In Vitro and In Vivo Improvement of Islet Survival Following Treatment with Nerve Growth Factor
Bibliographic record
Abstract
BACKGROUND: Nerve growth factor (NGF) has been reported to play an important regulatory role in pancreatic beta-cell function. However, the usefulness of NGF in a transplantation setting is unknown. METHODS: A marginal number of islet cells (260 islet equivalents/recipient) cultured for 24 hr with NGF (500 ng/ml) was syngeneically transplanted under the kidney capsule of streptozotocin-induced diabetic Balb/c mice. Fluorescence microscopy was used to evaluate islet viability. Islet function was evaluated in vitro and in vivo by static assay and glucose tolerance test, respectively. RESULTS: In vitro, improved viability and survival were found in murine islets cultured in serum-free medium for 96 hr with 500 ng/ml NGF (P<0.05). NGF-treated islets had more insulin secretion than islets cultured without NGF in response to 2.8 mmol/L glucose (P<0.05), and 20 mmol/L glucose conditions. In vivo, 67% of recipients with a submarginal number of islets cultured in NGF attained normoglycemia for more than 120 days, whereas transplanted islets without NGF treatment survived a maximum of 13 days in control mice. At posttransplant day 4, recipients of NGF-cultured islets showed significant improvement of glucose tolerance. On immunohistochemistry, the kidney capsules containing NGF-cultured islets displayed higher insulin content, and more dilated neoplastic microvessels than control renal capsules. The number of apoptotic cells using TUNEL staining decreased by nearly 50% in NGF-cultured islet grafts in comparison to control islet grafts. CONCLUSIONS: The above data suggest potential advantages of NGF for islet survival following transplantation. This neurotrophic approach may prove beneficial in human islet transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".