P3‐333: Targeting the hematopoietic system to treat Alzheimer's disease
Bibliographic record
Abstract
Strategies using bone marrow-derived cells (BMDC) as therapeutic agents for cerebral pathologies center on the beneficial effects of microglia. They are the brain resident macrophages, sole immunologic defenders of brain homeostasis and most important, of hematopoietic origin. A therapeutic role for these cells has been suggested in Alzheimer's disease (AD) using either bone marrow transplantation or hematopoietic cytokines. However, a controversy remains on the ability of BMDC to infiltrate into the CNS as previous experimental models always required the use of whole-body irradiation. A myeloablative chemotherapy regimen was adapted to replace whole-body irradiation. As no infiltration was seen in wild-type mice in a previous experiment, the regimen and bone marrow transplants were performed to APP/PS1 mice at 2 months of age, before they develop amyloid plaques and symptoms of Alzheimer's disease. They were sacrificed at 9 months of age, when signs of the pathology were fully developed. A similar protocol was also used in a curative model, between 6 and 9 months of age. The presence of Alzheimer's disease-like symptoms in mice was sufficient to induce an efficient migration of BMDC into the CNS. These cells were also highly efficient to treat the pathology, normalizing their behavior deficiencies and amyloid plaque load. Chemotherapy and irradiation were both equally efficient as a pretreatment for bone marrow transplant. These results demonstrate that although BMDC cannot migrate into the CNS in normal healthy conditions, they migrate to the sites of injury in conditions of cerebral pathology, such as Alzheimer's disease. These cells are highly efficient as a therapeutic agent to limit the deposition of amyloid plaques and the related cognitive decline. Taken together, our result suggest that treatments involving hematopoietic cytokines are prime candidates as new therapeutic strategies for Alzheimer's disease.
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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 | 0.000 | 0.001 |
| 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".