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Record W3171096887 · doi:10.3390/medicines8060027

Design, Syntheses, and Bioevaluations of Some Novel N2-Acryloylbenzohydrazides as Chemostimulants and Cytotoxic Agents

2021· article· en· W3171096887 on OpenAlexaff
Kinjal Lakhani, Edgar A. Borrego, Karla G. Cano, Jonathan R. Dimmock, Renato J. Aguilera, Swagatika Das, Praveen K. Roayapalley, Rajendra K. Sharma, Umashankar Das

Bibliographic record

VenueMedicines · 2021
Typearticle
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of General Medical SciencesUniversity of Texas at El Paso
KeywordsCytotoxicityCytotoxic T cellChemistryApoptosisStereochemistryHuman breastReactive oxygen speciesColorectal cancerCombinatorial chemistryCancer cellCell culturePharmacologyCancerBiochemistryBiologyIn vitroGenetics

Abstract

fetched live from OpenAlex

A series of novel N2-acryloylhydrazides 1a–m and a related series of compounds 6a–c were prepared as potential chemostimulants. In general, these compounds are cytotoxic to human HCT 116 colon cancer cells, as well as human MCF-7 and MDA-MB-231 breast cancer cell lines. A representative compound N1-(3,4-dimethoxyphenylcarbonyl)-N2-acryloylhydrazine 1m sensitized HCT 116 cells to the potent antineoplastic agent 3,5-bis(benzylidene)-4-piperidone 2a, and also to 5-fluorouracil. A series of compounds was prepared that incorporated some of the molecular features of 2a and related compounds with various N2-acryloylhydrazides in series 1. These compounds are potent cytotoxins. Two modes of action of representative compounds are the lowering of mitochondrial membrane potential and increasing the concentration of reactive oxygen species.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.091
GPT teacher head0.310
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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