NMR analysis of neutrophil activation in sputum samples from patients with cystic fibrosis
Bibliographic record
Abstract
Disorders of the respiratory system, such as cystic fibrosis (CF), involve the infiltration and activation of airway inflammatory cells, including neutrophils. This leads to the secretion of peroxidases, which react further with substrates in solution to produce oxidative metabolites, such as 3-chlorotyrosine. Elevated levels of modified tyrosine residues in the airways of patients with CF may be detectable by nuclear magnetic resonance (NMR) in correlation with inflammatory cell influx. In this study, high-resolution (500 MHz) 1H NMR was used to analyze the production of modified tyrosine residues resulting from in vitro stimulation of peripheral blood eosinophils and neutrophils, as well as in sputum samples from control subjects and patients with CF. Following in vitro stimulation, purified peripheral blood neutrophils generated 3-chlorotyrosine, while eosinophils produced predominantly 3-bromotyrosine and 3,5-dibromotyrosine. Chlorinated and brominated tyrosine residues were detected in sputum samples from patients with CF (N=7), but were not detected in the control group (N=9). Neutrophil counts in CF sputum correlated strongly with the presence of 3-chlorotyrosine (r2=0.869). Our findings indicate that neutrophil and eosinophil activation in CF is detectable by NMR. NMR may be a useful tool for the detection of biological markers of inflammatory processes in patient airways.
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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".