Human erythrocyte ghosts: Exploring the origins of multiexponential water diffusion in a model biological tissue with magnetic resonance
Bibliographic record
Abstract
A tissue model composed of erythrocyte ghosts was developed to study the effects of compartmentation on the MR signal acquired from biological tissues. This simple and flexible model offers control over the biophysical parameters that contribute to multicomponent signals arising from cellular systems. Cell density, size, intra- and extracellular composition, and membrane permeability can be independently altered. The effects of cell density and cell size on water diffusion properties were assessed. The data demonstrate non-monoexponential water diffusion in ghost cell suspensions of 17-67% cell density. Data were analysed with the widely employed two-compartment (biexponential) model, and with a two-compartment model that accounted for exchange between compartments. Water exchange between the intra- and extracellular compartments appeared to be significant over the range of diffusion times studied (7-35 ms). The biexponential fit to the ghost data appeared to be underparameterised as the ADCs and relative fractions of the fast and slow components were dependent on the experimental acquisition parameters, specifically the diffusion time. However, both analysis methods proved effective at tracking changes in the ghost model when it was perturbed. This was demonstrated with cell density variation, cell swelling and shrinkage experiments, and reduction of membrane water permeability using a water channel blocker (pCMBS). We anticipate that this model system could be used to investigate compartmental diffusion effects to simulate a range of pathologies, especially ischemic stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".