP3. Furthermore, experiments using Nlrp3 -/- mice verified that the NLRP3 inflammasome mediated the effect of TOF on γδT17 cell activation. Conclusions: Recovery of γδTreg/γδT17 cell balance was a novel mechanism by which TOF alleviated RA. Meanwhile, NLRP3 played a pivotal role in the process of TOF-mediated γδT17 cell activation.

We aimed to establish an artificial intelligence (AI) system based on deep learning and transfer learning for meibomian gland (MG) segmentation and evaluate the efficacy of MG density in the diagnosis of MG dysfunction (MGD). First, 85 eyes of 85 subjects were enrolled for AI system-based evaluation effectiveness testing. Then, from 2420 randomly selected subjects, 4006 meibography images (1620 upper eyelids and 2386 lower eyelids) graded by three experts according to the meiboscore were analyzed for MG density using the AI system. The updated AI system achieved 92% accuracy (intersection over union, IoU) and 100% repeatability in MG segmentation after 4 h of training. The processing time for each meibography was 100 ms. We discovered a significant and linear correlation between MG density and ocular surface disease index questionnaire (OSDI), tear break-up time (TBUT), lid margin score, meiboscore, and meibum expressibility score (all p < 0.05). The area under the curve (AUC) was 0.900 for MG density in the total eyelids. The sensitivity and specificity were 88% and 81%, respectively, at a cutoff value of 0.275. MG density is an effective index for MGD, particularly supported by the AI system, which could replace the meiboscore, significantly improve the accuracy of meibography analysis, reduce the analysis time and doctors’ workload, and improve the diagnostic efficiency.

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Meibomian glands dysfunction (MGD) is the main cause of dry eyes. Biological parameters of meibomian gland (MG) such as height, tortuosity and the degree of atrophy are closely related to its function. However, Thus, an effective quantitative diagnostic tool is needed for clinical diagnosis. Automatic quantification of MGs' morphological features could be a challenging task and play an important role in MGD diagnosis and classification. Our main objective is to develop an artificial intelligence (AI) system for evaluating MGs' morphology and explore the relationship between the morphological parameters and functions. We proposed a novel MGs extraction method based on convolutional neural network (CNN) with enhanced mini U-Net. A prospective study was conducted, 120 subjects were included and taken meibography. The training and validation sets encompassed 60 subjects; and the test set consisted of other 60 subjects with comprehensive examinations for ocular surface disease index questionnaire (OSDI), tear meniscus height (TMH), tear break-up time (TBUT), corneal fluorescein staining (CFS), lid margin score, and meibum expressibility score. The algorithm effectively extracted MGs from meibography even with this small training sample. As a result, while the intersection over union (IoU) achieved 0.9077, the repeatability was 100%. The processing time for each image was 100ms. Using this method, the investigators identified a significant and linear correlation between MG morphology and clinical parameters. This study provided a new method for quantification of MGs' morphological features obtained by meibography, which has advantages in reducing analysis time, improving diagnostic efficiency, and assisting ophthalmologists with limited clinical expertise.INDEX TERMS Deep learning, convolutional neural network (CNN), meibomian gland dysfunction (MGD), meibography.