The Fuzzy Border between the Functional and Dysfunctional Effects of Beta-Amyloid: A Synaptocentric View of Neuron–Glia Entanglement
Bibliographic record
Abstract
Recent observations from clinical trials using monoclonal antibodies against Aβ seem to suggest that Aβ-targeting is modestly effective and not sufficiently based on an effective challenge of the role of Aβ from physiological to pathological. After an accelerated approval procedure for aducanumab, and more recently lecanemab, their efficacy and safety remain to be fully defined despite previous attempts with various monoclonal antibodies, and both academic institutions and pharmaceutical companies are actively searching for novel treatments. Aβ needs to be clarified further in a more complicated context, taking into account both its accumulation and its biological functions during the course of the disease. In this review, we discuss the border between activities affecting early, potentially reversible dysfunctions of the synapse and events trespassing the threshold of inflammatory, self-sustaining glial activation, leading to irreversible damage. We detail a clear understanding of the biological mechanisms underlying the derangement from function to dysfunction and the switch of the of Aβ role from physiological to pathological. A picture is emerging where the optimal therapeutic strategy against AD should involve a number of allied molecular processes, displaying efficacy not only in reducing the well-known AD pathogenesis players, such as Aβ or neuroinflammation, but also in preventing their adverse effects.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".