Potential Benefits of Nrf2/Keap1 Targeting in Pancreatic Islet Cell Transplantation
Bibliographic record
Abstract
Permanent pancreatic islet cell destruction occurs in type 1 diabetes mellitus (T1DM) through the infiltration of inflammatory cells and cytokines. Loss of β-cell integrity secondary to oxidation leads to an inability to appropriately synthesize and secrete insulin. Allogenic islet cell transplantation (ICT) has risen as a therapeutic option to mitigate problematic hypoglycemia. Nevertheless, during the process of transplantation, islet cells are exposed to oxidatively caustic conditions that severely decrease the islet cell yield. Islet cells are at a baseline disadvantage to sustain themselves during times of metabolic stress as they lack a robust anti-oxidant defense system, glycogen stores, and vascularity. The Nrf2/Keap1 system is a master regulator of antioxidant genes that has garnered attention as pharmacologic activators have shown a protective response and a low side effect profile. Herein, we present the most recently studied Nrf2/Keap1 activators in pancreas for application in ICT: Dh404, dimethyl fumarate (DMF), and epigallocatechin gallate (EGCG). Furthermore, we discuss that Nrf2/Keap1 is a potential target to ameliorate oxidative stress at every step of the Edmonton Protocol.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".