Tau, Amyloid Beta and Deep Brain Stimulation: Aiming to Restore Cognitive Deficit in Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: The last two decades have seen a great advance in the data that supports the two current hypotheses in Alzheimer`s disease field, the amyloid beta hypothesis and the tau hypothesis. Not surprisingly, Aβ and tau proteins are currently the major therapeutic research targets for AD treatment. Unfortunately, nothing but moderate success has emerged from such therapeutic approaches. With this in mind, we will discuss deep brain stimulation as a promising therapeutic strategy that aims to restore brain activity. Lastly, in the scope of cognitive deficit restoration, we will discuss the relevance of the limbic formation as a promising neuroanatomical target for deep brain stimulation. METHODS: Immunohistochemistry for modified tau (phosphorylated at Ser199-202-Thr205 labelled by the antibody AT8) was performed on paraffin-embedded human brain sections providing a detailed characterization of NFT pathology. RESULTS: Abnormally phosphorylated tau protein is the key common marker in several brain diseases such as Alzheimer's disease, Parkinson`s disease, Pick Disease, Down syndrome and frontotemporal dementia and is capable of affecting synaptic events that are critical for memory formation. With this in mind, therapeutic strategies aiming to restore synaptic events could offer better outcomes. CONCLUSION: The humble success of current therapeutic strategies along with the lack of basic knowledge of the brain disease mechanisms calls for alternatives that benefit patients in the present moment. One of particular interest is the neurostimulation strategy that is already a well-established treatment for several movement disorders and when compared to current Alzheimer`s therapeutic strategies, deep brain stimulation does not directly interfere with the normal protein function, therefore increasing the probability of success.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".