Characterization of doxycycline-mediated inhibition of Marfan syndrome-associated aortic dilation by multiphoton microscopy
Bibliographic record
Abstract
Marfan syndrome (MFS) is a connective tissue disorder that results in aortic root widening and aneurysm if unmanaged. We have previously reported doxycycline, a nonselective matrix metalloproteinases (MMPs) inhibitor, to attenuate aortic root widening and improve aortic contractility and elasticity in MFS mice. We were also first to use multiphoton microscopy, a non-invasive and label-free imaging technique, to quantify and link the aortic ultrastructure to possible changes in the skin dermis. Here, we aimed to assess the effects of long-term doxycycline treatment on the aortic ultrastructure and skin dermis of MFS mice through immunohistochemical evaluation and quantification of elastic and collagen content and morphology using multiphoton microscopy. Our results demonstrate a rescue of aortic elastic fiber fragmentation and disorganization accompanied by a decrease in MMP-2 and MMP-9 expression within the aortic wall in doxycycline-treated MFS mice. At 12 months of age, reduced skin dermal thickness was observed in both MFS and control mice, but only dermal thinning in MFS mice was rescued by doxycycline treatment. MMP-2 and MMP-9 expression was reduced in the skin of doxycycline-treated MFS mice. A decrease in dermal thickness was found to be positively associated with increased aortic root elastin disorganization and wall thickness. Our findings confirm the beneficial effects of doxycycline on ultrastructural properties of aortic root as well as on skin elasticity and structural integrity in MFS mice.
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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".