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Record W2897185231 · doi:10.1080/1547691x.2018.1514444

The skin as a metabolic and immune-competent organ: Implications for drug-induced skin rash

2018· review· en· W2897185231 on OpenAlexaff
Amy Sharma, Yoshiro Saito, Wen‐Hung Chung, Dean J. Naisbitt, Jack Uetrecht, Jeanine L. Bussiere

Bibliographic record

VenueJournal of Immunotoxicology · 2018
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Toronto
FundersMedical Research Council
KeywordsImmune systemDrugRashImmunologyAdverse effectMedicineInnate immune systemMechanism (biology)Human skinBiologyPharmacologyDermatology

Abstract

fetched live from OpenAlex

Current advances in the study of cutaneous adverse drug reactions can be attributed to the recent understanding that the skin is both a metabolically and immunologically competent organ. The ability of the skin to serve as a protective barrier with limited drug biotransformation ability, yet highly active immune function, has provided insights into its biological capability. While the immune response of the skin to drugs is vastly different from that of the liver due to evolutionary conditioning, it frequently occurs in response to various drug classes and manifests as a spectrum of hypersensitivity reactions. The skin is a common site of adverse and idiosyncratic drug reactions; drug-specific T-cells, as well as involvement of an innate immune response, appear to be key mechanistic drivers in such scenarios. Association of other factors such as human leukocyte antigen (HLA) polymorphisms may play a significant role for particular drugs. This review aims to integrate emerging findings into proposed mechanisms of drug metabolism and immunity in the skin that are likely responsible for rashes and other local allergic responses. These unique biological aspects of the skin, and their translation into implications for drug development and the use of animal models, will be discussed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.375
Teacher spread0.332 · 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 designOther design
Domainnot available
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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