Metabolic and inflammatory responses to pulmonary exacerbation in adults with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: We hypothesized that increased resting energy expenditure in adults with cystic fibrosis was related to chronic inflammation secondary to pulmonary infection and could be modified by treatment of the underlying infection. METHOD: To determine the relationship between resting energy expenditure and the inflammatory and metabolic responses, we studied 22 adults with cystic fibrosis and chronic Pseudomonas aeruginosa infection before and after treatment of a respiratory exacerbation. Resting energy expenditure was measured by indirect calorimetry. Spirometry and circulating concentrations of C-reactive protein, neutrophil elastase alpha1-antiproteinase complex, catecholamines, non-esterified fatty acids and glycerol were determined. RESULTS: The mean (95% confidence interval)% predicted FEV1 was 28.5% (20.6, 36.4) and mean body weight 50.7 kg (47.4, 54.1). Following treatment, 1-s forced expiratory volume (FEV1) and weight increased, while C-reactive protein (P<0.0001) and neutrophil elastase alpha1-antiproteinase complex concentrations (P<0.0001) were reduced. Resting energy expenditure decreased from 6.8 (6.3, 7.2) to 6.25 (5.9, 6.6) MJ day-1 by day 15 (P<0.001). Changes in resting energy expenditure and C-reactive protein were related (r = 0.66, P< 0.0001). Weight gain was inversely related to resting energy expenditure (r = 0.43, P = 0.02) and unrelated to energy intake (r = 0.02, P = 0.47). Post-treatment reduction in norepinephrine was related to changes in heart rate (r = 0.57, P<0.01), resting energy expenditure (r = 0.51, P = 0.001) and non-esterified fatty acids (r = 0.42, P< 0.05). CONCLUSIONS: A parallel reduction in the host inflammatory and catabolic responses followed treatment of a respiratory exacerbation and may have contributed to weight gain.
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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 | 0.001 |
| 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".