Microfluidic impedance‐based flow cytometry
Bibliographic record
Abstract
Microfabricated flow cytometers can detect, count, and analyze cells or particles using microfluidics and electronics to give impedance-based characterization. Such systems are being developed to provide simple, low-cost, label-free, and portable solutions for cell analysis. Recent work using microfabricated systems has demonstrated the capability to analyze micro-organisms, erythrocytes, leukocytes, and animal and human cell lines. Multifrequency impedance measurements can give multiparametric, high-content data that can be used to distinguish cell types. New combinations of microfluidic sample handling design and microscale flow phenomena have been used to focus and position cells within the channel for improved sensitivity. Robust designs will enable focusing at high flowrates while reducing requirements for control over multiple sample and sheath flows. Although microfluidic impedance-based flow cytometers have not yet or may never reach the extremely high throughput of conventional flow cytometers, the advantages of portability, simplicity, and ability to analyze single cells in small populations are, nevertheless, where chip-based cytometry can make a large impact.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".