Glucose Can Protect Membranes against Dehydration Damage by Inducing a Glassy Membrane State at Low Hydrations
Bibliographic record
Abstract
The physical effects of small sugars on membranes have been studied for decades, primarily because of their membrane stabilization in cold or dehydrated environments. We studied the effects of up to 20 mol% glucose in bilayers made of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) at low hydration by combining X-ray diffraction and Molecular Dynamics (MD) simulations. In agreement with previous studies, we observe membrane thinning at low and membrane thickening at high sugar concentrations. Glucose was found to preferentially localize to the outer head region of phospholipid bilayers at all concentrations, and partitioning of sugar in the membranes was found to monotonically increase with increasing sugar concentration. While the number of gauche defects in the lipid acyl tails and the lipid packing in the presence of sugar resembled values of a fluid lipid bilayer, tail dynamics, as assessed by autocorrelation of the carbon atoms in the phospholipid tails, were slowed down significantly with increasing glucose content. Thus, our findings suggest that sugar leads to a a disordered, glassy state of the hydrophobic membrane core. The non-monotonic effect of glucose on membrane thickness was found to be an effect of fluidification at low concentrations and decreased interdigitation in the higher sugar concentration regime.
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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".