High throughput screening of airway constriction in mouse lung slices
Bibliographic record
Abstract
The level of airway constriction in thin slices of lung tissue is highly variable. Owing to the labor-intensive nature of these experiments, determining the number of airways to be analyzed in order to allocate a reliable value of constriction in one mouse is challenging. Herein, a new automated device for physiology and image analysis was used to facilitate high throughput screening of airway constriction in lung slices. Airway constriction was first quantified in slices of lungs from male BALB/c mice with and without experimental asthma that were inflated with agarose through the trachea or trans-parenchymal injections. Random sampling simulations were then conducted to determine the number of airways required per mouse to quantify maximal constriction. The constriction of 45 ± 12 airways per mouse in 32 mice were analyzed. Mean maximal constriction was 37.4 ± 32.0%. The agarose inflating technique did not affect the methacholine response. However, the methacholine constriction was affected by experimental asthma (p = 0.003), shifting the methacholine concentration-response curve to the right, indicating a decreased sensitivity. Simulations then predicted that approximately 35, 16 and 29 airways per mouse are needed to quantify the maximal constriction mean, standard deviation and coefficient of variation, respectively; these numbers varying between mice and with experimental 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".