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Record W4360949990 · doi:10.1038/s41536-023-00290-7

Neurotrophin signaling is a central mechanism of salivary dysfunction after irradiation that disrupts myoepithelial cells

2023· article· en· W4360949990 on OpenAlexaff
Alejandro Chibly, Vaishali Patel, Marit H. Aure, Mary Pasquale, Robert J. Morell, Daniel Martin Izquierdo, Erich T. Boger, Gemma E. Martin, Mousa Ghannam, Julianne Andrade, Noah G. Denegre, Colleen Simpson, David P. Goldstein, Fei‐Fei Liu, Isabelle M.A. Lombaert, Matthew P. Hoffman

Bibliographic record

Venuenpj Regenerative Medicine · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Institute of Dental and Craniofacial ResearchNational Institute on Deafness and Other Communication DisordersNHLBI Division of Intramural ResearchNational Institutes of Health
KeywordsDownregulation and upregulationCell biologyMyoepithelial cellNeurotrophinBiologySalivary glandRegeneration (biology)Nerve growth factorCancer researchReceptorNeuroscienceImmunologyImmunohistochemistryGene

Abstract

fetched live from OpenAlex

The mechanisms that prevent regeneration of irradiated (IR) salivary glands remain elusive. Bulk RNAseq of IR versus non-IR human salivary glands showed that neurotrophin signaling is highly disrupted post-radiation. Neurotrophin receptors (NTRs) were significantly upregulated in myoepithelial cells (MECs) post-IR, and single cell RNAseq revealed that MECs pericytes, and duct cells are the main sources of neurotrophin ligands. Using two ex vivo models, we show that nerve growth factor (NGF) induces expression of MEC genes during development, and upregulation of NTRs in adult MECs is associated with stress-induced plasticity and morphological abnormalities in IR human glands. As MECs are epithelial progenitors after gland damage and are required for proper acinar cell contraction and secretion, we propose that MEC-specific upregulation of NTRs post-IR disrupts MEC differentiation and potentially impedes the ability of the gland to regenerate.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.278
Teacher spread0.228 · 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 teacher head, 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

Citations27
Published2023
Admission routes1
Has abstractyes

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