Benefits and Detriments of Gadolinium from Medical Advances to Health and Ecological Risks
Bibliographic record
Abstract
Gadolinium (Gd)-containing chelates have been established as diagnostics tools. However, extensive use in magnetic resonance imaging has led to increased Gd levels in industrialized parts of the world, adding to natural occurrence and causing environmental and health concerns. A vast amount of data shows that metal may accumulate in the human body and its deposition has been detected in organs such as brain and liver. Moreover, the disease nephrogenic systemic fibrosis has been linked to increased Gd3+ levels. Investigation of Gd3+ effects at the cellular and molecular levels mostly revolves around calcium-dependent proteins, since Gd3+ competes with calcium due to their similar size; other reports focus on interaction of Gd3+ with nucleic acids and carbohydrates. However, little is known about Gd3+ effects on membranes; yet some results suggest that Gd3+ interacts strongly with biologically-relevant lipids (e.g., brain membrane constituents) and causes serious structural changes including enhanced membrane rigidity and propensity for lipid fusion and aggregation at much lower concentrations than other ions, both toxic and essential. This review surveys the impact of the anthropogenic use of Gd emphasizing health risks and discussing debilitating effects of Gd3+ on cell membrane organization that may lead to deleterious health consequences.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".