Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized pathologically by the deposition of beta-amyloid (A beta)-containing extracellular neuritic plaques, intracellular neurofibrillary tangles and neuronal loss. Much evidence supports the hypothesis that A beta peptide aggregation contributes to AD pathogenesis, however, currently approved therapeutic treatments do nothing to stop or reverse A beta deposition. The success of active and passive anti-A beta immunotherapies in both preventing and clearing parenchymal amyloid in transgenic mouse models led to the initiation of an active anti-A beta vaccination (AN1792) trial in human patients with mild-to-moderate AD, but was prematurely halted when 6% of inoculated patients developed aseptic meningoencephalitis. Autopsy results from the brains of four individuals treated with AN1792 revealed decreased plaque burden in select brain areas, as well as T-cell lymphocytes in three of the patients. Furthermore, antibody responders showed some improvement in memory task measures. These findings indicated that anti-A beta therapy might still be a viable option for the treatment of AD, if potentially harmful proinflammatory processes can be avoided. Over the past 6 years, this target has led to the development of novel experimental immunization strategies, including selective A beta epitope targeting, antibody and adjuvant modifications, as well as alternative routes and mechanisms of vaccine delivery, to generate anti-A beta antibodies that selectively target and remove specific A beta species without evoking autoimmunity. Results from the passive vaccination AD clinical trials that are currently underway will provide invaluable information about both the effectiveness of newly improved anti-A beta vaccines in clinical treatment, as well as the role of the A beta peptide in the pathogenesis of the disease.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".