P-Selectin-Mediated Monocyte–Cerebral Endothelium Adhesive Interactions Link Peripheral Organ Inflammation To Sickness Behaviors
Bibliographic record
Abstract
Sickness behaviors, such as fatigue, mood alterations, and cognitive dysfunction, which result from changes in central neurotransmission, are prevalent in systemic inflammatory diseases and greatly impact patient quality of life. Although, microglia (resident cerebral immune cells) and cytokines (e.g., TNFα) are associated with changes in central neurotransmission, the link between peripheral organ inflammation, circulating cytokine signaling, and microglial activation remains poorly understood. Here we demonstrate, using cerebral intravital microscopy, that in response to liver inflammation, there is increased monocyte specific rolling and adhesion along cerebral endothelial cells (CECs). Peripheral TNFα-TNFR1 signaling and the adhesion molecule P-selectin are central mediators of these monocyte-CEC adhesive interactions which were found to be closely associated with microglial activation, decreased central neural excitability and sickness behavior development. Similar monocyte-CEC adhesive interactions were also observed in another mouse model of peripheral organ inflammation (i.e., 2,4-dinitrobenzene sulfonic acid-induced colitis). Our observations provide a clear link between peripheral organ inflammation and cerebral changes that impact behavior, which can potentially allow for novel therapeutic interventions in patients with systemic inflammatory diseases.
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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".