Triggering receptor expressed on myeloid cells‐1 (<scp>TREM</scp>‐1) improves host defence in pneumococcal pneumonia
Bibliographic record
Abstract
Streptococcus (S.) pneumoniae is a common Gram-positive pathogen in community-acquired pneumonia and sepsis. Triggering receptor expressed on myeloid cells-1 (TREM-1) is a receptor on phagocytes known to amplify inflammatory responses. Previous studies showed that TREM-1 inhibition protects against lethality during experimental Gram-negative sepsis. We here aimed to investigate the role of TREM-1 in an experimental model of pneumococcal pneumonia, using TREM-1/3-deficient (Trem-1/3(-/-) ) and wild-type (Wt) mice. Additionally ex vivo responsiveness of Trem-1/3(-/-) neutrophils and macrophages was examined. S. pneumoniae infection resulted in a rapid recruitment of TREM-1-positive neutrophils into the bronchoalveolar space, while high constitutive TREM-1 expression on alveolar macrophages remained unchanged. TREM-1/3 deficiency led to increased lethality, accompanied by enhanced growth of S. pneumoniae at the primary site of infection and increased dissemination to distant organs. Within the first 3-6 h of infection, Trem-1/3(-/-) mice demonstrated a strongly impaired innate immune response in the airways, as reflected by reduced local release of cytokines and chemokines and a delayed influx of neutrophils. Trem-1/3(-/-) alveolar macrophages produced fewer cytokines upon exposure to S. pneumoniae in vitro and were less capable of phagocytosing this pathogen. TREM-1/3 deficiency did not influence neutrophil responsiveness to S. pneumoniae. These results identify TREM-1 as a key player in protective innate immunity during pneumococcal pneumonia, most likely by enhancing the early immune response of alveolar macrophages.
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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".