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Record W2407960705 · doi:10.1186/s12860-016-0094-8

Connexin and pannexin channels in cancer

2016· review· en· W2407960705 on OpenAlexaff
Jean X. Jiang, Silvia Peñuela

Bibliographic record

VenueBMC Cell Biology · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsWestern University
FundersNational Cancer Institute
KeywordsConnexinPannexinGap junctionBiologyCell biologyCancerConnexonCarcinogenesisCancer cellNeuroscienceIntracellularGenetics

Abstract

fetched live from OpenAlex

Communication among cells via direct cell-cell contact by connexin gap junctions, or between cell and extracellular environment via pannexin channels or connexin hemichannels, is a key factor in cell function and tissue homeostasis. Upon malignant transformation in different cancer types, the dysregulation of these connexin and pannexin channels and their effect in cellular communication, can either enhance or suppress tumorigenesis and metastasis. In this review, we will highlight the latest reports on the role of the well characterized connexin family and its ability to form gap junctions and hemichannels in cancer. We will also introduce the more recently discovered family of pannexin channels and our current knowledge about their involvement in cancer progression.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.343
Teacher spread0.295 · 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

Citations68
Published2016
Admission routes1
Has abstractyes

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