Occupational exposure to formaldehyde and alterations in lymphocyte subsets
Bibliographic record
Abstract
BACKGROUND: Formaldehyde is used in many occupational settings, most notably in manufacturing, health care, and embalming. Formaldehyde has been classified as a human carcinogen, but its mechanism of action remains uncertain. METHODS: We carried out a cross-sectional study of 43 formaldehyde-exposed workers and 51 unexposed age and sex-matched controls in Guangdong, China to study formaldehyde's early biologic effects. To follow up our previous report that the total lymphocyte count was decreased in formaldehyde-exposed workers compared with controls, we evaluated each major lymphocyte subset (i.e., CD4(+) T cells, CD8(+) T cells, natural killer [NK] cells, and B cells) and T cell lymphocyte subset (CD4(+) naïve and memory T cells, CD8(+) naïve and memory T cells, and regulatory T cells). Linear regression of each subset was used to test for differences between exposed workers and controls, adjusting for potential confounders. RESULTS: Total NK cell and T cell counts were about 24% (P = 0.037) and 16% (P = 0.0042) lower, respectively, among exposed workers. Among certain T cell subsets, decreased counts among exposed workers were observed for CD8(+) T cells (P = 0.026), CD8(+) effector memory T cells (P = 0.018), and regulatory T cells (CD4(+) FoxP3(+) : P = 0.04; CD25(+) FoxP3(+) : P = 0.008). CONCLUSIONS: Formaldehyde-exposed workers experienced decreased counts of NK cells, regulatory T cells, and CD8(+) effector memory T cells; however, due to the small sample size; these findings need to be confirmed in larger studies.
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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) | 0.001 | 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".