Nanoparticles in the diagnosis and treatment of vascular aging and related diseases
Bibliographic record
Abstract
Aging-induced alternations of vasculature structures, phenotypes, and functions are key in the occurrence and development of vascular aging-related diseases. Multiple molecular and cellular events, such as oxidative stress, mitochondrial dysfunction, vascular inflammation, cellular senescence, and epigenetic alterations are highly associated with vascular aging physiopathology. Advances in nanoparticles and nanotechnology, which can realize sensitive diagnostic modalities, efficient medical treatment, and better prognosis as well as less adverse effects on non-target tissues, provide an amazing window in the field of vascular aging and related diseases. Throughout this review, we presented current knowledge on classification of nanoparticles and the relationship between vascular aging and related diseases. Importantly, we comprehensively summarized the potential of nanoparticles-based diagnostic and therapeutic techniques in vascular aging and related diseases, including cardiovascular diseases, cerebrovascular diseases, as well as chronic kidney diseases, and discussed the advantages and limitations of their clinical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".