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Record W4413826768 · doi:10.3390/biom15091250

Ashes to Rashes: An Exploration of the Intersection Between Smoking and Cutaneous Lupus Erythematosus

2025· review· en· W4413826768 on OpenAlexaff
Rafael O. Homer, Ahmed Eldaboush, Darae Kang, Touraj Khosravi‐Hafshejani, Ming‐Lin Liu, Victoria P. Werth

Bibliographic record

VenueBiomolecules · 2025
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of British Columbia
FundersOffice of Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsCutaneous Lupus ErythematosusDermatologyIntersection (aeronautics)MedicineSystemic lupus erythematosusLupus erythematosusImmunologyGeographyCartographyDiseasePathology

Abstract

fetched live from OpenAlex

Cutaneous lupus erythematosus is an autoimmune skin disorder with a known association with cigarette smoking. Smokers with cutaneous lupus have a worse disease course and may be refractory to treatments. Despite many studies documenting this association, minimal work exists examining the molecular drivers of these clinical differences. This review delves into how cigarette smoke may influence key immunopathogenic pathways in cutaneous lupus, including oxidative stress, interferon signaling, inflammatory cell recruitment, extracellular vesicles, and immune regulation. Additionally, factors such as epigenetics and heat injury are considered as well. Here, we integrate the existing and emerging literature on the pathophysiology of cutaneous lupus with known effects of cigarette smoke on the skin and immune system and propose hypotheses that may explain clinical differences in smokers. Understanding the molecular underpinnings of these differences may yield a clearer picture of the disease and more effective treatment strategies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.087
GPT teacher head0.383
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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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