Lipid Structure Determines the Differential Impact of Single Metal Additions and Binary Mixtures of Manganese, Calcium and Magnesium on Membrane Fluidity and Liposome Size
Bibliographic record
Abstract
Unilamellar vesicles of the biologically relevant lipids phosphatidic acid (PA) and phosphatidylserine (PS) with fully saturated (DM-) or partly unsaturated (PO-) acyl side chains were exposed to Ca, Mn and Mg in single metal additions; in equimolar mixtures or by sequential additions of one metal at a time. Laurdan generalized polarization measured the membrane fluidity, while dynamic light scattering reported liposome size changes complemented by zeta potential. All metals induced membrane rigidity and increased liposome sizes across all systems. Mn had the strongest effect overall, but Mg was comparable for DMPS. Lipid side chain architecture was important as GP values for binary mixtures were higher than expected from the sum of values for single additions added to POPS but smaller for DMPS. Sequential additions were predominantly different for Ca:Mg mixtures. Mn induced the strongest increase of liposome size in saturated lipids whereas Ca effects dominated unsaturated matrices. Binary additions induced larger sizes than the sum of single additions for POPS, but much lower changes in DMPA. The order of addition was relevant for PS systems. Thus, lipid structure determines metal effects, but their impact is modulated by other ions. Thus, metal effects may differ with the local lipid architecture and metal concentrations within cells.
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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) | 0.001 | 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".