A porous polymeric–hydroxyapatite scaffold used for femur fractures treatment: fabrication,analysis, and simulation

Background

One of the most common fractures in the skeleton happens in the femur. One of the important reasons for this fracture is because it is the longest bone in the body and osteoporosis affect this part a lot. The geometric complexity and anisotropy properties of this bone have received a lot of attention in the orthopedic field.

Methods

In this research, a femur designed using 3D printing machine using the middle part of the hip made of polylactic acid–hydroxyapatite (PLA–HA) nanocomposite containing 0, 5, 10, 15, and 25 wt% of ceramic nanoparticle. Three different types of loadings, including centralized loading, full-scale, and partially loaded, were applied to the designed femur bone. The finite element analysis was used to analyze biomechanical components.

Results

The results of the analysis showed that it is possible to use the porous scaffold model for replacement in the femur having proper strength and mechanical stability. Stress–strain analysis on femoral implant with biometric HA and PLA after modeling was performed using the finite element method under static conditions in Abaqus software.

Conclusion

Three scaffold structures, i.e., mono-, hybrid, and zonal structures, that can be fabricated using current bioprinting techniques are also discussed with respect to scaffold design.

     

Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John’s, Newfoundland and Labrador (NL), Canada during 2009–2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver’s operating curve analysis. Two validating datasets, NL records of 2011–2014, and Canada-wide records of 2010–2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997–0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.