Placenta <i>hIGF1</i> nanoparticle treatment in guinea pigs mitigates fetal sex dependent FGR-associated effects on kidney structure and blood pressure-related signaling pathways
Bibliographic record
Abstract
ABSTRACT Fetal development in an adverse in utero environment significantly increases the risk of hypertension and cardiovascular disease. The kidneys play a pivotal role in the regulation of blood pressure and cardiovascular function, and perturbations in kidney structure and molecular profile are often demonstrated in offspring born fetal growth restricted (FGR). The aim of this study was to determine whether improving the in utero fetal growth environment with a placental nanoparticle gene therapy would ameliorate FGR-associated dysregulation of fetal kidney development. Using the guinea pig maternal nutrient restriction (MNR) model, we improved placenta efficiency and fetal weight following three placental administrations of a non-viral polymer-based nanoparticle gene therapy from mid-pregnancy (gestational day 35) until gestational day 52. The nanoparticle gene therapy transiently increased expression of human insulin-like growth factor 1 ( hIGF1 ) in placenta trophoblast. Fetal kidney tissue was collected near-term at gestational day 60. Differences in kidney structure, glomeruli size and gene expression of extracellular matrix (ECM) remodeling and blood pressure regulation-related factors were demonstrated in sham-treated FGR fetuses but not observed in FGR fetuses who received placental hIGF1 nanoparticle treatment. We speculate that mitigating the FGR-associated changes in kidney architecture and molecular profiles might confer protection against increased susceptibility to aberrant kidney physiology in later-life. Overall, this work opens avenues for future research to assess the long-term impact of the placental hIGF1 nanoparticle gene therapy on cardiovascular function in offspring.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".