Physically Masked Nanoflares for Accurate Biological Applications by Blocking Nucleases
Bibliographic record
Abstract
Abstract Nanoflare integrated with a gold nanoparticle (Au NP) core and a functional oligonucleotide shell is a powerful platform for bio‐applications, such as sensing, imaging, diagnosis, and therapy. However, degradation of nucleic acids by endogenous nucleases in vivo is an inevitable problem causing signal distortion. To solve this issue, in this work, a physically masked strategy was designed by trapping the nanoflare in a porous polymer nanocage. The porous structure of the nanocage allowed the penetration of small target molecules to react with the internal nanoflare, while bulky nucleases were blocked, resulting in high‐fidelity fluorescence signaling. Using this method, cancer cells were differentiated from normal cells by a masked nanoflare that can recognize a microRNA biomarker (miRNA‐21), and tumors were imaged with a high‐contrast signal. The physical masking strategy opened up a new path for improving the stability of nucleic acid probes against nucleases, facilitating accurate bio‐application.
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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".