Method of measuring nitric oxide release by vascular endothelial cells grown in microfluidic channels
Bibliographic record
Abstract
In this paper, a simple and versatile method is presented which enables detection of nitric oxide (NO) released from vascular endothelial cells (ECs) cultured in microfluidic structures. The culturing system and NO measurement method allow cell shape to be controlled in a non-invasive manner using microfluidic structures while NO release is monitored for cell shape versus function studies. The culturing system consists of arrays of polydimethylsiloxane (PDMS) fluidic channels 120 micrometers in depth and ranging from 100 micrometers to 3 mm in width. The number of channels in each array is varied to yield a constant cell culture surface area (75 mm2) independent of channel width. The channel surfaces are collagen-coated and ECs are cultured to confluence within the channels. A cell scraper is then used to scrape extraneous cells cultured between channels, and NO measurements are made 18 to 24 hours later. A chemiluminescence-based sensor system (NOA 280i, Sievers NO Analyzer) is utilized to measure sample NO. Initial results indicate that NO concentrations can be measured from different microfluidic channel-containing samples using this method. It is shown that there is no significant difference in NO concentration derived from channels of different widths even though the degree of cell elongation varies due to physical constraint by microfluidic channel walls. However, cells treated with TNFα release more NO than untreated cells in fluidic channels, which is comparable to the function of ECs cultured in conventional culturing systems such as culturing dishes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".