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Diseases in plants harm the quantity of the overall food production as well as the quality of the yield. Early detection, diagnosis and treatment can greatly reduce losses, both economic and ecological. Intuitively, reduction in the use of agrochemicals due to timely detection of the disease, would greatly help in mitigating the environmental impact. In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation (SE) Networks, before processing by the original Capsule networks (CapsNet) for classification, for estimating the disease severity in plants. Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule networks. Leaf images for the devastating Late Blight disease occurring in the Tomato crop have been utilized from the PlantVillage dataset. The images, divided into four severity stages i.e. healthy, early, middle and end, are downscaled, enhanced and given as input to the SE networks. The feature maps generated from the two networks are separately given as input to the Capsule Network for classification and their performances are compared with the original CapsNet, on two image sizes 32X32 and 64X64. SE-Alex-CapsNet achieves the highest accuracy of 92.1% and SE-Res CapsNet achieves the highest accuracy of 93.75% with 64X64 image size, as compared to CapsNet that results in 85.53% accuracy. The classification accuracies of six state-of-the-art CNN models namely AlexNet, SqueezeNet, ResNet50, VGG16, VGG19 and Inception V3 are also presented for comparison purposes. Accuracy as well as precision, recall, F1-score, validation loss etc. measures have been recorded and compared. The findings have been validated by implementing the proposed approaches with another dataset, achieving similar resultant accuracy measures. The implementation was also accomplished with datasets after noise addition in six different variations, to verify the robustness of the proposed model. Based on the performances, the proposed techniques can be exploited for disease severity assessment in other crops as well and can be extended to other areas of applications such as plant species classification, weed identification etc. In addition to improved performance, with reduced image size, the proposed methodology can be utilized to create a mobile application requiring low processing capabilities, to be installed on reasonably priced smartphones for practical usage by farmers. [ABSTRACT FROM AUTHOR]

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by autoantibody production and immune complex deposition in various organs. The pathogenesis of SLE is multifactorial, involving genetic, hormonal, environmental, and immune factors. Interleukin-10 (IL-10) is a pleiotropic cytokine produced by various immune cells and has conflicting roles in inflammation., Materials and Methods: This is a cross-sectional study involving 56 SLE patients and 30 healthy controls., Results and Analysis: We found a significant increase in T helper 10 (Th10) cells and IL-10 levels in SLE patients compared to controls. Disease activity, measured by Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score, correlated positively with Th10 cells and IL-10 levels. Further analysis categorized patients into active and inactive SLE, showing significant differences in laboratory parameters, including C3, C4, Th10 cells, and IL-10, between the two groups. Notably, Th10 cells and IL-10 exhibited a significant positive correlation with SLEDAI scores. The study also explored SLE patients with and without nephritis, a severe manifestation of the disease. Th10 cell expression was significantly higher in nephritis patients, while IL-10 levels did not differ significantly between the two groups., Conclusion: In conclusion, this study provides valuable insights into the association between Th10 cells, IL-10, and disease activity in SLE. The findings suggest that Th10 cells and IL-10 could serve as potential biomarkers for disease activity in SLE, offering a basis for further research into therapeutic interventions targeting these factors. These results contribute to our understanding of the complex immunological factors at play in SLE and may pave the way for more targeted and effective treatment approaches., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. The Institutional Ethics Committee of All India Institute of Medical Sciences (AIIMS), Raipur, issued approval AIIMSRPR/IEC/2020/515. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Verma et al.)