A Microfluidic Chamber To Study the Dynamics of Muscle-Contraction-Specific Molecular Interactions
Bibliographic record
Abstract
In vitro motility and laser trap assays are commonly used for molecular mechanics measurements. However, chemicals cannot be added during these measurements, because they create flows that alter the molecular mechanics. Thus, we designed a microfluidic device that allows the addition of chemicals without creating bulk flows. Biocompatibility of the components of this device was tested. A microchannel chamber was created by photolithography with the patterns transferred to polydimethylsiloxane (PDMS). The PDMS chamber was bound to a polycarbonate membrane, which itself was bound to a molecular mechanics chamber. The microchannels ensured rapid distribution of the chemicals over the membrane, whereas the membrane ensured efficient delivery to the mechanics chamber while preventing bulk flow. The biocompatibility of the materials was tested by comparing the velocity (ν(max)) of propulsion by myosin of fluorescently labeled actin filaments to that of the conventional assay; no difference in ν(max) was observed. To estimate total chemical delivery time, labeled bovine serum albumin was injected in the channel chamber and TIRF was used to determine the time to reach the assay surface (2.7 ± 0.1 s). Furthermore, the standard distance of a trapped microsphere calculated during buffer diffusion using the microfluidic device (14.9 ± 3.2 nm) was not different from that calculated using the conventional assay (15.6 ± 5.3 nm, p = 0.922). Finally, ν(max) obtained by injecting adenosine triphosphate (ATP) in the microchannel chamber (2.37 ± 0.48 μm/s) was not different from that obtained when ATP was delivered directly to the mechanics chamber (2.52 ± 0.42 μm/s, p = 0.822). This microfluidic prototype validates the design for molecular mechanics measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".