Investigation of cytoskeleton proteins in neurons of the cat lateral geniculate nucleus
Bibliographic record
Abstract
The gross structure of neurons is supported by proteins that compose the cytoskeleton. Neurofilaments are intermediate cytoskeletal proteins that contribute to neuron structure and function, and three neurofilament subunits different in their molecular mass assemble to form heteropolymers that produce a structure-providing intracellular scaffold. The light neurofilament subunit is obligatory and can assemble with either the medium or heavy subunit, indicating some degree of independence between subunits. The presence of the heavy subunit has been shown to be associated with mature cells and is linked to large neurons in the cerebral cortex and thalamus. Spectrin is a membrane-associated actin-binding protein that, like neurofilament, has been linked to neuron shape. In this study of the cat dorsal lateral geniculate nucleus (dLGN) we examined whether labeling for neurofilament subunits and spectrin is linked to neuron size. We found that about one-third of neurons contained a visible amount of labeling for each neurofilament subunit, and the bulk of these labeled cells were large in comparison to the general population of neurons. The distribution of neuron sizes was not different between neurofilament subunits, indicating that neurofilament subunit content is not determined by neuron size. Spectrin labeling was evident in most dLGN neurons, and was not related to the size of neurons. That reactivity for neurofilament was predominant in large cells led us to directly examine the relationship between neurofilament and interneurons. The large majority of neurofilament-positive neurons did not contain GABA, indicating that neurofilament is predominant in projection cells and not in interneurons.
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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 | 0.001 | 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 | 0.000 |
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".