A Universal Upconversion Sensing Platform for the Sensitive Detection of Tumour‐Related ncRNA through an Exo III‐Assisted Cycling Amplification Strategy
Bibliographic record
Abstract
upconversion nanoparticles by DNA hybridization, leading to quenching of the upconversion emission through fluorescence resonance energy transfer. A signal DNA (SDNA) sequence is designed to open Hp, recovering the upconversion emission. To achieve universality and high sensitivity of the nanoprobe, an exonuclease III (Exo III)-assisted cycling amplification strategy is introduced. A multifunctional hairpin DNA (mHp) containing ncRNA recognition sequence and SDNA sequence is designed to recognize ncRNA and trigger Exo III as a biocatalyst to stepwise disintegrate itself, releasing both ncRNA and SDNA. The released ncRNA can be reused to release more SDNA, which greatly improves the sensing sensitivity. By changing the recognition portion of mHp, various ncRNA can be detected. The sensitive detection of both homeobox (HOX) transcript antisense RNA segment and miR-21 is achieved with this novel strategy, even in human serum, indicating the universality and sensitivity of the proposed strategy. Additionally, the expression level of miR-21 in human breast cancer cell (MCF-7) lysate is successfully measured, suggesting its potential in clinical 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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".