Building the Environmental Chemical-Protein Interaction Network (eCPIN): An Exposome-Wide Strategy for Bioactive Chemical Contaminant Identification
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
Although advancements in nontargeted analysis have made it possible to detect hundreds of chemical contaminants in a single run, the current environmental toxicology approaches lag behind, precluding the transition from analytical chemistry efforts to health risk assessment. We herein highlighted a recently developed "top-down" bioanalytical method, protein Affinity Purification with Nontargeted Analysis (APNA), to screen for bioactive chemical contaminants at the "exposome-wide" level. To achieve this, a tagged functional protein is employed as a "bait" to directly isolate bioactive chemical contaminants from environmental mixtures, which are further identified by nontargeted analysis. Advantages of this protein-guided approach, including the discovery of new bioactive ligands as well as new protein targets for known chemical contaminants, were highlighted by several case studies. Encouraged by these successful applications, we further proposed a framework, i.e., the environmental Chemical-Protein Interaction Network (eCPIN), to construct a complete map of the 7 billion binary interactions between all chemical contaminants (>350,000) and human proteins (∼20,000) via APNA. The eCPIN could be established in three stages through strategically prioritizing the ∼20,000 human proteins, such as focusing on the 48 nuclear receptors (e.g., thyroid hormone receptors) in the first stage. The eCPIN will provide an unprecedented throughput for screening bioactive chemical contaminants at the exposome-wide level and facilitate the identification of molecular initiating events at the proteome-wide level.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it