Multimodal two-photon and three-photon endomicroscopy for 3D tissue imaging
Bibliographic record
Abstract
Multimodal multiphoton microscopy (MPM) can provide fast, label-free, non-invasive examination of cells, extracellular matrix, and lipids. Two-photon microscopy (2PM) can detect second harmonic generation (SHG) from fibrillar collagen and striated muscle myosin, whereas two-photon excitation fluorescence (2PEF) can detect intrinsic fluorophores such as NADH from cells. Meanwhile, three-photon microscopy (3PM) can detect third harmonic generation (THG) from lipids and tissue interfaces. We have developed a miniaturized multimodal multiphoton system which can perform label-free two-photon and three-photon imaging. An Er-doped fiber laser delivers fundamental pulses at 1580 nm and 80 fs for exciting THG. SHG and 2PEF are excited at 790 nm via the frequency doubling of 1580 nm pulses. For clinical applications, a compact probe is being developed with single-mode fiber for delivering the femtosecond excitation pulses and multi-mode fiber for collecting the MPM signals. A MEMS mirror performs lateral scanning at up to 4 frames/s. For objective lenses, a miniature aspherical lens (NA=0.64) is compared with a gradient index microobjective (NA=0.8). Shape memory alloy actuator used in smartphone cameras is evaluated for shifting the focal plane to acquire Z-stacks for 3D tissue imaging. High-resolution SHG, 2PEF, and THG images are acquired from biological tissues and show that multimodal MPM endomicroscopy has great potential for clinical applications as an alternative to histology.
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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) | 0.001 | 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 | 0.001 |
| 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".