Neutrophil recognition of bacterial DNA and Toll-like receptor 9-dependent and -independent regulation of neutrophil function
Bibliographic record
Abstract
Neutrophils are essential for host defense and detect the presence of invading microorganisms through recognition of pathogen-associated molecular patterns. Among these receptors are Toll-like receptors (TLRs). Neutrophils express all known TLRs except for TLR3. TLR9, localized intracellularly, is to date the best characterized sensor for bacterial DNA, containing short sequences of unmethylated CpG motifs, though TLR9-independent intracellular DNA recognition mechanism(s) may also exist. Bacterial DNA has profound impact on neutrophil functions; it promotes neutrophil trafficking in vivo, induces chemokine expression, regulates expression of adhesion molecules, enhances phagocyte activity, and rescues neutrophils from constitutive apoptosis. TLR9 stimulation results in alterations in cellular redox balance, peroxynitrite formation, activation of the mitogen-activated protein kinase, PI3-kinase, and Jun N-terminal kinase pathways and/or nuclear factor kappaB and AP-1. These features identify an important role for bacterial DNA and TLR9 signaling in the regulation of neutrophil functions that are critical for optimal expression as well as for resolution of the inflammatory response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".