Tissue Factor and CCL2/Monocyte Chemoattractant Protein-1 Released by Human Islets Affect Islet Engraftment in Type 1 Diabetic Recipients
Bibliographic record
Abstract
Islet survival in the early posttransplantation period is likely to be influenced by inflammatory events in and around islets. Twenty-seven human islet preparations were transplanted by 24 infusions into 14 patients with brittle type 1 diabetes under the Edmonton protocol. Patients were monitored for their coagulation [cross-linked fibrin degradation products (XDPs)] and liver function test [aspartate and alanine aminotransferase (AST and ALT)] as markers of early posttransplant complications, and these were correlated with in vitro islet number, purification, volume, monocyte-chemoattractant protein-1 (CCL2/MCP-1) and tissue factor (TF) islet release. Consistent with activation of coagulation pathways and hepatic damage, serum XDP values increased early after 11 infusions and transaminase after 13 of 24 infusions. TF and CCL2/MCP-1 were detected in supernatants of 21 and 22 islet preparations, respectively. Serum XDP peak values were correlated with TF/equivalent islets (EI) (r(2)=0.26, P = 0.001) and CCL2/MCP-1/EI (r(2) = 0.42; P < 0.001); serum transaminase areas under the curve in the first week posttransplantation were correlated with CCL2/MCP-1/EI (r(2) = 0.55; P < 0.001 for ALT and r(2) = 0.51; P = 0.001 for AST) and TF/EI (r(2) = 0.31; P = 0.002 for ALT, and r(2) = 0.36; P = 0.002 for AST). These data suggest that reducing the islet proinflammatory state may be a means to reduce the early posttransplant complications and perhaps improve islet engraftment.
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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) | 0.000 | 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 | 0.000 |
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".