Combined optical-resolution photoacoustic and fluorescence micro-endoscopy
Bibliographic record
Abstract
We present a new micro-endoscopy system combining real-time C-scan optical-resolution photoacoustic micro-endoscopy (OR-PAME), and a high-resolution fluorescence micro-endoscopy system for visualizing fluorescently labeled cellular components and optically absorbing microvasculature simultaneously. With a diode-pumped 532-nm fiber laser, the OR-PAM sub-system is capable of imaging with a resolution of ~ 7μm. The fluorescence sub-system consists of a diode laser with 445 nm-centered emissions as the light source, an objective lens and a CCD camera. Proflavine, a FDA approved drug for human use, is used as the fluorescent contrast agent by topical application. The fluorescence system does not require any mechanical scanning. The scanning laser and the diode laser light source share the same light path within an optical fiber bundle containing 30,000 individual single mode fibers. The absorption of Proflavine at 532 nm is low, which mitigates absorption bleaching of the contrast agent by the photoacoustic excitation source. We demonstrate imaging in live murine models. The system is able to provide cellular morphology with cellular resolution co-registered with the structural and functional information given by OR-PAM. Therefore, the system has the potential to serve as a virtual biopsy technique, helping researchers and clinicians visualize angiogenesis, effects of anti-cancer drugs on both cells and the microcirculation, as well as aid in the study of other diseases.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".