Immortalized neurons for the study of hypothalamic function
Bibliographic record
Abstract
The hypothalamus is a vital part of the central nervous system: it harbors control systems implicated in regulation of a wide range of homeostatic processes, including energy balance and reproduction. Structurally, the hypothalamus is a complex neuroendocrine tissue composed of a multitude of unique neuronal cell types that express a number of neuromodulators, including hormones, classical neurotransmitters, and specific neuropeptides that play a critical role in mediating hypothalamic function. However, neuropeptide and receptor gene expression, second messenger activation, and electrophysiological and secretory properties of these hypothalamic neurons are not yet fully defined, primarily because the heterogeneity and complex neuronal architecture of the neuroendocrine hypothalamus make such studies challenging to perform in vivo. To circumvent this problem, our research group recently generated embryonic- and adult-derived hypothalamic neuronal cell models by utilizing the novel molecular techniques of ciliary neurotrophic factor-induced neurogenesis and SV40 T antigen transfer to primary hypothalamic neuronal cell cultures. Significant research with these cell lines has demonstrated their value as a potential tool for use in molecular genetic analysis of hypothalamic neuronal function. Insights gained from hypothalamic immortalized cells used in conjunction with in vivo models will enhance our understanding of hypothalamic functions such as neurogenesis, neuronal plasticity, glucose sensing, energy homeostasis, circadian rhythms, and reproduction. This review discusses the generation and use of hypothalamic cell models to study mechanisms underlying the function of individual hypothalamic neurons and to gain a more complete understanding of the overall physiology of the hypothalamus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".