Data from: Loss of GD1-positive Lactobacillus correlates with inflammation in human lungs with COPD
Bibliographic record
Abstract
Objectives: The present study assesses the relationship between contents of GD1 (glycerol dehydratase)-positive Lactobacillus, presence of Lactobacillus and the inflammatory response measured in host lung tissue in mild to moderate chronic obstructive pulmonary disease (COPD). We hypothesise that there will be a loss of GD1 producing Lactobacillus with increasing severity of COPD and that GD1 has anti-inflammatory properties. Setting: Secondary care, 1 participating centre in Vancouver, British Columbia, Canada. Participants: 74 individuals who donated non-cancerous portions of their lungs or lobes removed as treatment for lung cancer (normal lung function controls (n=28), persons with mild (GOLD 1) (n=21) and moderate (GOLD 2) COPD (n=25)). Outcome measures: Primary outcome measure was GD1 positivity within each group and whether or not this impacted quantitative histological measures of lung inflammation. Secondary outcome measures included Lactobacillus presence and quantification, and quantitative histological measurements of inflammation and remodelling in early COPD. Results: Total bacterial count (p>0.05) and prevalence of Lactobacillus (p>0.05) did not differ between groups. However, the GD1 gene was detected more frequently in the controls (14%) than in either mild (5%) or moderate (0%) COPD (p<0.05) samples. Macrophage and neutrophil volume fractions (0.012±0.005 (mean±SD) vs 0.026±0.017 and 0.005±0.002 vs 0.015±0.014, respectively) in peripheral lung tissue were reduced in samples positive for the GD1 gene (p<0.0035). Conclusions: A reduction in GD1 positivity is associated with an increased tissue immune inflammatory response in early stage COPD. There is potential for Lactobacillus to be used as a possible therapeutic, however, validation of these results need to be completed before an anti-inflammatory role of Lactobacillus in COPD can be confirmed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".