Critical review on where CRISPR meets molecular diagnostics
Bibliographic record
Abstract
Abstract Simple yet powerful clustered regularly-interspaced short palindromic repeats (CRISPR) technology has led to the advent of numerous developments in life sciences, biotechnology, therapeutics, and molecular diagnostics, enabled by gene editing capability. By exploiting the CRISPR-Cas system’s nucleic acid sequence detection abilities, CRISPR-based molecular diagnostics have been developed. Here, we review the development of rapid, sensitive, and inexpensive CRISPR-based molecular diagnostics. We introduce the transition of CRISPR technology to precision molecular diagnostic devices from tube to device. Next, we discuss the various nucleic acid (NA) detection methods by CRISPR. We address the importance of significant sample preparation steps for a future sample-to-answer solution, which is lacking in current CRISPR-based molecular diagnostic technology. Lastly, we discuss the extension of CRISPR-based molecular diagnostics to various critical applications. We envision CRISPR technology holds great promise for widespread use in precision NA detection applications after particular technical challenges are overcome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".