<i>Thermus thermophilus</i> Argonaute-Mediated Single Particle Counting Platform for Multiplex Cancer-Related Biomarkers Detection
Bibliographic record
Abstract
The clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas) system has achieved remarkable success in the field of nucleic acid detection, while its Achilles’ heel lies in the difficulties encountered in flexibility regarding the multiplex detection. As a sister system of CRISPR-Cas, prokaryotic Argonautes (pAgos) have precise recognition, multiturnover, and more importantly multiple specific cleavage characteristics, which is a potential candidate for the next generation of multiplex detection. Herein, a single particle counting platform was developed for the simultaneous detection of three colorectal cancer-related miRNAs (miR-141, miR-31, and miR-21) by combining single particle inductively coupled plasma mass spectrometry (SP-ICPMS) with the Thermus thermophilus Argonaute protein (TtAgo), with nanoparticles as signal probes for cleavage. The platform demonstrated high sensitivity (aM level) and specificity due to the dual-cycle mechanism of exponential isothermal amplification (EXPAR) and TtAgo cleavage, as well as the combination of TtAgo’s specific cleavage capability and the multiplex detection advantages of metal stable isotope tagging. Additionally, the platform showed good robustness in human serum and cell extracts, indicating significant potential in clinical 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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".