Human Heart Glutamate Receptors—Implications for Toxicology, Food Safety, and Drug Discovery
Bibliographic record
Abstract
Excitatory amino acids (EAAs) mediate their effects through the glutamate receptors (GluRs) in the brain. GluRs play an important role in the treatment of a variety of neuropsychiatric conditions and are central to the neurotoxicity of EAAs such as domoic and kainic acid. Unstained histological preparations of human heart tissues were used for the histopathological assessment, the anatomical identification of specific cardiac structures and the presence of the GluRs. Immunohistochemical stains with the biomarkers protein gene product (PGP 9.5) and the neurofilaments (NF 160 and NF 200) were used to identify neural structures and the components of the conducting system. Several subtypes of GluRs were differentially expressed and each had a specific distribution. In contrast to nonhuman primates, GluRs are more widely expressed in humans, where the working myocardium and the wall of blood vessels stained for GluRs. The immunolabelling was observed within the specialized structures of the conducting system, intramural nerves, and ganglia cells. These receptors may be involved in important cardiac functions such as contraction, rhythm, coronary circulation, and thus may be implicated in the pathobiology of some cardiac disease. The GluRs in the heart could be targets for the effects of excitatory compounds and is therefore an important consideration for the safety evaluation of foods and therapeutic products.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".