Expression Profile and Function of Triggering Receptor Expressed on Myeloid Cells–1 during Melioidosis
Bibliographic record
Abstract
BACKGROUND: Triggering receptor expressed on myeloid cells-1 (TREM-1) amplifies Toll-like receptor-initiated responses against pathogens. We aimed to characterize TREM-1 expression and function during sepsis caused by Burkholderia pseudomallei (melioidosis). METHODS: TREM-1 expression was determined on leukocytes and plasma from 34 patients with melioidosis and 32 controls and in mice with experimentally induced melioidosis. Responsiveness toward B. pseudomallei of TREM-1(+) and TREM-1(-) leukocytes was tested in vitro. TREM-1 function was inhibited in mice by a synthetic peptide mimicking the ectodomain of this receptor. RESULTS: Patients demonstrated increased soluble (s) TREM-1 plasma levels and TREM-1 surface expression on monocytes but not granulocytes. Similarly, mice inoculated with B. pseudomallei displayed a gradual rise in sTREM-1 level and an increase in blood monocyte but not granulocyte TREM-1 expression. At the primary infection site, however, granulocyte TREM-1 expression was enhanced, and the rise in sTREM-1 level occurred earlier. Additionally, purified human TREM-1(-) granulocytes showed reduced responsiveness to B. pseudomallei relative to TREM-1(+)granulocytes, a difference not detected for TREM-1(-) and TREM-1(+) monocytes. Treatment with a peptide mimicking a conserved domain of sTREM-1 partially protected mice from B. pseudomallei-induced lethality. CONCLUSIONS: During melioidosis, TREM-1 expression is differentially regulated on granulocytes and monocytes; measurement of TREM-1 expression on blood granulocytes may not provide adequate information on granulocyte TREM-1 expression at the infection site. TREM-1 may be a therapeutic target in melioidosis.
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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) | 0.001 | 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".