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Record W2955438010 · doi:10.3389/fimmu.2019.01454

Proteases in Pemphigoid Diseases

2019· review· en· W2955438010 on OpenAlexafffund
Sho Hiroyasu, Christopher T. Turner, Katlyn C. Richardson, David J. Granville

Bibliographic record

VenueFrontiers in Immunology · 2019
Typereview
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsInternational Collaboration On Repair DiscoveriesVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsProteasesPemphigoidMedicineDermatologyImmunologyBullous pemphigoidBiologyEnzymeAntibodyBiochemistry

Abstract

fetched live from OpenAlex

Pemphigoid diseases are a subgroup of autoimmune skin diseases characterized by widespread tense blisters. Standard of care typically involves immunosuppressive treatments, which may be insufficient and are often associated with significant adverse events. As such, a deeper understanding of the pathomechanism(s) of pemphigoid diseases is necessary in order to identify improved therapeutic approaches. A major initiator of pemphigoid diseases is the accumulation of autoantibodies against proteins at the dermal-epidermal junction (DEJ), followed by protease activation at the lesion. The contribution of proteases to pemphigoid disease pathogenesis has been investigated using a combination of in vitro and in vivo models. These studies suggest proteolytic degradation of anchoring proteins proximal to the DEJ is crucial for dermal-epidermal separation and blister formation. In addition, proteases can also augment inflammation, expose autoantigenic cryptic epitopes, and/or provoke autoantigen spreading, which are all important in pemphigoid disease pathology. The present review summarizes and critically evaluates the current understanding with respect to the role of proteases in pemphigoid diseases.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.308
Teacher spread0.284 · 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 designNot applicable
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

Citations29
Published2019
Admission routes2
Has abstractyes

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