Miniaturized multimodal multiphoton microscope for simultaneous two-photon and three-photon imaging with a dual-wavelength Er-doped fiber laser
Bibliographic record
Abstract
A multimodal multiphoton microscopy (MPM) is developed to acquire both two-photon microscopy (2PM) and three-photon microscopy (3PM) signals. A dual-wavelength Er-doped fiber laser is used as the light source, which provides the fundamental pulse at 1580 nm to excite third harmonic generation (THG) and the frequency-doubled pulse at 790 nm to excite intrinsic two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG). Due to their different contrast mechanisms, the TPEF, SHG, and THG images can acquire complementary information about tissues, including cells, collagen fibers, lipids, and interfaces, all label-free. The compact MPM imaging probe is developed using miniature objective lens and a micro-electro-mechanical scanner. Furthermore, the femtosecond laser pulses are delivered by a single mode fiber and the signals are collected by a multimode fiber, which makes the miniaturized MPM directly fiber-coupled, compact, and portable. Design considerations on using the dual excitation wavelengths are discussed. Multimodal and label-free imaging by TPEF, SHG, and THG are demonstrated on biological samples. The miniaturized multimodal MPM is shown to have great potential for label-free imaging of thick and live tissues.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".