Harnessing BDNF Signaling to Promote Resilience in Aging
Bibliographic record
Abstract
As a key member of the neurotrophin family in the central nervous system, brain-derived neurotrophic factor (BDNF) plays a critical role in the maintenance and plasticity of the nervous system. Its innate neuroprotective advantage can also be shared with the brain when normal aging-dependent processes challenge neural circuits. The intricate relationship between BDNF and resilience during the aging process signifies the molecular mechanisms that underlie the maintenance and protection of brain function, such as cognition, movement and psychological well-being. As BDNF is crucial for neuronal growth and survival, it can also promote resilience against age-related functional decline and frailty, even if it fails to entirely prevent aging-related functional decline. In the present review, we discuss BDNF function from a neuroprotective perspective and how it may promote resilience in aging. We emphasize briefly the principal, well-known cellular hallmarks of brain aging and how BDNF may restrict such disabling molecular dynamics and enhance overall functional resilience in aging. Insight into the molecular pathways through which BDNF reduces age-related brain dysfunctions and/or improves resilience, provides a foundation for developing targeted interventions to promote mental well-being in an aging population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".