Spherical Nucleic Acids‐Directed Cryosynthesis of Manganese Nanoagents for Tumor Imaging and Therapy
Bibliographic record
Abstract
Abstract DNAzyme‐based theranostic nanotechnologies that can respond to specific tumor pathophysiological parameters hold great promise for tumor diagnostics and effective treatments. However, their clinical translation is hindered by insufficient intracellular availability of essential metal cofactors required for DNAzyme activation. To overcome this limitation, we developed a temperature‐controlled synthesis strategy for fabricating multifunctional DNA‐templated manganese carbonate nanoparticles (DtMnP). The process involves three critical phases: (i) spherical nucleic acid hybrids, DNAzyme‐functionalized AuNPs, serve as scaffolds for spatially controlled Mn 2+ deposition through phosphate coordination, initiating heterogeneous nucleation of MnCO 3 ; (ii) rapid liquid nitrogen freezing induces nanoparticle growth along DNA templates; and (iii) lyophilization‐mediated structural stabilization enables convenient long‐term storage. The DtMnP exhibits pH‐responsive dissolution, releasing 90% of Mn 2+ within 60 min under tumor microenvironment conditions (pH 5.5). The released Mn 2+ ion enables dual functionality: (i) superior magnetic resonance imaging (MRI) contrast of MCF‐7 xenograft models with enhanced biosafety, and (ii) synergistic therapeutic efficacy through DNAzyme‐mediated EGR‐1 gene silencing (60% mRNA downregulation) combined with Mn 2+ ‐catalyzed Fenton reactions generating cytotoxic hydroxyl radicals (45% apoptosis in MCF‐7 cells). The cryo‐encapsulated DtMnP exemplifies a flexible and efficient approach for integrating various functional components into a single nanoparticle for tumor theranostic applications.
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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".