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Record W2331525531 · doi:10.1177/1073858414546000

Secretory Leukocyte Protease Inhibitor (SLPI)

2014· review· en· W2331525531 on OpenAlexaff
Sari S. Hannila

Bibliographic record

VenueThe Neuroscientist · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSLPINeuroprotectionProtease inhibitor (pharmacology)Central nervous systemImmunologyMicrogliaMultiple sclerosisBiologyMyelinPharmacologyInflammationNeuroscienceVirus

Abstract

fetched live from OpenAlex

At first glance, secretory leukocyte protease inhibitor (SLPI) would appear to have little relevance to the central nervous system (CNS). This serine protease inhibitor is most commonly found in mucosal fluids such as saliva and is best known for its anti-inflammatory and antimicrobial properties. It has been shown to promote wound healing by reducing expression of pro-inflammatory cytokines, and it can also inhibit bacterial growth and block HIV infection of macrophages. In the past 10 years, however, several studies have reported that SLPI is strongly up-regulated in response to CNS injury and that exogenous administration of SLPI is neuroprotective. It has also been shown that SLPI can overcome inhibition by CNS myelin and promote axonal regeneration. In this review, we will discuss these studies, examine the molecular mechanisms underlying SLPI's effects, and consider SLPI's potential for therapeutic use in cerebral ischemia, spinal cord injury, and multiple sclerosis.

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)0.001
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 score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.034
GPT teacher head0.316
Teacher spread0.281 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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