A simple breathing circuit allowing precise control of inspiratory gases for experimental respiratory manipulations
Bibliographic record
Abstract
BACKGROUND: Respiratory manipulations modulating blood flow and oxygenation levels have become an important component of modern functional MRI applications. Manipulations often consist of temporarily switching inspired fractions of CO2 and O2; and have typically been performed using simple oxygen masks intended for applications in respiratory therapy. However, precise control of inspired gas composition is difficult using this type of mask due to entrainment of room air and resultant dilution of inspired gases. We aimed at developing a gas delivery apparatus allowing improved control over the fractional concentration of inspired gases, to be used in brain fMRI studies. FINDINGS: The breathing circuit we have conceived allowed well controlled step changes in FiO2 and FiCO2, at moderate flow rates achievable on standard clinical flow regulators. In a two run test inside the scanner we demonstrate that tightly controlled simple gas switching manipulations can afford good intra-subject reproducibility of induced hyperoxia/hypercapnia responses. Although our approach requires a non-vented mask fitting closely to the subject's face, the circuit ensures a continuous supply of breathable air even if the supply of medical gases is interrupted, and is easily removable in case of an emergency. The apparatus we propose is also compact and MRI compatible, allowing subject placement in confined spaces such as an MRI scanner for brain examinations. CONCLUSIONS: We have reported a new approach for the controlled administration of medical gases, and describe an implementation of the breathing circuit that is MRI compatible and uses commercially available parts. The resultant apparatus allows simple, safe and precise manipulations of FiO2 and FiCO2.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".