Current State of Intellectual Property in Microfluidic Nucleic Acid Analysis
Bibliographic record
Abstract
The development of novel fabrication methods, materials and surface chemistries to implement nucleic acid analysis brings reduced cost, reduced reagent consumption, increased analysis efficiency, portability, ease of use and reliability to todays genomic approach. This trend, as evident by the exponential growth in the number of patent applications, granted patents and commercialized systems, is motivated by the promise for significant breakthroughs and benefits of nucleic acid analysis to drug discovery and point-of-care diagnosis. This review paper aims at identifying the enabling technologies and key patents in microfluidics for nucleic acid analysis. In particular, it seeks to identify granted and pending patents for cell sorting and lysis, nucleic acid extraction and purification, followed by nucleic acid amplification, separation and detection. Additionally, it presents an overview of the current intellectual property environment and seeks to identify trends for the future development. Much of this development is geared increasingly toward fully integrated systems. The convergence of technology and interdisciplinary interests is expected to foster further breakthroughs and commercialization. Keywords: Microfluidic nucleic acid analysis, cell sorting, cell lysis, nucleic acid extraction, nucleic acid separation, polymerase chain reaction, electrophoresis, integrated systems, Lab-on-a-Chip
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".