Sensing molecular carbon dioxide: a translational focus for respiratory disease
Bibliographic record
Abstract
The last two decades of research on carbon dioxide have demonstrated that CO 2 is far more than a waste product of aerobic metabolism leading to acidosis and that it elicits biological responses directly via non-pH-dependent molecular interactions. New specialized methodologies have mapped CO 2 incorporation into specific regions of CO 2 -sensitive proteins and linked these events to altered cellular function. CO 2 affects a host of biological responses related to respiratory disease, including control of respiration, protein maturation, alveolar fluid homeostasis, wound repair, innate immunity, host defense, and airway contractility. Elevated CO 2 (hypercapnia) appears to be primarily deleterious in pulmonary diseases, leading to a heightened interest in strategies to reduce excess CO 2 in patients with hypercapnic respiratory failure. Here, we summarize recently generated knowledge on molecular CO 2 sensing and signaling and the potential translational relevance of these processes in the context of respiratory disease. We need to grow this field further by encouraging experts in basic and translational science to contribute to more fully elucidating CO 2 sensing, signaling, and downstream effects. Understanding the biology and clinical consequences of perturbations in CO 2 homeostasis should no longer be considered secondary to studying oxygen sensing and signaling in respiratory medicine.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".