Development of a protocol for whole-lung in vivo lung perfusion-assisted photodynamic therapy using a porcine model
Bibliographic record
Abstract
SignificanceStandard treatments for isolated lung metastases remain a clinical challenge. In vivo lung perfusion technique provides flexibility to overcome the limitations of photodynamic therapy (PDT) by replacing the blood with acellular perfusate, allowing greater light penetration.AimUsing Monte Carlo-based simulations, we will evaluate the abilities of a light delivery system to irradiate the lung homogenously. Afterward, we aim to demonstrate the feasibility and safety profile of a whole-lung perfusion-assisted PDT protocol using 5-ALA and Chlorin e6.ApproachA porcine model of a simplified lung perfusion procedure was used. PDT was performed at 630 or 660 nm with 5-ALA or Chlorin e6, respectively. Light fluence rate measurements and computed tomography (CT) scan segmentations were used to create in silico models of light propagation. Physiologic, gross, CT, and histological assessment of lung toxicity was performed 72 h post-PDT.ResultsDose-volume histograms showed homogeneity of light intensity throughout the lung. Predicted and measured fluence rates showed strong reliability. The photodynamic threshold of 5-ALA was 2.10×1017±8.24×1016 hν/cm3, whereas Chlorin e6 showed negligible uptake in lung tissue.ConclusionsWe lay the groundwork for personalized preoperative in silico dosimetry planning to achieve desired treatment volumes within the therapeutic range. Chlorin e6 demonstrated the greatest therapeutic potential, with a minimal uptake in healthy lung tissues.
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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 | 0.001 | 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".