Dynamic imaging of transient metabolic processes by small-animal PET for the evaluation of photosensitizers in photodynamic therapy of cancer.
Bibliographic record
Abstract
UNLABELLED: This study evaluated the potential use of dynamic PET to monitor transient metabolic processes and to investigate the mechanisms of action of new photosensitizing drugs in the photodynamic therapy (PDT) of cancer. METHODS: Rats bearing 2 mammary tumors received different phthalocyanine-based photosensitizers. The following day, the animals were positioned in a Sherbrooke small-animal PET scanner and continuously infused with 18F-FDG while dynamic images were acquired for 2 h. During that period, one of the 2 tumors was exposed for 30 min to red light delivered by a small diode laser to activate PDT. RESULTS: 18F-FDG time-activity curves during PDT showed distinct transient patterns characterized by a drop and subsequent recovery of tumor 18F-FDG uptake rates. Variations in these rates and response delay parameters revealed tumoral and systemic metabolic processes that correlated with differences in mechanism of action between drugs, that is, direct tumor cell kill or initial vascular shutdown. CONCLUSION: Real-time follow-up of tumor response to PDT as monitored by dynamic 18F-FDG PET has been shown to correlate with the mechanisms of action of photosensitizing drugs in vivo. This new imaging paradigm can be exploited to monitor a variety of transient cellular and molecular processes as they occur in vivo, enabling the mechanisms of action of therapeutic interventions to be scrutinized and their efficacy predicted in real time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".