Kinin B2 Receptor Activation Prevents the Evolution of Alzheimer’s Disease Pathological Characteristics in a Transgenic Mouse Model
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease is mainly characterized by remarkable neurodegeneration in brain areas related to memory formation. This progressive neurodegeneration causes cognitive impairment, changes in behavior, functional disability, and even death. Our group has demonstrated changes in the kallikrein-kinin system (KKS) in Alzheimer's disease (AD) experimental models, but there is a lack of evidence about the role of the KKS in Alzheimer's disease. AIM: In order to answer this question, we evaluated the potential of the kinin B2 receptors (BKB2R) to modify AD characteristics, particularly memory impairment, neurodegeneration, and Aβ peptide deposition. METHODS: To assess the effects of B2, we used transgenic Alzheimer's disease mice treated with B2 receptor (B2R) agonists and antagonists, and performed behavioral and biochemical tests. In addition, we performed organotypic hippocampal culture of wild-type (WT) and transgenic (TG) animals, where the density of cytokines, neurotrophin BDNF, activated astrocyte marker S100B, and cell death were analyzed after treatments. RESULTS: Treatment with the B2R agonist preserved the spatial memory of transgenic mice and decreased amyloid plaque deposition. In organotypic hippocampal culture, treatment with B2R agonist decreased cell death, neuroinflammation, and S100B levels, and increased BDNF release. CONCLUSIONS: Our results indicate that the kallikrein-kinin system plays a beneficial role in Alzheimer's disease through B2R activation. The use of B2R agonists could, therefore, be a possible therapeutic option for patients diagnosed with 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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".