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Record W4312035675 · doi:10.21203/rs.3.rs-2306404/v1

Highly efficient platelet generation in lung vasculature reproduced by microfluidics

2022· preprint· en· W4312035675 on OpenAlexaff
Xiaojuan Zhao, Dominic Alibhai, Tony G. Walsh, Nathalie Tarassova, Maximilian Englert, Semra Zuhal Birol, Yong Li, Christopher Williams, Chris Neal, Philipp Burkard, Elizabeth W. Aitken, Amie K. Waller, José Ballester‐Beltrán, Peter W. Gunning, Edna C. Hardeman, Ejaife O. Agbani, Bernhard Nieswandt, Ingeborg Hers, Cédric Ghevaert, Alastair W. Poole

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Calgary
FundersDeutsche ForschungsgemeinschaftUniversity of BristolBritish Heart FoundationWellcome Trust
KeywordsMicrofluidicsLungPlateletNanotechnologyInternal medicineMedicineMaterials science

Abstract

fetched live from OpenAlex

Abstract Platelets, small hemostatic blood cells, are derived from megakaryocytes (MKs). It is accepted that both bone marrow (BM) and lung are principal sites of thrombogenesis although underlying mechanisms remain unclear. Outside the body, however, our ability to generate platelets, and retain their functionality, is poor at present. Here we show that perfusion of MKs ex vivo through the mouse lung vasculature generates substantial platelet numbers, up to 3,000 per MK. Despite their large size, MKs were able repeatedly to passage through the lung vasculature, leading to enucleation and subsequent platelet generation intravascularly. Using the ex vivo lung and a novel in vitro microfluidic chamber we determined how oxygenation, ventilation and endothelial cell health support platelet generation. Our data also show a critical role for the actin regulator TPM4 in the final steps of platelet formation in lung vasculature. The findings could inform new approaches to large scale generation of platelets.

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 score0.002

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 head0.046
GPT teacher head0.369
Teacher spread0.323 · 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
Published2022
Admission routes1
Has abstractyes

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