Bibliographic record
Abstract
Abstract Since the discovery that there is a series of pattern recognition receptors that allow the immune system to detect pathogens, there has been a lot of work to elucidate the signaling molecules that contribute to this detection system. Although the cell types involved would seem to be just as important, our understanding of which cells are critical remains less well explored. Using intravital imaging to visualize the different cell types, we were surprised to find that in almost all conditions that we examined, platelets were rapidly recruited to afflicted tissues. Under some conditions, platelets bound vascular macrophage including Kupffer cells and helped contain bacteria. These were instantaneous responses. At later times, platelets bound neutrophils and induced the production of neutrophil extracellular traps (NETs) that helped to catch bacteria as well as viruses but did induce some local vascular injury. In some scenarios, platelets bound endothelial cells and whether this was to wall off and contain infections or a hijacking of platelets by bacteria to induce vascular dysfunction and poor perfusion remains unclear. Visualizing platelets in sterile injury also revealed important contributions to helping recruit other immune cells that help to heal. The role of the platelet as an effector in infections and inflammation will be discussed. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".