Low-dose X-ray radiodynamic therapy solely based on gold nanoclusters for efficient treatment of deep hypoxic solid tumors combined with enhanced antitumor immune response
Bibliographic record
Abstract
Background: Radiodynamic therapy (RDT) is an emerging novel anti-cancer treatment based on the generation of cytotoxic reactive oxygen species (ROS) at the lesion site following the interaction between low-dose X-ray and a photosensitizer (PS) drug.For a classical RDT, scintillator nanomaterials loaded with traditional PSs are generally involved to generate singlet oxygen ( 1 O2).However, this scintillator-mediated strategy generally suffers from insufficient energy transfer efficiency and the hypoxic tumor microenvironment, and finally severely impedes the efficacy of RDT.Methods: Gold nanoclusters were irradiated by low dose of X-ray (called RDT) to investigate the production of ROS, killing efficiency of cell level and living body level, antitumor immune mechanism and biosafety.Results: A novel dihydrolipoic acid coated gold nanoclusters (AuNC@DHLA) RDT, without additional scintillator or photosensitizer assisted, has been developed.In contrast to scintillator-mediated strategy, AuNC@DHLA can directly absorb the X-ray and exhibit excellent radiodynamic performance.More importantly, the radiodynamic mechanism of AuNC@DHLA involves electron-transfer mode resulting in O2 -• and HO•, and excess ROS has been generated even under hypoxic conditions.Highly efficient in vivo treatment of solid tumors had been achieved via only single drug administration and low-dose X-ray radiation.Interestingly, enhanced antitumor immune response was involved, which could be effective against tumor recurrence or metastasis.Negligible systemic toxicity was also observed as a consequence of the ultra-small size of AuNC@DHLA and rapid clearance from body after effective treatment.Conclusions: Highly efficient in vivo treatment of solid tumors had been achieved, enhanced antitumor immune response and negligible systemic toxicity were observed.Our developed strategy will further promote the cancer therapeutic efficiency under low dose X-ray radiation and hypoxic conditions, and bring hope for clinical cancer treatment.
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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".