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The interface pressure between the residual limb and prosthetic socket has a significant effect on the amputee’s mobility and level of comfort with their prosthesis. This paper presents a socket interface pressure (SIFP) system to compare the interface pressure differences during gait between two different types of prosthetic sockets for a transtibial amputee. The system evaluates the interface pressure in six critical regions of interest (CROI) of the lower limb amputee and identifies the peak pressures during certain moments of the gait cycle. The six sensors were attached to the residual limb in the CROIs before the participant with transtibial amputation donned a prosthetic socket. The interface pressure was monitored and recorded while the participant walked on a treadmill for 10 min at 1.4 m/s. The results show peak pressure differences of almost 0.22 kgf/cm2 between the sockets. It was observed that the peak pressure occurred at 50% of the stance phase of the gait cycle. This SIFP system may be used by prosthetists, physical therapists, amputation care centers, and researchers, as well as government and private regulators requiring comparison and evaluation of prosthetic components, components under development, and testing.

Background Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. Methods To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. Results The classifier with the highest metrics was the four antibody-based Perfecto–Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. Conclusions Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification. Electronic supplementary material The online version of this article (10.1186/s12967-019-1951-y) contains supplementary material, which is available to authorized users.

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Porous scaffolds have been widely explored for tissue regeneration and engineering in vitro three-dimensional models. In this review, a comprehensive literature analysis is conducted to identify the steps involved in their generation. The advantages and disadvantages of the available techniques are discussed, highlighting the importance of considering pore geometrical parameters such as curvature and size, and summarizing the requirements to generate the porous scaffold according to the desired application. This paper considers the available design tools, mathematical models, materials, fabrication techniques, cell seeding methodologies, assessment methods, and the status of pore scaffolds in clinical applications. This review compiles the relevant research in the field in the past years. The trends, challenges, and future research directions are discussed in the search for the generation of a porous scaffold with improved mechanical and biological properties that can be reproducible, viable for long-term studies, and closer to being used in the clinical field.