Expression of von Willebrand factor, pulmonary intravascular macrophages, and Toll-like receptors in lungs of septic foals
Bibliographic record
Abstract
Sepsis causes significant mortality in neonatal foals; however, there is little data describing the cellular and molecular pathways of lung inflammation in septic foals. This study was conducted to characterize lung inflammation in septic foals. Lung tissue sections from control (n = 6) and septic (n = 17) foals were compared using histology and immunohistology. Blinded pathologic scoring of hematoxylin and eosin stained samples revealed increased features of lung inflammation such as thickened alveolar septa and sequestered inflammatory cells in septic foals. Septic foal lungs showed increased expression of von Willebrand factor in blood vessels, demonstrating vascular inflammation. Use of MAC387 antibody to detect calprotectin as a reflection of mononuclear cell infiltration revealed a significant increase in their numbers in alveolar septa of lungs from septic foals compared to those from control foals. The mononuclear cells appeared to be mature macrophages and were located in the septal capillaries, suggesting they were pulmonary intravascular macrophages (PIMs). Finally, lungs from septic foals showed increased expression of Toll-like receptor 4 and 9 in mononuclear cells relative to the control. Taken together, this study is the first to show the expression of inflammatory molecules and an increase in PIMs in lungs from foals that died from sepsis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".