The potential of autofluorescence for the detection of single living cells for label‐free cell sorting in microfluidic systems
Bibliographic record
Abstract
A novel method for studying unlabeled living mammalian cells based on their autofluorescence (AF) signal in a prototype microfluidic device is presented. When combined, cellular AF detection and microfluidic devices have the potential to facilitate high-throughput analysis of different cell populations. To demonstrate this, unlabeled cultured cells in microfluidic devices were excited with a 488 nm excitation light and the AF emission (> 505 nm) was detected using a confocal fluorescence microscope (CFM). For example, a simple microfluidic three-port glass microstructure was used together with conventional electroosmotic flow (EOF) to switch the direction of the fluid flow. As a means to test the potential of AF-based cell sorting in this microfluidic device, granulocytes were successfully differentiated from human red blood cells (RBCs) based on differences in AF. This study demonstrated the use of a simple microfabricated device to perform high-throughput live cell detection and differentiation without the need for cell-specific fluorescent labeling dyes and thereby reducing the sample preparation time. Hence, the combined use of microfluidic devices and cell AF may have many applications in single-cell analysis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".