Stress significantly increases mortality following a secondary bacterial respiratory infection
Bibliographic record
Abstract
A variety of mechanisms contribute to the viral-bacterial synergy which results in fatal secondary bacterial respiratory infections. Epidemiological investigations have implicated physical and psychological stressors as factors contributing to the incidence and severity of respiratory infections and psychological stress alters host responses to experimental viral respiratory infections. The effect of stress on secondary bacterial respiratory infections has not, however, been investigated. A natural model of secondary bacterial respiratory infection in naive calves was used to determine if weaning and maternal separation (WMS) significantly altered mortality when compared to calves pre-adapted (PA) to this psychological stressor. Following weaning, calves were challenged with Mannheimia haemolytica four days after a primary bovine herpesvirus-1 (BHV-1) respiratory infection. Mortality doubled in WMS calves when compared to calves pre-adapted to weaning for two weeks prior to the viral respiratory infection. Similar results were observed in two independent experiments and fatal viral-bacterial synergy did not extend beyond the time of viral shedding. Virus shedding did not differ significantly between treatment groups but innate immune responses during viral infection, including IFN-γ secretion, the acute-phase inflammatory response, CD14 expression, and LPS-induced TNFα production, were significantly greater in WMS versus PA calves. These observations demonstrate that weaning and maternal separation at the time of a primary BHV-1 respiratory infection increased innate immune responses that correlated significantly with mortality following a secondary bacterial respiratory infection.
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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) | 0.001 | 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 | 0.001 |
| 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".