MétaCan
Menu
Back to cohort

Function of Angiostatin in Acute Lung Inflammation

2010· article· en· W3169805768 on OpenAlexaff
Gurpreet Kaur Aulakh, Lixin Liu, Sarabjeet Singh Suri, Baljit Singh

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAngiostatinBronchoalveolar lavageInflammationLungChemokineLipopolysaccharideImmunologyMedicineAngiogenesisChemistryCancer researchInternal medicine

Abstract

fetched live from OpenAlex

ALI is characterized by the migration of activated neutrophils into lungs and causes significant mortality. Although necessary, activated neutrophils also cause significant tissue damage. The mechanisms of neutrophils migration and pathogenesis of ALI are not fully understood. Angiostatin binds to ATP synthase and integrins, and inhibits angiogenesis. We investigated effect of subcutaneous administration of angiostatin in a mouse model of E. coli lipopolysaccharide (LPS) induced ALI. Immunohistochemistry showed higher expression of angiostatin/plasminogen in the airways and blood vessels in the inflamed lungs. Angiostatin expression was increased in inflamed lung extracts as well as bronchoalveolar lavage (BAL) supernatant (P<0.007). Angiostatin treatment of the LPS‐treated mice decreased total cell counts and protein concentration (P<0.05) but increased (P<0.05) apoptotic neutrophils in BAL and decreased (P<0.05) myeloperoxide in lung extracts. The protein and mRNA expression of cytokines and chemokines (IL‐1β, KC, MCP‐1 and MIP‐1α) was not altered in LPS+angiostatin mice. We conclude that angiostatin expression is increased in inflamed lungs and angiostatin treatment inhibits neutrophil migration into the inflamed lungs and lung inflammation. (Funding NSERC Discovery Grant) Grant Funding Source : NSERC Discovery Grant

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicImmune cells in cancerFrench-language works237,207