Seeding of proteins into amyloid structures by metabolite assemblies may clarify certain unexplained epidemiological associations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The accumulation of various metabolites appears to be associated with diverse human diseases. However, the aetiological link between metabolic alteration and the observed diseases is still elusive. This includes the correlation between the abnormally high levels of homocysteine and quinolinic acid in Alzheimer's disease, as well as the accumulation of oncometabolites in malignant processes. Here, we suggest and discuss a possible mechanistic insight into metabolite accumulation in conditions such as neurodegenerative diseases and cancer. Our hypothesis is based on the demonstrated ability of metabolites to form amyloid-like structures in inborn error of metabolism disorders and the potential of such metabolite amyloids to promote protein aggregation. This notion can provide a new paradigm for neurodegeneration and cancer, as both conditions were linked to loss of function due to protein aggregation. Similar to the well-established observation of amyloid formation in many degenerative disorders, the formation of amyloids by tumour-suppressor proteins, including p53, was demonstrated in malignant states. Moreover, this new paradigm could fill the gap in understanding the high occurrence of specific types of cancer among genetic error of metabolism patients. This hypothesis offers a fresh view on the aetiology of some of the most abundant human maladies and may redirect the efforts towards new therapeutic developments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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 it