Biomedical Engineering: Applications, Basis and Communications

Objective: In the present study, we investigate the biological performance of a calcium phosphate ceramics (CPC) bone substitute combined with poly-hydroxybutyrate-co-hydroxyvalerate (PHBV). Materials and Methods: A particulate CPC [45% beta-tricalcium phosphate ($\beta$-TCP) and 55% of dihydrated dicalcium phosphate (DCPD)] was incorporated into a biodegradable copolymer PHBV. Two series of the composite, 1 and 2, with CPC–PHBV weight ratios of (40%–60% and 60%–40%), respectively, were prepared using chloroform for dissolving the polymer and a pressure molding process for shaping the composite samples. After particle size analysis, the two composites were characterized by scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS). In a second step, a 10mm bony segmental defect created in the tibias of 20 New Zealand White Rabbits was filled randomly with either composite 1 for group 1 or composite 2 for group 2. There were 10 animals in each group. Clinical, radiological and histological assessments were then carried out to evaluate the biological properties of developed CPC–PHBV composites. Results: For both variants of the developed CPC–PHBV biocomposite, there was evidence of osseous consolidation within three months. An in vivo investigation revealed the biological properties of the biocomposite, namely, biocompatibility, bioactivity, biodegradability and osteoconductivity. The morphological characteristics, granule size and chemical composition, were indeed found to be favorable for osseous cell development. This study likewise showed lower mortality for the variant with weight ratio (40%CPC–60%PHBV). Conclusion: An in vivo investigation revealed that the new biomaterial composed of CPC and PHBV exhibits manifest osteoconductivity and bioactivity with better degradation kinetics than the CPC. Moreover, the variant with 40%CPC/60%PHBV appeared more resistant to infection than the 60%CPC/40%PHBV which is an indicator of biocompatibility.

Heart sound (HS) analysis or auscultation is a standout amongst the most simple, non-invasive and costless methods used to evaluate heart health and is one of the basic and foremost routine of a doctor while reviewing a patient. Detecting cardiac abnormality by auscultation demands a physician’s experience and even then there is a high scope of committing error. In this paper, a low cost electronic stethoscope is built to acquire HS in a novel manner by taking one from each ventricular and auricular area and superimposed, to get a resultant signal of both distinct lub-dub sound. Then, a light, fast and low computation speed beat track method followed by wavelet reconstruction is presented for correct detection of S1 and S2. It is done without ECG reference, and can be used satisfactorily on both normal and pathological HSs. Moreover, heartbeats can be identified in both de-noised and noised environment as it is independent of external disturbances. Significant features are extracted from the resultant HSs with detected S1 and S2 and feed-forward back propagation method. It is used to classify the HS nature into normal and pathological. This algorithm has been implemented on 24 pairs of HSs, extracted from 24 patients of 15 pathological and nine normal subjects and the classification yields a result of 91.7% accuracy with 81.8% sensitivity. The overall performance suggests a good performance to cost ratio. This system can be used as first diagnosis tool by the medical professionals.

The cerebellum receives a variety of inputs and it accomplishes information processing based on its internal rules. Purkinje cells (PCs) play a major role in the information processing of the cerebellum. These cells can learn input patterns from parallel fiber (PF) by long-term depression (LTD) in PF to PC synapses, but it is not clear how PCs encode information in their firing activity. Previous studies reported different mechanisms for information processing in PCs, and some studies mentioned that PCs encode weak and strong PF input patterns using different mechanisms. Thus, it remains a controversial issue how PCs encode PF patterns in their output. Therefore, in this study, pattern recognition in PCs was explored using a multi-compartmental model of a PC. Simulation results indicated that the PC responded to PF input patterns with a short burst followed by a quiescent period, and a proper metric for patterns recognition could be the number of spikes in the burst. So, learned patterns by LTD as well as novel patterns could be discriminated based on the number of spikes in the burst, and there were no different mechanisms of information processing for weak PF input patterns versus strong patterns.

Purpose: Diffusion tensor magnetic resonance imaging (DT-MRI) has the ability to gather information on tissue microstructure by using the indices that quantify the anisotropy of water diffusion in tissues. This study was designed to find out the impact of patient clinical disability on diffusion tensor (DT) indices. Method: In this paper, images were generated from the data acquired at 3T among 25 MS patients (mean age $40\pm 15$ years old). DT indices including fractional anisotropy (FA), mean diffusivity (MD), relative anisotropy (RA), and volume ratio (VR) of multiple sclerosis (MS) lesions are calculated and the correlation between Kurtzke expanded disability status scale (EDSS) score and the values of DT indices of 25 MS patients lesions are obtained. The correlation comparison procedure is done between the EDSS scores and DT indices in MS lesions attained, respectively, from fluid attenuated inversion recovery (FLAIR) images and black holes of T1-weighted (T1-w) images. Results: EDSS scores and DT indices of MS lesions extracted from the black holes present in T1-w images are more correlated in comparison with those extracted from FLAIR images. It is found that MD has a significant correlation with EDSS in both types of images. The results of FLAIR image processing indicates that any increase in EDSS leads to decreasing FA and RA on one hand and increasing MD and VR on the other hand. The correlation between the identical size of lesions in different parts of brain with EDSS shows that the location of lesion in brain also has a correlation with EDSS. Conclusion: The results showed, MD is the best biomarker of clinical disability between these four DT indices. It is also concluded that the correlation between DT indices of frontal brain lesions and EDSS is higher than the other selected brain parts.

Background and aim: Healing of fire-induced wounds has been still a challenge in clinical issues. The aim of this study was to fabricate a nanofibrous poly (L-lactic acid)/collagen (PLLA/COL) scaffold with sustained release of aloe vera (AV) gel using a chitosan (CT)-coated layer for skin tissue engineering applications. Material and methods: Morphology, porosity, tensile strength, hydrophilicity, degradation rate, water vapor permeability and water uptake ratio of the scaffold were characterized. The behaviors of mouse fibroblasts (L929) were evaluated on the scaffold. Results: We observed that although the porosity of the scaffold was decreased, other characteristics were enhanced by coating a CT layer. The scaffold supports attachment, viability and proliferation of mouse fibroblasts. Conclusion: Consequently, the PLLA/COL scaffold coated with CT for sustained release of AV gel can be considered as a desirable scaffold for skin tissue engineering.

Human biometrics is a popular method for security applications of personal identification. In past years, most research topics focused on face, iris, and fingerprint or signature recognition. Apart from these features, human noses can also be used as a biometric feature for gender classification according to our daily experience. In this work, four distinct features are extracted from nose images to distinguish between male and female faces. By comparing with other nose features obtained from 3D stereoscopic photographs, the proposed features are easily extracted from ordinary face images. Image preprocessing methods are applied to extract those four features automatically, and the linear discriminant analysis (LDA) method is applied to classify those extracted features for gender classification. Experimental results demonstrate that average classification accuracy can reach to 77%. In particular, the curvature feature calculated from the nose wing achieves the best classification performance.

Computer-aided tools help in shortening and eradicating numerous repetitive tasks that reduce the gap between digital model and the actual product. Use of these tools assist in realizing free-form objects such as custom fit products as described by an stringent interaction with the human body. Development of such model presents a difficult situation for reverse engineering (RE), which are not analogous with the requirement for generating simple geometric models. Hence, an alternating way of producing more accurate three-dimensional models is proposed. For creating accurate 3D models, point clouds are processed through filtering technique, segmentation, mesh smoothing and surface generation. These processes help in converting the initial unorganized point data into a 3D digital model and simultaneously influence the quality of the model. This study provides an optimum balance for the best accuracy obtainable with maximum allowable deviation to lesser computer handling and processing time. In this paper, a realistic nontrivial case study of the free-form prosthetic socket is considered. The accuracy obtained for the developed model is acceptable for the use in medical applications and FEM analysis.

Purpose: An important work in making the treatment plan and following the therapeutic effects of multiple sclerosis (MS) patients is quantification of MS lesion subtypes. This study was designed to propose a new automatic method for classification of MS lesion subtypes based on conventional magnetic resonance (c-MR) and diffusion tensor MR (DT-MR) images. Method: In this paper, fuzzy $K$-nearest neighbor (F-KNN) algorithm is used for classifying MS lesions into three subtypes including black holes (chronic), enhancing lesions (acute) and T2-lesions. In order to achieve this classification, F-KNN algorithm is used with features of c-MR images including T1-weighted (T1-w), fluid attenuated inversion recovery (FLAIR), gadolinium enhanced T1-weighted (Gad-E-T1-w) and T2-weighted (T2-w) and then with added features extracted from DT-MR images (diffusion indices) including mean diffusivity (MD) and fractional anisotropy (FA). Results: Evaluation of the proposed method was performed by manual classification of chronic, acute and T2-w lesions using the criterion of sensitivity (Se), specificity (Sp) and accuracy (Acc). The results showed that application of the c-MR with the DT-MR images makes possible the classification of MS lesion subtypes with high sensitivity and allows ability to evaluate MS disease in the treatment process.