Calmodulin Binding Proteins and Alzheimer’s Disease: Biomarkers, Regulatory Enzymes and Receptors That Are Regulated by Calmodulin
Bibliographic record
Abstract
The integral role of calmodulin in the amyloid pathway and neurofibrillary tangle formation in Alzheimer's disease was first established leading to the "Calmodulin Hypothesis". Continued research has extended our insight into the central function of the small calcium sensor and effector calmodulin and its target proteins in a multitude of other events associated with the onset and progression of this devastating neurodegenerative disease. Calmodulin's involvement in the contrasting roles of calcium/CaM-dependent kinase II (CaMKII) and calcineurin (CaN) in long term potentiation and depression, respectively, and memory impairment and neurodegeneration are updated. The functions of the proposed neuronal biomarker neurogranin, a calmodulin binding protein also involved in long term potentiation and depression, is detailed. In addition, new discoveries into calmodulin's role in regulating glutamate receptors (mGluR, NMDAR) are overviewed. The interplay between calmodulin and amyloid beta in the regulation of PMCA and ryanodine receptors are prime examples of how the buildup of classic biomarkers can underly the signs and symptoms of Alzheimer's. The role of calmodulin in the function of stromal interaction molecule 2 (STIM2) and adenosine A2A receptor, two other proteins linked to neurodegenerative events, is discussed. Prior to concluding, an analysis of how targeting calmodulin and its binding proteins are viable routes for Alzheimer's therapy is presented. In total, calmodulin and its binding proteins are further revealed to be central to the onset and progression of 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".