Passive immunotherapies targeting Aβ and tau in Alzheimer's disease
Bibliographic record
Abstract
Amyloid-β (Aβ) and tau proteins currently represent the two most promising targets to treat Alzheimer's disease. The most extensively developed method to treat the pathologic forms of these proteins is through the administration of exogenous antibodies, or passive immunotherapy. In this review, we discuss the molecular-level strategies that researchers are using to design an effective therapeutic antibody, given the challenges in treating this disease. These challenges include selectively targeting a protein that has misfolded or is pathological rather than the more abundant, healthy protein, designing strategic constructs for immunizing an animal to raise an antibody that has the appropriate conformational selectivity to achieve this end, and clearing the pathological protein species before prion-like cell-to-cell spread of misfolded protein has irreparably damaged neurons, without invoking damaging inflammatory responses in the brain that naturally arise when the innate immune system is clearing foreign agents. The various solutions to these problems in current clinical trials will be discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".