Interaction between endoplasmic/sarcoplasmic reticulum stress (ER/SR stress), mitochondrial signaling and Ca<sup>2+</sup> regulation in airway smooth muscle (ASM)
Bibliographic record
Abstract
Airway inflammation is a key aspect of diseases such as asthma. Several inflammatory cytokines (e.g., TNFα and IL-13) increase cytosolic Ca(2+) ([Ca(2+)]cyt) responses to agonist stimulation and Ca(2+) sensitivity of force generation, thereby enhancing airway smooth muscle (ASM) contractility (hyper-reactive state). Inflammation also induces ASM proliferation and remodeling (synthetic state). In normal ASM, the transient elevation of [Ca(2+)]cyt induced by agonists leads to a transient increase in mitochondrial Ca(2+) ([Ca(2+)]mito) that may be important in matching ATP production with ATP consumption. In human ASM (hASM) exposed to TNFα and IL-13, the transient increase in [Ca(2+)]mito is blunted despite enhanced [Ca(2+)]cyt responses. We also found that TNFα and IL-13 induce reactive oxidant species (ROS) formation and endoplasmic/sarcoplasmic reticulum (ER/SR) stress (unfolded protein response) in hASM. ER/SR stress in hASM is associated with disruption of mitochondrial coupling with the ER/SR membrane, which relates to reduced mitofusin 2 (Mfn2) expression. Thus, in hASM it appears that TNFα and IL-13 result in ROS formation leading to ER/SR stress, reduced Mfn2 expression, disruption of mitochondrion-ER/SR coupling, decreased mitochondrial Ca(2+) buffering, mitochondrial fragmentation, and increased cell proliferation.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".