The Use of Spinning-Disk Confocal Microscopy for the Intravital Analysis of Platelet Dynamics in Response to Systemic and Local Inflammation
Bibliographic record
Abstract
Platelets are central players in inflammation and are an important component of the innate immune response. The ability to visualize platelets within the live host is essential to understanding their role in these processes. Past approaches have involved adoptive transfer of labelled platelets, non-specific dyes, or the use of fluorescent antibodies to tag platelets in vivo. Often, these techniques result in either the activation of the platelet, or blockade of specific platelet receptors. In this report, we describe two new methods for intravital visualization of platelet biology, intravenous administration of labelled anti-CD49b, which labels all platelets, and CD41-YFP transgenic mice, in which a percentage of platelets express YFP. Both approaches label endogenous platelets and allow for their visualization using spinning-disk confocal fluorescent microscopy. Following LPS-induced inflammation, we were able to measure a significant increase in both the number and size of platelet aggregates observed within the vasculature of a number of different tissues. Real-time observation of these platelet aggregates reveals them to be large, dynamic structures that are continually expanding and sloughing-off into circulation. Using these techniques, we describe for the first time, platelet recruitment to, and behaviour within numerous tissues of the mouse, both under control conditions and following LPS induced inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".