ArticleDrug target insights
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Article in Drug target insights. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Diabetes mellitus (DM), particularly type 2 DM (T2DM), is a chronic metabolic disorder requiring novel therapeutic approaches as the available therapies are not meeting the current challenges. This study investigates the anti-diabetic potential of Vigna unguiculata using a network pharmacology approach, supported by Methods: The plant was collected from Khyber Pakhtunkhwa, Pakistan, and subjected to hydroalcoholic extraction and fractionation. Results: IC50 values were determined using IBM SPSS Statistics 21 software. The data underwent analysis using one-way ANOVA followed by Dunnett's multiple comparison test. Significance value was determined at *p 0.05, **p 0.01 and ***p 0.001. In-vitro assays demonstrated significant α-amylase, α-glucosidase, and aldose reductase inhibitory activities. Phytochemical screening identified several bioactive compounds. Functional annotation and KEGG pathway analysis confirmed these genes' roles in crucial metabolic pathways. Virtual screening revealed strong binding affinities of compounds like Stigmasterol, Luteoline, and Quercetin with GSK3B, PTGS2, and TLR4. The Molecular Dynamics (MD) simulation, binding free energy calculations (MM-PBSA and MM-GBSA), confirmed the results of Virtual screening. Conclusion: In short, these findings underscore
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.