Precision cut lung slices: A novel method for examining mechanisms underlying respiratory diseases
Bibliographic record
Abstract
Bacterial infections and smoking have been linked to exacerbations of many respiratory diseases. To this end, responses to toll-like receptor (TLR) agonists and bacterial pathogens were studied in precision cut lung slices (PCLS) from room air and smoke-exposed mice. Ex vivo cultured PCLS were either left untreated or stimulated with toll-like receptor agonists, lipopolysaccharide (LPS), or Pam3CSK. An additional set of PCLS were challenged with either live or heat-killed Haemophilus influenzae, or Streptococcus pneumoniae. RNA was isolated and microarray analysis performed. Principal component analysis showed that live S. pneumoniae stimulation of PCLS led to a distinct response and a greater number of differentially expressed genes (DEGs) when compared to the responses elicited by the two TLR agonists or H. influenzae . Unsupervised hierarchical clustering analysis was performed on 1846 DEGs identified 24 hours post-stimulation in room air exposed PCLS, and two distinct clusters were present, confirming the principal component analysis. Of the 1354 genes identified following live S. pneumoniae challenge of room air PCLS, several signaling cascades were identified following ingenuity pathways analysis (IPA); these included the IL-1 and IL-10 signaling cascades. In the context of cigarette smoke exposure, IPA analysis captured pathways involved in airway pathology in COPD. These data highlight the strength of this technique for evaluating pathways that may be linked to disease (or exacerbation) susceptibility. Finally, mechanisms that have been implicated in COPD pathogenesis are captured in this model, and therefore increase the validity of this method to test interventions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".