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Record W2109478922 · doi:10.1177/1076029611412371

Characterization of In Vitro Hemostatic Peptide Effects by Thromboelastography

2011· article· en· W2109478922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2011
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsMcGill UniversityJewish General HospitalDefence Research and Development Canada
FundersDefence Research and Development Canada
KeywordsThromboelastographyPeptideMelittinHemostasisCoagulationClotting timeChemistryFibrinogenPharmacologyMedicineBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

In this study, we validated a thromboelastography (TEG) method to evaluate the hemostatic effects of 3 peptides. The first peptide is an ideal amphipathic peptide composed of 22 leucine and lysine in a ratio of 2:1. At a very low concentration, the peptide had a procoagulant effect shown by decreases in reaction time (R) and coagulation time (K) but was impaired by a decrease in maximum amplitude (MA). At higher concentrations, the peptide had an anticoagulant effect. The α angle was minimally affected by the peptide. The second peptide is melittin derived from bee venom. Melittin showed procoagulant effects reflected by a decrease in clotting time but led to lower MA. The third peptide derived from fibrinogen γ chain promoted hemostasis only at an optimal concentration and became anticoagulant at a higher concentration. The hemostatic mechanisms of each peptide were discussed. Our study would facilitate further development of peptides for either hemorrhage control or thrombosis treatment.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.374
Teacher spread0.283 · 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