Hybrid near-infrared-activated luminescent gold nanoparticle platform for efficient cancer therapy
Bibliographic record
Abstract
Abstract The development of a multifunctional therapy nanoplatform is of crucial importance to tackle the complex challenges associated with cancer. Despite significant advancements in tumor treatment, the efficacy of these traditional approaches remains insufficient. Recurrence and metastasis following tumor treatment continue to represent a significant contributor to tumor-related mortality. This paper presents an improved, facile, and relatively green fabrication of (5-mercapto-1,3,4-thiadiazol-2-ylthio) acetic acid (TMT)-coated luminescent gold nanoparticles (L-AuNP@TMT), which exhibit highly membrane-targeting capacity and superior photodynamic properties. Furthermore, in vivo tumor-bearing mouse model experiments indicated that the L-AuNP@TMT could be used as a two-photon excited nanomedicine via pyroptosis-mediated anti-tumor immunity for effectively eliminating colorectal cancer (CRC), the third most common malignancy and the second deadliest cancer, without evident toxic side effects or tumor metastasis/recurrence. According to its facile and green fabrication approach, near-infrared light-activatable highly efficient photodynamic cancer therapy, and noninvasive imaging mode, this multifunctional nanoplatform offers significant advantages over traditional monotherapy techniques, providing an alternative for the precise clinical treatment of cancer.
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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 | 0.001 | 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".