A Versatile DIY 3D Printed Device for the Preparation of Gian Lipid Vesicles as Model Membranes by Electroformation: Design and Fabrication
Bibliographic record
Abstract
Una forma sencilla de estudiar las propiedades de la membrana celular y su interacción con otras moléculas es mediante el uso de sistemas modelo con la misma estructura básica. Estos utilizan unos pocos componentes básicos que simulan las condiciones de la membrana plasmática y se han utilizado ampliamente para estudiar las propiedades de las membranas biológicas. Entre estos sistemas modelo, podemos encontrar mono capas de Langmuir, bicapas soportadas y vesículas lipídicas. Las vesículas lipídicas son particularmente interesantes debido a que su estructura esférica imita a las membranas celulares. Entre los diferentes procedimientos para preparar GUV's (vesículas unilamelares gigantes), la electroformación destaca porque las vesículas producidas por este método son en su mayoría más grandes que 100 nm y unilamelares, lo que las hace fácilmente visibles mediante microscopía óptica convencional. Aunque existen dispositivos comerciales disponibles, generalmente se fabrican equipos personalizados de manera artesanal. Este trabajo describe el diseño y la fabricación de un dispositivo sencillo y versátil impreso en 3D para preparar vesículas lipídicas gigantes utilizando el método de electroformación. El dispositivo ha sido diseñado para fijarse en la platina del microscopio.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".