Atf7ip Inhibits Osteoblast Differentiation via Negative Regulation of the Sp7 Transcription Factor.

Epigenetic modifications are critical for cell differentiation and growth. As a regulator of H3K9 methylation, Setdb1 is implicated in osteoblast proliferation and differentiation. The activity and nucleus localization of Setdb1 are regulated by its binding partner, Atf7ip. However, whether Atf7ip is involved in the regulation of osteoblast differentiation remains largely unclear. In the present study, we found that Atf7ip expression was upregulated during the osteogenesis of primary bone marrow stromal cells and MC3T3-E1 cells, and was induced in PTH-treated cells. The overexpression of Atf7ip impaired osteoblast differentiation in MC3T3-E1 cells regardless of PTH treatment, as measured by the expression of osteoblast differentiation markers, Alp-positive cells, Alp activity, and calcium deposition. Conversely, the depletion of Atf7ip in MC3T3-E1 cells promoted osteoblast differentiation. Compared with the control mice, animals with Atf7ip deletion in the osteoblasts (Oc-Cre;Atf7ipf/f) showed more bone formation and a significant increase in the bone trabeculae microarchitecture, as reflected by µ-CT and bone histomorphometry. Mechanistically, Atf7ip contributed to the nucleus localization of Setdb1 in MC3T3-E1, but did not affect Setdb1 expression. Atf7ip negatively regulated Sp7 expression, and through specific siRNA, Sp7 knockdown attenuated the enhancing role of Atf7ip deletion in osteoblast differentiation. Through these data, we identified Atf7ip as a novel negative regulator of osteogenesis, possibly via its epigenetic regulation of Sp7 expression, and demonstrated that Atf7ip inhibition is a potential therapeutic measure for enhancing bone formation.

The Feature Ambiguity Mitigate Operator model helps improve bone fracture detection on X-ray radiograph.

This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture detection on radiographs of various body parts. A total of 9040 radiographic studies were extracted. These images were classified into several body part types including 1651 hand, 1302 wrist, 406 elbow, 696 shoulder, 1580 pelvic, 948 knee, 1180 ankle, and 1277 foot images. Instance segmentation was annotated by radiologists. The ResNext-101+FPN was employed as the baseline network structure and the FAMO model for processing. The proposed FAMO model and other ablative models were tested on a test set of 20% total radiographs in a balanced body part distribution. To the per-fracture extent, an AP (average precision) analysis was performed. For per-image and per-case, the sensitivity, specificity, and AUC (area under the receiver operating characteristic curve) were analyzed. At the per-fracture level, the controlled experiment set the baseline AP to 76.8% (95% CI: 76.1%, 77.4%), and the major experiment using FAMO as a preprocessor improved the AP to 77.4% (95% CI: 76.6%, 78.2%). At the per-image level, the sensitivity, specificity, and AUC were 61.9% (95% CI: 58.7%, 65.0%), 91.5% (95% CI: 89.5%, 93.3%), and 74.9% (95% CI: 74.1%, 75.7%), respectively, for the controlled experiment, and 64.5% (95% CI: 61.3%, 67.5%), 92.9% (95% CI: 91.0%, 94.5%), and 77.5% (95% CI: 76.5%, 78.5%), respectively, for the experiment with FAMO. At the per-case level, the sensitivity, specificity, and AUC were 74.9% (95% CI: 70.6%, 78.7%), 91.7%% (95% CI: 88.8%, 93.9%), and 85.7% (95% CI: 84.8%, 86.5%), respectively, for the controlled experiment, and 77.5% (95% CI: 73.3%, 81.1%), 93.4% (95% CI: 90.7%, 95.4%), and 86.5% (95% CI: 85.6%, 87.4%), respectively, for the experiment with FAMO. In conclusion, in bone fracture detection, FAMO is an effective preprocessor to enhance model performance by mitigating feature ambiguity in the network.