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Record W2955038926 · doi:10.1002/ddr.21562

Epigenetic treatment of dermatologic disorders

2019· article· en· W2955038926 on OpenAlexaff
Walter H. Moos, Douglas V. Faller, Ioannis P. Glavas, David N. Harpp, Iphigenia Kanara, Carl A. Pinkert, Whitney R. Powers, Konstantina Sampani, Kosta Steliou, Demetrios G. Vavvas, Krishna Kodukula, Robert Zamboni

Bibliographic record

VenueDrug Development Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsMcGill University
FundersMitoCure
KeywordsPsoriasisEpigeneticsMedicineSkin cancerDiseaseDampFunction (biology)InflammationCancerBioinformaticsPharmacologyBiologyDermatologyImmunologyPathologyBiochemistryInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract Healthy skin protects us against a multitude of insults, but injured or maladapted skin can lead to infection, inflammation, or worse. Fortunately, naturally occurring bioactive products, many commonly found in olive oil and other plant and vegetable extracts, have shown utility in treating skin and related diseases as well as conditioning the skin to maintain its healthy function. Powerful agents targeting nuclear regulatory pathways continue to hold promise as new or repurposed therapies for a wide variety of ills and skin conditions. Epigenetic approaches that activate Nrf2 to effect detoxification, redox balance, DNA repair, and mitochondrial function are noteworthy. Some of the disease applications being actively investigated range from eczema and psoriasis to skin cancer and diabetes‐related wound healing to name just a few.

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 score0.203
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.323
Teacher spread0.296 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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