| 191. |
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| 192. |
- Moscatelli, Ilana, et al.
(författare)
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Lentiviral gene transfer of TCIRG1 into peripheral blood CD34(+) cells restores osteoclast function in infantile malignant osteopetrosis.
- 2013
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Ingår i: Bone. - : Elsevier BV. - 1873-2763 .- 8756-3282. ; 57:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Infantile malignant osteopetrosis (IMO) is a rare, lethal, autosomal recessive disorder characterized by non-functional osteoclasts. More than 50% of the patients have mutations in the TCIRG1 gene, encoding for a subunit of the osteoclast proton pump. The aim of this study was to restore the resorptive function of IMO osteoclasts by lentiviral mediated gene transfer of the TCIRG1 cDNA. CD34(+) cells from peripheral blood of five IMO patients and from normal cord blood were transduced with lentiviral vectors expressing TCIRG1 and GFP under a SFFV promoter, expanded in culture and differentiated on bone slices to mature osteoclasts. qPCR analysis and western blot revealed increased mRNA and protein levels of TCIRG1, comparable to controls. Vector corrected IMO osteoclasts generated increased release of Ca(2+) and bone degradation product CTX-I into the media as well as increased formation of resorption pits in the bone slices, while non-corrected IMO osteoclasts failed to resorb bone. Resorption was approximately 70-80% of that of osteoclasts generated from cord blood. Furthermore, transduced CD34(+) cells successfully engrafted in NSG-mice. In conclusion we provide the first evidence of lentiviral-mediated correction of a human genetic disease affecting the osteoclastic lineage.
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| 193. |
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| 194. |
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| 195. |
- Nguyen, Hanh H., et al.
(författare)
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AFFnet - a deep convolutional neural network for the detection of atypical femur fractures from anteriorposterior radiographs
- 2024
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Ingår i: Bone. - : ELSEVIER SCIENCE INC. - 8756-3282 .- 1873-2763.
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Tidskriftsartikel (refereegranskat)abstract
- Despite well-defined criteria for radiographic diagnosis of atypical femur fractures (AFFs), missed and delayed diagnosis is common. An AFF diagnostic software could provide timely AFF detection to prevent progression of incomplete or development of contralateral AFFs. In this study, we investigated the ability for an artificial intelligence (AI)-based application, using deep learning models (DLMs), particularly convolutional neural networks (CNNs), to detect AFFs from femoral radiographs. A labelled Australian dataset of pre-operative complete AFF (cAFF), incomplete AFF (iAFF), typical femoral shaft fracture (TFF), and non-fractured femoral (NFF) X-ray images in anterior-posterior view were used for training (N = 213, 49, 394, 1359, respectively). An AFFnet model was developed using a pretrained (ImageNet dataset) ResNet-50 backbone, and a novel Box Attention Guide (BAG) module to guide the model's scanning patterns to enhance its learning. All images were used to train and internally test the model using a 5-fold cross validation approach, and further validated by an external dataset. External validation of the model's performance was conducted on a Sweden dataset comprising 733 TFF and 290 AFF images. Precision, sensitivity, specificity, F1-score and AUC were measured and compared between AFFnet and a global approach with ResNet-50. Excellent diagnostic performance was recorded in both models (all AUC >0.97), however AFFnet recorded lower number of prediction errors, and improved sensitivity, F1-score and precision compared to ResNet-50 in both internal and external testing. Sensitivity in the detection of iAFF was higher for AFFnet than ResNet-50 (82 % vs 56 %). In conclusion, AFFnet achieved excellent diagnostic performance on internal and external validation, which was superior to a pre-existing model. Accurate AI-based AFF diagnostic software has the potential to improve AFF diagnosis, reduce radiologist error, and allow urgent intervention, thus improving patient outcomes.
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| 196. |
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| 197. |
- Nguyen, Jacqueline, et al.
(författare)
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CYLD, a mechanosensitive deubiquitinase, regulates TGFβ signaling in load-induced bone formation
- 2020
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Ingår i: Bone. - : Elsevier BV. - 8756-3282. ; 131
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Tidskriftsartikel (refereegranskat)abstract
- Many signaling pathways involved in bone homeostasis also participate in the anabolic response of bone to mechanical loading. For example, TGFβ signaling coordinates the maintenance of bone mass and bone quality through its effects on osteoblasts, osteoclasts, and osteocytes. TGFβ signaling is also essential for the mechanosensitive formation of new bone. However, the mechanosensitive mechanisms controlling TGFβ signaling in osteocytes remain to be determined, particularly those that integrate TGFβ signaling with other early responses to mechanical stimulation. Here, we used an in vivo mouse hindlimb loading model to identify mechanosensitive molecules in the TGFβ pathway, and MLO-Y4 cells to evaluate their interactions with the prostaglandin E2 (PGE2) pathway, which is well-known for its rapid response to mechanical stimulation and its role in bone anabolism. Although mRNA levels for several TGFβ ligands, receptors, and effectors were unchanged, the level of phosphorylated Smad2/3 (pSmad2/3) was reduced in tibial bone as early as 3 h after early mechanical stimulation. We found that PGE2 and its receptor, EP2, repress pSmad2/3 levels and transactivation of Serpine1 in osteocytes. PGE2 and EP2 control the level of pSmad2/3 through a proteasome-dependent mechanism that relies on the deubiquitinase CYLD. CYLD protein levels were also reduced in the tibiae within 3 h of mechanical loading. Using CYLD-deficient mice, we found that CYLD is required for the rapid load-mediated repression of pSmad2/3 and for load-induced bone formation. These data introduce CYLD as a mechanosensitive deubiquitinase that participates in the prostaglandin-dependent repression of TGFβ signaling in osteocytes.
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| 198. |
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