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Record W4385497991 · doi:10.3389/fcell.2023.1264800

Editorial: Pannexin and connexin signaling in cell death and tissue health and disease

2023· editorial· en· W4385497991 on OpenAlexaff
Marc Mesnil, Andrew K. J. Boyce, Catherine Wright

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2023
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPannexinConnexinCell biologySignal transductionDiseaseProgrammed cell deathBiologyGap junctionMedicinePathologyGeneticsApoptosis

Abstract

fetched live from OpenAlex

Editorial on the Research Topic Pannexin and connexin signaling in cell death and tissue health and diseaseThis Research Topic of Frontiers in Cell and Developmental Biology, "Pannexin and connexin signaling in cell death and tissue health and disease," brings together seven new articles exemplifying the role of connexin and pannexin signaling in human health, with the aim of increasing understanding of the biology of this area and feeding into work developing novel related therapies.The first time that gap junctions were associated with a human disease was in 1966, when Loewenstein and Kanno observed a lack of gap junctional intercellular communication between liver cancer cells (Loewenstein and Kanno, 1966).During the following five decades, this association has been confirmed in many in vitro and in vivo models; however, there is yet no clear answer about the precise molecular involvement of gap junctions and their constitutive proteins, the connexins, in tumor progression.How connexins are involved in cancer cell growth and/or invasion is still an open question, whose possible answers depend on tumor stage and cancer type.The lack of clear molecular mechanisms linking connexin signaling to cancer is probably due to the complexity of the disease evolution and heterogeneity of its microenvironment (Aasen et al., 2019).Since the seminal observation of Loewenstein and Kanno, it took almost 30 years to causally link connexin gene mutations to a human disease (in X-linked Charcot-Marie-Tooth disease) outside the cancer field (Bergoffen et al., 1993).This finding was unexpected, as the particular connexin, Connexin32 (Cx32), which was thought to be an epithelialspecific connexin, was identified in Schwann cells with a critical role in myelin maintenance.This work inspired the description of many more connexin-related genetic diseases, growing over 20 years, with the parallel discovery of new connexin isoforms, establishing connexins as members of a multigene family.Connexin mutations are now known to cause a wide range of genetic diseases affecting hearing (cochlea), skin keratinization, heart function, lens transparency, etc., (Srinivas et al., 2018).Many of these discoveries were confirmed by the increased use of transgenic strategies in mice permitting visualization of the pathophysiological effects of modulating connexin expression.In parallel, investigations of connexin function and biology showed that at least some connexins could not only mediate direct intercellular communication through gap junctions, but also communicate between intracellular and extracellular domains through unopposed connexin channels (connexons) in the plasma membrane.This so-called hemichannel

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)0.006
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.001
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0030.003
Research integrity0.0220.017
Insufficient payload (model declined to judge)0.0280.015

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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