Low-Flow Nasal Cannula Hydrogen Therapy
Bibliographic record
Abstract
Background: Molecular hydrogen (H 2 ) is a biologically active gas that is widely used in the healthcare sector. In recent years, on-site H 2 gas generators, which produce high-purity H 2 by water electrolysis, have begun to be introduced in hospitals, clinics, beauty salons, and fitness clubs because of their ease of use. In general, these generators produce H 2 at a low-flow rate, so physicians are concerned that an effective blood concentration of H 2 may not be ensured when the gas is delivered through a nasal cannula. Therefore, this study aimed to evaluate blood concentrations of H 2 delivered from an H 2 gas generator via a nasal cannula. Methods: We administered 100% H 2 , produced by an H 2 gas generator, at a low-flow rate of 250 mL/min via a nasal cannula to three spontaneously breathing micro miniature pigs. An oxygen mask was placed over the nasal cannula to administer oxygen while minimizing H 2 leakage, and a catheter was inserted into the carotid artery to monitor the arterial blood H 2 concentration. Results: During the first hour of H 2 inhalation, the mean (standard error (SE)) H 2 concentrations and saturations in the arterial blood of the three pigs were 1,560 (413) nL/mL and 8.85% (2.34%); 1,190 (102) nL/mL and 6.74% (0.58%); and 1,740 (181) nL/mL and 9.88% (1.03%), respectively. These values are comparable to the concentration one would expect if 100% of the H 2 released from the H 2 gas generator is taken up by the body. Conclusions: Inhalation of 100% H 2 produced by an H 2 gas generator, even at low-flow rates, can increase blood H 2 concentrations to levels that previous non-clinical and clinical studies demonstrated to be therapeutically effective. The combination of a nasal cannula and an oxygen mask is a convenient way to reduce H 2 leakage while maintaining oxygenation. J Clin Med Res. 2020;12(10):674-680 doi: https://doi.org/10.14740/jocmr4323
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".