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Record W87824810

Epidermal Notch1 recruits innate lymphoid cells to orchestrate normal skin repair

2014· dissertation· en· W87824810 on OpenAlexfundno aff
Zhi Li

Bibliographic record

VenueDurham e-Theses (Durham University) · 2014
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsnot available
FundersBritish Skin FoundationYork University
KeywordsInnate lymphoid cellInterleukin 22Wound healingImmunologyInflammationImmune systemSkin repairInnate immune systemKeratinocyteBiologyCCL20Cell biologyChemokineCancer researchCytokineInterleukinCell cultureChemokine receptor
DOInot available

Abstract

fetched live from OpenAlex

Skin constitutes a barrier between our body and outside environment providing the first line defence against microbial infection. Epithelial repair and skin wound healing starts with inflammation to clear up invading pathogens and debris followed by cell proliferation and tissue remodelling. The immune response is vital for protecting the body from infection and diseases, however, it remains controversial whether the immune cells contribute to wound closure and tissue repair, or cause scarring and pathology. In this thesis, I investigate the role of Notch signalling in epithelial tissue repair. I demonstrate Notch1 signalling activation in epidermal keratinocytes following acute skin injury recruits innate lymphoid cells (i.e. ILC3s) to the site of injury in a TNF-α/CCL20-dependent mechanism and controls macrophage/monocyte recruitment via ILC3-dependent CCL3. Notch1 also induces epidermal production of IL23 which facilitates ILC3s to produce IL22 for re-epithelialization and skin repair.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDurham e-Theses (Durham University)Same topicIL-33, ST2, and ILC PathwaysFrench-language works237,207