RETRACTED: Monocytes play different roles in stimulating T cells in obese diabetic individuals
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a chronic metabolic disorder, which was also found to involve a series of inflammatory disorders, including accumulation of macrophages and T cells in the adipose tissue, increased proinflammatory cytokine production, shifting of macrophage composition toward M1-type, and skewing of peripheral blood T cells toward IL-17 productions. However, these studies were primarily conducted in obese mouse models and/or human subjects with higher BMI, and may not reflect the role of the immune system in non-obese T2D pathogenesis. Here, we examined T cell and monocyte cytokine expression and function in both non-obese and obese T2D patients. We found that IFN-g production by circulating T cells were increased in both non-obese and obese T2D subjects, while IL-17 is only upregulated in obese T2D subjects. Also, circulating monocytes from obese T2D subjects had significantly higher IL-6 production than their counterparts in non-obese T2D subjects. Moreover, monocytes from non-obese T2D subjects could support IFN-g but not IL-17 production in vitro, while that from obese T2D subjects supported both IFN-g and IL-17 production. Together, our results revealed that the role immune system plays in T2D pathogenesis is more complicated than previously thought, and is affected by the person's BMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".