Ultrasensitive Quantitative Migration Sensor for Monitoring the Quantitative Viscosity–Cell Migration Relationship
Bibliographic record
Abstract
The relationship between extracellular viscosity and the cells’ migration is a new and crucial clue indicating tumor growth and metastasis. However, their quantitative relationship has not yet been revealed. In this study, an ultrasensitive quantitative migration sensor (UQMS) that can quantitatively monitor the abnormal change of viscosities and the cell migration rate under abnormal extracellular viscosities with a record-breaking detection limit of 3 cells is developed for the first time. In this UQMS, a robust glucose/O 2 fuel cell (GFC) that can work steadily in body fluids and can output a continuous electrical signal serves as the energy driver and signal generator. At the anode of the GFC, we design a cell growth area two millimeters away from the electroactive area to ensure that the electroactive area is initially free from cell interference. The raised extracellular viscosity impedes mass transfer, leading to an instantaneous and linear decrease in the current output of the GFC. With the time going, the cancer cells migrate to the electroactive area on the anode, which further blocks the electron and mass transfer, leading to a time- and cell-number-dependent decrease in the current output of the GFC. By analyzing changes of the GFC’s current output during different timeframes, the UQMS can quantitatively detect the extracellular viscosity in a wide range (1 cP–27 cP) that could distinguish the normal and abnormal viscosity; moreover, the quantitative relationship between long-term adherent cell migration and viscosities can be built at a level as low as 3 cells. Both of the migrations of adherent cells (ATCs) and circulating tumor cells (CTCs) under different viscosities can be quantitatively monitored by this UQMS. And we observe that the high viscosity enables the ATC to deform to migrate rapidly in an energy-efficient mode but slows down CTC migration; what is more, the migration of CTCs is significantly faster than that of ATCs. This work is expected to be highly helpful in assessing the risk of tumor metastasis from the migration of both ATCs and CTCs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".