<i>In vivo</i> imaging of infection‐induced cogulopathy in the microcirculation
Bibliographic record
Abstract
Patients with systemic infection often develop disseminated intravascular coagulation (DIC), a condition leading to clot formation throughout the vasculature. This systemic coagulation can impair blood flow within the tissue microvasculature and has been associated with organ damage and dysfunction. Infection also triggers the release of Neutrophil Extracellular Traps (NETs) within the vasculature. NETs are comprised of extracellular DNA covered in a number of nuclear and antimicrobial proteins. This immune response is designed to catch and kill pathogens, but also has the potential to bind platelets and initiate coagulation. Currently, most studies are limited to the histological analysis of tissue sections or in vitro biochemical assays, providing only snapshots of these complex and dynamic processes. As such the specific interaction between NETs, platelets and coagulation are poorly understood. Using intravital microscopy (IVM), can directly visualize NET formation and fibrin deposition in real‐time, in the blood vessels of live mice following infection. Additionally, we have developed a novel imaging protocol using an enzyme‐activatable fluorescent probe to track the time and location of thrombin activation within the vasculature of live mice. Importantly, IVM allows us to understand not only the complex interactions between the pathogen, immunity and coagulation, but to also the effect of these interactions on vascular perfusion and tissue damage. Following i.v. challenge with bacteria, we measure significant NET deposition within the liver sinusoids. These NETs in turn activate thrombin leading to fibrin deposition, vascular occlusion and tissue damage. Importantly, treatments that prevent NET formation, break‐down NETs within the vasculature, or block key NET components significantly inhibit thrombin activation, improve perfusion and attenuate tissue damage. Support or Funding Information This work is supported by grants from the Critical Care Strategic Clinical Network and the Natural Sciences and Engineering Research Council of Canada (NSERC)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".