Pathophysiology of Brain Aging: A Brief Account on Molecular Changes
Bibliographic record
Abstract
Being the second most populous country in the world, India houses a large geriatric population. Increasing geriatric population with increasing age related ailments has necessitated research in the field of Aging. Thus, the study of Biological mechanism of aging is not merely a topic of scientific curiosity, but also a crucial area of research in the current scenario. “Aging” is one of the most fascinating topics that have interested philosophers and scientists for centuries. Over the years, the researchers have postulated several theories to explain the aging phenomena. Denham Harman postulated that aging is a deleterious, progressive, intrinsic, and universal process, which is a progressive accumulation of alteration as a function of time associated with or responsible for the everincreasing susceptibility to age-related disease and death.[1] Aging is associated with (a) progressive loss of physiologic functions; (b) atrophy to most of the organs; (c) increased susceptibility to infections, trauma and neurodegeneration (d) susceptibility to malignancy, and (e) decreased gaseous exchange during respiration.[2]
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".