Early Postnatal Tobacco Smoke Exposure Aggravates Experimental Autoimmune Encephalomyelitis in Adult Rats
Bibliographic record
Abstract
Abstract Although substantial evidence supports smoking as a risk factor for the development of multiple sclerosis (MS) in adulthood, it remains controversial as to whether early-life exposure to environmental tobacco smoke (ETS) increases the risk of MS later in life. Here, using experimental autoimmune encephalomyelitis (EAE) as an animal model for MS, we show that exposing neonatal rats during the 1 st week (ETS1-EAE), but not the 2 nd week (ETS2-EAE) and the 3 rd week (ETS3-EAE) after birth, increased the severity of EAE in adulthood in comparison to pups exposed to filtered compressed air (AIR-EAE). The EST1-EAE rats showed a worse neurological deficit score and a significant increase in CD4 + cell infiltration, demyelination, and axonal injury in the spinal cord compared to AIR-EAE, ETS2-EAE, and ETS3-EAE groups. Flow cytometry analysis showed that the ETS1 group had decreased numbers of regulatory T (Treg) cells and increased effector T (Teff) cells in the brain and spinal cord. The expressions of Treg upstream regulator Foxp3 and downstream cytokines such as IL-10 were also altered accordingly. Together, these findings demonstrate that neonatal ETS exposure suppresses Treg functions and aggravates the severity of EAE, confirming early-life exposure to EST as a potential risk factor for multiple sclerosis in adulthood.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".