Explainable deep learning model for automatic mulberry leaf disease classification

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world's raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models' findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves.

Diethyl methyl chitosan as an intestinal paracellular enhancer: ex vivo and in vivo studies

Chitosan exhibits favorable biological properties such as no toxicity, biocompatibility and biodegradability; therefore, it has attracted great attention in both pharmaceutical and biomedical fields. Chitosan exhibits poor solubility at pH values above 6 that prevents enhancing effects at the sites of absorption of drugs. In the present work, N-diethyl methyl chitosan (DEMC) was prepared and the enhancing effect of this polymer was investigated. Ex vivo studies have shown a significant increase in absorption of brilliant blue in the presence of diethyl methyl chitosan in comparison with chitosan. DEMC with positive charges is able to interact with tight junctions of colon epithelial cells and hence increases permeability of brilliant blue across the tight junctions. In vivo investigations have exhibited the absorption enhancer effects of DEMC on the colon absorption of insulin in normal and diabetic rats. The insulin absorption from the rat's colon was evaluated by its hypoglycemic effect. A significant decrease in blood glucose was observed, when mixture of insulin and DEMC was introduced in ascending colon of rats.