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Record W4414410467 · doi:10.3390/curroncol32090526

Cytotoxicity of Esculetin Compared with Vinblastine and Paclitaxel in PC-3 Prostate Cancer Cells

2025· article· en· W4414410467 on OpenAlexvenueno aff
Ana I. Garcı́a-Pérez, Virginia Rubio, Ángel Herráez, Lilian Puebla, José C. Díez

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
FundersUniversidad de Alcalá
KeywordsCytotoxicityProstate cancerPaclitaxelVinblastineProstateCell cultureMechanism of action

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: Metastatic prostate cancer is among the therapy-resistant human neoplasms. PC-3 is a commonly used experimental cell line that does not express androgen receptors. We compared the cytotoxicity of esculetin with that of vinblastine and paclitaxel on prostatic tumour PC-3 cells. METHODS: Cells were treated with either esculetin (100 or 250 μM), vinblastine (50 μM) or paclitaxel (100 or 200 μM) for 19 to 72 h. Cells were assessed for metabolic viability, membrane integrity, DNA fragmentation and cell cycle analysis. Apoptosis was checked with annexin and propidium iodide. RESULTS: Esculetin decreased the metabolic activity of PC-3 cells in a time- and concentration-dependent way. The metabolic activity of vinblastine- and paclitaxel-treated cells did not show time-dependence. Cells treated with 250 µM esculetin for 48 or 72 h showed apoptosis levels similar to those produced by 50 µM vinblastine at these incubation times or by 200 µM paclitaxel at 19 h. Vinblastine and paclitaxel produced cell cycle arrest in the G2/M phase after incubation for 19 h. In contrast, esculetin did not significantly affect the cell cycle. CONCLUSIONS: A differential action of esculetin on PC-3 prostate cells may be inferred. This may be relevant for novel therapies against resistant prostate cancer.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.418
Teacher spread0.354 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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