Simulated Night Shift Disrupts Circadian Rhythms of Immune Functions in Humans
Bibliographic record
Abstract
Recent research unveiled a circadian regulation of the immune system in rodents, yet little is known about rhythms of immune functions in humans and how they are affected by circadian disruption. In this study, we assessed rhythms of cytokine secretion by immune cells and tested their response to simulated night shifts. PBMCs were collected from nine participants kept in constant posture over 24 h under a day-oriented schedule (baseline) and after 3 d under a night-oriented schedule. Monocytes and T lymphocytes were stimulated with LPS and PHA, respectively. At baseline, a bimodal rhythmic secretion was detected for IL-1β, IL-6, and TNF-α: a night peak was primarily due to a higher responsiveness of monocytes, and a day peak was partly due to a higher proportion of monocytes. A rhythmic release was also observed for IL-2 and IFN-γ, with a nighttime peak due to a higher cell count and responsiveness of T lymphocytes. Following night shifts, with the exception of IL-2, cytokine secretion was still rhythmic but with peak levels phase advanced by 4.5-6 h, whereas the rhythm in monocyte and T lymphocyte numbers was not shifted. This suggests distinct mechanisms of regulation between responsiveness to stimuli and cell numbers of the human immune system. Under a night-oriented schedule, only cytokine release was partly shifted in response to the change in the sleep-wake cycle. This led to a desynchronization of rhythmic immune parameters, which might contribute to the increased risk for infection, autoimmune diseases, cardiovascular and metabolic disorders, and cancer reported in shift workers.
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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".