Werner coordination chemistry and neurodegeneration
Bibliographic record
Abstract
Neurodegenerative diseases are capturing the world's attention as being the next set of diseases we must tackle collectively. Not only are the patients experiencing gradual cognitive and physical decline in most cases, but these diseases are fatal with no prevention currently available. As these diseases are progressive, providing care and symptom treatment for the ageing population is becoming both a medical and a financial challenge. This review discusses how Werner coordination chemistry plays a role in three diseases - those of Alzheimer's, Parkinson's, and prions. Metal ions are considered to be involved in these diseases in part via their propensity to cause toxic aggregation of proteins. First, the coordination of metal ions, with emphasis on copper(II), to metalloproteins that are hallmarks of these diseases - amyloid β, α-synuclein, and prion, respectively - will be discussed. We will present the current understanding of the metal coordination environments created by the amino acids of these proteins, as well as metal binding affinity. Second, a diverse set of examples of rationally designed metal chelators to outcompete this deleterious binding will be examined based on coordination mode and affinity toward bio-relevant metal ions. Overall, this review will give a general overview of protein and metal chelator coordination environments in neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.002 |
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".