Prevalence of Hepatic Steatosis After Islet Transplantation and Its Relation to Graft Function
Bibliographic record
Abstract
Islet allotransplantation can provide insulin independence in selected individuals with type 1 diabetes. The long-term effects of these transplants on the liver are unknown. Recently, two cases of periportal steatosis after islet transplantation have been described. In this study, we performed ultrasound and magnetic resonance imaging (MRI) in 30 C-peptide-positive islet transplant recipients to detect steatosis and to explore the association of the radiological findings with clinical and metabolic factors. Steatosis was observed on MRI in six (20%) subjects. Histological findings of hepatic steatosis concurred with the imaging findings. Steatosis completely resolved in one subject whose graft failed. More subjects with steatosis required supplementary exogenous insulin than not (67 vs. 21%; P < 0.05). The clinical features of subjects with and without steatosis were otherwise similar, although C-peptide levels were higher in insulin-independent subjects with steatosis (0.98 +/- 0.12 vs. 0.70 +/- 0.18 nmol/l; P = 0.05), despite similar blood glucose levels. Serum triglycerides and the use of exogenous insulin were associated with increased odds of steatosis in a logistic regression model (chi(2) [degrees freedom] = 13.6 [2]); P = 0.001). MRI-detected steatosis is a common finding; the steatosis appears to be due to a paracrine action of insulin secreted from intrahepatic islets. Hepatic steatosis may be associated with insulin resistance or graft dysfunction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".