Bibliographic record
Abstract
BACKGROUND: Neutrophils, eosinophils and inflammatory cells contribute to asthmatic inflammation. The anti-bactericidal/permeability-increasing protein (BPI), produced by neutrophils, peripheral blood monocytes or epithelial cells, can neutralize lipopolysaccharide activity and enhance phagocytosis regulation function. This study aimed to assess the clinical significance of BPI in asthmatic patients. METHODS: We recruited 18 controlled asthma, 39 uncontrolled asthma and 35 healthy controls individuals. Clinical characteristics (age, gender, allergy history, body mass index (BMI) and smoking history), clinical indicators [whole blood count, forced expiratory volume in one second as percentage of predicted volume (FEV1% predicted), IgE level, high sensitivity C-reactive protein (hs-CRP) and fractional expiratory nitric oxide (FeNO)] and serum BPI levels were measured to compare among each group. We then evaluated the correlation between BPI, clinical characteristics and clinical indicators. Finally, linear regression analysis was performed to exclude the influence of other factors and to find the independent influencing factors of BPI. RESULTS: Our results showed that the serum BPI levels increased by twofold in the controlled asthma group (12.83 ± 6.04 ng/mL) and threefold in the uncontrolled asthma group (18.10 ± 13.48 ng/mL), compared to the healthy control group (6.00 ± 2.58 ng/mL) (p < 0.001). We further found that serum BPI levels were positively correlated with the hs-CRP (p = 0.002). There was no significant association among BPI, age, gender, BMI, allergy, blood eosinophils, blood neutrophils, IgE, FeNO or FEV1% predicted. CONCLUSION: BPI levels were increased in asthma and positively correlated with hs-CRP. BPI as a potential asthma biomarker that still needs further research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".