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Based on the implementing grain security projects and instituting other relevant grain security policies, China has promoted the process of digitalization and intelligence of grain storage and built the world's largest grain Internet of Things, since the 18th National Congress of the Communist Party of China. But, the resulting large amount of grain data, mainly based on temperature, can not effectively support decision-making and management on grain storage. Based on the study of the multi-field coupling theory of grain pile ecosystem, our research team further extracted the continuity principle, periodicity principle, and coordination principle of digital supervision of grain reserves, and proposed a series of intelligent strategies such as AID, ABC, 6R, SIN, CAE, O, and U. However, the application of these strategies directly faces the original data, which has some problems such as low efficiency and large error. To apply the intelligent strategies efficiently and correctly, the basic data manual of intelligent analysis of grain condition has been developed, according to the original grain condition data of typical granaries and main grain varieties in different grain storage ecological regions in China. The development of this manual is expected to consolidate the foundation of intelligent analysis of grain situation, which could promote the digital monitoring of grain storage quantity and quality, and realize the transformation from "manual defense" to “technical defense”.

Diquat (DQ) poisoning can cause multiple organ damage, and the kidney is considered to be the main target organ. Increasing evidence shows that alleviating oxidative stress and inflammatory response has promising application prospects. Epigallocatechin gallate (EGCG) has potent antioxidant and anti-inflammatory effects. In this study, red blood cell membrane (RBCm)-camouflaged polylactic-co-glycolic acid (PLGA) nanoparticles (NPs) were synthesized to deliver EGCG (EGCG-RBCm/NPs) for renal injury induced by DQ. Human renal tubular epithelial cells (HK-2 cells) were stimulated with 600 μM DQ for 12 h and mice were intraperitoneally injected with 50 mg/kg b.w. DQ, followed by 20 mg/kg b.w./day EGCG or EGCG-RBCM/NPs for 3 days. The assessment of cellular vitality was carried out using the CCK-8 assay, while the quantification of reactive oxygen species (ROS) was performed through ROS specific probes. Apoptosis analysis was conducted by both flow cytometry and TUNEL staining methods. Pathological changes in renal tissue were observed. The expressions of NLRP3, IL-1β, IL-18, NFκB and Caspase1 were detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunohistochemistry, immunofluorescence, and Western blot. The results showed that the DQ group had increased ROS expression, increased the level of oxidative stress, and increased apoptosis rate compared with the control group. Histopathological analysis of mice in the DQ group showed renal tubular injury and elevated levels of blood urea nitrogen (BUN), serum creatinine (SCr), kidney injury molecule-1 (KIM-1), and cystatin C (Cys C). Furthermore, the DQ group exhibited heightened expression of NLRP3, p-NFκB p65, Caspase1 p20, IL-1β, and IL-18. However, EGCG-RBCm/NPs treatment mitigated DQ-induced increases in ROS, apoptosis, and oxidative stress, as well as renal toxicity and decreases in renal biomarker levels. Meanwhile, the expression of the above proteins were significantly decreased, and the survival rate of mice was ultimately improved, with an effect better than that of the EGCG treatment group. In conclusion, EGCG-RBCm/NPs can improve oxidative stress, inflammation, and apoptosis induced by DQ. This effect is related to the NF-κB/NLRP3 inflammasome pathway. Overall, this study provides a new approach for treating renal injury induced by DQ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Qu, Pei, Li, Li, Chen, Zhang and Lu.)