The Effect of Warming of the Abdomen and of Herbal Medicine on Superior Mesenteric Artery Blood Flow – a Pilot Study
Bibliographic record
Abstract
BACKGROUND: In traditional Japanese and Chinese medicine, warming the abdomen with moxibustion or herbal medicines has been used for various diseases. However, the effects of these therapies on hemodynamics have not been clear. We clarify the physiological effects of these therapies on the superior mesenteric artery (SMA) blood flow. PARTICIPANTS AND METHODS: 28 healthy male volunteers were randomly assigned to groups A and B. Group A (n = 14) underwent local thermal stimulation of the paraumbilical region for 20 min at a temperature of 40 °C; this simulated the heat and mechanical pressure effects of moxibustion. Group B (n = 14) took the herbal medicine Daikenchuto (TJ-100; 5.0 g) with distilled water. As a control, group C (n = 14) took distilled water alone. Blood flow volume in the SMA was measured by ultrasound from rest to 50 min after the start of each intervention. RESULTS: The SMA blood flow volume increased significantly between 10 to 40 min after the start of thermal stimulation (p < 0.05), and it also increased significantly between 10 to 50 min after administration of TJ-100 (p < 0.01) as compared to the resting volume. However, SMA blood flow volume did not change significantly after administration of water alone. There was no significant difference in SMA blood flow changes between groups A and B. CONCLUSIONS: The results suggest that one of the physiological effects of warming the abdomen according to a traditional concept in thermal stimulation and herbal medicine is an increase of SMA blood flow volume.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".