Journal of Biomimetics, Biomaterials and Biomedical Engineering Vol. 42

Abstract: The aim of this research work is to assess the muscles fatigue of the male runner during 400 meters (m) running with three types of running strategies. The Electromyography (EMG) signals from the Rectus Femoris (RF), Biceps Femoris (BF), Gluteus Maximus (GM), Gastrocnemius Lateralis (GL), and Gastrocnemius Medialis (GMS) were collected by using bipolar electrodes from the right lower extremity’s muscles. EMG signals were collected during the run on the tartan athletic track. Five subjects (non-athletes) had run 400m with three various types of running strategies. The first type: the first 200m running 85-93% of full speed and the last 200m sprinting (full speed), second type: the first 300m running 85-93% of sprinting and the last 100m sprinting, and third type: running 85-93% of sprinting for 400m. The EMG signals were transformed to the time-frequency domain using Short Time Fourier Transform to calculate the instantaneous mean frequency (IMNF) and instantaneous median frequency (IMDF). The less index fatigues were during 1^st strategy, while the RF, BF, GM, and GL muscles got recovered with IMNF and IMDF with the three strategies, and the GMS muscle has less negative regression slope value with IMNF with 1^st strategy during the 4^th 100m of the 400m running event. From the results, it can be concluded the running with the 1^st strategy get less fatigues compared with the 2^nd and 3^rd strategy based on the results of time-frequency domain features (IMNF and IMDF).

Abstract: The number of blindness is tend to be increased year by year. One of the blindness cause is cornea ulcer.The cause of cornea ulcer is bacteria, fungi, and herpes simplex virus. Cornea transplantation is the only treatment which could widely accepted for blindness. Transplant by donor network becomes the only treatment that is acceptable on a large for blindness. However, treatment donor transplants have many shortcomings in complications post surgery such as host response, donor limitations, incompatibility and the length of time healing. As technology develops, there are many corneal substitutes based on natural ingredients derived from collagen or their derivatives because they promise better properties in biocompatibility. The aim of research are to conduct the synthesis and characterization of collagen- chitosan- glycerol - HPMC as artificial cornea such functional cluster test, cytotoxycity test, morphological test and antibacterial test. Based on functional cluster test, there are functional groups of all components of composite materials. While from cytotoxicity test, all samples have a percentage of living cells above 85%. The morphology test is showed that the pore size of sample B with composition collagen-chitosan-glycerol-HPMC is in accordance with the standard pore size for keratoprothesis. Sample A (collagen-chitosan-glycerol) and sample B (collagen-chitosan-glycerol-HPMC) have strong antibacterial properties.Biocomposite of collagen-chitosan-glycerol could be considered as artificial cornea due to the proximity with the corneal characteristics.

Abstract: This work aims to review current trends in research within the field of iron-based scaffolds for orthopaedic applications. Current research is trapped in a ‘see-saw’ type problem where an increase in corrosion rate of the base metal is required to accelerate the degradation process making the resorption time compatible with the healing time. This is done via several methods including porosity control, cathodic element addition and/or patterning and alloying. In turn, this increase in corrosion rate causes the local concentration of metallic ions to increase beyond the toxicity limit for osteoblast type cells, thus negatively effecting cytocompatibility. This is most pronounced when considering the orthopaedic environment, in which static conditions provide for increased local ion concentrations, resulting in local toxicity. However, research from the medical field of Thalassemia may help solve this dilemma by providing chelation medicine for patients undergoing implantation of resorbable orthopaedic scaffolds, throughout the resorption period. Excretion of iron would then be provided mainly through bowel movement and urination.

Abstract: The dental tissue scaffold must provide a favorable surface for dental pulp stem cell attachment and proliferation. Employing nanohydroxyapatite (HA) and nanofluorohydroxyapatite (FHA) beside synthetic and organic polymer in favor of scaffolds would be used in bone and dental tissue engineering. In this research, nanoHA and FHA/chitosan scaffolds were synthesized by freeze-drying technique. Surface morphology, chemical composition and hydrophilicity have a great impact on initial cell attachment which will further affect the cell viability and proliferation which evaluated by SEM, XRD and contact angle measurement. Bioactivity of scaffolds was investigated by immersion in simulated body fluid (SBF) and cell proliferation assay. In freeze-drying technique percentage usage of hydroxyapatite could be risen up to 40% and shown better macro-mechanical and physical properties and bioactivity. According to obtained results by adding chitosan, contact angle was decreased by %54 and %37 for polycaprolactone (PCL)/HA and PCL/FHA scaffolds. In addition, addition of chitosan causes significant increase in the cell proliferation for PCL/HA and PCL/FHA up to 81% and 164%, respectively. These results indicate that PCL/FHA/chitosan scaffold represent a big potential for dental tissue engineering.

Abstract: The reliability and specificity of the Pap smear rely on the eyewitness to perceive and an assortment of cell variations in clinically obtained cervical specimens. The improvement in early diagnosis to distinguish Human Papillomavirus (HPV) infection has been an issue. ELISA has been intended to analyze the immune response against HPV, and they can be utilized to distinguish the phase of the infection. The objective of this study to characterize the performance of In Vitro Nucleic Acid Hybridization with a correlation with Liquid-Based Cytology and HPV DNA Genotyping Test to assess its use as the potential for the identification of high-risk HPV in cervical clinical specimens. Hybridization utilizing an in vitro nucleic acid DNA-based ELISA method was performed with an improved chemiluminescent for the qualitative and semi-quantitative identification of high-risk HPV in cervical specimens. For analyzing the reliability to identify HPV DNA in cervical Pap smear, we studied the effectiveness of FDA-approved Hybrid Capture II (HCII) utilizing cervical swapping from 20 patient clinical specimens. Two scrapes-outs were positive for the occurrence of HPV. Only specimens displayed positive with HCII test were further confirmed by HPV Genotyping Test. Specimen 2 and 13 were positive for one of the 13 high-risk types in HCII test. Specimen 2 was confirmed by HPV genotyping test as a positive high-risk HPV type 58.

Abstract: Current imaging systems available in the study of tissue hemodynamics and vascular reactivity are typically complex and bulky, hence limiting their applications to laboratory use. The aim of this study is to present the dynamics of skin oxygen level with changes in the microcirculatory perfusion monitored using a developed field portable, handheld tri-wavelength imaging system. The skin oxygen saturation level (S_tO₂) was measured in-vivo in a span of 12 minutes in the ventral forearm of seven healthy humans at rest, before and after supra-diastolic and supra-systolic pressure inflations. The findings of this work showed statistical significance in the difference between the mean S_tO₂ values in baseline and that following ischemic episodes with ρ ≤ 0.03. The values returned to baseline, although of different magnitude for each individual, within 2 min (ρ =0.217) during reperfusion could suggest the time required for the resume of normal autoregulation mechanisms and vasomotion reactivities in the recruits. This study concluded that the developed imaging system could find potential application in self-healthcare management and may help to improve patient care in remote or rural locations.

Abstract: A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with transfer learning and optimization gradient descent adaptive momentum learning rate (ADAM). The proposed method is applied on Archive International Skin Imaging Collaboration (ISIC) database to classify images into three main classes (benign, melanoma, seborrheic keratosis) under the two scenarios; segmented and non-segmented lesion images. The overall accuracy of the non-segmented classification database is 92.2% and 89.8% for the non-segmented dataset. Utilizing optimization algorithm (ADAM) leads to a significant improvement in the classification results when they are compared with previous studies.

Abstract: In this paper, a modified adaptive K-means (MAKM) method is proposed to extract the region of interest (ROI) from the local and public datasets. The local image datasets are collected from Bethezata General Hospital (BGH) and the public datasets are from Mammographic Image Analysis Society (MIAS). The same image number is used for both datasets, 112 are abnormal and 208 are normal. Two texture features (GLCM and Gabor) from ROIs and one CNN based extracted features are considered in the experiment. CNN features are extracted using Inception-V3 pre-trained model after simple preprocessing and cropping. The quality of the features are evaluated individually and by fusing features to one another and five classifiers (SVM, KNN, MLP, RF, and NB) are used to measure the descriptive power of the features using cross-validation. The proposed approach was first evaluated on the local dataset and then applied to the public dataset. The results of the classifiers are measured using accuracy, sensitivity, specificity, kappa, computation time and AUC. The experimental analysis made using GLCM features from the two datasets indicates that GLCM features from BGH dataset outperformed that of MIAS dataset in all five classifiers. However, Gabor features from the two datasets scored the best result with two classifiers (SVM and MLP). For BGH and MIAS, SVM scored an accuracy of 99%, 97.46%, the sensitivity of 99.48%, 96.26% and specificity of 98.16%, 100% respectively. And MLP achieved an accuracy of 97%, 87.64%, the sensitivity of 97.40%, 96.65% and specificity of 96.26%, 75.73% respectively. Relatively maximum performance is achieved for feature fusion between Gabor and CNN based extracted features using MLP classifier. However, KNN, MLP, RF, and NB classifiers achieved almost 100% performance for GLCM texture features and SVM scored an accuracy of 96.88%, the sensitivity of 97.14% and specificity of 96.36%. As compared to other classifiers, NB has scored the least computation time in all experiments.