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Record W4405223739 · doi:10.29105/qh13.02-412

A Versatile DIY 3D Printed Device for the Preparation of Gian Lipid Vesicles as Model Membranes by Electroformation: Design and Fabrication

2024· article· es· W4405223739 on OpenAlexaff
Ana Salinas de Frías, Cristina Flores Cadengo, Pablo Luis Hernández‐Adame, Francisco Bañuelos Ruedas, Lazaro Canizalez DavaIos, Leo Alvarado Perea, A López

Bibliographic record

VenueQuimica Hoy · 2024
Typearticle
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesVesicleNanotechnologyCrystallographyChemistryMembraneMaterials sciencePhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.284
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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