Tumor necrosis factor-α potentiates the effects of angiotensin II on subfornical organ neurons
Bibliographic record
Abstract
Inflammation is thought to play a fundamental role in the pathophysiology of hypertension and heart failure, although the mechanisms for this remain unclear. Proinflammatory cytokines, such as tumor necrosis factor-α (TNF-α), influence the subfornical organ (SFO) to modulate sympathetic activity and blood pressure. The pressor effects of TNF-α in the SFO are partially mediated by angiotensin II (ANG II) receptor type 1 (AT1R), and TNF-α is known to potentiate ANG II-induced hypertension. However, the cellular mechanism of the interaction between TNF-α and ANG II/AT1R signaling remains unknown. In the present study, we performed Ca2+imaging on dissociated SFO neurons in vitro from male Sprague-Dawley rats to determine whether TNF-α modulates ANG II-induced increases in intracellular Ca2+in SFO neurons. We first established that a proportion of SFO neurons respond to ANG II, an effect that required AT1R signaling and extracellular Ca2+. We then tested the hypothesis that TNF-α may modulate the effects of ANG II on SFO neurons by examining the effects of TNF-α treatment on the ANG II-induced rise in intracellular Ca2+. We discovered that TNF-α potentiated the ANG II-induced rise in intracellular Ca2+, an effect that was dependent on the duration of TNF-α treatment. Finally, we determined that this potentiation of ANG II-induced Ca2+activity relied on tetrodotoxin-sensitive voltage-gated Na+(vgNa+) channels. These data suggest that the potentiation of ANG II/AT1R activity by TNF-α in SFO neurons results from the previously demonstrated ability of this cytokine to modulate the activation threshold of vgNa+currents.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".