Impact of obesity on neutrophil function and phenotype in severe asthma
Bibliographic record
Abstract
Obesity is one of the main risk factors associated with poor asthma outcomes. Obese asthma is known for the presence of increased airway neutrophils. Yet the exact link between obesity and neutrophilic inflammation remains unclear. Neutrophils, a key component in inflammatory responses, play a significant role in the pathophysiology of severe asthma. Our study aims to investigate the effect of obesity on neutrophil behaviour. Neutrophils isolated from healthy donors and asthmatic patients peripheral blood, were exposed to obese (ObACM) and lean adipocyte-conditioned media (LACM). Migration was measured at 4 and 24h, and cell viability was assessed at 24 and 48h. Sputum samples from obese and lean asthmatic patients were immunostained for neutrophil markers (CD15, CD95, CD92L). Significantly higher migration of healthy neutrophils towards LACM after 24h was observed compared to ObACM and controls. Asthmatic neutrophils showed reduced migratory responses compared to healthy neutrophils. The viability of healthy neutrophils, unlike asthmatic neutrophils, showed an enhanced survival rate in ObACM after 48h. Immunofluorescence staining showed a predominance of pro-inflammatory N1 neutrophils (CD15+CD95HighCD62L+) in obese asthmatic patients, while lean asthmatic patients had a higher propotion of anti-inflammatory N2 neutrophils(CD15+CD95lowCD62Llow). In conclusion, while neutrophils migrate more effectively towards LACM, an enhanced survival rate is shown in ObACM. These findings are reflected in the phenotypic shift of N1 neutrophils in obese asthmatics from N2 in lean asthmatic patients. This differential response provides an insight into the role of obesity in neutrophilic inflammation in obese associated asthma.
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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 | 0.001 |
| 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".