Time course of transforming growth factor-?1 (TGF-?1) mRNA expression in the host reaction to alginate-poly-L-lysine microcapsules following implantations into rat epididymal fat pads
Bibliographic record
Abstract
Microencapsulation of islets of Langerhans within semipermeable membranes has been proposed to prevent their immune destruction after transplantation. However, the successful application of this method is impaired by a pericapsular reaction, which eventually induces graft failure. Our goal is to study the role of cytokines in the pathogenesis of this reaction, using the model of alginate-poly-L-lysine microcapsule implantation into Wistar rat epididymal fat pads (EFP). The specific objective of this study was to determine the time course of transforming growth factor (TGF)-beta(1) mRNA expression by semi-quantitative reverse transcriptase-polymerase chain reaction. Microcapsules induced an increase of TGF-beta(1) mRNA expression that reached a maximum 14 days after implantation. Seven, 14, 30, and 60 days after microcapsule implantation, the expression of TGF-beta(1) mRNA was significantly higher in pericapsular infiltrate cells than in nonimplanted EFP cells (p<0.05, p<0.0001, p<0.005, and p<0.01, respectively). Injection of physiological saline induced a small and gradual augmentation of TGF-beta(1) mRNA expression with a maximum 30 days after injection (p<0.01 vs. nonimplanted EFP cells). These results demonstrated that microcapsule implantation, in comparison with saline injection, induce an early, extended, and amplified TGF-beta(1) mRNA expression. This suggests that TGF-beta(1) plays a role in the pathogenesis of the pericapsular host reaction.
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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".