ROLE OF THE IRE/XBP-1 PATHWAY IN CIGARETTE SMOKE AFFECTED MACROPHAGE POLARIZATION IN VITRO
Bibliographic record
Abstract
Cigarette smoke contributes to 90% of lung cancer cases and 80% of COPD cases. These concerns loom large as lung cancer represents 13% of all cancer deaths and estimates report by 2020 COPD will be the third leading cause of death in the world. The master regulator of the ER stress response, IRE-1, in the context of cigarette smoke exposure lacks study. Interestingly, its downstream pathways are activated. In fact, the 2014 Surgeon General’s report on the health consequences of smoking highlighted the endoplasmic reticulum (ER) stress response as a potential mechanism leading to the development of lung cancer and Chronic Obstructive Pulmonary Disorder (COPD). Following acute cigarette smoke exposure, mouse lung homogenates exhibited increased levels of XBP-1 along with downstream mediators of IRE-1 activation— GRP-78 and CHOP. Specifically observing macrophages, an important immune cell and source of acute inflammation, cigarette smoke induced activation of IRE-1/XBP-1 pathway through splicing of XBP-1 mRNA. However, upon assaying for pro-inflammatory cytokines we were unable to determine that cigarette smoke directly caused inflammation in vitro. Furthermore, cigarette smoke inhibited the activation of M2 macrophages, an anti-inflammatory and tissue healing subset seen through CCL18 inhibition. A majority of M2 and M1 macrophage markers were decreased from IRE-1/XBP-1 inhibition. This suggests a different phenotype than classical M1 or M2 polarization being induced by cigarette smoke. In addition, it suggests the IRE-1/XBP-1 pathway having a robust role in controlling gene expression and balance of cellular proteomics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".