The Monkey (Macaca fascicularis) Heart Neural Structures and Conducting System: An Immunochemical Study of Selected Neural Biomarkers and Glutamate Receptors
Bibliographic record
Abstract
The neural markers, protein gene product 9.5 (PGP 9.5), neurofilaments (NF) and glutamate receptors (GluRs) were visualized by immunohistochemistry in the monkey heart. PGP 9.5 showed the greatest affinity for intramural ganglia cells and nerve fibres. Structural components of the conducting system were also stained, particularly the bundle of His, AV node and Purkinje fibres. Anti-NF 200 and NF 160 showed strong, preferential affinity to nerve fibres and ganglia throughout the heart. Further studies concentrated on the presence and the distribution of glutamate receptors: NMDAR 1, GluR 1, GluR 2/3, GluR 5/6/7, mGluR 2/3, and mGluR 5. Positive immunoreactivity of GluRs was evident in nerve terminals within the atrium, myocardium, intramural ganglia and elements of the conducting system. The intensity of the stain varied for each antibody according to the anatomical distribution within neural structures and conducting system. The specificity of immunolabelling was confirmed by absorption studies with each corresponding peptide. There is preferential affinity to and differential distribution of staining with PGP 9.5, NFs and several subtypes of GluRs in the various components of the cardiac conducting system in adult monkeys. The expression of specific neural markers and glutamate receptors common to nerve fibers and ganglia cells is consistent to our previous report in rodents. These expressions suggests that such structures in the heart share common characteristics with a variety of neural tissues and hence are potential targets for neurotoxins. Furthermore, the strong affinity and specific distribution of several subtypes of GluRs in the monkey heart fosters our view that these receptors may be able to influence the physiology and pathophysiology of cardiac rhythm and excitation. Hence as in the brain, the GluRs may be involved in the mediation of excitatory effects in the heart.
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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) | 0.002 | 0.000 |
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".