Extracellular bactericidal functions of porcine neutrophils (133.20)
Bibliographic record
Abstract
Abstract Neutrophils are one of the main effector cells of innate immunity and were shown to kill bacteria via phagocytosis more than 100 years ago. New developments, however, also show that neutrophils are capable of antimicrobial activity by producing extracellular structures named neutrophil extracellular traps (NETs). The antimicrobial effects of NETs can be attributed to cathelicidins, a category of antimicrobial peptides stored as inactive proforms. These proforms are cleaved by neutrophils elastase during cell activation in response to pathogens. This project focuses on the cathelicidin peptides contained within NETs and their effectiveness in killing several common bacterial pathogens of swine. Subsequent investigations address the contribution of cathelicidins to extracellular bactericidal activity of porcine neutrophils. Activated secretions of neutrophils are effective in killing 100% of Escherichia coli K12, 21% of Streptococcus suis, and 74% of Actinobacillus suis. Pasteurella multocida appears to survive in the presence of activated neutrophil secretions demonstrating a 38% increase in growth when compared to P. multocida exposed to non activated neutrophils secretions. Identification of the contribution of cathelicidins to this bactericidal activity is pending. These data suggest that activated neutrophil secretions have bactericidal activity against common pathogens in swine.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".