Optofluidic Device Based Microflow Cytometers for Particle/Cell Detection: A Review
Bibliographic record
Abstract
Optofluidic devices combining micro-optical and microfluidic components bring a host of new advantages to conventional microfluidic devices. Aspects, such as optical beam shaping, can be integrated on-chip and provide high-sensitivity and built-in optical alignment. Optofluidic microflow cytometers have been demonstrated in applications, such as point-of-care diagnostics, cellular immunophenotyping, rare cell analysis, genomics and analytical chemistry. Flow control, light guiding and collecting, data collection and data analysis are the four main techniques attributed to the performance of the optofluidic microflow cytometer. Each of the four areas is discussed in detail to show the basic principles and recent developments. 3D microfabrication techniques are discussed in their use to make these novel microfluidic devices, and the integration of the whole system takes advantage of the miniaturization of each sub-system. The combination of these different techniques is a spur to the development of microflow cytometers, and results show the performance of many types of microflow cytometers developed recently.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".