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Record W2950897493 · doi:10.1101/463315

Transmission of mechanical information by purinergic signaling

2018· preprint· en· W2950897493 on OpenAlexafffund
Nicholas Mikolajewicz, Simon Sehayek, Paul W. Wiseman, Svetlana V. Komarova

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchRéseau de Recherche en Santé Buccodentaire et OsseuseNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsPurinergic receptorExtracellularStimulationPurinergic signallingCell biologyIntracellularBiophysicsChemistryCalciumBiologyBiochemistryNeuroscienceReceptor

Abstract

fetched live from OpenAlex

1 Abstract The human skeleton constantly interacts and adapts to the physical world. We have previously reported that physiologically-relevant mechanical forces lead to small, repairable membrane injuries in bone-forming osteoblasts, resulting in the release of ATP and stimulation of purinergic (P2) calcium responses in neighbouring cells. The goal of this study was to develop a theoretical model describing injury-related ATP and ADP release, extracellular diffusion and degradation, and purinergic responses in neighboring cells. The model was validated using experimental data obtained by measuring intracellular free calcium ([Ca 2+ ] i ) elevations following mechanical stimulation of a single osteoblast. The validated single-cell injury model was then scaled to a tissue-level injury to investigate how purinergic responses communicate information about injuries with varying geometries. We found that total ATP released, peak extracellular ATP concentration and the ADP-mediated signaling component contributed complementary information regarding the mechanical stimulation event. The total amount of ATP released governed the maximal distance from the injury at which purinergic responses were stimulated, as well as the overall number of responders. The peak ATP concentration reflected the severity of an individual cell injury and determined signal propagation velocity and temporal synchrony of responses. Peak ATP concentrations also discriminated between minor and severe injuries that led to the release of similar total amounts of ATP due to differences in injury repair dynamics. The third component was ADP-mediated signaling which became relevant only in larger tissue-level injuries, and it conveyed information about the distance to the injury site and its geometry. Taken together, this study identified specific features of extracellular ATP/ADP spatiotemporal signals that encode the severity of the mechanical stimulus, the distance from the stimulus, as well as the mechano-resilient status of the tissue.

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 communication0.0010.001
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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
Published2018
Admission routes2
Has abstractyes

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