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Record W4388598213 · doi:10.1016/j.rpth.2023.100566

OC 58.5 Cooperation between Platelet β1 and β3 Integrins in the Arrest of Bleeding Under Inflammatory Conditions in Mice

2023· article· en· W4388598213 on OpenAlexaff
Emily Janus‐Bell, Nicolas Receveur, Luc Mercier, Clarisse Mouriaux, Béatrice Hechler, Jochen Reiser, Benoît Ho‐Tin‐Noé, Pierre Mangin

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2023
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsIntegrinPlateletMedicineImmunologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

ceramides 18:1/16:0 and 18:1/18:0, which are positively correlated with platelet hyperresponsiveness to multiple agonists and heightened thrombus formation in aged mice.Employing Mfsd2b-siRNA, we reveal that MkPs contribute to reduced expression of cellular senescence markers γ-H2A.X (telomere shortening) and p53 (cell cycle arrest), and caspase-3 (apoptosis), and increased expression of Sirt1, which is known to delay cellular senescence and rejuvenate aged cells.Conclusion(s): Our studies demonstrated that aging affects Mfsd2b expression in specific MkP subpopulations that regulate circulating S1P and ceramides.These findings indicate that modulation of sphingolipids and ceramides by deletion of Mfsd2b strongly regulates aging-related platelet hyperreactivity and thrombus formation, which suggests that targeting Mfsd2b in specific MkP subpopulations could contribute to important clues to future personalized medicine for the prevention of thrombotic disorders in aging.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.197
GPT teacher head0.459
Teacher spread0.262 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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