Short-term impact of the cooking aerosol on the human brain wavepattern
Bibliographic record
Abstract
Translational research examining the impact of indoor ultrafine particles on human health is important because humans spend more than 85% of their time indoors. Cooking is the major modifiable source contributing to indoor UFP (particles <100nm) exposures. Cooking emits a copious number of UFPs containing trace elements with different morphology. Healthy volunteers (N=30) over 25 were recruited for this study. The experiments were conducted for two consecutive days in an apartment under controlled conditions. The first day was a control experiment (without cooking), and the second was an exposure experiment (with cooking). An Electroencephalograph (QEEG) was used to measure the brain wave pattern. Volunteers entered the apartment at 8:00 am. The brain EEG was measured in 21 steps thatstarted one hour after arrival (9 am) and continued at (10:00 am, 10:30 am, 11:00 am, 11:30 am, 12:00, 14:00, 16:00, 18:00, and 20:00). The last measurement was at 9:00 am the third day (97 hours after arrival). Frying chicken drumsticks and French fries in sunflower oil using a gas stove was conducted without ventilation at 9:30 am on the second day (cooking day). UFPs, particular matter, CO2, indoor temperature, RH, and oil temperatures were monitored continuously throughout the experiments. During cooking the UFP concentration reached its mximum value to be 5.310 5 particles/cm 3 . Our preliminary results showed that the brain wave pattern underwent statistically significant changes at different lobes as a result of the exposure. However, the brain reverted back to the normal after 24 hours.All frequency bands including beta 1, beta 2, beta 3, alpha, delta and theta underwent changes as a result of the exposure.
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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 | 0.000 |
| 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) | 0.003 | 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".