Novel Neutrophil-Derived Proteins in Bronchoalveolar Lavage Fluid Indicate an Exaggerated Inflammatory Response in Pediatric Cystic Fibrosis Patients
Bibliographic record
Abstract
BACKGROUND: Airway inflammation in cystic fibrosis (CF) is exaggerated and characterized by neutrophil-mediated tissue destruction, but its genesis and mechanisms remain poorly understood. To further define the pulmonary inflammatory response, we conducted a proteome-based screen of bronchoalveolar lavage fluid (BALF) collected from young children with and without CF experiencing endobronchial infection. METHODS: We collected BALF samples from 45 children younger than 5 years and grouped them according to the presence of respiratory pathogens: > or = 1 x 10(5) colony-forming units (CFU)/mL BALF (18 and 12 samples with and without CF, respectively) and <1 x 10(5) CFU/mL (23 and 15 samples). BALF proteins were analyzed with SELDI-TOF mass spectrometry (MS) and H4 ProteinChips. Proteins were identified and characterized using trypsin digestion, tandem MS, Fourier transform ion cyclotron resonance MS, immunoblotting, and ELISA. RESULTS: The SELDI-TOF MS BALF profiles contained 53 unique, reliably detected proteins. Peak intensities of 24 proteins differed significantly between the CF and non-CF samples. They included the neutrophil proteins, alpha-defensin 1 and 2, S100A8, S100A9, and S100A12, as well as novel forms of S100A8 and S100A12 with equivalent C-terminal deletions. Peak intensities of these neutrophil proteins and immunoreactive concentrations of selected examples were significantly higher in CF than non-CF samples. CONCLUSIONS: Small neutrophil-derived BALF proteins, including novel C-terminal truncated forms of S100A proteins, are easily detected with SELDI-TOF MS. Concentrations of these molecules are abnormally high in early CF lung disease. The data provide new insights into CF lung disease and identify novel proteins strongly associated with CF airway inflammation.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".