MétaCan
Menu
Back to cohort
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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Explore more

Same venueFrontiers in ImmunologySame topicAutoimmune Bullous Skin DiseasesFrench-language works237,207