Malat1 deficiency prevents hypoxia-induced lung dysfunction by protecting the access to alveoli
Bibliographic record
Abstract
Hypoxia is common in lung diseases and a potent stimulator of the long non-coding RNA Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1). Herein, we investigated the impact of Malat1 on hypoxia-induced lung dysfunction in mice. Malat1-deficient mice and their wild-type littermates were tested after 8 days of normoxia or hypoxia (10% oxygen). Hypoxia decreased elastance of the lung by increasing lung volume and caused in vivo hyperresponsiveness to methacholine without altering the contraction of airway smooth muscle. Malat1 deficiency also modestly decreased lung elastance but only when tested at low lung volumes and without altering lung volume and airway smooth muscle contraction. The in vivo responsiveness to methacholine was also attenuated by Malat1 deficiency, at least when elastance, a readout sensitive to small airway closure, was used to assess the response. More impressively, in vivo hyperresponsiveness to methacholine caused by hypoxia was virtually absent in Malat1-deficient mice, especially when hysteresivity, a readout sensitive to small airway narrowing heterogeneity, was used to assess the response. Malat1 deficiency also increased the coefficient of oxygen extraction and decreased ventilation in conscious mice, suggesting improvements in gas exchange and in clinical signs of respiratory distress during natural breathing. Combined with a lower elastance at low lung volumes at baseline, as well as a decreased propensity for small airway closure and narrowing heterogeneity during a methacholine challenge, these findings represent compelling evidence suggesting that the lack of Malat1 protects the access to alveoli for air entering the lung.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".