Live imaging of platelets and neutrophils during antibody-mediated neurovascular thrombosis
Bibliographic record
Abstract
Immune complexes form in systemic disorders such as rheumatological, autoimmune, and allergic diseases or in response to infections or medications. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) adenoviral vector vaccines have been associated with rare yet serious thrombotic complications in the brain due to the formation of immune complexes that activate platelets. There are currently no data visualizing the interplay of platelets with leukocytes and the brain vasculature endothelium in response to immune complexes. This is in part due to the absence of FcγRIIA in mice, a receptor for immune complexes implicated in these thrombotic incidents. Here, we describe and illustrate events at the cellular level that take place in the brain vasculature in response to systemic administration of surrogate immune complexes. We used Ly6gCre+/-::Rosa26-TdT+/-::CD41-YFP+/- mice expressing the FcγRIIA transgene and fluorescence in neutrophils and platelets. Using real-time videomicroscopy to capture high-velocity events in conjunction with unbiased computer-assisted analyses, we provide images and quantifications of the cellular responses downstream of FcγRIIA stimulation. We observed transient and stable platelet-neutrophil interactions, platelets forming thrombi, and neutrophil adhesion to blood vessel walls. This imaging approach in a quadruple transgenic animal model can be used for the study of the pathogenic roles of immune complexes in disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".