Pulmonary expression and regulation of Cldn6 by tobacco smoke (834.3)
Bibliographic record
Abstract
Smoking is a major risk factor for several chronic diseases including chronic obstructive pulmonary disease (COPD), a condition involving both emphysema and inflammation of the airways. COPD severity directly relates to abnormalities of pulmonary epithelial cells. Claudins contribute to tight junctions that prevent paracellular transport of extracellular fluid and diverse substances and Claudin 6 (Cldn6) is a protein expressed prominently in the lung parenchyma. To determine whether Cldn6 was differentially influenced by tobacco smoke, Cldn6 was evaluated by q‐PCR, immunoblitting, and immunofluorescence following exposure to tobacco smoke. Q‐PCR and immunoblotting revealed that Cldn6 was increased in alveolar type II‐like epithelial cells (A549) but not in Bease2B cells, a cell line derived from proximal airway epithelium. Luciferase assays incorporating 0.5kb, 1.0kb, or 2.0kb of the Cldn6 promoter also revealed increased transcription of the gene when cells were exposed to cigarette smoke extract. Cldn6 was also markedly increased in the lungs of Balb/C mice exposed to tobacco smoke delivered by an automated smoke machine (InExpose, Scireq, Canada) compared to animals exposed to room air. These data reveal for the first time that tight junctional proteins are differentially regulated by tobacco smoke exposure and that Cldns are potentially involved as neighboring epithelial cells respond to tobacco smoke. Further research may show that complexes between cells contribute to impaired structural integrity of the lung coincident with smoking. Grant Funding Source : Supported by the Flight Attendant’s Medical Research Institute (FAMRI) and a BYU MEG Award (PRR).
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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) | 0.002 | 0.001 |
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".