Human Elimination of Organochlorine Pesticides: Blood, Urine, and Sweat Study
Bibliographic record
Abstract
Background. Many individuals have been exposed to organochlorinated pesticides (OCPs) through food, water, air, dermal exposure, and/or vertical transmission. Due to enterohepatic reabsorption and affinity to adipose tissue, OCPs are not efficiently eliminated from the human body and may accrue in tissues. Many epidemiological studies demonstrate significant exposure-disease relationships suggesting OCPs can alter metabolic function and potentially lead to illness. There is limited study of interventions to facilitate OCP elimination from the human body. This study explored the efficacy of induced perspiration as a means to eliminate OCPs.Methods. Blood, urine, and sweat (BUS) were collected from 20 individuals. Analysis of 23 OCPs was performed using dual-column gas chromatography with electron-capture detectors.Results. Various OCPs and metabolites, including DDT, DDE, methoxychlor, endrin, and endosulfan sulfate, were excreted into perspiration. Generally, sweat samples showed more frequent OCP detection than serum or urine analysis. Many OCPs were not readily detected in blood testing while still being excreted and identified in sweat. No direct correlation was found among OCP concentrations in the blood, urine, or sweat compartments.Conclusions. Sweat analysis may be useful in detecting some accrued OCPs not found in regular serum testing. Induced perspiration may be a viable clinical tool for eliminating some OCPs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".