Erratum to: Age, atherosclerosis and type 2 diabetes reduce human mesenchymal stromal cell-mediated T-cell suppression
Bibliographic record
Abstract
Atherosclerosis is an age-associated, multifactorial process driven by immune activation and inflammation. Ongoing clinical trials aim to establish the role of mesenchymal stromal cells (MSCs) as therapeutic agents in atherosclerosis. The beneficial effects of MSCs derive from their immune-modulatory properties. Understanding the impact of aging and age-associated conditions (i.e., type 2 diabetes mellitus and atherosclerosis) on MSC function is key to maximizing their therapeutic potency. The aim of this study was to assess the effect of chronological and biological aging on human MSC-mediated CD4+ T-cell suppression. To this end human MSCs were isolated from adipose tissue and the MSC:CD4+ T-cell suppression was assessed in a co-culture system. MSCs from elderly donors (≥65 years) had significantly lower T-cell suppressive capacity compared to those from donors <65 years ( p = 0.003). Furthermore, MSCs from patients with atherosclerosis and type 2 diabetes mellitus were less efficient at suppressing T-cell proliferation (atherosclerosis, p = 0.02; type 2 diabetes mellitus, p = 0.04; compared to non-disease controls). Sex and tobacco use did not impact the immunosuppressive capacity of MSCs. In summary, this study demonstrates that advanced age, atherosclerosis and type 2 diabetes mellitus reduce the functional potency of MSCs. Optimizing the criteria for the selection of MSC donors could enhance the results of cell-based therapies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".