E-Cigarette Exposure Alters Neuroinflammation Gene and Protein Expression in a Murine Model: Insights from Perinatally Exposed Offspring and Post-Birth Mothers
Bibliographic record
Abstract
The use of E-cigarettes, often considered a safer alternative to traditional smoking, has been associated with high rates of cellular toxicity, genetic alterations, and inflammation. Neuroinflammatory impacts of cigarette smoking during pregnancy have been associated with increased risks of adverse childhood health outcomes; however, it is still relatively unknown if the same propensity is conferred on offspring by maternal vaping during gestation. Results from our previous mouse inhalation studies suggest such a connection. In this earlier study, pregnant C57BL/6 mice were exposed daily to inhaled E-cig aerosols (i.e., propylene glycol and vegetable glycerin, [PG/VG]), with or without nicotine (16 mg/mL) by whole-body inhalation throughout gestation (3 h/d; 5 d/week; total ~3-week) and continuing postnatally from post-natal day (PND) 4-21. As neuroinflammation is involved in the dysregulation of glucose homeostasis and weight gain, this study aimed to explore genes associated with these pathways in 1-mo.-old offspring (equivalent in humans to 12-18 years of age). Results in the offspring demonstrated a significant increase in glucose metabolism protein levels in both treatment groups compared to filtered air controls. Gene expression analysis in the hypothalamus of 1 mo. old offspring exposed perinatally to E-cig aerosols, with and without nicotine, revealed significantly increased gene expression changes in multiple genes associated with neuroinflammation. In a second proof-of-principal parallel study employing the same experimental design, we shifted our focus to the hippocampus of the postpartum mothers. We targeted the mRNA levels of several neurotrophic factors (NTFs) indicative of neuroinflammation. While there were suggestive changes in mRNA expression in this study, levels failed to reach statistical significance. These studies highlight the need for ongoing research on E-cig-induced alterations in neuroinflammatory pathways.
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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) | 0.001 | 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".