Photoacoustic imaging of angiogenesis in a subcutaneous islet transplant site in a murine model
Bibliographic record
Abstract
Islet transplantation (IT) is an established clinical therapy for select patients with type-1 diabetes. Clinically, the hepatic portal vein serves as the site for IT. Despite numerous advances in clinical IT, limitations remain, including early islet cell loss posttransplant, procedural complications, and the inability to effectively monitor islet grafts. Hence, alternative sites for IT are currently being explored, with the subcutaneous space as one potential option. When left unmodified, the subcutaneous space routinely fails to promote successful islet engraftment. However, when employing the previously developed subcutaneous “deviceless” technique, a favorable microenvironment for islet survival and function is established. In this technique, an angiocatheter was temporarily implanted subcutaneously, which facilitated angiogenesis to promote subsequent islet engraftment. This technique has been employed in preclinical animal models, providing a sufficient means to develop techniques to monitor functional aspects of the graft such as angiogenesis. Here, we utilize photoacoustic imaging to track angiogenesis during the priming of the subcutaneous site by the implanted catheter at 1 to 4 weeks postcatheter. Quantitative analysis on vessel densities shows gradual growth of vasculature in the implant position. These results demonstrate the ability to track angiogenesis, thus facilitating a means to optimize and assess the pretransplant microenvironment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".