Roncoroni Re‐Visited: The Neuronal Intranuclear Rodlet Comes of Age
Bibliographic record
Abstract
Despite myriad technological advances in neuroscience, the nervous system harbors morphological phenomena that continue to defy explanation. First described by the classical microscopists, including Santiago Ramon y Cajal, at the end of the 19th century, the neuronal intranuclear rodlet (INR) has mystified neurohistologists and microscopists for centuries. In this review article, we will provide an overview of the discovery of the INR as well as the subsequent attempts to elucidate its nature and functional significance. We outline our own studies of this structure over the past three decades, focusing on its elusive nature, its interactions with other nuclear organelles, and on disease-related quantitative changes in Alzheimer's disease. We then describe our somewhat serendipitous discovery that these structures are filamentous aggregates of the nucleotide-synthesizing metabolic enzyme inosine monophosphate dehydrogenase. The filamentation of metabolic enzymes to form mesoscale cellular structures called "rods and rings" or "cytoophidia" (Greek for "cellular snakes") is a recently described phenomenon that remains to be systematically investigated in the nervous system. Thus, this review provides an intriguing historical juxtaposition in neuroscience, inculcating the neuronal INR, once a mere morphological curiosity, into one of the most rapidly evolving fields in contemporary cell biology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".