CRISPR-Driven Biosensors: A New Frontier in Rapid and Accurate Disease Detection
Bibliographic record
Abstract
This comprehensive review delves into the advancements and challenges in biosensing, with a strong emphasis on the transformative potential of CRISPR technology for early and rapid detection of infectious diseases. It underscores the versatility of CRISPR/Cas systems, highlighting their ability to detect both nucleic acids and non-nucleic acid targets, and their seamless integration with isothermal amplification techniques. The review provides a thorough examination of the latest developments in CRISPR-based biosensors, detailing the unique properties of CRISPR systems, such as their high specificity and programmability, which make them particularly effective for detecting disease-associated nucleic acids. While the review focuses on nucleic acid detection due to its critical role in diagnosing infectious diseases, it also explores the broader applications of CRISPR technology in detecting non-nucleic acid targets, thereby acknowledging the technology's broader potential. Additionally, the review identifies existing challenges, such as the need for improved signal amplification and real-world applicability, and offers future perspectives aimed at overcoming these hurdles. The ultimate goal is to advance the development of highly sensitive and specific CRISPR-based biosensors that can be used widely for improving human health, particularly in point-of-care settings and resource-limited environments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".