[18F]-FDG CT and PET Scans in a Monocrotaline Rat Model to Understand the Role of the Brain in Pulmonary Arterial Hypertension Development
Bibliographic record
Abstract
Introduction/Problem statement: Pulmonary arterial hypertension (PAH), classified as Group 1 pulmonary hypertension is a rare, progressive disease with a 50% survival rate. Common symptoms include dyspnea, fatigue, and lightheadedness. The fatal effects of the disease occur because of right ventricular failure exacerbated by elevated mean arterial pressure and chronic pulmonary vascular resistance. The role of the brain in PAH is not well understood, however elevated sympathetic nerve activity implicates the baro- and chemo-flex, physiological mechanisms thate modulate sympathetic tone through changes in pressure, and blood chemistry respectively. Methods/Approach: Sprague Dawley rats were separated into treatment and control groups, receiving 60mg/kg subcutaneous injections of monocrotaline (MCT) or phosphate buffer saline (PBS), respectively. Rats then underwent echocardiography to confirm the onset of PAH. Under isoflurane anesthesia, [18F]-FDG was administered intravenously, which was utilized by metabolically active cells. Using a Tri-modality scanner (Mi Labs; Netherlands), CT and PET scans were acquired so that a protocol to assess the metabolic differences between MCT and PBS control rats can be optimized so that differences in tissues such as the heart and brain can be assessed. Findings/Implications: This research aims to provide pilot data which will help to optimize CT and PET scanning using [18F]-FDG in rats with PAH compared to normotensive controls. Our findings will help inform future studies that rely on these data as end-point measurements and so enhance our understanding of PAH mechanisms, ultimately guiding future research and treatment strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".