Artesunate prevents type 1 diabetes in NOD mice mainly by inducing protective IL‐4—producing T cells and regulatory T cells
Bibliographic record
Abstract
ABSTRACT Type 1 diabetes (T1D) is an autoimmune disease characterized by the immune‐mediated destruction of insulin‐producing β cells. Recent studies showed that in addition to malaria, artemisinin and its derivative, artesunate (AS), could alleviate several autoimmune diseases. However, whether AS has a role in the prevention or treatment of T1D is still unknown. Therefore, in this study we administrated AS or DMSO in the drinking water of nonobese diabetic (NOD) mice, a mouse model of T1D. We found that AS administration significantly prevented the incidence of T1D. The frequency of IL‐4—producing CD4 + single‐positive T cells and CD8 + T cells was significantly elevated, and IFN‐γ—producing T cells were reduced in the spleen and pancreatic lymph nodes. In the pancreas, the skewing to IL‐4—producing T cells was also observed. In addition, more regulatory T cells were found in the pancreas. mRNA levels of proinflammatory cytokines, including TNF‐α and IL‐6, were decreased. In addition, AS administration promoted the functional maturity of β cells in vitro . Our findings demonstrate that AS administration can prevent T1D in NOD mice mainly by reducing autoimmune T cells and increasing protective T cells. Our data constitute the first functional study of AS in T1D, which may provide a new rationale for future translational studies.—Li, Z., Shi, X., Liu, J., Shao, F., Huang, G., Zhou, Z., Zheng, P. Artesunate prevents type 1 diabetes in NOD mice mainly by inducing protective IL‐4—producing T cells and regulatory T cells. FASEB J. 33, 8241–8248 (2019). www.fasebj.org
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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) | 0.001 | 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) | 0.002 | 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".