CFTR deletion affects mouse osteoblasts in a gender‐specific manner
Bibliographic record
Abstract
Abstract Advancements in research and care have contributed to increase life expectancy of individuals with cystic fibrosis (CF). With increasing age comes a greater likelihood of developing CF bone disease, a comorbidity characterized by a low bone mass and impaired bone quality, which displays gender differences in severity. However, pathophysiological mechanisms underlying this gender difference have never been thoroughly investigated. We used bone marrow‐derived osteoblasts and osteoclasts from Cftr+/+ and Cftr−/− mice to examine whether the impact of CF transmembrane conductance regulator (CFTR) deletion on cellular differentiation and functions differed between genders. To determine whether in vitro findings translated into in vivo observations, we used imaging techniques and three‐point bending testing. In vitro studies revealed no osteoclast‐autonomous defect but impairment of osteoblast differentiation and functions and aberrant responses to various stimuli in cells isolated from Cftr−/− females only. Compared with wild‐type controls, knockout mice exhibited a trabecular osteopenic phenotype that was more pronounced in Cftr−/− males than Cftr−/− females. Bone strength was reduced to a similar extent in knockout mice of both genders. In conclusion, we find a trabecular bone phenotype in Cftr−/− mice that was slightly more pronounced in males than females, which is reminiscent of the situation found in patients. However, at the osteoblast level, the pathophysiological mechanisms underlying this phenotype differ between males and females, which may underlie gender differences in the way bone marrow–derived osteoblasts behave in absence of CFTR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".