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Record W2896665954 · doi:10.1002/jcp.26768

Ovarian cancer stem cell: A potential therapeutic target for overcoming multidrug resistance

2018· review· en· W2896665954 on OpenAlex
Ainaz Mihanfar, Javad Aghazadeh Attari, Iraj Mohebbi, Maryam Majidinia, Mojtaba Kaviani, Mehdi Yousefi, Bahman Yousefi

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

VenueJournal of Cellular Physiology · 2018
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsAcadia University
Fundersnot available
KeywordsOvarian cancerCancer stem cellCancerMultiple drug resistanceMetastasisStem cellCancer researchMedicineBiologyOncologyBioinformaticsDrug resistanceInternal medicineGenetics

Abstract

fetched live from OpenAlex

The cancer stem cell (CSC) model encompasses an advantageous paradigm that in recent decades provides a better elucidation for many important biological aspects of cancer initiation, progression, metastasis, and, more important, development of multidrug resistance (MDR). Such several other hematological malignancies and solid tumors and the identification and isolation of ovarian cancer stem cells (OV-CSCs) show that ovarian cancer also follows this hierarchical model. Gaining a better insight into CSC-mediated resistance holds promise for improving current ovarian cancer therapies and prolonging the survival of recurrent ovarian cancer patients in the future. Therefore, in this review, we will discuss some important mechanisms by which CSCs can escape chemotherapy, and then review the recent and growing body of evidence that supports the contribution of CSCs to MDR in ovarian 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.334
Teacher spread0.289 · 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