Intratumoral synthesis of transformable metal-phenolic nanoaggregates with enhanced tumor penetration and retention for photothermal immunotherapy
Bibliographic record
Abstract
Rationale: Effective photothermal therapy (PTT) remains a great challenge due to the difficulties of delivering photothermal agents with both deep penetration and prolonged retention at tumor lesion spatiotemporally.Methods: Here, we report an intratumoral self-assembled nanostructured aggregate named FerH, composed of a natural polyphenol and a commercial iron supplement.FerH assemblies possess size-increasing dynamic kinetics as a pseudo-stepwise polymerization from discrete nanocomplexes to microscale aggregates.Results: The nanocomplex can penetrate deeply into solid tumors, followed by prolonged retention (> 6 days) due to the in vivo growth into nanoaggregates in the tumor microenvironment.FerH performs a targeting ablation of tumors with a high photothermal conversion efficiency (60.2%).Importantly, an enhanced immunotherapeutic effect on the distant tumor can be triggered when co-administrated with checkpoint-blockade PD-L1 antibody.Conclusions: Such a therapeutic approach by intratumoral synthesis of metal-phenolic nanoaggregates can be instructive to address the challenges associated with malignant tumors.
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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) | 0.000 | 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".