Health Risks of Chronic Exposure to Small Doses of Microcystins: An Integrative Metabolomic and Biochemical Study of Human Serum
Bibliographic record
Abstract
Health risks of chronic exposure to microcystins (MCs), a family of aquatic contaminants produced mainly by cyanobacteria, are critical yet unsolved problems. Despite a few epidemiological studies, the metabolic profiles of humans exposed to MCs remain unknown, hindering the deep understanding of the molecular toxicity mechanisms. Here, sensitive nuclear magnetic resonance (NMR)- and liquid chromatography-mass spectrometry (LC-MS)-based metabolomics were applied to investigate the serum metabolic profiles of humans living near Lake Chao, where toxic cyanobacterial blooms occur annually. MCs were positively detected in 92 of 144 sera by ultra-high-pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with a median concentration of 0.016 μg/L. The estimated daily intake (0.15-0.27 μg MC-LReq/day) was less than the tolerable daily intake (TDI, 2.4 μg MC-LR for 60 kg adults) recommended by the World Health Organization (WHO). Obvious disruptions of the amino acid metabolism were confirmed and played important roles in renal impairments associated with serum MC burdens. Chronic oral exposure of mice to 30 μg MC-LR/kg body mass, which is less than the no observed adverse effect level, also led to obvious renal lesions and metabolic dysfunction. These observations provide the first evidence of metabolic disturbance of humans exposed to MCs and indicate that the WHO's TDI value determined traditionally should be lessened to protect human health effectively.
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 | 0.000 | 0.000 |
| 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 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".