Calcium-Sensing Receptors of Human Astrocyte-Neuron Teams: Amyloid-β-Driven Mediators and Therapeutic Targets of Alzheimer’s Disease
Bibliographic record
Abstract
It is generally assumed that the neuropathology of sporadic (late-onset or nonfamilial) Alzheimer's disease (AD) is driven by the overproduction and spreading of first Amyloid-βx-42 (Aβ42) and later hyperphosphorylated (hp)-Tau oligomeric "infectious seeds". Hitherto, only neurons were held to make and spread both oligomer types; astrocytes would just remove debris. However, we have recently shown that exogenous fibrillar or soluble Aβ peptides specifically bind and activate the Ca(2+)-sensing receptors (CaSRs) of untransformed human cortical adult astrocytes and postnatal neurons cultured in vitro driving them to produce, accrue, and secrete surplus endogenous Aβ42. While the Aβ-exposed neurons start dying, astrocytes survive and keep oversecreting Aβ42, nitric oxide (NO), and vascular endothelial growth factor (VEGF)-A. Thus astrocytes help neurons' demise. Moreover, we have found that a highly selective allosteric CaSR agonist ("calcimimetic"), NPS R-568, mimics the just mentioned neurotoxic actions triggered by Aβ●CaSR signaling. Contrariwise, and most important, NPS 2143, a highly selective allosteric CaSR antagonist ("calcilytic"), fully suppresses all the Aβ●CaSR signaling-driven noxious actions. Altogether our findings suggest that the progression of AD neuropathology is promoted by unceasingly repeating cycles of accruing exogenous Aβ42 oligomers interacting with the CaSRs of swelling numbers of astrocyte-neuron teams thereby recruiting them to overrelease additional Aβ42 oligomers, VEGF-A, and NO. Calcilytics would beneficially break such Aβ/CaSR-driven vicious cycles and hence halt or at least slow the otherwise unstoppable spreading of AD neuropathology.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".