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Record W4303958829 · doi:10.3390/molecules27196718

Novel Unsymmetric 3,5-Bis(benzylidene)-4-piperidones That Display Tumor-Selective Toxicity

2022· article· en· W4303958829 on OpenAlexaff
Aruna Chhikara, Praveen K. Roayapalley, Hiroshi Sakagami, Shigeru Amano, Keitaro Satoh, Yoshihiro Uesawa, Umashankar Das, Swagatika Das, Edgar A. Borrego, Cristina D. Guerena, Clare R. Hernandez, Renato J. Aguilera, Jonathan R. Dimmock

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute on Minority Health and Health Disparities
KeywordsADMECytotoxicityChemistryApoptosisCell cultureProgrammed cell deathDepolarizationToxicityStereochemistryLeukemiaLymphomaBiochemistryCancer researchPharmacologyBiophysicsBiologyIn vitroImmunologyOrganic chemistryGenetics

Abstract

fetched live from OpenAlex

were designed as candidate antineoplastic agents. These compounds display potent cytotoxicity towards two colon cancers, as well as several oral squamous cell carcinomas. These compounds are less toxic to various non-malignant cells giving rise to large selectivity index (SI) figures. Many of the compounds are also cytotoxic towards CEM lymphoma and HL-60 leukemia cells. Representative compounds induced apoptotic cell death characterized by caspase-3 activation and subG1 accumulation in some OSCC cells, as well as the depolarization of the mitochondrial membrane potential in CEM cells. A further line of inquiry was directed to finding if the SI values are correlated with the atomic charges on the olefinic carbon atoms. The potential of these compounds as antineoplastic agents was enhanced by an ADME (absorption, distribution, metabolism, and excretion) evaluation of five lead molecules, which revealed no violations.

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 score0.004

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 head0.024
GPT teacher head0.229
Teacher spread0.205 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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