Prenatal microRNA miR-200b Therapy Improves Nitrofen-induced Pulmonary Hypoplasia Associated With Congenital Diaphragmatic Hernia
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the use of miR-200b as a prenatal transplacental therapy in the nitrofen rat model of abnormal lung development and congenital diaphragmatic hernia (CDH). BACKGROUND: Pulmonary hypoplasia (PH) and pulmonary hypertension determine mortality and morbidity in CDH babies. There is no safe medical prenatal treatment available. We previously discovered that higher miR-200b is associated with better survival in CDH babies. Here, we investigate the role of miR-200b in the nitrofen rat model of PH and CDH and evaluate its use as an in vivo prenatal therapy. METHODS: We profiled miR-200b expression during nitrofen-induced PH using RT-qPCR and in situ hybridization in the nitrofen rat model of PH and CDH. The effects of nitrofen on downstream miR-200b targets were studied in bronchial lung epithelial cells using a SMAD luciferase assay, Western blotting and Immunohistochemistry. We evaluated miR-200b as a lung growth promoting therapy ex vivo and in vivo using lung explant culture and transplacental prenatal therapy in the nitrofen rat model. RESULTS: We show that late lung hypoplasia in CDH is associated with (compensatory) upregulation of miR-200b in less hypoplastic lungs. Increasing miR-200b abundance with mimics early after nitrofen treatment decreases SMAD-driven TGF-β signaling and rescues lung hypoplasia both in vitro and in vivo. Also, prenatal miR-200b therapy decreases the observed incidence of CDH. CONCLUSIONS: Our data indicate that miR-200b improves PH and decreases the incidence of CDH. Future studies will further exploit this newly discovered prenatal therapy for lung hypoplasia and CDH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".