Investigation into Cannabidiol as a Potential Treatment for Preeclampsia in B6D2F1 Mice with Placenta Specific Human Transgene hsFLT1: A Research Protocol
Bibliographic record
Abstract
Preeclampsia is a common pregnancy complication that leaves the affected individual to choose between preterm delivery or risking death. These outcomes are far from ideal and the search for a better treatment is underway. Previous studies have implicated whole flower commercial cannabis use as a risk factor for the development of preeclampsia as well as other partum complications. However, commercial cannabis is high in Δ9-Tetrahydrocannabinol and other psychoactive cannabinoids and low in cannabidiol. Therefore, it is imperative that the use of isolated CBD as a potential therapy is investigated. In this study novel mouse models of preeclampsia will be utilized to demonstrate the effect of cannabidiol on expecting mothers who are predisposed to preeclampsia. This will be demonstrated using B6D2F1 mice with placenta specific human transgene hsFLT1 to simulate preeclampsia. Cannabidiol will be introduced at different stages of gestation and symptoms of preeclampsia will be measured through blood pressure, protein urine content, and fetal mortality rate. The group with mice receiving cannabidiol prior to implantation are anticipated to show the lowest incidences of preeclampsia symptoms. With so many studies suggesting cannabidiol as a treatment method for a variety of the most dangerous symptoms of preeclampsia, it may be possible that cannabis will allow future mothers afflicted with Preeclampsia to bring their child to full-term.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".