A novel permalloy based magnetic single cell micro array
Bibliographic record
Abstract
Devices capable of automatically aligning cells onto geometrical arrays are of great interest to biomedical researchers. Such devices can facilitate the study of numerous cells while the cells remain physically separated from one another. In this way, cell arrays reduce cell-to-cell interactions while the cells are all subjected to common stimuli, which allows individual cell behaviour to be revealed. The use of arrays allows for the parallel analysis of single cells, facilitates data logging, and opens the door to the use of automated machine-based single cell analysis techniques. A novel permalloy based magnetic single cell micro array (MSCMA) is presented in this paper. The MSCMA creates an array of magnetic traps by generating magnetic flux density peaks at predefined locations. When using cells labelled with immunomagnetic labels, the cells will interact with the magnetic fields, and can be captured at the magnetic trap sites. Prototypes of the MSCMA have been successfully fabricated and tested using both fixed and live Jurkat cells (10 microm average diameter) that were labelled. The prototypes performed as predicted during experimental trials. The experimental results show that the MSCMA can randomly array up to 136 single cells per square mm. The results also show that the number of single cells captured is a function of the trap site density of the MSCMA design and the cell density in the fluid sample.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".