Lighting Up MicroRNA in Living Cells by the Disassembly of Lock‐Like DNA‐Programmed UCNPs‐AuNPs through the Target Cycling Amplification Strategy
Bibliographic record
Abstract
Abstract Intracellular microRNAs imaging based on upconversion nanoprobes has great potential in cancer diagnostics and treatments. However, the relatively low detection sensitivity limits their application. Herein, a lock‐like DNA (LLD) generated by a hairpin DNA (H1) hybridizing with a bolt DNA (bDNA) sequence is designed, which is used to program upconversion nanoparticles (UCNPs, NaYF4@NaYF4:Yb, Er@NaYF4) and gold nanoparticles (AuNPs). The upconversion emission is quenched through luminescence resonance energy transfer (LRET). The multiple LLD can be repeatedly opened by one copy of target microRNA under the aid of fuel hairpin DNA strands (H2) to trigger disassembly of AuNPs from the UCNP, resulting in the lighting up of UCNPs with a high detection signal gain. This strategy is verified using microRNA‐21 as model. The expression level of microRNA‐21 in various cells lines can be sensitively measured in vitro, meanwhile cancer cells and normal cells can be easily and accurately distinguished by intracellular microRNA‐21 imaging via the nanoprobes. The detection limit is about 1000 times lower than that of the previously reported upconversion nanoprobes without signal amplification. This is the first time a nonenzymatic signal amplification method has been combined with UCNPs for imaging intracellular microRNAs, which has great potential for cancer diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".