Effect of glycosaminoglycan degradation on lung tissue viscoelasticity
Bibliographic record
Abstract
We tested the hypothesis that matrix glycosaminoglycans contribute to lung tissue viscoelasticity. We exposed lung parenchymal strips to specific degradative enzymes (chondroitinase ABC, heparitinase I, and hyaluronidase) and determined whether the mechanical properties of the tissue were affected. Subpleural parenchymal strips were obtained from Sprague-Dawley rats and suspended in a Krebs-filled organ bath. One end of the strip was attached to a force transducer and the other to a servo-controlled lever arm that effected sinusoidal oscillations. Recordings of tension and length at different amplitudes and frequencies of oscillation were recorded before and after enzyme exposure. Resistance, dynamic elastance, and hysteresivity were estimated by fitting the equation of motion to changes in tension and length. Quasi-static stress-strain curves were also obtained. Exposure to chondroitinase and heparitinase I caused significant increases in hysteresivity, no decrement in resistance, and similar decreases in dynamic elastance relative to control strips exposed to Krebs solution only. Conversely, measures of static elastance were different in treated versus control strips. Hyaluronidase treatment did not alter any of the mechanical measures. These data demonstrate that digestion of chondroitin sulfate and heparan sulfate alters the mechanical behavior of lung parenchymal tissues.
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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 | 0.000 |
| 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".