Multimodal Ultrasound-Photoacoustic Imaging of Tissue Engineering Scaffolds and Blood Oxygen Saturation In and Around the Scaffolds
Bibliographic record
Abstract
Preclinical, noninvasive imaging of tissue engineering polymeric scaffold structure and/or the physiological processes such as blood oxygenation remains a challenge. In vitro or ex vivo, the widely used scaffold characterization modalities such as porosimetry, electron or optical microscopy, and X-ray microcomputed tomography have limitations or disadvantages-some are invasive or destructive, others have limited tissue penetration (few hundred micrometers) and/or show poor contrast under physiological conditions. Postmortem histological analysis, the most robust technique for the evaluation of neovascularization is obviously not appropriate for acquiring physiological or longitudinal data. In this study, we have explored the potential of ultrasound (US)-coregistered photoacoustic (PA) imaging as a noninvasive multimodal imaging modality to overcome some of the above challenges and/or provide complementary information. US-PA imaging was employed to characterize poly(lactic-co-glycolic acid) (PLGA) polymer scaffolds or single-walled carbon nanotube (SWCNT)-incorporated PLGA (SWCNT-PLGA) polymer scaffolds as well as blood oxygen saturation within and around the scaffolds. Ex vivo, PLGA and SWCNT-PLGA scaffolds were placed at 0.5, 2, and 6 mm depths in chicken breast tissues. PLGA scaffolds could be localized with US imaging, but generate no PA signal (excitation wavelengths 680 and 780 nm). SWCNT-PLGA scaffolds generated strong PA signals at both wavelengths due to the presence of the SWCNTs and could be localized with both US and PA imaging depths between 0.5-6 mm (lateral resolution = 90 μm, axial resolution = 40 μm). In vivo, PLGA and SWCNT-PLGA scaffolds were implanted in subcutaneous pockets at 2 mm depth in rats, and imaged at 7 and 14 days postsurgery. The anatomical position of both the scaffolds could be determined from the US images. Only SWCNT-PLGA scaffolds could be easily detected in the US-PA images. SWCNT-PLGA scaffolds had significant four times higher PA signal intensity compared with the surrounding tissue and PLGA scaffolds. In vivo blood oxygen saturation maps around and within the PLGA scaffolds could be obtained by PA imaging. There was no significant difference in oxygen saturation for the PLGA scaffolds at the two time points. The blood oxygen saturation maps complemented the histological analysis of neovascularization of the PLGA scaffolds.
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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 | 0.000 |
| 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".