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Record W1990099521 · doi:10.1186/1755-8166-7-s1-p97

In silico docking studies for designing potent anti-diabetic derivatives of swertiamarin with enzyme HMG COA reductase

2014· article· en· W1990099521 on OpenAlex
Jayshil Bhatt, Hitesh Vaidya, Varun Khanna, Naisargee Patel, Ramesh K. Goyal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecular Cytogenetics · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and Biological Activities
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIn silicoDocking (animal)HMG-CoA reductasePharmacologyMedicineReductaseHuman geneticsEnzymeComputational biologyChemistryBioinformaticsBiochemistryBiology

Abstract

fetched live from OpenAlex

Background Swertiamarin, a secoiridoid glycoside is found in abundant quantity in Enicostemma Littorale herb and is the main constituent responsible for anti-diabetic and anti-obesity effects of the plant extract. It has been reported to act on various enzymes and transcription factors involved in glucose and lipid metabolism, including inhibition of the enzyme HMG-CoA reductase which might be one of the mechanisms responsible for the antihyperlipidaemic activity. However, owing to its high water solubility, it has a low plasma half life; and thus we have designed its derivatives which bind more efficiently with HMG CoA Reductase.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.253
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it