Abstract 2211 Differential interactions of Gd with key mammalian lipids contained in brain membranes affect liposome fluidity and size
Bibliographic record
Abstract
Gadolinium containing chelates are important for magnetic resonance imaging because they increase the contrast and improve the proper analysis of the patient scans. In some cases, gadolinium was found to deposit in both brain and kidney tissues. Moreover, environmental gadolinium has been found surrounding healthcare systems that utilize Gd in MRI. Given the potential for gadolinium deposition in the brain, the objective of this study was to characterize the extent of the metal interactions with key mammalian brain lipids to assess potential detrimental effects at the level of the cell membrane. Fluorescence spectroscopy and dynamic light scattering were used to determine metal induced changes in membrane fluidity and liposome size for samples comprised of phosphatidylcholines, sphingomyelins, brain polar extracts, and a biomimetic model of myelin. Electrostatic interactions promote complexation of gadolinium ions to anionic lipids like phosphatidylserine yet increases in both membrane rigidity and liposome size were also observed for zwitterionic lipids such as phosphatidylcholine and sphingomyelin. Moreover, effects were stronger for lipids with fully saturated acyl chain architecture compared to monounsaturated lipids. Gd induced these changes at low micromolar concentrations compared to the effects of the highly toxic metals cadmium and lead, which required millimolar concentrations for equivalent changes. The interactions of gadolinium change the membrane dynamics of key brain lipids which may impact important processes. These data demonstrate that more research is needed to better understand the potentially serious effects of gadolinium use in the healthcare industry. This research was funded by the Natural Sciences and Engineering Research Council of Canada through a Discovery Grant to E.J.P.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".