How does angiotensin AT2 receptor activation help neuronal differentiation and improve neuronal pathological situations?
Bibliographic record
Abstract
The angiotensin type 2 (AT(2)) receptor of angiotensin II has long been thought to be limited to few tissues, with the primary effect of counteracting the angiotensin type 1 (AT(1)) receptor. Functional studies in neuronal cells have demonstrated AT(2) receptor capability to modulate neuronal excitability, neurite elongation, and neuronal migration, suggesting that it may be an important regulator of brain functions. The observation that the AT(2) receptor was expressed in brain areas implicated in learning and memory led to the hypothesis that it may also be implicated in cognitive functions. However, linking signaling pathways to physiological effects has always proven challenging since information relative to its physiological functions has mainly emerged from indirect observations, either from the blockade of the AT(1) receptor or through the use of transgenic animals. From a mechanistic standpoint, the main intracellular pathways linked to AT(2) receptor stimulation include modulation of phosphorylation by activation of kinases and phosphatases or the production of nitric oxide and cGMP, some of which are associated with the Gi-coupling protein. The receptor can also interact with other receptors, either G protein-coupled such as bradykinin, or growth factor receptors such as nerve growth factor or platelet-derived growth factor receptors. More recently, new advances have also led to identification of various partner proteins, thus providing new insights into this receptor's mechanism of action. This review summarizes the recent advances regarding the signaling pathways induced by the AT(2) receptor in neuronal cells, and discussed the potential therapeutic relevance of central actions of this enigmatic receptor. In particular, we highlight the possibility that selective AT(2) receptor activation by non-peptide and selective agonists could represent new pharmacological tools that may help to improve impaired cognitive performance in Alzheimer's disease and other neurological cognitive disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".