A review of sorting, separation and isolation of cells and microbeads for biomedical applications: microfluidic approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several biomedical analyses are performed on particular types of cells present in body samples or using functionalized microparticles. Success in such analyses depends on the ability to separate or isolate the target cells or microparticles from the rest of the sample. In conventional procedures, multiple pieces of equipment, such as centrifuges, magnets, and macroscale filters, are used for such purposes, which are time-consuming, associated with human error, and require several operational steps. In the past two decades, there has been a tendency to develop microfluidic techniques, so-called lab-on-a-chip, to miniaturize and automate these procedures. The processes used for the separation and isolation of the cells and microparticles are scaled down into a small microfluidic chip, requiring very small amounts of sample. Differences in the physical and biological properties of the target cells from the other components present in the sample are the key to the development of such microfluidic techniques. These techniques are categorized as filtration-, hydrodynamic-, dielectrophoretic-, acoustic- and magnetic-based methods. Here we review the microfluidic techniques developed for sorting, separation, and isolation of cells and microparticles for biomedical applications. The mechanisms behind such techniques are thoroughly explained and the applications in which these techniques have been adopted are reviewed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it