Ultraviolet-C sourced LED (UVC-LED) has been widely used for disinfection purposes due to its germicidal spectrum. In this study, the efficiencies of UVC-LED for Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) disinfections were investigated at three exposure distances (1, 1.5, and 2 cm) and two exposure times (30 and 60 s). The respective bacterial inhibition zones were measured, followed by a morphological analysis under SEM. The viabilities of human skin fibroblast cells were further evaluated under the treatment of UVC-LED with the adoption of aforesaid exposure parameters. The inhibition zones were increased with the increment of exposure distances and times. The highest records of 5.40 ± 0.10 cm P. aeruginosa inhibition and 5.43 ± 0.11 cm S. aureus inhibition were observed at the UVC-LED distance of 2 cm and 60-s exposure. Bacterial physical damage with debris formation and reduction in size were visualized following the UVC-LED exposures. The cell viability percentages were in a range of 75.20-99.00% and 82-100.00% for the 30- and 60-s exposures, respectively. Thus, UVC-LED with 275-nm wavelength is capable in providing bacterial disinfection while maintaining accountable cell viability which is suitable to be adopted in wound treatment. Bacterial disinfection and human skin fibroblast cell assessment using UVC-LED.
Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.
Complication rates of anterior cruciate ligament reconstruction (ACL-R) were reported to be around 15% although it is a common arthroscopic procedure with good outcomes. Breakage and migration of fixators are still possible even months after surgery. A fixator with optimum stability can minimise those two complications. Factors that affect the stability of a fixator are its configuration, material, and design. Thus, this paper aims to analyse the biomechanical effects of different types of fixators (cross-pin, interference screw, and cortical button) towards the stability of the knee joint after ACL-R. In this study, finite element modelling and analyses of a knee joint attached with double semitendinosus graft and fixators were carried out. Mimics and 3-Matic softwares were used in the development of the knee joint models. Meanwhile, the graft and fixators were designed by using SolidWorks software. Once the meshes of all models were finished in 3-Matic, simulation of the configurations was done using MSC Marc Mentat software. A 100-N anterior tibial load was applied onto the tibia to simulate the anterior drawer test. Based on the findings, cross-pin was found to have optimum stability in terms of stress and strain at the femoral fixation site for better treatment of ACL-R.
This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.
Wearable electronics and sensors are increasingly popular for personal health monitoring, including smart shirts containing electrocardiography (ECG) electrodes. Optimal electrode performance requires careful selection of the electrode position. On top of the electrophysiological aspects, practical aspects must be considered due to the dynamic recording environment. We propose a new method to obtain optimal electrode placement by considering multiple dimensions. The electrophysiological aspects were represented by P-, R-, and T-peak of ECG waveform, while the shirt-skin gap, shirt movement, and regional sweat rate represented the practical aspects. This study employed a secondary data set and simulations for the electrophysiological and practical aspects, respectively. Typically, there is no ideal solution that maximizes satisfaction degrees of multiple electrophysiological and practical aspects simultaneously; a compromise is the most appropriate approach. Instead of combining both aspects-which are independent of each other-into a single-objective optimization, we used multi-objective optimization to obtain a Pareto set, which contains predominant solutions. These solutions may facilitate the decision-makers to decide the preferred electrode locations based on application-specific criteria. Our proposed approach may aid manufacturers in making decisions regarding the placement of electrodes within smart shirts.
Asymmetrical stiff knee gait is a mechanical pathology that can disrupt lower extremity muscle coordination. A better understanding of this condition can help identify potential complications. This study proposes the use of dynamic musculoskeletal modelling simulation to investigate the effect of induced mechanical perturbation on the kneeand to examine the muscle behaviour without invasive technique. Thirty-eight healthy participants were recruited. Asymmetrical gait was simulated using knee brace. Knee joint angle, joint moment and knee flexor and extensor muscle forces were computed using OpenSim. Differences inmuscle force between normal and abnormal conditions were investigated using ANOVA and Tukey-Kramer multiple comparison test.The results revealed that braced knee experienced limited range of motion with smaller flexion moment occuring at late swing phase. Significant differences were found in all flexormuscle forces and in several extensor muscle forces (p<0.05). Normal knee produced larger flexor muscle force than braced knee. Braced knee generated the largest extensor muscle force at early swing phase. In summary, musculoskeletal modelling simulation can be a computational tool to map and detect the differences between normal and asymmetrical gaits.
Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
Pilon fractures can be caused by high-energy vertical forces which may result in long-term patient immobilization. Many experts in orthopedic surgery recommend the use of a Delta external fixator for type III Pilon fracture treatment. This device can promote immediate healing of fractured bone, minimizing the rate of complications as well as allowing early mobilization. The characteristics of different types of the Delta frame have not been demonstrated yet. By using the finite element method, this study was conducted to determine the biomechanical characteristics of six different configurations (Model 1 until Model 6). CT images from the lower limb of a healthy human were used to reconstruct three-dimensional models of foot and ankle bones. All bones were assigned with isotropic material properties and the cartilages were assigned to exhibit hyperelasticity. A linear link was used to simulate 37 ligaments at the ankle joint. Axial loads of 70 and 350 N were applied at the proximal tibia to simulate the stance and swing phase. The metatarsals and calcaneus were fixed distally in order to prevent rigid body motion. A synthetic ankle bone was used to validate the finite element model. The simulated results showed that Delta3 produced the highest relative micromovement (0.09 mm, 7 μm) during the stance and swing phase, respectively. The highest equivalent von Mises stress was found at the calcaneus pin of the Delta4 (423.2 MPa) as compared to others. In conclusion, Delta1 external fixator was the most favorable option for type III Pilon fracture treatment. Graphical abstract ᅟ.
Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.
Even though the mechanical heart valve (MHV) has been used routinely in clinical practice for over 60 years, the occurrence of serious complications such as blood clotting remains to be elucidated. This paper reviews the progress that has been made over the years in terms of numerical simulation method and the contribution of abnormal flow toward blood clotting from MHVs in the aortic position. It is believed that this review would likely be of interest to some readers in various disciplines, such as engineers, scientists, mathematicians and surgeons, to understand the phenomenon of blood clotting in MHVs through computational fluid dynamics.
This study aims to investigate the measurement of left ventricular flow propagation velocity, V p, using phase contrast magnetic resonance imaging and to assess the discrepancies resulting from inflow jet direction and individual left ventricular size. Three V p measuring techniques, namely non-adaptive (NA), adaptive positions (AP) and adaptive vectors (AV) method, were suggested and compared. We performed the comparison on nine healthy volunteers and nine post-infarct patients at four measurement positions, respectively, at one-third, one-half, two-thirds and the conventional 4 cm distances from the mitral valve leaflet into the left ventricle. We found that the V p measurement was affected by both the inflow jet direction and measurement positions. Both NA and AP methods overestimated V p, especially in dilated left ventricles, while the AV method showed the strongest correlation with the isovolumic relaxation myocardial strain rate (r = 0.53, p
Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health. Graphical abstract ᅟ.
Congenital anomalies are not only one of the main killers for infants but also one of the major causes of deaths under 5. Among congenital anomalies, Down syndrome or trisomy 21 (T-21) and neural tube defects (NTDs) are considered the most common. Expectant mothers in developing countries may not have access to or may not afford the advanced prenatal screening tests. To solve this issue, this paper explores the practicality of using only the basic risk factors for developing prediction models as a tool for initial risk assessment. The prediction models are based on logistic regression. The results show that the prediction models do not have a high balanced classification rate. However, these models can still be used as an effective tool for initial risk assessment for T-21 and NTDs by eliminating at least 50% of the cases with no or low risk. Graphical Abstract Prenatal Risk Assessment of Trisomy-21 and Neural Tube Defects.
Diabetic retinopathy (DR) is a chronic eye condition that is rapidly growing due to the prevalence of diabetes. There are challenges such as the dearth of ophthalmologists, healthcare resources, and facilities that are unable to provide patients with appropriate eye screening services. As a result, deep learning (DL) has the potential to play a critical role as a powerful automated diagnostic tool in the field of ophthalmology, particularly in the early detection of DR when compared to traditional detection techniques. The DL models are known as black boxes, despite the fact that they are widely adopted. They make no attempt to explain how the model learns representations or why it makes a particular prediction. Due to the black box design architecture, DL methods make it difficult for intended end-users like ophthalmologists to grasp how the models function, preventing model acceptance for clinical usage. Recently, several studies on the interpretability of DL methods used in DR-related tasks such as DR classification and segmentation have been published. The goal of this paper is to provide a detailed overview of interpretability strategies used in DR-related tasks. This paper also includes the authors' insights and future directions in the field of DR to help the research community overcome research problems.
Coronary artery disease (CAD) is an important cause of mortality across the globe. Early risk prediction of CAD would be able to reduce the death rate by allowing early and targeted treatments. In healthcare, some studies applied data mining techniques and machine learning algorithms on the risk prediction of CAD using patient data collected by hospitals and medical centers. However, most of these studies used all the attributes in the datasets which might reduce the performance of prediction models due to data redundancy. The objective of this research is to identify significant features to build models for predicting the risk level of patients with CAD. In this research, significant features were selected using three methods (i.e., Chi-squared test, recursive feature elimination, and Embedded Decision Tree). Synthetic Minority Over-sampling Technique (SMOTE) oversampling technique was implemented to address the imbalanced dataset issue. The prediction models were built based on the identified significant features and eight machine learning algorithms, utilizing Acute Coronary Syndrome (ACS) datasets provided by National Cardiovascular Disease Database (NCVD) Malaysia. The prediction models were evaluated and compared using six performance evaluation metrics, and the top-performing models have achieved AUC more than 90%. Graphical abstract.
Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values
The purpose of this study is to investigate the effect of various degrees of percentage stenosis on hemodynamic parameters during the hyperemic flow condition. 3D patient-specific coronary artery models were generated based on the CT scan data using MIMICS-18. Numerical simulation was performed for normal and stenosed coronary artery models of 70, 80 and 90% AS (area stenosis). Pressure, velocity, wall shear stress and fractional flow reserve (FFR) were measured and compared with the normal coronary artery model during the cardiac cycle. The results show that, as the percentage AS increase, the pressure drop increases as compared with the normal coronary artery model. Considerable elevation of velocity was observed as the percentage AS increases. The results also demonstrate a recirculation zone immediate after the stenosis which could lead to further progression of stenosis in the flow-disturbed area. Highest wall shear stress was observed for 90% AS as compared to other models that could result in the rupture of coronary artery. The FFR of 90% AS is found to be considerably low.
This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis "Mercedes Benz" sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.
Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.