Biomimetic Model Membrane Systems Serve as Increasingly Valuable in Vitro Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biological membranes contain a multitude of lipids, proteins, and carbohydrates unique for any given cell or organism, and are a critical component of many biological processes. Animal and cell cultures have been used to understand these biological processes at the membrane level and more traditionally, to assess toxicity. However, the complex composition does not allow understanding of the detailed role of each membrane component, such as individual lipid species. This insight can be obtained from using simplified model systems, which include various kinds of vesicles (unilamellar or multilamellar), micelles, monolayers at an air-water interface, planar lipid bilayers/black lipid membranes, bicelles (bilayered micelles) and supported bilayers. All systems allow detailed control of composition and experimental conditions, and have been used to mimic various different membrane types, such as mammalian and bacterial. Using various physicochemical techniques including nuclear magnetic resonance (NMR), differential scanning calorimetry (DSC), isothermal calorimetry (ITC), electron spin resonance, fluorescence spectroscopy, and X-ray diffraction, it is possible to investigate the mechanisms of membrane toxicity through differential changes in acyl chain melting temperature, membrane fluidity, and permeability of these different membrane models upon ligand binding. Moreover, the effects of ions (Na + , K + , Li + , Ca 2+ , Mg 2+ , Ba 2+ ), toxic heavy metals (Hg 2+ , Cd 2+ ) and a variety of drugs (e.g. Ellipticine for tumors and H1N1 virus or cyclosporine A to prevent graft rejection) have been evaluated on mammalian systems. For bacterial model membranes, the effects of antimicrobial peptides, antibiotics, the interaction of proteins with model membranes, and the insertion or reconstitution of membrane proteins into such systems have also been investigated. When interpreting the results, it is important to note that some models may be better representatives of the natural membrane than others, and consequently, some results more relevant than others. Factors to consider include -but are not limited to -lipid composition, membrane curvature, or ionic strength of the solution, which all impart certain characteristics on the membrane model, influencing the results. Thus, while a singlecomponent lipid model can be informative, it is important to consider its applications and limitations. Overall, this chapter will provide insight as to the different lipid models used to mimic mammalian and bacterial membranes and how they have been found to be effective and useful research tools. Future development of these membrane models to more closely mimic www.intechopen.com Advances in Biomimetics 252 the composition and complexity of the natural membrane will provide further insight into the mechanisms of membrane processes in biological systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it