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Record W2116481874 · doi:10.5482/ha-1187

FSAP, a new player in inflammation?

2012· review· en· W2116481874 on OpenAlexaff
Frank Stephan, L Aarden, Sacha Zeerleder

Bibliographic record

VenueHämostaseologie · 2012
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsInflammationFactor VIIFibrinolysisSerine proteaseCell biologyTissue factorBiologyUrokinaseCoagulationImmunologyChemistryProteaseMedicineBiochemistryEnzymeGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Factor VII-activating protease (FSAP) is a serine protease in plasma that has a role in coagulation and fibrinolysis. FVII could be activated by purified FSAP in a tissue factor independent manner and pro-urokinase has been demonstrated to be a substrate for purified FSAP in-vitro. However, the physiological role of FSAP in haemostasis remains unclear. More recently FSAP is suggested to be involved in inflammation. It modulates vascular permeability directly and indirectly by the generation of bradykinin. Furthermore, FSAP is activated by dead cells induced by the inflammatory response and subsequently removes nucleosomes from apoptotic cells. FSAP activation can be detected in sepsis patients as well. However, whether FSAP activation upon inflammation is beneficial or detrimental remains an open question. In this review the structure, activation mechanisms and the possible role of FSAP in inflammation are discussed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.223
GPT teacher head0.401
Teacher spread0.179 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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