Dissolved oxygen minimally affects magnetic susceptibility in biologically relevant conditions
Bibliographic record
Abstract
Abstract Purpose To investigate the potential of quantitative susceptibility mapping (QSM) with MRI as a biomarker for tissue oxygenation in fat-water mixture. Oxygen molecules (O 2 ) are paramagnetic. This suggests that dissolved O 2 in tissue should affect the measured magnetic susceptibility. However, direct measurements of dissolved O 2 in tissues is challenging with QSM as the induced change in susceptibility is below the sensitivity of existing algorithms. QSM in regions that contain fat could be sensitive enough to be used as a marker of tissue oxygenation as oxygen has a larger solubility in fat than in water. Methods The relationship between dissolved O 2 concentration and magnetic susceptibility was investigated based on MRI measurements using phantoms made of fat-water emulsions. Dairy cream was used to approximate fat-containing biological tissues. Phantoms based on dairy cream with 35 % fat were designed with controlled concentrations of dissolved O 2 . O 2 was bubbled into the dairy cream to reach O 2 concentrations above the concentration at atmospheric pressure, while nitrogen was bubbled in cream to obtain O 2 concentrations below atmospheric pressure. Magnetic susceptibility was expected to increase, becoming more paramagnetic, as O 2 concentration was increased. Results Magnetic susceptibility from MRI-based QSM measurements did not reveal a dependence on O 2 concentration in fat-water mixture phantoms. The relationship between susceptibility and O 2 was weak and inconsistent among the various phantom experiments. Conclusion QSM in fat-water mixture appears to be minimally sensitive to dissolved O 2 based on phantom experiments. This suggests that QSM is not likely to be sensitive enough to be proposed as a marker for tissue oxygenation, as the change in magnetic susceptibility induced by the change in dissolved O 2 concentration is below the current detection limit, even in the presence of fat.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".