The purpose of this study was to look into the effects of green coconut mesocarp juice extract (CMJE) on diabetes-related problems in streptozotocin- (STZ-) induced type 2 diabetes, as well as the antioxidative functions of its natural compounds in regulating the associated genes and biochemical markers. CMJE's antioxidative properties were evaluated by the standard antioxidant assays of 1,1-diphenyl-2-picrylhydrazyl (DPPH), superoxide radical, nitric oxide, and ferrous ions along with the total phenolic and flavonoids content. The α-amylase inhibitory effect was measured by an established method. The antidiabetic effect of CMJE was assayed by fructose-fed STZ-induced diabetic models in albino rats. The obtained results were verified by bioinformatics-based network pharmacological tools: STITCH, STRING, GSEA, and Cytoscape plugin cytoHubba bioinformatics tools. The results showed that GC-MS-characterized compounds from CMJE displayed a very promising antioxidative potential. In an animal model study, CMJE significantly (P < 0.05) decreased blood glucose, serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, uric acid, and lipid levels and increased glucose tolerance as well as glucose homeostasis (HOMA-IR and HOMA-b scores). The animal's body weights and relative organ weights were found to be partially restored. Tissue architectures of the pancreas and the kidney were remarkably improved by low doses of CMJE. Compound-protein interactions showed that thymine, catechol, and 5-hydroxymethylfurfural of CMJE interacted with 84 target proteins. Of the top 15 proteins found by Cytoscape 3.6.1, 8, CAT and OGG1 (downregulated) and CASP3, COMT, CYP1B1, DPYD, NQO1, and PTGS1 (upregulated), were dysregulated in diabetes-related kidney disease. The data demonstrate the highly prospective use of CMJE in the regulation of tubulointerstitial tissues of patients with diabetic nephropathy.
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.