Systemic oxidative stress in children with cystic fibrosis with bacterial infection including <i>Pseudomonas Aeruginosa</i>
Bibliographic record
Abstract
INTRODUCTION: Oxidative stress (OS) occurs in cystic fibrosis (CF). OBJECTIVE: The objective of this work is to evaluate the influence of bacterial infection on biomarkers of OS (catalase [CAT], glutathione peroxidade [GPx], reduced glutathione [GSH]), markers of oxidative damage (protein carbonyls [PC], thiobarbituric acid reactive substances [TBARS]), together with the nutritional status and lung function in children with CF. METHODS: Cross-sectional study including CF group (CFG, n = 55) and control group (CG, n = 31), median age: 3.89 and 4.62 years, respectively. CFG was distributed into CFG negative bacteriology (CFGB-, n = 27) or CFG positive bacteriology (CFGB+, n = 28), and CFG negative Pseudomonas aeruginosa (CFGPa-, n = 36) or CFG positive Pseudomonas aeruginosa (CFGPa+, n = 19). RESULTS: Compared with CG, CFG (P = .034) and CFGB+ (P = .042) had lower body mass index-for-age z-score; forced expiratory volume in the first second was lower in CFGB+ and CFGPa+ (both P < .001). After adjusting for confounders and compared with CG: CFG showed higher TBARS (P ≤ .001) and PC (P = .048), and lower CAT (P = .004) and GPx (P = .003); the increase in PC levels was observed in CFGB+ (P = .011) and CFGPa+ (P = .001) but not in CFGB- (P = .510) and CFGPa- (P = .460). CONCLUSIONS: These results indicate a systemic OS in children with CF. The presence of bacterial infection particularly Pseudomonas aeruginosa seems to be determinant to exacerbate the oxidative damage to proteins, in which PC may be a useful biomarker of OS in CF.
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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".