PERSISTENCE OF ACCELERATED VASCULAR AGING FOLLOWING THERAPY IN OLDER CANCER SURVIVORS
Bibliographic record
Abstract
Abstract Chemotherapy destroys cells indiscriminantly and releases proinflammatory factors into the bloodstream that can adhere to endothelial cells (ECs); resulting in phenotypical changes consistent with “accelerated vascular aging”. This pilot study examined associations between markers of EC integrity, vascular aging, and cognition in 15 female breast cancer survivors 12–18 months after chemotherapy (median age: 57 years) and 2 non-cancer controls (58/59 years). EC integrity was evaluated using frequency-dependent electrical impedance (Z4000Hz) and levels of apoptosis (caspase-3/7), inflammation (NFkB activation), and oxidative stress (NRF2 activation) in cultured human endothelial cells (EC) exposed to the subject’s serum. Vascular aging was characterized by low serum insulin growth factor ([IGF1a] <85mg/dL) and laterality in cerebral oxygenation (Lat-FLOX), an in-vivo marker of altered cerebral blood flow (near-infrared spectroscopy). Z4000Hz were higher in cells treated with serum from survivors (520-1100 ohms) than controls (400-450 ohms). Higher Z4000Hz was associated with higher amounts of EC oxidative stress (rNRF2= -.55), inflammation (rNFkB= .43), apoptosis (rcapsase=.76), and Lat-FLOX (r=.39). Higher Z4000Hz (r= -.48) and Lat-FLOX (r= -.66) were associated with lower cognitive function, as measured by the Montreal Cognitive Assessment (MOCA). At lower Z4000Hz, combined Lat-FLOX and low IGF1a characterized persons with cognitive impairment (MOCA < 26). These findings suggest that proinflammatory factors related to cancer and/or its treatment persist for months following active treatment. Cognitive symptoms occur when EC damage results in alterations in cerebral blood flow. With depletion of growth factors, like IGF-1, these symptoms may occur at lower levels of EC damage.
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 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".