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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".