Cigarette Smoke-Induced Proteostasis Imbalance in Obstructive Lung Diseases
Bibliographic record
Abstract
The airway and alveolar surface is exposed daily to 8,000 L of air containing oxygen, particles, bacteria, allergens and pollutants, all of which have the potential to induce oxidative stress within cells. If one is also a cigarette smoker, then the exposure to reactive oxidants increases exponentially. More than any other tissue, the lung is at risk of undergoing oxidative changes in protein expression, structure and function. The oxidant burden of chronic cigarette smoke exposure can overwhelm the lung cells' capacity to maintain proteostasis, a process of regulated protein synthesis, folding and turnover. Somewhat surprisingly, most chronic cigarette smokers do not develop chronic obstructive pulmonary disease (COPD), likely because cells initiate a highly effective unfolded protein response (UPR) in the presence of oxidant-derived endoplasmic reticulum (ER) stress that allows cells to survive. The UPR initiates several signaling pathways that decrease protein translation, limit cell cycle progression, increase protein degradation and chaperone-mediated protein folding, and activate the transcription factor Nrf2 that induces antioxidant gene expression. Each of these actions decreases ER stress in a process of "healthy proteostasis". If these responses are insufficient, apoptosis ensues. In this article, we review the mechanisms of healthy and dysfunctional proteostasis related to cigarette smoke exposure and COPD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".