MétaCan
Menu
Back to cohort
Record W2154701937 · doi:10.2174/092986607782110202

The Molecular Nature and Consequences of Lipoprotein (A)s Association with Platelets

2007· review· en· W2154701937 on OpenAlexaff
D. Edward Barre

Bibliographic record

VenueProtein and Peptide Letters · 2007
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsCape Breton University
Fundersnot available
KeywordsPlateletFibrinolysisFibrinogenChemistryLipoprotein(a)Apolipoprotein BEndocrinologyInternal medicinePlasminPlatelet activationBiochemistryBiologyCholesterolMedicine

Abstract

fetched live from OpenAlex

Lipoprotein (a) (Lp (a)) may be pro-thrombotic in humans due to its apolipoprotein (a) (apo(a))-mediated decreases in fibrinolysis. Such decreased fibrinolysis arises putatively from interference with plasminogen conversion to plasmin due to the considerable homology between apolipoprotein (a) and plasminogen. However, in vitro, most studies have shown that human Lp (a) decreases agonist-stimulated platelet aggregation while in vivo it appears to decrease aggregation as implied by increased bleeding times with higher blood serum concentrations of Lp(a). Lp (a) binding to platelets mediated by apo (a) increases platelet intracellular c-AMP levels in resting platelets, and decreases platelet production of thromboxane A2 and fibrinogen binding to platelets all of which reduce platelet aggregation. One, though not the only, explanation of these conflicting data may be that Lp(a) self-regulates its interference with fibrinolysis by reducing platelet aggregation and platelet binding of fibrinogen and hence the degree of requirement for fibrinolysis. However, it is concluded more in vivo work needs to be done to fully understand whether, if at all, Lp(a) in varying concentrations and isoforms, favours reduced platelet aggregation or fibrinolysis.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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.

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

Citations10
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProtein and Peptide LettersSame topicAntiplatelet Therapy and Cardiovascular DiseasesFrench-language works237,207