Calcium Homeostasis and Ionic Mechanisms in Pulmonary Fibroblasts
Bibliographic record
Abstract
Fibroblasts are key cellular mediators of many chronic interstitial lung diseases, including idiopathic pulmonary fibrosis, scleroderma, sarcoidosis, drug-induced interstitial lung disease, and interstitial lung disease in connective tissue disease. A great deal of effort has been expended to understand the signaling mechanisms underlying the various cellular functions of fibroblasts. Recently, it has been shown that Ca(2+) oscillations play a central role in the regulation of gene expression in human pulmonary fibroblasts. However, the mechanisms whereby cytosolic [Ca(2+)] are regulated and [Ca(2+)] oscillations transduced are both poorly understood. In this review, we present the general concepts of [Ca(2+)] homeostasis, of ionic mechanisms responsible for various Ca(2+) fluxes, and of regulation of gene expression by [Ca(2+)]. In each case, we then also summarize the original findings that pertain specifically to pulmonary fibroblasts. From these data, we propose an overall signaling cascade by which excitation of the fibroblasts triggers pulsatile release of internally sequestered Ca(2+), which, in turn, activates membrane conductances, including voltage-dependent Ca(2+) influx pathways. Collectively, these events produce recurring Ca(2+) oscillations, the frequency of which is transduced by Ca(2+)-dependent transcription factors, which, in turn, orchestrate a variety of cellular events, including proliferation, synthesis/secretion of extracellular matrix proteins, autoactivation (production of transforming growth factor-β), and transformation into myofibroblasts. That unifying hypothesis, in turn, allows us to highlight several specific cellular targets and therapeutic intervention strategies aimed at controlling unwanted pulmonary fibrosis. The relationships between Ca(2+) signaling events and the unfolded protein response and apoptosis are also explored.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".