Generation of a Phenotypic Array of Hypothalamic Neuronal Cell Models to Study Complex Neuroendocrine Disorders
Bibliographic record
Abstract
Knowledge of how the brain achieves its diverse central control of basic physiology is severely limited by the virtual absence of appropriate cell models. Isolation of clonal populations of unique peptidergic neurons from the hypothalamus will facilitate these studies. Herein we describe the mass immortalization of mouse primary hypothalamic cells in monolayer culture, resulting in the generation of a vast representation of hypothalamic cell types. Subcloning of the heterogeneous cell populations resulted in the establishment of 38 representative clonal neuronal cell lines, of which 16 have been further characterized by analysis of 28 neuroendocrine markers. These cell lines represent the first available models to study the regulation of neuropeptides associated with the control of feeding behavior, including neuropeptide Y, ghrelin, urocortin, proopiomelanocortin, melanin-concentrating hormone, neurotensin, proglucagon, and GHRH. Importantly, a representative cell line responds appropriately to leptin stimulation and results in the repression of neuropeptide Y gene expression. These cell models can be used for detailed molecular analysis of neuropeptide gene regulation and signal transduction events involved in the direct hormonal control of unique hypothalamic neurons, not yet possible in the whole brain. Such studies may contribute information necessary for the strategic design of therapeutic interventions for complex neuroendocrine disorders, such as obesity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".