Artemisinin inhibits innate immune cell chemotaxis, cytokine production and NET release
Bibliographic record
Abstract
Abstract Immune cell chemotaxis to the sites of pathogen invasion is critical for fighting infection, but in life-threatening conditions such as sepsis and Covid-19, excess activation of the innate immune system is thought to cause a damaging invasion of immune cells into tissues and a consequent excessive release of cytokines. In these circumstances, tempering excessive activation of the innate immune system may, paradoxically, promote recovery. Here we identify the antimalarial compound artemisinin as a potent and selective inhibitor of neutrophil and macrophage chemotaxis induced by many chemotactic agents. Artemisinin released calcium from intracellular stores in a similar way to thapsigargin, a known inhibitor of the Sarco/Endoplasmic Reticulum Calcium ATPase pump (SERCA), but unlike thapsigargin, artemisinin blocks only the SERCA3 isoform. Inhibition of SERCA3 by artemisinin was irreversible and was inhibited by iron chelation, suggesting iron-catalysed alkylation of a specific cysteine residue in SERCA3 as the mechanism by which artemisinin inhibits immune cell motility. In murine infection models, artemisinin potently suppressed immune cell invasion into both peritoneum and lung in vivo and inhibited the release of cytokines/chemokines and neutrophil extracellular traps (NETs). This work suggests that artemisinin may have value as a therapy in conditions such as sepsis and Covid-19 in which over-activation of the innate immune system causes tissue injury that can lead to death.
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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".