SP091MATRIX METALLOPROTEINASE INHIBITION ALTERS PATTERNS OF VASCULAR MINERAL ACCRUAL IN EXPERIMENTAL CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction and Aims: The matrix metalloproteinases (MMPs) degrade the extracellular matrix (ECM) of tissues. In chronic kidney disease (CKD) they are up-regulated and linked as critical to the process of vascular calcification (VC), a high risk factor for cardiovascular disease (CVD). The study’s aim was to examine the role of ECM constituents, specifically the MMPs, on vascular mineral accrual in a progressive model of CKD using doxycycline, a common antibiotic known to inhibit MMP activity. Methods: Male Sprague Dawley rats were administered standard rat chow or a CKD-inducing diet (0.25% adenine) for 3 weeks. At 3 weeks, animals were stratified based on serum creatinine levels (uM) into 2 groups: CKD (0.25% adenine, n=8, creatinine: 472.3±81.58) and CKD-DX (0.30mg/kg doxycycline twice daily with 0.25% adenine, n=9, creatinine: 382.2±63.99). An additional age-matched healthy Control group (n=6, creatinine: 41.62±4.267) was included. Animals were treated for 4 weeks and then sacrificed. Results: Both CKD and CKD-DX showed significant elevations in serum PO43- compared to control (p<0.0001), as well as CKD-DX being significantly higher than CKD (p < 0.05). CKD-DX rats showed significant reductions in the proportion of vessels that calcified (defined as Ca2+ > 30nmol/mg tissue and PO43- > 18nmol/mg tissue; p < 0.05). Further, CKD-DX rats showed significant differences in their pattern of accrual for both Ca2+ and PO43- (p < 0.05).
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".