Lung inflammation associated with acute necrotizing pancreatitis in dogs (LB513)
Bibliographic record
Abstract
There is evidence that dogs suffering from acute necrotizing pancreatitis (ANP) can develop respiratory disease. However, there are no data describing the lung histopathology in clinical cases of ANP in dogs. Therefore, we hypothesized that lungs of dogs that died due to ANP (n=21) will be inflamed along with recruitment of PIMs, a pro‐inflammatory cell normally present in ruminants and equines, compared to lungs of control dogs (n=6). Histologic sections of the pancreas graded independently by two blinded pathologists showed the presence of necrosis and infiltration of inflammatory cells (scores 3‐10) whereas control dogs received a score of 0. Lungs from ANP dogs showed no (score of 0/6) to moderate (score 3/6) inflammation, but there was no statistical difference between the lung histological score of the control (median ± SEM: 0.5±0.3) and the ANP (median ± SEM: 1.5±0.2) dogs. However, immunohistochemistry revealed an increase in the numbers of septal macrophages/monocytes in lungs of ANP dogs (median ± SEM: 138±49) compared to control dogs (median ± SEM: 1.5±0,2; p<0,01). There was a moderate correlation (r=0.46) between the recruitment of macrophages/monocytes in the lungs and the total inflammatory cells score in the pancreas of ANP dogs. Only ANP dogs showed expression of vWF in alveolar septal capillaries and septal inflammatory cells. iNOS was expressed only in alveolar macrophages of ANP dogs. TLR4 and IL6 were variably expressed and localized in alveolar macrophages and septal inflammatory cells in lungs from both ANP and control dogs. We conclude that dogs suffering from ANP have pronounced lung inflammation along with recruitment of PIMs and increased expression of vWF. The PIMs may increase the susceptibility of the host to lung inflammation in response to circulating endotoxins. Grant Funding Source : Supported by Companion Animal Health Fund
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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".