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Record W4319312627 · doi:10.1039/d2md00431c

Development of thiazole-appended novel hydrazones as a new class of α-amylase inhibitors with anticancer assets: an<i>in silico</i>and<i>in vitro</i>approach

2023· article· en· W4319312627 on OpenAlex

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

VenueRSC Medicinal Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersUniversity Grants Commission
KeywordsIn silicoThiazoleIn vitroChemistryCombinatorial chemistryClass (philosophy)Computational biologyPharmacologyStereochemistryBiochemistryBiologyComputer scienceGeneArtificial intelligence

Abstract

fetched live from OpenAlex

values from 0.23 ± 0.003 to 0.5 ± 0.0 μM. Along with this, the proliferations of the HCT-116, A549 and MDA-MB-231 cells were inhibited when treated with the synthesized compounds. Notable cancer cell growth inhibition was observed for compounds 5e, 5f and 5y, which correlated with their α-amylase inhibition. Additionally, the kinetics investigation revealed that 5b, 5e, 5f and 5y exhibit uncompetitive inhibition. 5b was found to be the least cytotoxic and most potent α-amylase inhibitor and was further validated by absorption and fluorescence quenching technique.

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 score0.007
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.256
Teacher spread0.232 · 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