Tumor Necrosis Factor-α Production by Human Islets Leads to Postisolation Cell Death
Bibliographic record
Abstract
BACKGROUND: Recent successes in islet transplantation highlight the importance of islet isolation by experienced centers and minimization of cell injury as crucial to the achievement of insulin independence. Islet injury may manifest as cell death by apoptosis, shorter graft survival, and the need for retransplantation. Although an inflammatory cytokine response at the graft site is known to inhibit engraftment, recent evidence indicates that islet cells may contribute to this response. METHODS: Isolated human islets were cultured for up to one week in serum-free CMRL-1066 with 25 microM of tumor necrosis factor (TNF)alpha inhibitor RDP58. Gene expression was measured by reverse transcriptase polymerase chain reaction, apoptosis and TNFalpha secretion by enzyme-linked immunosorbent assay and enzyme-linked immunospot, and islet function by stimulated insulin secretion. RESULTS: Isolation induced a twofold increase in TNFalpha expression between days one and three (P<0.05), while TNFalpha secretion peaked at day one. RDP58 reduced TNFalpha secretion by 70.6% (P<0.02), though TNFalpha gene expression was unaffected. RDP58 reduced the frequency of TNFalpha-secreting islets by 64.4% (P<0.05) and reduced apoptotic levels by 26.4% within 24 hr postisolation (P<0.05). The reduction in apoptosis was maintained throughout the week (P<0.01), while apoptosis increased in control cultures. Finally, RDP58-treated islets displayed increased insulin secretion in response to both elevated glucose (1915.0+/-396.6 vs. 825.3+/-261.1 mU/L, P<0.01) and secretagogues (2294.3+/-529.5 vs. 939.8+/-333.7 mU/L, P<0.02). CONCLUSIONS: These data demonstrate that intraislet cytokine production should be considered as a factor leading to islet cell death postisolation and postengraftment, and strategies aimed at countering islet cytokine production represent a novel target for improving islet viability and function.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".