Quantitative diffuse optical spectroscopy and T1 mapping of gadolinium-incorporated porphysome nanoparticles for guided theranostics of oral cancer in mice
Bibliographic record
Abstract
Porphysomes are liposomal nanoparticles composed of photoactive porphyrin-lipid conjugates that exist either in intact or dissociated states. When intact, their tight packing density quenches fluorescence while their 100 nm diameter causes preferential accumulation in tumors through the enhanced permeability and retention effect. When processed by tumor cells, porphysomes dissociate into their subunits, unquenching their fluorescence and increasing their photoactivity. Magnetic resonance imaging (MRI) contrast is enabled by including gadolinium into the porphyrin-lipid conjugates. The passive tumor targeting and multimodal contrast present opportunities for novel oncological theranostic workflows. For instance, preoperative T1-weighted MRI enables margin assessment and/or specific uptake of the agent, while intraoperative optical measurements can distinguish residual tumor or aid in photodynamic therapy planning and monitoring. Here we compare MRI tumor contrast of gadolinium porphysomes (GPs) or gadolinium J-porphysomes (JGPs) versus Gadovist, and determine if diffuse reflectance and quantitative fluorescence spectroscopic (DRS/QFS) point measurements of porphysome concentration (GPs or JGPs) enable delineation of residual tumor. These tests used an oral squamous cell carcinoma (MOC2) tongue xenograft mouse model. Five mice were injected intravenously with GPs and JGPs, then measured at 15 minutes, 24 hours, and 44 hours post injection with T1 mapping in a 7T MRI, and with the DRS/QFS probe. Frank contrast was observed between tumor and healthy tissue in both MRI and spectroscopic measurements with broad agreement of total porphysome concentration between these measures at all measured time points. GPS and GJPS had significantly longer half-lives than Gadovist, with greater tumor specific uptake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".