Effects of low-frequency ultrasound combined with microbubbles on benign prostate hyperplasia
Bibliographic record
Abstract
INTRODUCTION: Our objective is to assess the effects of low-frequency ultrasound combined with microbubbles on benign prostate hyperplasia (BPH). METHODS: Sixteen Beagle dogs with BPH were randomly assigned into 4 groups (n = 4): control group (without treatment), G1 group (injection with 2 mL of microbubble contrast agent); G2 group (21 kHz ultrasound); and G3 group (injection with 2 mL of micro-bubble contrast agent +21 kHz ultrasound). The histopathological damage to prostate cells was assessed via transmission electron microscopy and optical microscopy. The protein expressions of prostate-specific antigen (PSA), inducible nitric oxide synthase (iNOS), superoxide dismutase (SOD) of vessels were detected by enzyme-linked immunosorbent assay (ELISA). RESULTS: Histopathologically, the prostate cells exhibited nuclear chromatin contraction, mitochondrial swelling, degranulation of rough endoplasmic reticulum, basement membrane rupture and cell apoptosis in the G2 and G3 groups; it was especially obvious in the G3 group, while no changes were observed in the control and G1 groups. Although prostate volume using imaging was not significantly changed in all groups after treatment, PSA was significantly reduced in the G2 and G3 groups, and especially obvious in the G3 group (p < 0.05). The iNOS and SOD, which are important oxidative stress factors, significantly increased after treatment in the G2 and G3 groups, but not in the control and G1 groups (p < 0.05). CONCLUSIONS: Low-frequency ultrasound is effective in treating BPH; low-frequency ultrasound combined with microbubbles improves the treatment efficacy.
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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".