Mineralization of the Connective Tissue: A Complex Molecular Process Leading to Age-Related Loss of Function
Bibliographic record
Abstract
Age-related metastatic mineralization of soft tissues has been considered a passive and spontaneous process. Recent data have demonstrated that calcium salt deposition in soft tissues could be a highly regulated process. Although calcification occurs in any tissue type, vascular calcification has been of particular interest due to association with atherosclerosis, chronic kidney disease (CKD), and osteoporosis. Different mechanisms underlying calcium apatite accumulation are explored with these age-related disorders. In the case of atherosclerotic plaques, oxy-lipids trigger release of the pro-inflammatory cytokines and inflammation that activate calcification processes in aorta intimae. In CKD patients, renal failure alters the balance between calcium and phosphate levels usually regulated by fibroblast growth factor-23 (FGF23), Klotho, and vitamin D, and vascular smooth muscle cells (VSMCs) begin to explore an osteoblastosteoblast-like phenotype. Calcification could affect extracellular matrix along with VSMCs. Collagen is a major component of extracellular matrix and its modifications accumulate with age. The formation of cross-links between collagen fibers is regulated by the action of lysine hydroxylases and lysyl oxidase and could occur spontaneously. Oxidation-induced advanced glycation end products (AGEs) are a major type of spontaneous cross-links that accelerate with age and may result in tissue stiffness, problems with recycling, and potential accumulation of calcium apatite. Applying strategies for clearing the AGEs proposed by de Grey may be more difficult in the highly mineralized extracellular matrix. We performed bioinformatic analysis of the molecular pathways underlying calcification in atherosclerotic and CKD patients, signaling pathways of collagen cross-links formation, and bone mineralization, and we propose new potential targets and review drugs for calcification treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".