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Record W3035408975 · doi:10.1038/s41392-020-0187-x

The mechanisms of sorafenib resistance in hepatocellular carcinoma: theoretical basis and therapeutic aspects

2020· review· en· W3035408975 on OpenAlexaff
Weiwei Tang, Ziyi Chen, Wenling Zhang, Ye Cheng, Betty Zhang, Fan Wu, Qian Wang, Shouju Wang, Dawei Rong, Florian P. Reiter, Enrico N. De Toni, Xuehao Wang

Bibliographic record

VenueSignal Transduction and Targeted Therapy · 2020
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSorafenibHepatocellular carcinomaResistance (ecology)MedicineOncologyBioinformaticsInternal medicineCancer researchComputational biologyBiology

Abstract

fetched live from OpenAlex

Sorafenib is a multikinase inhibitor capable of facilitating apoptosis, mitigating angiogenesis and suppressing tumor cell proliferation. In late-stage hepatocellular carcinoma (HCC), sorafenib is currently an effective first-line therapy. Unfortunately, the development of drug resistance to sorafenib is becoming increasingly common. This study aims to identify factors contributing to resistance and ways to mitigate resistance. Recent studies have shown that epigenetics, transport processes, regulated cell death, and the tumor microenvironment are involved in the development of sorafenib resistance in HCC and subsequent HCC progression. This study summarizes discoveries achieved recently in terms of the principles of sorafenib resistance and outlines approaches suitable for improving therapeutic outcomes for HCC patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.267
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1,132
Published2020
Admission routes1
Has abstractyes

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