Blood Oxygen Level-Dependent Magnetic Resonance Imaging of the Human Liver
Bibliographic record
Abstract
OBJECTIVE: To investigate blood oxygen level-dependent (BOLD) response of human liver to hyperoxic exposure under fasting and postprandial conditions. METHODS: Twelve healthy volunteers and 1 patient with chronic liver disease underwent liver BOLD magnetic resonance imaging at 3.0T. The BOLD images of a single slice were collected (1 image per second) during 3 breathing cycles of hyperoxia (3 minutes, 100% oxygen) with 5 minutes medical air (20.8% oxygen) in both preprandial and postprandial states. The BOLD signal time courses were correlated with a predefined stimulus paradigm. RESULTS: Eight healthy subjects showed increased BOLD signal within 44.6% +/- 21.1% liver area in the fasting state with gas cycling. Two showed slightly reduced BOLD signal within 23.4% +/- 14.9% liver area in the fasting state with gas cycling. In the postprandial state, the degree of change in BOLD signal with gas cycling was reduced for both the subjects having increased and those having decreased signal with gas cycling (13.4% +/- 12.6% positive, 10.9% +/- 10.1% negative; P < 0.05). Chronic liver disease also demonstrated increased BOLD signal with gas cycling, but the correlation decreased postprandially. CONCLUSIONS: The proposed physiological challenges induce certain BOLD signal response patterns in healthy and diseased livers, which may be useful for assessing liver function.
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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 | 0.000 |
| 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".