Noninvasive ablation of rabbit fetal and placental tissue targets in utero using magnetic resonance‐guided high‐intensity focused ultrasound
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is a potential noninvasive therapy for fetal conditions. In utero MRgHIFU delivery and proton resonance frequency shift (PRFS) thermometry monitoring will control accuracy of HIFU ablation and confirm in situ tissue heating in a rabbit model. METHODS: High-resolution 3T MR images were acquired in late-gestation rabbits (approximately 30 days, n = 5). HIFU sonications, using magnetic resonance (MR) thermometry as a guide, were delivered to achieve necrosis in relevant fetal targets. Thermometry, posttreatment magnetic resonance imaging (MRI), and follow-up histology confirmed ablation. RESULTS: Placentas (n = 14) were treated with 127 ± 34 Wac; thermometry-indicated temperatures reached 67°C. Lungs (n = 8) were treated with 85 ± 15 Wac and reached 73°C, livers (n = 6) with 80 ± 15 Wac and reached 74°C, and kidneys (n = 5) with 100 Wac and reached 66°C. Histological changes showed focal areas of necrosis with circumferential hemorrhage and/or vasodilation, which transitioned abruptly to healthy tissue. CONCLUSION: MRgHIFU therapy can effectively target and thermally treat specific in utero organs in this acute fetal rabbit model. PRFS gives in situ temperature control of therapy on tissues. Conceivably, MRgHIFU therapy may be applicable to specific fetal organ anomalies clinically and has the potential to improve the overall fetal outcome over traditional invasive surgical procedures.
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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 | 0.000 |
| 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 | 0.001 |
| 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".