Experimental Parameters Leading to Optimal Bilayers for Total Internal Reflection Fluorescence Microscopy Visualization
Bibliographic record
Abstract
Supported lipid bilayer systems were evaluated following various experimental procedures in an effort to determine their appropriateness for visualization using total internal reflection fluorescence (TIRF) microscopy. The incorporation and distribution of Texas Red® 1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (TR-DHPE) was studied when incorporated into bilayers of variable lipid composition using different forms of mechanical shearing. Results showed that 0.8 mol% TR-DHPE provides the most optimum TIRF images. At this concentration, a sufficient level of photostability can be achieved without an undesirable increase in TR-DHPE aggregates caused by excess probe molecules. Solutions composed of a 3:1 molar ratio of DOPC:DPPC with 0.8 mol% TR-DHPE produce bilayers that consistently display clear, distinct, rounded domains, whereas other lipid compositions did not. This optimum phase separation appears to be influenced by an increase in mechanical shearing during the vesicle formation process, when the lipid solutions were exposed to sonication and extrusion processes. The combination of a sonication and extrusion process also helped with eliminating the presence of TR-DHPE aggregates within the model membranes. It was also shown that bilayers formed on conditioned glass, placed on a slide, produced more highly detailed bilayers in which distinct lipid phase separation could be optimally visualized using TIRF microscopy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".