Multimodal 3D photoacoustic remote sensing and confocal fluorescence microscopy imaging
Bibliographic record
Abstract
SIGNIFICANCE: Complementary absorption and fluorescence contrast could prove useful for a wide range of biomedical applications. However, current absorption-based photoacoustic microscopy systems require the ultrasound transducers to physically touch the samples, thereby increasing contamination and limiting strong optical focusing in reflection mode. AIM: We sought to develop an all-optical system for imaging cells and tissues using the three combined imaging modalities: photoacoustic remote sensing (PARS), epifluorescence, and confocal laser scanning microscopy (CLSM). APPROACH: A PARS subsystem with ultraviolet excitation was used to obtain label-free absorption-contrast images of nucleic acids in ex vivo tissue samples. Co-integrated epifluorescence and CLSM subsystems were used to verify the 2D and 3D nuclei distribution. RESULTS: Complementary absorption and fluorescence contrast were demonstrated in phantom imaging experiments and subsequent cell and tissue imaging experiments. Lateral and axial resolution of ultraviolet-PARS (UV-PARS) is shown to be 0.39 and 1.6 μm, respectively, with 266-nm light. CLSM lateral and axial resolution was measured as 0.97 and 2.0 μm, respectively. This resolution is sufficient to image individual cell layers with fine optical sectioning. UV-PARS images of cell nuclei are validated in thick tissue using CLSM. CONCLUSIONS: Multimodal absorption and fluorescence contrast are obtained with a non-contact all-optical microscopy system for the first time and utilized to obtain images of cells and tissues with subcellular resolution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".