Highly accurate measurement of varying drug dosage for real-time analysis of chemo-mechanical response of cardiomyocytes
Bibliographic record
Abstract
A recent advancement in the study of drug development for cardiovascular diseases is based on measuring the mechanical response of a single cardiomyocyte to various drug concentrations. This method requires delivering a specific dose of the drug over a short period of time while measuring the forces exerted by a cell that is kept inside a microchamber. However, the exact drug dosage is difficult to control for rapid variations in drug concentration, which hinders the accuracy of the measurements. This paper reports a highly sensitive technique for accurate and real-time measurement of minute variations in drug concentration. The fluid electrical conductivity is monitored using an array of electrodes along a micro-channel that eventually leads to the microchamber where the cardiomyocyte is placed. The microfluidic setup is fabricated through bonding of a moulded Polydimethylsiloxane (PDMS) layer to a glass substrate with patterned gold electrodes. The real-time differential measurements let us measure the local drug concentration with accuracies of better than 10pMol/mL. By using the data from all of the array electrodes, the profile of the drug plug as it travels along the microchannel from the injection point to the cell location can be derived with high precision. The multidomain numerical simulations of the microfluidic setup are in line with the measured experimental data. Our technique can be easily integrated into many existing and new designs thus providing a robust approach for label-free measurement of fluid properties in cell viability studies.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".