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Record W4281484949 · doi:10.1016/j.abd.2021.09.008

Update on the pathogenesis of vitiligo

2022· review· pt· W4281484949 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnais Brasileiros de Dermatologia · 2022
Typereview
Languagept
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsVitiligoPathogenesisImmunologyBiologyAutoimmunityMelanocyteDiseaseCell adhesion moleculeProinflammatory cytokineOxidative stressInflammationMedicineCancer researchImmune systemPathologyMelanomaEndocrinology

Abstract

fetched live from OpenAlex

Vitiligo is a complex disease whose pathogenesis results from the interaction of genetic components, metabolic factors linked to cellular oxidative stress, melanocyte adhesion to the epithelium, and immunity (innate and adaptive), which culminate in aggression against melanocytes. In vitiligo, melanocytes are more sensitive to oxidative damage, leading to the increased expression of proinflammatory proteins such as HSP70. The lower expression of epithelial adhesion molecules, such as DDR1 and E-cadherin, facilitates damage to melanocytes and exposure of antigens that favor autoimmunity. Activation of the type 1-IFN pathway perpetuates the direct action of CD8+ cells against melanocytes, facilitated by regulatory T-cell dysfunction. The identification of several genes involved in these processes sets the stage for disease development and maintenance. However, the relationship of vitiligo with environmental factors, psychological stress, comorbidities, and the elements that define individual susceptibility to the disease are a challenge to the integration of theories related to its pathogenesis.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.047
GPT teacher head0.305
Teacher spread0.258 · 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