ABSTRACT
The placement of a polymeric electrospun scaffold is among the most promising strategies to improve nerve regeneration after critical neurotmesis. It is of great interest to investigate the effect of these structures on Schwann cells (SCs), as these cells lead nerve regeneration and functional recovery. The aim of this study was to assess SC viability and morphology when cultured on polyhydroxybutyrate (PHB) electrospun scaffolds with varied microfiber thicknesses and pore sizes. Six electrospun scaffolds were obtained using different PHB solutions and electrospinning parameters. All the scaffolds were morphologically characterized in terms of fiber thickness, pore size, and overall appearance by analyzing their SEM images. SCs seeded onto the scaffolds were analyzed in terms of viability and morphology throughout the culture period through MTT assay and SEM imaging. The SCs were cultured on three scaffolds with homogeneous smooth fibers (fiber thicknesses: 2.4 µm, 3.1 µm, and 4.3 µm; pore sizes: 16.7 µm, 22.4 µm, and 27.8 µm). SC infiltration and adhesion resulted in the formation of a three-dimensional network composed of intertwined fibers and cells. The SCs attached to the scaffolds maintained their characteristic shape and size throughout the culture period. Bigger pores and thicker fibers resulted in higher SC viability.
ABSTRACT
In the last two decades, artificial scaffolds for nerve regeneration have been produced using a variety of polymers. Polyhydroxybutyrate (PHB) is a natural polyester that can be easily processed and offer several advantages; hence, the purpose of this review is to provide a better understanding of the efficacy of therapeutic approaches involving PHB scaffolds in promoting peripheral nerve regeneration following nerve dissection in animal models. A systematic literature review was performed following the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" (PRISMA) criteria. The revised databases were: Pub-Med/MEDLINE, Web of Science, Science Direct, EMBASE, and SCOPUS. Sixteen studies were included in this review. Different animal models and nerves were studied. Extension of nerve gaps reconnected by PHB scaffolds and the time periods of analysis were varied. The additives included in the scaffolds, if any, were growth factors, neurotrophins, other biopolymers, and neural progenitor cells. The analysis of the quality of the studies revealed good quality in general, with some aspects that could be improved. The analysis of the risk of bias revealed several weaknesses in all studies. The use of PHB as a biomaterial to prepare tubular scaffolds for nerve regeneration was shown to be promising. The incorporation of additives appears to be a trend that improves nerve regeneration. One of the main weaknesses of the reviewed articles was the lack of standardized experimentation on animals. It is recommended to follow the currently available guidelines to improve the design, avoid the risk of bias, maximize the quality of studies, and enhance translationality.
ABSTRACT
Una interfaz cerebro computadora (ICC) es un sistema que provee una forma de comunicación directa entre el cerebro de una persona y el mundo exterior. Para el presente trabajo se utilizaron ICC basadas en EEG utilizando el paradigma de potenciales evocados relacionados con eventos (PRE). El objetivo de este trabajo es resolver en forma eficiente el problema de clasificación, en el cual se tienen dos clases posibles: registros con respuesta (PRE) y registros sin respuesta. Para esto se propone evaluar el desempeño de una ICC utilizando la transformada wavelet diádica discreta (DDWT, del inglés Dyadic Discrete Wavelet Transform) y la transformada wavelet packet (WPT, del inglés Wavelet Packet Transform) como métodos de extracción de características para la detección de la señal de PRE. La base de datos utilizada posee registros de EEG de época única de diez sujetos sanos. A partir de los patrones temporales (registros sin post-procesamiento) se generaron cinco conjuntos de patrones wavelet luego de aplicar la DDWT y WPT mediante diferentes técnicas. Se evaluó el desempeño de cada conjunto de patrones wavelet y de los patrones temporales mediante un clasificador lineal de Fisher. Se encontró que los patrones DDWT filtrados a 16 Hz presentan resultados de clasificación superiores a los patrones temporales. De esta manera al mejorar la etapa de extracción de características se mejora la clasificación, y consecuentemente, el desempeño del sistema completo de una ICC.
A brain-computer interface (BCI) is a system that provides a direct communication between the brain of a person and the outside world. For the present work we used an EEG-based event-related evoked potentials BCI. This paper aims to efficiently solve the problem of classification, which has two possible classes: recordings with evoked-potentials (ERP) and recordings without them. We proposed to evaluate the performance of a BCI using the discrete dyadic wavelet transform (DDWT) and the wavelet packet transform (WPT) as feature extraction methods for ERP signal detection. The database consisted of single-epoch EEG recordings from ten healthy subjects. From temporal patterns (recordings without any post-processing), five wavelet patterns were generated after applying DDWT and WPT via different techniques. The performance of the wavelet and temporal patterns were analyzed with the Fisher linear classifier finding that DDWT patterns, filtered at 16 Hz, presented better classification results than temporal patterns. This means that improving the feature extraction step, improves classification, and consequently, the performance of the entire BCI system.
Uma interface cérebro-computador (BCI) é um sistema que fornece uma forma de comunicação direta entre o cérebro de uma pessoa e o mundo exterior. Para este trabalho foram utilizados ICC baseado EEG evocados usando o paradigma de potenciais relacionados a eventos (ERP). O objetivo deste trabalho é resolver de forma eficiente o problema de classificação, em que há duas classes possíveis: registros Respondidas (PRE) e registros sem resposta. Para isso é avaliar o desempenho de uma ICC usando a wavelet diádica transformada discreta (DDWT, Discrete Wavelet Diádica Inglês Transform) e transformar pacote wavelet (WPT Transformada Wavelet Packet Inglês) como métodos de extração de características para a detecção de sinal PRE. A base de dados utilizada tem apenas EEG registra o tempo de dez indivíduos saudáveis. A partir dos padrões temporais (sem registros de pósprocessamento), cinco conjuntos de padrões após a aplicação wavelet e WPT DDWT gerado por várias técnicas. O desempenho de cada conjunto de padrões de wavelet e padrões temporais usando um classificador linear Fisher foi avaliado. Descobrimos que os padrões DDWT filtrados para 16 Hz apresentaram resultados acima da classificação padrões temporais. Assim, para melhorar a classificação de estágio de extração de características é melhorada, e, consequentemente, o desempenho de todo o sistema no ICC.
ABSTRACT
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.