MétaCan
Menu
Back to cohort
Record W4396228518 · doi:10.1111/febs.17143

Can the <scp>HB‐EGF</scp>/<scp>EGFR</scp> pathway restore injured neurons?

2024· article· en· W4396228518 on OpenAlexfundno aff
Colin Adrain, Marina Badenes

Bibliographic record

VenueFEBS Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsnot available
FundersQueen's University BelfastQueen's UniversityCalouste Gulbenkian Foundation
KeywordsCell biologyChemistryNeuroscienceBiology

Abstract

fetched live from OpenAlex

Heparin-binding epidermal growth factor-like growth factor (HB-EGF) is a transmembrane protein that, when cleaved by metalloproteases through a process called ectodomain shedding, binds to the EGF receptor (EGFR), activating downstream signaling. The HB-EGF/EGFR pathway is crucial in development and is involved in numerous pathophysiological processes. In this issue of The FEBS Journal, Sireci et al. reveal a previously unexplored function of the HB-EGF/EGFR pathway in promoting neuronal progenitor proliferation and sensory neuron regeneration in the zebrafish olfactory epithelium in response to injury.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFEBS JournalSame topicHER2/EGFR in Cancer ResearchFrench-language works237,207