Swelling of phospholipid membranes by divalent metal ions depends on the location of the ions in the bilayers
Bibliographic record
Abstract
The Hofmeister series illustrates how salts produce a wide range of effects in biological systems, which are not exclusively explained by ion charge. In lipid membranes, charged ions have been shown to bind to lipids and either hydrate or dehydrate lipid head groups, and also to swell the water layer in multi-lamellar systems. Typically, Hofmeister phenomena are explained by the interaction of the ions with water, as well as with biological interfaces, such as proteins or membranes. We studied the effect of the divalent cations Mg(2+), Ca(2+), Fe(2+), and Zn(2+) on oriented, stacked, phospholipid bilayers made of dimyristoylphosphatidylcholine (DMPC). Using high-resolution X-ray diffraction, we observed that the cations lead to a swelling of the water layer between the bilayers, without causing significant changes to the bilayer structure. The cations swelled the bilayers in different amounts, in the order Fe(2+) > Mg(2+) > Ca(2+) > Zn(2+). By decomposing the total bilayer electron density into different molecular groups, Zn(2+) and Ca(2+) were found to interact with the glycerol groups of the lipid molecules and cause minor swelling of the bilayers. Mg(2+) and Fe(2+) were found to position near the phosphate groups and cause a strong increase in the number of hydration water molecules. Our results present a molecular mechanism-of-action for the Hofmeister series in phospholipid membranes.
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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".