Bibliographic record
Abstract
Introduction and Objective: Multiphoton microscopy is a novel technology that permits acquisition of bioimages using several low-energy photons to induce autofluorescence of cellular components without damaging live tissue.When combined with imaging of a quantum optics phenomenon called second-harmonic generation (SHG), tissue discrimination may be enhanced.We report our results in both rat and fresh human prostatectomy specimen models. Materials and Methods:The prostate, cavernous nerves, seminal vesicles and periprostatic tissue was excised from 15 euthanized male Sprague Dawley rats and imaged under an Olympus X61WI upright fluorescence microscope.Twenty-five ex vivo human prostatectomy specimens were also imaged immediately after robotic-assisted radical prostatectomy.A femtosecond pulsed Titanium/sapphire laser at 780 nm wavelength was used to excite the cellular tissue.Second-harmonic generation signals were collected at 390 (standard deviation 35) nm and autofluorescence registered at 380-530 nm.Bioimages were merged for better tissue differentiation.Tissues were labelled and correlated with H&E images at final histopathology.Results: High-resolution images of the prostatic capsule, periprostatic vessels, smooth muscle cells, nerves and periprostatic inflammation were documented in rat and human prostatectomy specimens.Histopathological confirmation of these structures with H&E was closely congruent with MPM images.Conclusion: Multiphoton microscopy with SHG delivers superior real-time high-resolution cellular bioimages.Our pilot feasibility study demonstrates the potential for improving potency and cancer clearance outcomes during radical prostatectomy through augmented real-time visualization/ preservation of the periprostatic structures with eventual integration of the technology into laparoscopic and robotic platforms.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.513 | 0.232 |
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".