Study about Overall Adhesion-Spreading Process of Liposomes on a Gold Electrode. Influence of the Presence of CdTe Quantum Dots
Bibliographic record
Abstract
The utilization of quantum dots (QDs) it has been grown up into the material science area, because these nanoparticles have particular optical and electronic properties. These properties are dependent on the particle size, which can be controlled by the modification of some experimental conditions: temperature, reaction time, pH and molar ratio of precursors. QDs present a wide absorption spectrum, narrow emission fluorescence spectrum, high photo-stability, tunable band gap, high quantum yield, among others. These properties have allowed its application in different areas such as photovoltaic cells, biomedicine, chemical analysis, biosensors and biomarkers. However, particularly in the medicine field is very important to know what is the effect of QDs in contact with cell membranes. An approximation to this process could be the utilization of structures like lipid vesicles or liposomes, which also can be used as drug and biomarkers carriers. Some authors [1], [2] have used electrochemical methods in the study of liposomes, because the vesicles deposition process (adhesion and spreading processes) on metallic electrodes are similar to the lipid membranes fusion, providing information about e.g. exo- and endocytosis processes. In this context, this work is related with the influence of the interaction between CdTe QDs and 1,2-dimyristoyl-sn-phosphatidylcholine (DMPC) liposomes on the overall adhesion-spreading processes of liposomes modified by QDs. Synthesis of CdTe QDs was carried out in aqueous media, by using CdCl2 and Na2TeO3 as precursors, mercaptosuccinic acid (MSA) as capping agent and NaBH4 as reducing agent. Using a Doehlert’s experimental design was possible the optimization of the QDs sizes controlling the synthesis variables, i.e. temperature, reaction time, pH and molar ratio of precursors. After QDs were purified through ultracentrifugation with 1:1 water:isopropanol mixture and re-suspended in a buffer solution (borate buffer; pH 9.20). Finally, these were characterized by UV-Vis spectroscopy, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). On the other hand, DMPC liposomes were prepared by dissolving DMPC in chloroform, then evaporating solvent with Argon and suspending the lipids in borate buffer. Lipid suspension was cooled with liquid nitrogen and then heated below the phase transition temperature. Finally, lipids were extruded to obtain large unilamellar vesicles. After, the DMPC liposomes were deposited on gold electrode and characterized by CV observing the coverage degree by charge analysis. Additionally, the overall adhesion-spreading process of liposomes on gold electrode was characterized by means of chronoamperometry technique analyzing the corresponding current-time transients. Both analysis were performed after the mixing with CdTe QDs. The results show a decrease in the constant rate values of the adhesion-spreading processes of DMPC liposomes on gold electrode suggesting that the interaction CdTe(QD)-DMPC produces an increase in the activation energy of the lipid membranes fusion. References [1] V. A. Hernández and F. Scholz, “The Electrochemistry of Liposomes,” Isr. J. Chem., vol. 48, no. 3–4, pp. 169–184, 2008. [2] O. Pierrat, N. Lechat, C. Bourdillon, and J.-M. Laval, “Electrochemical and Surface Plasmon Resonance Characterization of the Step-by-Step Self-Assembly of a Biomimetic Structure onto an Electrode Surface,” Langmuir, vol. 13, no. 15, pp. 4112–4118, 1997.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".