Targeting the Lowest Concentration of a Toxin That Induces a Detectable Metabolic Response in Living Organisms: Time-Resolved <i>In Vivo</i> 2D NMR during a Concentration Ramp
Bibliographic record
Abstract
Abstract In vivo nuclear magnetic resonance (NMR) is a powerful analytical tool for probing complex biological processes inside living organisms. However, due to magnetic susceptibility broadening, which produces broad lines in one-dimensional NMR, 1H–13C two-dimensional (2D) NMR is required for metabolite monitoring in vivo. As each 2D experiment is time-consuming, often hours, this limits the temporal resolution over which in vivo processes can be monitored. Furthermore, to understand concentration-dependent responses, studies are traditionally repeated using different contaminant and toxin concentrations, which can make studies prohibitively long (potentially months). In this study, time-resolved non-uniform sampling NMR is performed in the presence of a contaminant concentration sweep. The result is that the lowest concentration that elicits a metabolic response can be rapidly detected, while the metabolic pathways impacted provide information about the toxic mode of action of the toxin. The lowest concentration of bisphenol A (BPA) that induces a response was ∼0.1 mg/L (detected in just 16 min), while changes in different metabolites suggest a complex multipathway response that leads to protein degradation at higher BPA concentrations. This proof of concept shows it is possible, on the basis of “real-time” organism responses, to identify the sublethal concentration at which a toxin impacts an organism and thus represents an essential analytical tool for the next generation of toxicity-based research and monitoring.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".