Long-Term Dabigatran Treatment Delays Alzheimer’s Disease Pathogenesis in the TgCRND8 Mouse Model
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder with important vascular and hemostatic alterations that should be taken into account during diagnosis and treatment. OBJECTIVES: This study evaluates whether anticoagulation with dabigatran, a clinically approved oral direct thrombin inhibitor with a low risk of intracerebral hemorrhage, ameliorates AD pathogenesis in a transgenic mouse model of AD. METHODS: TgCRND8 AD mice and their wild-type littermates were treated for 1 year with dabigatran etexilate or placebo. Cognition was evaluated using the Barnes maze, and cerebral perfusion was examined by arterial spin labeling. At the molecular level, Western blot and histochemical analyses were performed to analyze fibrin content, amyloid burden, neuroinflammatory activity, and blood-brain barrier (BBB) integrity. RESULTS: Anticoagulation with dabigatran prevented memory decline, cerebral hypoperfusion, and toxic fibrin deposition in the AD mouse brain. In addition, long-term dabigatran treatment significantly reduced the extent of amyloid plaques, oligomers, phagocytic microglia, and infiltrated T cells by 23.7%, 51.8%, 31.3%, and 32.2%, respectively. Dabigatran anticoagulation also prevented AD-related astrogliosis and pericyte alterations, and maintained expression of the water channel aquaporin-4 at astrocytic perivascular endfeet of the BBB. CONCLUSIONS: Long-term anticoagulation with dabigatran inhibited thrombin and the formation of occlusive thrombi in AD; preserved cognition, cerebral perfusion, and BBB function; and ameliorated neuroinflammation and amyloid deposition in AD mice. Our results open a field for future investigation on whether the use of direct oral anticoagulants might be of therapeutic value in AD.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".