Activable Photodynamic DNA Probe with an “AND” Logic Gate for Precision Skin Cancer Therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) has emerged as a promising approach for squamous cell carcinoma treatment but hindered by tumor hypoxia, acquired resistance, phototoxicity, and so on. To address these issues, we developed a smart strategy utilizing activable photosensitizers delivered by an aptamer-functionalized DNA probe (ADP). The ADP incorporated an AS1411 aptamer for tumor targeting and a linear antisense oligonucleotide (ASO) for recognition of Survivin mRNA. In the absence of the target, PDT remained quenched, thereby avoiding phototoxicity during circulation and nonselective distribution. With the aid of the aptamer, ADP achieved selective targeting of tumors. Upon internalization, ADP targeted recognized Survivin mRNA, triggering PDT activation, and releasing ASO to down-regulate Survivin expression and reverse tumor resistance. Consequently, the activable photosensitizers exhibited an “AND” logic gate, combining tumor-targeting delivery and tumor-related gene activation, thus enhancing its specificity. Additionally, the incorporation of hemin into the ADP provided catalase activity, converting tumor-abundant H 2 O 2 into O 2 , thereby ameliorating tumor hypoxia. The resulting functionalized G-quadruplex/hemin–DNA probe complex demonstrated targeted delivery and activation, minimized side effects, and enhanced PDT efficacy in both xenograft tumor-bearing mice and patient-derived xenograft models. This study offers a unique and promising platform for efficient and safe PDT, thus holding great potential for future clinical translation and improved cancer therapy.
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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".