Photoacoustic imaging of angiogenesis in subdermal islet transplant sites
Bibliographic record
Abstract
Exogenous insulin administration is the mainstay treatment therapy for patients with Type-1 diabetes mellitus (T1DM). However, for select patients, clinical islet transplantation is an alternative therapeutic treatment. In this procedure, islets are transplanted into the hepatic portal vein, and despite improved success within the last decade, obstacles are still associated with this approach. It has been discovered that the subcutaneous space may be an effective alternative site for islet transplantation, and may provide advantages of easy access and potential for simple monitoring. The ability to monitor islet viability and the transplant microenvironment may be key to future success in islet transplantation. A subcutaneous device-less technique has been developed to facilitate angiogenesis in the islet transplant site, however, a method for monitoring the potential engraftment site have yet to be explored fully. Here we demonstrate the ability to track angiogenesis in mice with 1, 2, 3 and 4 weeks post-catheter implant on both sides of the abdomen using a FujiFilm VisualSonics Vevo-LAZR system. Quantitative analysis on vessel densities exhibited gradual vessel growth successfully induced by catheter implantation. Our study demonstrates the ability of employing photoacoustic and micro-ultrasound imaging to track angiogenesis around the catheter site prior to islet transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".