Probing bioinorganic chemistry processes in the bloodstream to gain new insights into the origin of human diseases
Bibliographic record
Abstract
In the context of elucidating the origin of human diseases, past poisoning epidemics have revealed that exceedingly small doses of inorganic environmental pollutants can result in dramatic effects on human health. Today, numerous organic and inorganic pollutants have been quantified in human blood, but the interpretation of these concentrations remains--from a public health point of view--problematic. Conversely, the biomolecular origin for several grievous human diseases is essentially unknown. Taken together and viewed in the context of recent bioinorganic research findings, the established human blood concentrations of toxic metals and metalloids may be functionally connected with the etiology of specific human diseases. To unravel the underlying biomolecular mechanisms, and taking into account the basic flow of dietary matter through mammalian organisms, a better understanding of the bioinorganic chemistry of toxic metals and metalloid compounds in the bloodstream is emerging as a promising avenue for future research. To this end, the concerted application of modern proteomic methodologies, synchrotron-based X-ray absorption spectroscopy and established spectroscopic techniques will contribute to better define the role that blood-based bioinorganic chemistry-related processes play in the origin of human diseases. The application of this and other modern proteomic methodologies could contribute to a better understanding of the role that blood-based bioinorganic chemistry-related processes play in the origin and etiology of human 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".