Waveguide Evanescent Field Fluorescence Microscopy Images of Osteoblast Cells: The effect of trypsin and image processing using TrackMate
Bibliographic record
Abstract
Waveguide evanescent field fluorescence microscopy (WEFF) is an evanescent based microscopy utilizes a confined thin film of light, around 100 nm, to image the plasma membrane of cells attached to a waveguide. Low photobleaching and low background beside its high axial resolution allows time-lapse imaging to investigate changes in cell morphology in the presence or absence of chemical agents. Both large field of view (FOV) and uniform illumination are very important while imaging cell-substrate contacts with an evanescent field. In the current work, we demonstrate that the WEFF microscope is capable of large FOVs with a uniform illumination source and imaging over a very long time period with a simple and an inexpensive experimental setup. The interaction of the trypsin with plasma membranes of live osteoblast cells is investigated. To analyze cell images (250 images), instead of relying on manual tracking, which is time-consuming and can introduce numerous errors, we performed image processing using TrackMate to investigate the dynamic response of cells upon exposure to trypsin. This helps to save time and increase the accuracy of the analysis. The powerful tracking and analysis capabilities of the TrackMate plugin in ImageJ is used to automatically detect the cells boarder and traces each cluster of cells. The reduction in cell area is accompanied by a notable increase in mean intensity, reflecting changes in the intracellular environment. However, the background did not change during the experiment, which proves that the fluorescence material remains attached to the cell membrane and does not leak into the cell medium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".