Author response: Defective mesenchymal Bmpr1a-mediated BMP signaling causes congenital pulmonary cysts
Bibliographic record
Abstract
Congenital disorders are medical conditions that are present from birth. Although many congenital disorders are rare, they can have a severe impact on the quality of life of those affected. For example, congenital pulmonary airway malformation (CPAM) is a rare congenital disorder that occurs in around 1 out of every 25,000 pregnancies. In CPAM, abnormal, fluid-filled sac-like pockets of tissue, known as cysts, form within the lungs of unborn babies. After birth, these cysts become air-filled and do not behave like normal lung tissue and stop a baby’s lungs from working properly. In severe cases, babies with CPAM need surgery immediately after birth. We still do not understand exactly what the underlying causes of CPAM might be. CPAM is not considered to be hereditary – that is, it does not appear to be passed down in families – nor is it obviously linked to any environmental factors. CPAM is also very difficult to study, because researchers cannot access tissue samples during the critical early stages of the disease. To overcome these difficulties, Luo et al. wanted to find a way to study CPAM in the laboratory. First, they developed a non-human animal ‘model’ that naturally forms CPAM-like lung cysts, using genetically modified mice where the gene for the signaling molecule Bmpr1a had been deleted in lung cells. Normally, Bmpr1a is part of a set of the molecular instructions, collectively termed BMP signaling, which guide healthy lung development early in life. However, mouse embryos lacking Bmpr1a developed abnormal lung cysts that were similar to those found in CPAM patients, suggesting that problems with BMP signalling might also trigger CPAM in humans. Luo et al. also identified several other genes in the Bmpr1a-deficient mouse lungs that had abnormal patterns of activity. All these genes were known to be controlled by BMP signaling, and to play a role in the development and organisation of lung tissue. This suggests that when these genes are not controlled properly, they could drive formation of CPAM cysts when BMP signaling is compromised. This work is a significant advance in the tools available to study CPAM. Luo et al.’s results also shed new light on the molecular mechanisms underpinning this rare disorder. In the future, Luo et al. hope this knowledge will help us develop better treatments for CPAM, or even help to prevent it altogether.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.104 | 0.041 |
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".