Estimation of the Alveolar Partial Pressure of Oxygen using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study
Bibliographic record
Abstract
Estimation of the Alveolar Partial Pressure of Oxygen using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study Purpose: Hyperpolarized (HP) 3He MRI investigates lung structure and function without subjecting patients to ionizing radiation like CT or V/Q scans. We aimed to determine whether the estimation of alveolar oxygen partial pressure (pAO2) by HP MRI, reflecting the balance between inhaled oxygen and its uptake into the blood, can be used as a biomarker for COPD and severity of pulmonary emphysema subtypes. Methods: The MESA COPD Study recruited COPD cases and controls with 10+ pack-years of smoking. pAO2 and mean apparent diffusion coefficients (ADC) were estimated on HP 3He MRI (1.5T) and CT-assessed pulmonary emphysema was quantified visually and by densitometry (%emphysema) within six lung zones defined by cranial-caudal thirds. Regions with non-physiologic negative values were considered to be unventilated. Linear and linear mixed models were adjusted for age, sex, race/ethnicity, and smoking status. Results: Among 54 participants (29 with COPD), pAO2 standard deviation (SD) and %unventilated were positively associated with ADC mean (p=0.002) and %emphysema (p=0.01), whereas pAO2 mean did not vary significantly. In regional analyses, pAO2 SD and %unventilated were positively associated with visual emphysema severity (p=0.0004 and p=0.01, respectively). Conclusion: pAO2 measurements using HP noble gas MRI provide a safe means of studying emphysema pathophysiology and may act as a biomarker for regional alveolar damage and ventilation impairment. Funding NIH/NHLBI R01-HL093081R01-HL077612R01-HL121270
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".