Dry eye disease and high disease activity score (DAS-28) in rheumatoid arthritis: an underrated combination

Objective: To determine the association of dryness of eyes with rheumatoid arthritis severity. Method: The cross-sectional, observational study was conducted at the Jinnah Medical College Hospital, Karachi, from December 2020 to May 2021, and comprised adult patients of either gender with rheumatoid arthritis who were diagnosed on the basis of clinical and serological investigations. Data was collected using a structured pre-tested questionnaire. Ocular Surface Disease Index questionnaires with Tear Film Breakup Time were used to assess the severity of dry eyes. Disease Activity Score-28 with erythrocyte sedimentation rate was used to assess the severity of rheumatoid arthritis. Association between the two was explored. Data was analysed using SPSS 22. Results: Of the 61 patients, 52(85.2%) were females and 9(14.8%) were males. The overall mean age was 41.7±12.8 years, with 4(6.6%) aged 60years. Further, 46(75.4%) subjects had sero-positive rheumatoid arthritis, 25(41%) had high severity, 30(49.2%) had severe Occular Surface Density Index score and 36(59%) had decreased Tear Film Breakup Time. Logistic Regression analysis showed there were 5.45 times higher odds of having severe disease among the people with Occular Surface Density Index score >33 (p=0.003). In patients with positive Tear Film Breakup Time, there were 6.25 higher odds of having increased disease activity score (p=0.001). Conclusion: Disease activity scores of rheumatoid arthritis were found to have strong association with dryness of eyes, high Ocular Surface Disease Index score and increased erythrocyte sedimentation rate. Key Words: Rheumatoid arthritis, Tear film breakup time, Disease activity score-28, Dry eye, Ocular surface disease index

Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval

Abstract Deep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset