Evaluation Effect of Low-Intensity Pulsed Ultrasound on Blood Insulin Secretion due to Pancreatic Stimulation in Type 2 Diabetic Male Rats
Bibliographic record
Abstract
Background: Ultrasound is a noninvasive, nonionizing radiation that can be focused to transfer acoustic energy into the body, inducing mechanical stimulation in cells. In this study, we evaluated the effect of low-intensity pulsed ultrasound (LIPUS) on insulin release in type 2 diabetes (T2D) male rats. Methods: Twenty-eight white male Wistar rats were divided into four groups: non-diabetic control, diabetic control and diabetic treated with ultrasound at intensities of 1 W/cm 2 and 1.5 W/cm 2 (frequency = 1 MHz; pulsed = 1:2; exposure time = 15 min) in vivo . Diabetes was induced by a high-fat diet (HFD) and a low dose (35 mg/kg) of streptozotocin (STZ). After 14 days of LIPUS treatment, blood and tissue samples were analyzed. Results: LIPUS treatment significantly increased insulin levels by 79.55% (P < 0.001) - 94.80% (P < 0.001) and decreased glucose levels by 44.55% (P < 0.001) - 45.64% (P < 0.01). Additionally, glucagon levels increased by 31.39% (P < 0.05) - 45.69% (P < 0.01), while somatostatin levels decreased by 12.12-21.50% (P > 0.05). The pancreas and surrounding tissues, such as the liver and spleen, were not affected by the LIPUS treatment. Conclusions: Our findings indicate that LIPUS treatment improved glycemic control, insulin secretion and beta-cell function in T2D caused by HFD and low dose STZ (35 mg/kg) without tissue damage. J Endocrinol Metab. 2024;14(4):184-193 doi: https://doi.org/10.14740/jem1002
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".