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Record W3036839811 · doi:10.3390/cancers12061624

Exploring the Therapeutic Potential of Membrane Transport Proteins: Focus on Cancer and Chemoresistance

2020· review· en· W3036839811 on OpenAlexafffund
Shekoufeh Almasi, Yassine El Hiani

Bibliographic record

VenueCancers · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsDalhousie UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova ScotiaNova Scotia Health Research Foundation
KeywordsDruggabilityCancerCarcinogenesisCancer cellEconomic shortageMedicineAnticancer drugDrugCancer therapyDrug resistanceCancer researchBioinformaticsPharmacologyBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Improving the therapeutic efficacy of conventional anticancer drugs represents the best hope for cancer treatment. However, the shortage of druggable targets and the increasing development of anticancer drug resistance remain significant problems. Recently, membrane transport proteins have emerged as novel therapeutic targets for cancer treatment. These proteins are essential for a plethora of cell functions ranging from cell homeostasis to clinical drug toxicity. Furthermore, their association with carcinogenesis and chemoresistance has opened new vistas for pharmacology-based cancer research. This review provides a comprehensive update of our current knowledge on the functional expression profile of membrane transport proteins in cancer and chemoresistant tumours that may form the basis for new cancer treatment strategies.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.285
Teacher spread0.220 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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