Targeting Endogenous Mechanisms of Brain Resilience for the Treatment and Prevention of Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease is a neurodegenerative disorder which contributes to millions of cases of dementia worldwide. The dominant theoretical models of Alzheimer's disease propose that the brain passively succumbs to disruptions in proteostasis, neuronal dysfunction, inflammatory and other processes, ultimately leading to neurodegeneration and dementia. However, an emerging body of evidence suggests that the adult brain is endowed with endogenous mechanisms of resilience which may enable individuals to remain cognitively intact for years despite underlying pathology. In this brief review, we discuss evidence from basic neuroscience and clinical research which demonstrates the existence of endogenous molecular signaling pathways that can promote resilience to neurodegeneration. The p75 neurotrophin receptor provides one such pathway of resilience due to its role as a fundamental signaling switch which determines neuronal survival or degeneration. We highlight a series of preclinical studies targeting the p75 neurotrophin receptor in mouse models which demonstrate resilience to amyloid. We briefly discuss the design and goals of a recent clinical trial of p75 neurotrophin receptor modulation in patients with mild to moderate Alzheimer's disease. Unique challenges for developing therapeutics and biomarkers which are optimized for targeting and detecting endogenous mechanisms of resilience are also discussed. Altogether, this review motivates further trial work of therapeutics modulating the p75 neurotrophin receptor and other deep biology targets.
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 | 0.002 | 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 | 0.002 |
| 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".