Transgenic Expression of Hsc70 in Pancreatic Islets Enhances Autoimmune Diabetes in Response to β Cell Damage
Bibliographic record
Abstract
Inflammation following tissue damage promotes lymphocyte recruitment, tissue remodeling, and wound healing while maintaining self tolerance. Endogenous signals associated with tissue damage and cell death have been proposed to initiate and instruct immune responses following injury. In this study, we have examined the effects of elevated levels of a candidate endogenous danger signal, heat shock cognate protein 70 (hsc70), on stimulation of inflammation and autoimmunity following cell damage. We find that damage to pancreatic beta cells expressing additional cytosolic hsc70 leads to an increased incidence of diabetes in a transgenic mouse model. Steady-state levels of activated APC and T cell populations in the draining lymph node were enhanced, which further increased following streptozotocin-induced beta cell death. In addition, proinflammatory serum cytokines, and lymphocyte recruitment were increased in hsc70 transgenic mice. Islet Ag-specific T cells underwent a greater extent of proliferation in the lymph nodes of mice expressing hsc70 following beta cell damage, suggesting elevated Ag presentation following release of Ag in the presence of hsc70. These findings suggest that an elevated content of hsc70 in cells undergoing necrotic or apoptotic cell death can increase the extent of sterile inflammation and increase the susceptibility to autoimmunity.
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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 | 0.001 |
| 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".