New insight into Alzheimer's disease: Light reverses Aβ‐obstructed interstitial fluid flow and ameliorates memory decline in APP/PS1 mice
Bibliographic record
Abstract
Abstract Introduction Pharmacological therapies to treat Alzheimer's disease (AD) targeting “Aβ” have failed for over 100 years. Low levels of laser light can disassemble Aβ. In this study, we investigated the mechanisms that Aβ‐blocked extracellular space (ECS) induces memory disorders in APP/PS1 transgenic mice and addressed whether red light (RL) at 630 nm rescues cognitive decline by reducing Aβ‐disturbed flow of interstitial fluid (ISF). Methods We compared the heating effects on the brains of rats illuminated with laser light at 630, 680, and 810 nm for 40 minutes, respectively. Then, a light‐emitting diode with red light at 630 nm (LED‐RL) was selected to illuminate AD mice. The changes in the structure of ECS in the cortex were examined by fluorescent double labeling. The volumes of ECS and flow speed of ISF were quantified by magnetic resonance imaging. Spatial memory behaviors in mice were evaluated by the Morris water maze. Then, the brains were sampled for biochemical analysis. Results RL at 630 nm had the least heating effects than other wavelengths associated with ~49% penetration ratio into the brains. For the molecular mechanisms, Aβ could induce formaldehyde (FA) accumulation by inactivating FA dehydrogenase. Unexpectedly, in turn, FA accelerated Aβ deposition in the ECS. However, LED‐RL treatment not only directly destroyed Aβ assembly in vitro and in vivo but also activated FA dehydrogenase to degrade FA and attenuated FA‐facilitated Aβ aggregation. Subsequently, LED‐RL markedly smashed Aβ deposition in the ECS, recovered the flow of ISF, and rescued cognitive functions in AD mice. Discussion Aβ‐obstructed ISF flow is the direct reason for the failure of the developed medicine delivery from superficial into the deep brain in the treatment of AD. The phototherapy of LED‐RL improves memory by reducing Aβ‐blocked ECS and suggests that it is a promising noninvasive approach to treat AD.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".