Bibliographic record
Abstract
By 2050, the aging population is predicted to expand by over 100%. Considering this rapid growth, and the additional strain it will place on healthcare resources because of age-related impairments, it is vital that researchers gain a deeper understanding of the cellular interactions that occur with normal aging. A variety of mammalian cell types have been shown to become compromised with age, each with a unique potential to contribute to disease formation in the aging body. Astrocytes represent the largest group of glial cells and are responsible for a variety of essential functions in the healthy central nervous system (CNS). Like other cell types, aging can cause a loss of normal function in astrocytes which reduces their ability to properly maintain a healthy CNS environment, negatively alters their interactions with neighboring cells, and contribute to the heightened inflammatory state characteristic of aging. The goal of this review article is to consolidate the knowledge and research to date regarding the role of astrocytes in aging. In specific, this review article will focus on the morphology and molecular profile of aged astrocytes, the consequence of astrocyte dysfunction on homeostatic functions during aging, and the role of astrocytes in age-related neurodegenerative diseases.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".