Effects of MMP-9 inhibition by doxycycline on proteome of lungs in high tidal volume mechanical ventilation-induced acute lung injury
Bibliographic record
Abstract
BACKGROUND: Although mechanical ventilation (MV) is a major supportive therapy for patients with acute respiratory distress syndrome, it may result in side effects including lung injury. In this study we hypothesize that MMP-9 inhibition by doxycycline might reduce MV-related lung damage. Using a proteomic approach we identified the pulmonary proteins altered in high volume ventilation-induced lung injury (VILI). Forty Wistar rats were randomized to an orally pretreated with doxycycline group (n = 20) or to a placebo group (n = 20) each of which was followed by instrumentation prior to either low or high tidal volume mechanical ventilation. Afterwards, animals were euthanized and lungs were harvested for subsequent analyses. RESULTS: Mechanical function and gas exchange parameters improved following treatment with doxycycline in the high volume ventilated group as compared to the placebo group. Nine pulmonary proteins have shown significant changes between the two biochemically analysed (high volume ventilated) groups. Treatment with doxycycline resulted in a decrease of pulmonary MMP-9 activity as well as in an increase in the levels of soluble receptor for advanced glycation endproduct, apoliporotein A-I, peroxiredoxin II, four molecular forms of albumin and two unnamed proteins. Using the pharmacoproteomic approach we have shown that treatment with doxycycline leads to an increase in levels of several proteins, which could potentially be part of a defense mechanism. CONCLUSION: Administration of doxycycline might be a significant supportive therapeutic strategy in prevention of VILI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".