N-Formyl Methionine Peptide-Mediated Neutrophil Activation in Systemic Sclerosis
Bibliographic record
Abstract
Exaggerated neutrophil activation and formation of neutrophil extracellular traps (NETs) are reported in systemic sclerosis (SSc) but its involvement in SSc pathogenesis is not clear. In the present study we assessed markers of neutrophil activation and NET formation in SSc patients in relation to markers of inflammation and disease phenotype. Factors promoting neutrophil activation in SSc remain largely unknown. Among the neutrophil activating factors, mitochondrial-derived N-formyl methionine (fMet) has been reported in several autoinflammatory conditions. The aim of the current study is to assess whether SSc patients have elevated levels of fMet and the role of fMet in neutrophil-mediated inflammation on SSc pathogenesis. Markers of neutrophil activation (calprotectin, NETs) and levels of fMet were analyzed in plasma from two SSc cohorts (n=80 and n=20, respectively) using ELISA. Neutrophil activation assays were performed in presence or absence of formyl peptide receptor 1 (FPR1) inhibitor cyclosporin H. Elevated levels of calprotectin and NETs were observed in SSc patients as compared to healthy controls (p<0.0001) associating with SSc clinical disease characteristics. Further, SSc patients had elevated levels of circulating fMet as compared to healthy controls (p<0.0001). Consistent with a role for fMet-mediated neutrophil activation, fMet levels correlated with levels of calprotectin and NETs (r=0.34, p=0.002; r=0.29, p<0.01 respectively). Additionally, plasma samples from SSc patients with high levels of fMet induced de novo neutrophil activation through FPR1-dependent mechanisms. Our data for the first time implicates an important role for the mitochondrial component fMet in promoting neutrophil-mediated inflammation in SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".