To draw a comparison of the pullout strengths of buttress thread, barb thread, and reverse buttress thread bone screws. Buttress thread, barb thread, and reverse buttress thread bone screws were inserted into synthetic cancellous bone blocks. Five screw-block constructs per group were tested to failure in an axial pullout test. The pullout strengths were calculated and compared. A finite element analysis (FEA) was performed to explore the underlying failure mechanisms. FEA models of the three different screw-bone constructs were developed. A pullout force of 250 N was applied to the screw head with a fixed bone model. The compressive and tensile strain contours of the midsagittal plane of the three bone models were plotted and compared.Aims
Methods
To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence.
We investigated whether strontium-enriched calcium
phosphate cement (Sr-CPC)-treated soft-tissue tendon graft results
in accelerated healing within the bone tunnel in reconstruction
of the anterior cruciate ligament (ACL). A total of 30 single-bundle
ACL reconstructions using tendo Achillis allograft were performed
in 15 rabbits. The graft on the tested limb was treated with Sr-CPC,
whereas that on the contralateral limb was untreated and served
as a control. At timepoints three, six, nine, 12 and 24 weeks after
surgery, three animals were killed for histological examination.
At six weeks, the graft–bone interface in the control group was
filled in with fibrovascular tissue. However, the gap in the Sr-CPC
group had already been completely filled in with new bone, and there
was evidence of the early formation of Sharpey fibres. At 24 weeks,
remodelling into a normal ACL–bone-like insertion was found in the
Sr-CPC group. Coating of Sr-CPC on soft tissue tendon allograft
leads to accelerated graft healing within the bone tunnel in a rabbit
model of ACL reconstruction using Achilles tendon allograft. Cite this article:
