Various cytokines, including interferon (IFN)-γ and IL-17, are augmented, and autoreactive T cells and B cells are activated in the immune pathogenesis of Sjögren’s syndrome (SS). In particular, IFNs are involved in both the early stages of innate immunity by high level of type I IFN in glandular tissue and sera and the later stages of disease progression by type I and type II IFN producing T cells and B cells through B cell activating factor in SS. Genetically modified mouse models for some of these molecules have been reported and will be discussed in this review. New findings from human SS and animal models of SS have elucidated some of the mechanisms underlying SS-related dry eye. We will discuss IFN-γ and several other molecules that represent candidate targets for treating inflammation in SS-related dry eye.

Tear film breakup time (TFBUT) is an essential parameter used to diagnose dry eye disease (DED). However, a robust method for examining TFBUT in murine models has yet to be established. We invented an innovative device, namely, the "Smart Eye Camera", which addresses several problems associated with existing methods and is capable of evaluating TFBUT in a murine DED model. We compared images taken by existing devices and the Smart Eye Camera in a graft-versus-host disease-related DED murine model. We observed that the quality of the images obtained by the Smart Eye Camera were sufficient for practical use. Moreover, this new technique could be used to obtain measurements for several consecutive ocular phenotypes in a variety of environments. Here, we demonstrate the effectiveness of our new invention in the examination of ocular phenotypes, including TFBUT in a murine model. We highlight the potential for future translational studies adopting the Smart Eye Camera in clinical settings.

The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome these issues, we used the Smart Eye Camera (SEC), a video-recordable slit-lamp device, and collected videos of the anterior segment of the eye. This study aimed to evaluate the accuracy of the AI algorithm in estimating the tear film breakup time and apply this model for the diagnosis of DED according to the Asia Dry Eye Society (ADES) DED diagnostic criteria. Using the retrospectively corrected DED videos of 158 eyes from 79 patients, 22,172 frames were annotated by the DED specialist to label whether or not the frame had breakup. The AI algorithm was developed using the training dataset and machine learning. The DED criteria of the ADES was used to determine the diagnostic performance. The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769–0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861–0.893). The sensitivity and specificity of this AI model for the diagnosis of DED was 0.778 (95% CI 0.572–0.912) and 0.857 (95% CI 0.564–0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED. Our diagnostic model has the potential to enable ophthalmology examination outside hospitals and clinics.

To elucidate the correlation between lactoferrin concentration in the tear film and signs and symptoms of severe dry eye disease (DED) using a novel microfluidic paper-based analytical device (μPAD) and enzyme-linked immunosorbent assay (ELISA). Twenty-four patients were recruited at the Keio University Hospital. Using a novel μPAD, lactoferrin concentrations were measured in 4 patients with GVHD-related DED, 3 patients with other types of DED and 2 controls (Group A). For validation by ELISA, 22 patients (7 patients from Group A) comprising 9 patients with GVHD-related DED, 6 patients with other types of DED and 7 controls were examined (Group B). The link between lactoferrin concentration and clinical data about the severity of aqueous tear deficient DED was also investigated by both μPAD and ELISA. The lactoferrin concentration in tear fluid of the DED patients was positively correlated between μPAD and ELISA (p = 0.006, r = 0.886). The tear fluid of the GVHD patients showed low or undetectable lactoferrin concentration. Analysis by ELISA demonstrated that lactoferrin concentrations in the tear film from the GVHD patients were significantly lower than those from the non-GVHD patients (p = 0.010576). ELISA revealed lactoferrin concentration correlated with the value of Schirmer test and tear film breakup time, whereas it was inversely correlated with OSDI, fluorescein and rose bengal scores. The novel μPAD may pave the way for measuring lactoferrin concentration in tear fluid from DED patients. Our results suggested that lactoferrin concentration in tear fluid reflect the severity of DED.

Background: Visual impairments and age-related eye diseases need to be detected and treated in a timely manner. However, this is often hampered by lack of appropriate medical equipment. We have invented a portable, recordable, and smartphone-attachable slit-lamp device, called the Smart Eye Camera (SEC). The aim of this study was to compare evaluating nuclear cataract (NUC) between the SEC and the conventional, non-portable slit-lamp microscope. Methods: A total of 128 eyes of 64 Japanese patients (mean age: 73.95 ± 9.28 years; range: 51‒92 years; female: 34) were enrolled. The NUC was classified into four grades (grade 0 to 3) based on three standard photographs of nuclear opacities according to the WHO classification by ophthalmologists. An ophthalmic healthcare assistant (non-ophthalmologist) filmed the eyes in video mode by the SEC and an ophthalmologist graded the NUC. Grade correlation and inter-rater reproducibility were determined. Results: NUC grading by the two approaches correlated significantly (both eyes: r = 0.871 [95%CI: 0.821 to 0.907; p < 0.001]). Inter-rater agreement was high (weighted κ = 0.807 [95%CI: 0.798 to 0.816; p < 0.001]). Conclusions: This study suggests that the SEC is as reliable as the conventional non-portable slit-lamp microscope for evaluating NUC.