Zn<sup>2+</sup> Induced Irreversible Aggregation, Stacking, and Leakage of Choline Phosphate Liposomes
Bibliographic record
Abstract
The interaction between lipids and metal ions is important for metal sensing, cellular signal transduction, and oxidative lipid damage. While most previous work overlooked the phosphate group of lipids for metal binding, we herein highlight its importance. Phosphocholine (PC) and its headgroup inversed choline phosphate (CP) lipids were used to prepare liposomes. From dynamic light scattering (DLS), Zn 2+ causes significant aggregation or fusion of the CP liposomes, but not PC liposomes. The size change induced by Zn 2+ is not fully reversed by adding EDTA, implying liposome fusion induced by Zn 2+ . Isothermal titration calorimetry (ITC) shows that binding between Zn 2+ and CP liposomes is endothermic with a K d of 110 μM Zn 2+, suggesting an entropy driven reaction likely due to the release of bound water. In comparison, no heat was detected by titrating Zn 2+ into PC liposomes or Ca 2+ into CP liposomes. Furthermore, Zn 2+ causes a transient leakage of the CP liposomes, and further leakage is observed upon removing Zn 2+ by EDTA. Transmission electron microscopy (TEM) with negative stained samples showed multilamellar CP lipid structures attributable to Zn 2+ sandwiched between lipid bilayers, leading to a proposed reaction mechanism. This work provides an interesting system for studying metal interacting with terminal phosphate groups in liposomes, affecting the size, charge, and membrane integrity of the liposomes.
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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 | 0.000 |
| 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 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".