Interaction between Respiratory Syncytial Virus and Particulate Matter in Guinea Pig Alveolar Macrophages
Bibliographic record
Abstract
Alveolar macrophages (AM) play a pivotal role in host lung defense mechanisms. Respiratory syncytial virus (RSV) stimulates secretion of proinflammatory cytokines in AM while it suppresses the cell's phagocytic ability. However, exposure of AM to ambient particulate matter (PM10) has been reported to inhibit RSV uptake. The mechanisms involved in the interaction between RSV and PM10 in AM are not known. We hypothesize that the cellular response of AM to RSV and PM10 is dependent on the sequence in which AM are exposed to these agents. In this study, we compared the sequential effect of RSV and PM10 exposure in vitro on the phagocytic function of guinea pig AM, the RSV Yield in AM, and the production of proinflammatory cytokines (interleukin [IL]-6, IL-8, and tumor necrosis factor [TNF]-alpha). The ability of AM to phagocytose PM10 was not affected by sequential exposure to RSV and PM10. RSV Yield was severely decreased in PM10-exposed AM, regardless of sequence of exposure, compared with AM that were not exposed to PM10 (P < 0.004). Exposure of AM to RSV and/or PM10 resulted in enhanced secretion of bioactive TNF-alpha compared with controls (P < 0.02), without synergistic or inhibitory interaction of these agents on TNF-alpha production. By contrast, exposure of AM to PM10 significantly decreased the production of RSV-induced IL-6 (P < 4 x 10(-6)) and IL-8 (P < 0.003). In summary, our findings suggest that PM10 exposure may interfere with mechanisms of RSV replication and viral-induced cytokine production in guinea pig AM, independent of the sequence of exposure to these agents.
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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 | 0.001 |
| 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 | 0.001 | 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".