Distribution of zincergic neurons in the mouse forebrain
Bibliographic record
Abstract
Synaptically released zinc is thought to play an important role in neuronal signaling by modulating excitatory and inhibitory receptors and intracellular signaling proteins. Consequently, neurons that release zinc have been implicated in synaptic plasticity underlying learning and memory as well as neuropathological processes such as epilepsy, stroke, and Alzheimer's disease. To characterize the distribution of these neurons, investigators have relied on a technique that involves the retrograde transport of zinc-selenium crystals from axonal boutons to the cell bodies of origin. However, one major problem with this method is that labeling of cell bodies is obscured by high levels of staining in synaptic boutons, particularly within forebrain structures where this staining is most intense. Here, we used a modification of the retrograde labeling method that eliminates terminal staining for zinc, thereby enabling a clear and comprehensive description of these neurons. Zincergic neurons were found in all cerebral cortical regions and were arranged in a distinct laminar pattern, restricted to layers 2/3, 5, and 6 with no labeling in layer 4. In the hippocampus, labeling was present in CA1, CA3, and the dentate gyrus but not in CA2. Labeled cell bodies were also observed in most amygdaloid nuclei, anterior olfactory nuclei, claustrum, tenia tecta, endopiriform region, lateral ventricle, lateral septum, zona incerta, superior colliculus, and periaqueductal gray. Moreover, retrograde labeling was also noted in the dorsomedial and lateral hypothalamus, regions that previously were thought to be devoid of neurons with a zincergic phenotype. Collectively these data show that zincergic neurons comprise a large population of neurons in the murine forebrain and will provide an anatomical framework for understanding the functional importance of these neurons in the mammalian brain.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".