Photodynamic therapy enables tumor-specific ablation in preclinical models of thyroid cancer
Bibliographic record
Abstract
The incidence of differentiated thyroid cancer has increased significantly during the last several decades. Surgical resection is the primary treatment for thyroid cancer and is highly effective, resulting in 5-year survival rates greater than 98%. However, surgical resection can result in short- and long-term treatment-related morbidities. Additionally, as this malignancy often affects women less than 40 years of age, there is interest in more conservative treatment approaches and, an unmet need for therapeutic options that minimize the risk of surgery-related morbidities while simultaneously providing an effective cancer treatment. Photodynamic therapy (PDT) has the potential to reduce treatment-related side effects by decreasing invasiveness and limiting toxicity. Owing to multiple advantageous properties of the porphyrin-HDL nanoparticle (PLP) as a PDT agent, including preferential accumulation in tumor, biodegradability and unprecedented photosensitizer packing, we evaluate PLP-mediated PDT as a minimally invasive, tumor-specific treatment for thyroid cancer. On both a biologically relevant human papillary thyroid cancer (K1) mouse model and an anatomically relevant rabbit squamous carcinoma (VX2)-implanted rabbit thyroid model, the intrinsic fluorescence of PLP enabled tracking of tumor preferential accumulation and guided PDT. This resulted in significant and specific apoptosis in tumor tissue, but not surrounding normal tissues including trachea and recurrent laryngeal nerve (RLN). A long-term survival study further demonstrated that PLP-PDT enabled complete ablation of tumor tissue while sparing both the normal thyroid tissue and RLN from damage, thus providing a safe, minimally invasive, and effective alternative to thyroidectomy for thyroid cancer therapies.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".