Physiological roles for the subfornical organ: a dynamic transcriptome shaped by autonomic state
Bibliographic record
Abstract
The subfornical organ (SFO) is a circumventricular organ recognized for its ability to sense and integrate hydromineral and hormonal circulating fluid balance signals, information which is transmitted to central autonomic nuclei to which SFO neurons project. While the role of SFO was once synonymous with physiological responses to osmotic, volumetric and cardiovascular challenge, recent data suggest that SFO neurons also sense and integrate information from circulating signals of metabolic status. Using microarrays, we have confirmed the expression of receptors already described in the SFO, and identified many novel transcripts expressed in this circumventricular organ including receptors for many of the critical circulating energy balance signals such as adiponectin, apelin, endocannabinoids, leptin, insulin and peptide YY. This transcriptome analysis also identified SFO transcripts, the expressions of which are significantly changed by either 72 h dehydration, or 48 h starvation, compared to fed and euhydrated controls. Expression and potential roles for many of these targets are yet to be confirmed and elucidated. Subsequent validation of data for adiponectin and leptin receptors confirmed that receptors for both are expressed in the SFO, that discrete populations of neurons in this tissue are functionally responsive to these adipokines, and that such responsiveness is regulated by physiological state. Thus, transcriptomic analysis offers great promise for understanding the integrative complexity of these physiological systems, especially with development of technologies allowing description of the entire transcriptome of single, carefully phenotyped, SFO neurons. These data will ultimately elucidate mechanisms through which these uniquely positioned neurons respond to and integrate complex circulating signals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".