Interleukin-10 modulates the synthesis of inflammatory mediators in the sensory circumventricular organs: implications for the regulation of fever and sickness behaviors
Bibliographic record
Abstract
BACKGROUND: Whereas the role played by interleukin (IL)-10 in modulating fever and sickness behavior has been linked to it targeting the production of pro-inflammatory cytokines in the circulation, liver and spleen, it is not known whether it could directly target the local production of pro-inflammatory cytokines within the sensory circumventricular organs (CVOs) situated within the brain, but outside the blood-brain barrier. Using inactivation of IL-10, we, therefore, investigated whether IL-10 could modulate the synthesis of pro-inflammatory cytokines within the sensory CVOs, in particular the organum vasculosum laminae terminalis (OVLT) and area postrema (AP). FINDINGS: Primary OVLT and AP microcultures were established from topographically excised rat pup brain tissue. The microcultures were pretreated with either IL-10 antibodies (AB) (10 μl/350 μl medium) or phosphate-buffered saline (PBS) (10 μl/350 μl medium) before being incubated with lipopolysaccharide (LPS) (100 μg/ml) or PBS in complete medium for 6 h. Supernatants were removed from the microcultures after 6 h of incubation with LPS and used for the determination of IL-6 and tumor necrosis factor (TNF)-α. Pre-treating the OVLT and AP microcultures with IL-10 antibodies significantly enhanced the LPS-induced increase in TNF-α and IL-6 in the supernatant obtained from the microcultures. CONCLUSIONS: Our results show for the first time that the LPS-induced release of pro-inflammatory cytokines in cells cultured from the AP and OVLT can be modulated in the presence of IL-10 antibodies. Thus, we have identified that the sensory CVOs may have a key role to play in both the initiation and modulation of neuroinflammation.
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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 | 0.000 |
| 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".