Immunopathology of Type 1 Diabetes and Immunomodulatory Effects of Stem Cells: A Narrative Review of the Literature
Bibliographic record
Abstract
Type 1 Diabetes (T1D) is a complex autoimmune disorder which occurs as a result of an intricate series of pathologic interactions between pancreatic β-cells and a wide range of components of both the innate and the adaptive immune systems. Stem-cell therapy, a recently-emerged potentially therapeutic option for curative treatment of diabetes, is demonstrated to cause significant alternations to both different immune cells such as macrophages, natural killer (NK) cells, dendritic cells, T cells, and B cells and non-cellular elements, including serum cytokines and different components of the complement system. Although there exists overwhelming evidence indicating that the documented therapeutic effects of stem cells on patients with T1D are primarily due to their potential for immune regulation rather than pancreatic tissue regeneration, to date, the precise underlying mechanisms remain obscure. On the other hand, immune-mediated rejection of stem cells remains one of the main obstacles to regenerative medicine. Moreover, the consequences of efferocytosis of stem-cells by the recipients' lung-resident macrophages have recently emerged as a mechanism responsible for some immune-mediated therapeutic effects of stem-cells. This review focuses on the nature of the interactions amongst different compartments of the immune systems which are involved in the pathogenesis of T1D and provides an explanation as to how stem cell- based interventions can influence immune system and maintain the physiologic equilibrium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".