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Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches.

Abstract Long non‐coding RNA (lncRNA) miR‐17‐92a‐1 cluster host gene (MIR17HG) is oncogenic in several cancers. This study was aimed to probe into its expression characteristics and biological functions in retinoblastoma (RB) and to explore its role in regulating microRNA‐155‐5p (miR‐155‐5p) and hypoxia‐inducible factor‐1α (HIF‐1α). In this study, paired RB samples were collected, and expression levels of MIR17HG, miR‐155‐5p, and HIF‐1α were examined by quantitative real‐time polymerase chain reaction (qRT‐PCR); the proliferation, migration, and invasion of RB cells were detected by cell counting kit‐8 (CCK‐8) and transwell assays; qRT‐PCR and Western blot were used to analyze the changes of miR‐155‐5p expression and HIF‐1α expression. We found that MIR17HG expression was significantly up‐regulated in RB samples, which was negatively correlated with miR‐155‐5p expression. The proliferation, migration, and invasion of RB cells were promoted by MIR17HG overexpression but inhibited by MIR17HG knockdown. MiR‐155‐5p suppressed the proliferation, migration, and invasion of RB cells. MIR17HG positively regulated the expression of HIF‐1α on both mRNA and protein levels in RB cells. Additionally, miR‐155‐5p was identified as a target of MIR17HG. The data in this study suggest that MIR17HG exerts oncogenic effects in RB via the miR‐155‐5p/HIF‐1α axis.

Curcumin, a naturally occurring poorly water-soluble polyphenol with a broad spectrum, is a typical BCS IV drug. The objective of this study was to develop curcumin nanocrystals liposomes with the aim of improving bioavailability. In this study, we prepared cationic curcumin nanocrystals with a particle size of only 29.42 nm; such a phenomenal range of particle sizes is very rare. Moreover, we summarized and evaluated the parameters of the nanocrystal preparation process, including methods, formulations, etc., and the rules we concluded can be generalized to other nanocrystal preparation processes. To counteract the instability of the nanocrystals in the digestive tract, cationic curcumin nanocrystals were loaded into negatively charged liposomes through gravitational force between different charges. Unexpectedly, chitosan oligosaccharide was found to promote the self-assembly process of curcumin nanocrystal liposomes. In vitro and in vivo experiments demonstrated that chitosan-modified curcumin nanocrystal liposomes exhibited enhanced resistance to enzyme barriers, mucus barriers, and cellular barriers, resulting in a 5.4-fold increase in bioavailability compared to crude powder formulations. It can be concluded that cationic nanocrystals liposomes represent an appropriate novel strategy for improving the dissolution rate and bioavailability of poorly soluble natural products such as curcumin. [ABSTRACT FROM AUTHOR]