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Record W2120623774 · doi:10.3892/or.2015.3841

Synergy of BID with doxorubicin in the killing of cancer cells

2015· article· en· W2120623774 on OpenAlexfundno aff
Emilia J. Orzechowska-Licari, Agnieszka Girstun, Krzysztof Staroń, Joanna Trzcińska‐Danielewicz

Bibliographic record

VenueOncology Reports · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersInstitute of GeneticsInstytut Biologii Doswiadczalnej im. M. Nenckiego PANUniwersytet Warszawski
KeywordsDoxorubicinApoptosisLNCaPHeLaCamptothecinCancer researchCancer cellProgrammed cell deathCell cultureCell cycleCancerChemistryCellBiologyPharmacologyMedicineInternal medicineChemotherapyBiochemistry

Abstract

fetched live from OpenAlex

Overexpression of the BH3-interacting domain death agonist (BID) protein sensitizes certain cancer cell lines to apoptosis induced by anticancer agents, particularly by those acting through death receptors (e.g. TRAIL). Previously, we showed that recombinant BID fused with TAT cell penetrating peptide (TAT-BID) allowed for controlled delivery of BID to different cancer cell lines and moderately sensitized some of them to TRAIL or slightly to camptothecin. In the present study, we showed that TAT-BID delivered to HeLa cells strongly sensitized them to doxorubicin, as identified by cell viability and apoptosis assays. Another cell line sensitized to doxorubicin was PC3, whereas A549 and LNCaP cells were sensitized moderately or not at all, respectively. Sensitization was more pronounced at 1 µM doxorubicin administered for 48 h than for lower doses and shorter treatments. TAT-BID and doxorubicin may thus be considered as a potential therapeutic combination for cervical carcinoma and advanced prostate cancer treatment.

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 head0.019
GPT teacher head0.298
Teacher spread0.279 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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