A preclinical research platform to evaluate photosensitizers for transbronchial localization and phototherapy of lung cancer using an orthotopic mouse model
Bibliographic record
Abstract
Background: Establishing the efficacy of novel photosensitizers (PSs) for phototherapy of lung cancer requires in vivo study prior to clinical evaluation. However, previously described animal models are not ideal for assessing transbronchial approaches with such PSs. Methods: An ultra-small parallel-type composite optical fiberscope (COF) with a 0.97 mm outer diameter tip. The integration of illumination and laser irradiation fibers inside the COF allows simultaneous white-light and fluorescence imaging, as well as real-time monitoring of tip position during laser phototherapy. An orthotopic lung cancer mouse model was created with three human lung cancer cell lines transbronchially inoculated into athymic nude mice. The COF was inserted transbronchially into a total of 15 mice for tumor observation. For in vivo fluorescence imaging, an organic nanoparticle, porphysome, was used as a PS. Laser excitation through the COF was performed at 50 mW using a 671 nm source. Results: The overall success rate for creating orthotopic lung tumors was 71%. Transbronchial white light images were successfully captured by COF. Access to the left main bronchus was successful in 87% of mice (13/15), the right main bronchus to the cranial lobe bronchus level in 100% (15/15), and to the right basal trifurcation of the middle lobe, caudal lobe and accessory lobe in 93% (14/15). For transbronchial tumor localization of orthotopic lung cancer tumors, PS-laden tumor with the strong signal was clearly contrasted from the normal bronchial wall. Conclusions: The ultra-small COF enabled reliable transbronchial access to orthotopic human lung cancer xenografts in vivo. This method could serve as a versatile preclinical research platform for PS evaluation in lung cancer, enabling transbronchial approaches in in vivo survival models inoculated with human lung cancer cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".