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Loop-mediated isothermal amplification or LAMP has been identified to be an efficient technology for point-of-care diagnostics. Paper-based LAMP technique has tremendous potential in replacing the existing tube-based technology as the manufacturing cost of a paper-based device is comparatively lower and easy-to-use. LAMP-based paper diagnostic device for Mycobacterium tuberculosis (MTB) detection is of extreme importance as it will help in early and rapid diagnosis of the affected patients. The fabrication of these devices requires assessment of design parameters on the extent of LAMP amplification reaction. Hence, CFD studies would be extremely beneficial from the design perspective. The current work presents an insight into the CFD simulations for LAMP amplification reaction on a porous paper membrane (nitrocellulose membrane). The convection-diffusion-reaction model is solved on a COMSOL Multiphysics 5.0 platform. Studies on effect of pore size, aspect ratio and initial DNA concentration on the extent of DNA amplification reaction have been carried out. The current paper-based technique is effective in detecting a minimum of 5 copies of DNA contrasting the previous semi-quantitative technique which demonstrated the detection of minimum 98 copies. Overall, the simulation results displayed almost 96% enhancement in the DNA amplification rate on paper membrane. Graphical abstract Graphical abstract for the computational study of DNA amplification reaction via LAMP technique on a porous paper membrane. [ABSTRACT FROM AUTHOR]

Mechanisms of atrial fibrillation and the susceptibility to reentries can be impacted by the repolarization across the atria. Studies into atrial fibrillation ignore cell-to-cell heterogeneity due to electrotonic coupling. Recent studies show that cellular variability may have a larger impact on electrophysiological behaviour than assumed. This paper aims to determine the impact of cellular heterogeneity on the repolarization phase across the AF remodelled atria. Using a population of models approach, 10 anatomically identical atrial models were created to include cellular heterogeneity. Atrial models were compared with an equivalent homogenous model. Activation, APD90, and repolarization maps were used to compare models. The impact of electrotonic coupling in the tissue was determined through a comparison of RMP, APD20, APD50, APD90, and triangulation between regional atrial tissue and the single cell populations. After calibration, cellular heterogeneity does not impact atrial depolarization. Repolarization patterns were significantly impacted by cellular heterogeneity, with the APD90 across the LA increasing due to heterogeneity and the reverse occurring in the RA. Electrotonic coupling caused a reduction in variability across all biomarkers but did not fully remove variability. Electrotonic coupling resulted in an increase in APD20 and APD50, and reduced triangulation compared to isolated cell populations. Heterogeneity also caused a reduction in triangulation compared with regionally homogeneous atria. [ABSTRACT FROM AUTHOR]

Interventional surgery is widely used in the treatment of cardiovascular and cerebrovascular diseases, and the development of surgical robots can greatly reduce the fatigue and radiation risks brought to surgeons during surgery. In this paper, we present a novel interventional surgical robot which allows surgeons to fully use their operating skills during remote control. Fuzzy control theory is used to guarantee control precision during the master-slave operation. The safety force feedback control is designed based on the catheter and guidewire spring model, and the force-position control is designed to decrease the potential damage due to the control delay. This study first evaluates the force-position control strategy using a vascular model experiment, and then an in vivo experiment is used to evaluate the precision of the surgical robot controlling the catheter and guidewire to the designated position. The in vivo experiment results and surgeon's feedback demonstrate that the proposed surgical robot is able to perform complex remote surgery in clinical application. Graphical abstract Surgeons perform remote interventional animal surgery using interventional surgical robots. [ABSTRACT FROM AUTHOR]

With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis technology is needed to meet the requirement of the diagnosis and classification of various kinds of diseases using biomedical images. However, the current biomedical image classification methods and general non-biomedical image classifiers cannot extract more compact biomedical image features or capture the tiny differences between similar images with different types of diseases from the same category. In this paper, we propose a novel fused convolutional neural network to develop a more accurate and highly efficient classifier for biomedical images, which combines shallow layer features and deep layer features from the proposed deep neural network architecture. In the analysis, it was observed that the shallow layers provided more detailed local features, which could distinguish different diseases in the same category, while the deep layers could convey more high-level semantic information used to classify the diseases among the various categories. A detailed comparison of our approach with traditional classification algorithms and popular deep classifiers across several public biomedical image datasets showed the superior performance of our proposed method for biomedical image classification. In addition, we also evaluated the performance of our method in modality classification of medical images using the ImageCLEFmed dataset. Graphical abstract The graphical abstract shows the fused, deep convolutional neural network architecture proposed for biomedical image classification. In the architecture, we can clearly see the feature-fusing process going from shallow layers and the deep layers. [ABSTRACT FROM AUTHOR]

The kinematics of the body center of mass (bCoM) may provide crucial information supporting the rehabilitation process of people with transfemoral amputation. The use of magneto-inertial measurement units (MIMUs) is promising as it may allow in-the-field bCoM motion monitoring. Indeed, bCoM acceleration might be obtained by fusing the estimated accelerations of body segments' centers of mass (sCoM), the formers being computed from the measured accelerations by segment-mounted MIMUs and the known relative position between each pair of MIMU and underlying sCoM. This paper investigates how erroneous identifications of MIMUs positions impact the accuracy of estimated 3D sCoM and bCoM accelerations in transfemoral amputee gait. Using an experimental design approach, 215 simulations of erroneous identifications of MIMUs positions (up to 0.02 m in each direction) were simulated over seven recorded gait cycles of one participant. MIMUs located on the trunk and sound lower limbs were shown to explain up to 77% of the variance in the accuracy of the estimated bCoM acceleration, presumably due to the higher mass and/or angular velocity of these segments during gait of lower-limb amputees. Therefore, a special attention should be paid when identifying the positions of MIMUs located on segments contributing the most to the investigated motion. Sensitivity of the estimated vertical body center of mass acceleration to erroneous identifications of MIMU positions in the anteroposterior (AP), mediolateral (ML), and vertical (V) directions, expressed in percentage of the total variance of the estimation accuracy. [ABSTRACT FROM AUTHOR]

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis. [ABSTRACT FROM AUTHOR]

Cardiac resynchronization therapy (CRT) is an effective treatment for those patients with severe heart failure. Regrettably, there are about one third of CRT "non-responders", i.e. patients who have undergone this form of device therapy but do not respond to it, which adversely affects the utility and cost-effectiveness of CRT. In this paper, we assess the ability of a novel surface ECG marker to predict CRT response. We performed a retrospective exploratory study of the ECG previous to CRT implantation in 43 consecutive patients with ischemic (17) or non-ischemic (26) cardiomyopathy. We extracted the QRST complexes (consisting of the QRS complex, the S-T segment, and the T wave) and obtained a measure of their energy by means of spectral analysis. This ECG marker showed statistically significant lower values for non-responder patients and, joint with the duration of QRS complexes (the current gold-standard to predict CRT response), the following performances: 86% accuracy, 88% sensitivity, and 80% specificity. In this manner, the proposed ECG marker may help clinicians to predict positive response to CRT in a non-invasive way, in order to minimize unsuccessful procedures. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the effects of mindfulness meditation training in electrophysiological signals, recorded during a concentration task. Longitudinal experiments have been limited to the analysis of psychological scores through depression, anxiety, and stress state (DASS) surveys. Here, we present a longitudinal study, confronting DASS survey data with electrocardiography (ECG), electroencephalography (EEG), and electrodermal activity (EDA) signals. Twenty-five university student volunteers (mean age = 26, SD = 7, 9 male) attended a 25-h mindfulness-based stress reduction (MBSR) course, over a period of 8 weeks. There were four evaluation periods: pre/peri/post-course and a fourth follow-up, after 2 months. All three recorded biosignals presented congruent results, in line with the expected benefits of regular meditation practice. In average, EDA activity decreased throughout the course, -64.5%, whereas the mean heart rate displayed a small reduction, -5.8%, possibly as a result of an increase in parasympathetic nervous system activity. Prefrontal (AF3) cortical alpha activity, often associated with calm conditions, saw a very significant increase, 148.1%. Also, the number of stressed and anxious subjects showed a significant decrease, -92.9% and -85.7%, respectively. Easy to practice and within everyone's reach, this mindfulness meditation can be used proactively to prevent or enhance better quality of life. 25 volunteers attended a Mindfulness-Based Stress Reduction (MBSR) course in 4 evaluation periods: Pre/Peri/Post-course and a fourth follow-up after two months. A Depression, Anxiety and Stress State (DASS) survey is completed in each period. Electrodermal Activity (EDA), Electrocardiography (ECG) and Electroencephalography (EEG) are also recorded and processed. By integrating self-reported surveys and electrophysiological recordings there is strong evidence of evolution in wellbeing. Mindfulness meditation can be used proactively to prevent or enhance better quality of life. [ABSTRACT FROM AUTHOR]

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data. Graphical abstract. [ABSTRACT FROM AUTHOR]

In previous studies, the numerical simulation models of industrial airbags were verified to have high accuracy regarding their actual dynamics. However, numerical methods were scarcely utilized to simulate and investigate the inflation height behaviors of nursing bed airbag. For this problem, this study constructs a numerical simulation model illustrating the association between the internal pressure and inflating height of nursing bed airbag, under various external loads. Firstly, based on an averaged pressure prerequisite, an airbag dynamic model is established by the control volume approach (the air inside the airbag follows the gas state equation of Poisson's law). Besides, the elastic mechanical behaviors of airbag film material are determined according to a material constitutive model built by the quasi-static uniaxial tensile test. The obtained data are used as the boundary conditions, for the numerical dynamics modeling of the nursing bed airbag. Verification experiments clarify that this numerical modeling is accurate for describing airbag inflation behaviors, and then can be effectively applied to the design and optimization phases of nursing bed airbags. Based on the simulation modeling above, the mathematical equation of controlling airbag inflating height by its internal pressure is obtained. It provides a vital basis for the differentiated and intelligent control of the airbag nursing bed. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort in patients. There has been a lack of noninvasive method capable of predicting immunohistochemistry index for breast cancer patients. This paper proposes a machine learning method to predict the immunohistochemical index of breast cancer patients by using noninvasive contrast-enhanced ultrasound. A total of 119 breast cancer patients were included in this retrospective study. Each patient implemented the pathological examination of immunohistochemical expression and underwent contrast-enhanced ultrasound imaging of breast tumor. The multi-dimension features including 266 three-dimension features and 837 two-dimension dynamic features were extracted from the contrast-enhanced ultrasound sequences. Using the machine learning prediction method, 21 selected multi-dimension features were integrated to generate a model for predicting the immunohistochemistry index noninvasively. The immunohistochemical index of human epidermal growth factor receptor-2 (HER2) was predicted based on multi-dimension features in contrast-enhanced ultrasound sequence with the sensitivity of 71%, and the specificity of 79% in the testing cohort. Therefore, the noninvasive contrast-enhanced ultrasound can be used to predict the immunohistochemical index. To our best knowledge, no studies have been reported about predicting immunohistochemical index by using contrast-enhanced ultrasound sequences for breast cancer patients. Our proposed method is noninvasive and can predict immunohistochemical index by using contrast-enhanced ultrasound in several minutes, instead of relying totally on the invasive and biopsy-based histopathological examination. Graphical abstract Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most diagnosable forms of cancer worldwide. The early diagnoses of pulmonary nodules in computed tomography (CT) chest scans are crucial for potential patients. Recent researches have showed that the methods based on deep learning have made a significant progress for the medical diagnoses. However, the achievements on identification of pulmonary nodules are not yet satisfactory enough to be adopted in clinical practice. It is largely caused by either the existence of many false positives or the heavy time of processing. With the development of fully convolutional networks (FCNs), in this study, we proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from their environments and then removes the false positives. Especially, it optimizes the network architecture for the identification of nodules rapidly and accurately. In order to remove the false positives, the suspected nodules are reduced using the 2D models. Furthermore, according to the significant differences between nodules and non-nodules in 3D shapes, the false positives are eliminated by integrating into the 3D models and classified via 3D CNNs. The experiments on 1000 patients indicate that our proposed method achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s. Graphical Abstract This paper has proposed a new method of identifying the pulmonary nodules. The method segments the suspected nodules from CT images and removes the false positives. As shown in the above, the proposed approach consists of three stages. In stage I, raw data are filtered and normalized. The clean normalized data are then segmented in stage II to extract the suspected nodular lesions through 2D FCNs. Stage III is to remove some false positives generated at stage II via 3D CNNs and outputs the final results. The experiments on 1000 patients indicate that our proposed method has achieved 97.78% sensitivity rate for segmentation and 90.1% accuracy rate for detection. The maximum response time was less than 30 s and the average time was about 15 s. [ABSTRACT FROM AUTHOR]

TiO2 scaffolds have previously shown to have promising osteoconductive properties in previous in vivo experiments. Appropriate mechanical stimuli can further promote this osteoconductive behaviour. However, the complex mechanical environment and the mechanical stimuli enhancing bone regeneration for porous bioceramics have not yet been fully elucidated. This paper aims to compare and evaluate mechanical environment of TiO2 scaffold with three commercial CaP biomaterials, i.e. Bio-Oss, Cerabone and Maxresorb under simulated perfusion culture conditions. The solid phase and fluid phase were modelled as linear elastic material and Newtonian fluid, respectively. The mechanical stimulus was analysed within these porous scaffolds quantitatively. The results showed that the TiO2 had nearly heterogeneous stress distributions, however lower effective Young's modulus than Cerabone and Maxresorb. The permeability and wall shear stress (WSS) for the TiO2 scaffold was significantly higher than other commercial bone substitute materials. Maxresorb and Bio-Oss showed lowest permeability and local areas of very high WSS. The detailed description of the mechanical performance of these scaffolds could help researchers to predict cell behaviour and to select the most appropriate scaffold for different in vitro and in vivo performances. Graphical abstract Schematic representation of the establishment procedure. Take the establishment process of Cerabone as an example. Left shows a slice of micro-CT image from Cerabone, and 1.5 mm × 1.5 mm region of interest was shown in the red box. A 1.5-mm3 cube was cut out by Boolean operation in Mimics (Materialise, Belgium), and the cubic model was remeshed in 3-Matic 6.0 (Materialise, Belgium). The cubic model is shown in blue, and the empty space in red. [ABSTRACT FROM AUTHOR]

Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes (seizure and non-seizure), and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The wavelet transform with five scales is first employed to process the raw EEG signals. Next, the test EEG samples are collaboratively represented on the training sets by the kernel R-ProCRC and they are categorized by checking which class has the maximum likelihood. Finally, post-processing is deployed to reduce misjudgment and acquire more stable results. This method is evaluated on two EEG databases and yields an accuracy of 99.3% for interictal and ictal EEGs on the Bonn database. In addition, the average sensitivity of 97.48% and specificity of 96.81% are achieved from the Freiburg database. Graphical abstract Visual inspection of EEG recordings for epilepsy diagnosis is very time-consuming. Therefore, many researchers are devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this paper, a kernel version of the robust probabilistic collaborative representation based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes, i.e., seizure and non-seizure, and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The main procedures of the proposed method are exhibited in the two figures as following, Fig. 1 The main procedures of the proposed method. (a) The schematic diagram of EEG classification based on the Freiburg database. (b) The detailed procedures of the kernel R-ProCRC This method has been evaluated on two different types of EEG databases and shows superior performance. [ABSTRACT FROM AUTHOR]

Social and medical problems associated with stress are increasing globally and seriously affect mental health and well-being. However, an effective stress-level monitoring method is still not available. This paper presents a quantitative method for monitoring acute stress levels in healthy young people using biomarkers from physiological signals that can be unobtrusively monitored. Two states were induced to 40 volunteers, a basal state generated with a relaxation task and an acute stress state generated by applying a standard stress test that includes five different tasks. Standard psychological questionnaires and biochemical markers were utilized as ground truth of stress levels. A multivariable approach to comprehensively measure the physiological stress response is proposed using stress biomarkers derived from skin temperature, heart rate, and pulse wave signals. Acute physiological stress levels (total-range 0-100 au) were continuously estimated every 1 min showing medians of 29.06 au in the relaxation tasks, while rising from 34.58 to 47.55 au in the stress tasks. Moreover, using the proposed method, five statistically different stress levels induced by the performed tasks were also measured. Results obtained show that, in these experimental conditions, stress can be monitored from unobtrusive biomarkers. Thus, a more general stress monitoring method could be derived based on this approach. Graphical abstract Stress measurements of different healthy young people throughout a Stress Session that includes a pre-relax stage (BLs), memory test (ST and MT), stress anticipation time (SA), video display (VD) and arithmetic task. [ABSTRACT FROM AUTHOR]

Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals. Graphical abstract The schematic of the proposed HSS-ELM algorithm. [ABSTRACT FROM AUTHOR]

Infants born at 35-37 weeks' gestational age (GA) are at higher risk for a range of pathological conditions and poorer neurodevelopmental outcomes. However, mechanisms responsible are not fully understood. The purpose of this paper is to use traditional and novel techniques to assess newborn autonomic development as a function of GA at birth, focusing on cardiorespiratory regulation. ECG and respiration were acquired during sleep on 329 healthy newborns. Infants were divided into GA groups: 35-36 weeks (late preterm (LPT)), 37-38 weeks (early term (ET)), and 39-40 weeks (full term (FT)). Time domain, frequency domain, and non-linear measures were calculated. Increased heart rate short-term variability and complexity as a function of GA were observed in time domain and non-linear measures. Decreasing inter-breath interval variability was found as a function of GA, with increasing linear cardiorespiratory coupling. A complexity parameter (quadratic sample entropy) was less affected by arrhythmias and artifacts when compared to traditional measures. Results suggest lower maturation in LPT, with less developed cardiorespiratory regulation. This may confer risk for altered outcome, convergent with epidemiological findings. Reported examples show that a combination of methodological approaches can be beneficial to characterize autonomic maturation. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Artificial cervical disc replacement surgery has become an effective and main treatment method for cervical disease, which has become a more common and serious problem for people with sedentary work. To improve cervical disc replacement surgery significantly, a 6-DOF parallel bone-grinding robot is developed for cervical bone-grinding by image navigation and surgical plan. The bone-grinding robot including mechanical design and low level control is designed. The bone-grinding robot navigation is realized by optical positioning with spatial registration coordinate system defined. And a parametric robot bone-grinding plan and high level control have been developed for plane grinding for cervical top endplate and tail endplate grinding by a cylindrical grinding drill and spherical grinding for two articular surfaces of bones by a ball grinding drill. Finally, the surgical flow for a robot-assisted cervical disc replacement surgery procedure is present. The final experiments results verified the key technologies and performance of the robot-assisted surgery system concept excellently, which points out a promising clinical application with higher operability. Finally, study innovations, study limitations, and future works of this present study are discussed, and conclusions of this paper are also summarized further. This bone-grinding robot is still in the initial stage, and there are many problems to be solved from a clinical point of view. Moreover, the technique is promising and can give a good support for surgeons in future clinical work. [ABSTRACT FROM AUTHOR]

Measurements of vessel-wall-plus-plaque thickness (VWT) from 3D carotid ultrasound have been shown to be sensitive to the effect of pharmaceutical interventions. Since the geometry of carotid arteries is highly subject-specific, quantitative comparison of the distributions of point-wise VWT measured for different patients or for the same patients at different ultrasound scanning sessions requires the development of a mapping strategy to adjust for the geometric variability of different carotid surface models. In this paper, we present an algorithm mapping each 3D carotid surface to a 2D carotid template with an emphasis on preserving the local geometry of the carotid surface by minimizing local angular distortion. The previously described arc-length scaling (AL) approach was applied to generate an initial 2D VWT map. Using results established in the quasi-conformal theory, a new map was computed to compensate for the angular distortion incurred in AL mapping. As the 2D carotid template lies on an L-shaped non-convex domain, one-to-one correspondence of the mapping operation was not guaranteed. To address this issue, an iterative Beltrami differential chopping and smoothing procedure was developed to enforce bijectivity. Evaluations performed in the 20 carotid surface models showed that the reduction in average angular distortion made by the proposed algorithm was highly significant (P = 2.06 × 10-5). This study is the first study showing that a bijective conformal map to a non-convex domain can be obtained using the iterative Beltrami differential chopping and smoothing procedure. The improved consistency exhibited in the 2D VWT map generated by the proposed algorithm will allow for unbiased quantitative comparisons of VWT as well as local geometric and hemodynamic quantities in population studies. [ABSTRACT FROM AUTHOR]

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved. [ABSTRACT FROM AUTHOR]

As one of the most frequently used grafts in anterior cruciate ligament (ACL) reconstruction, hamstring tendon (HT) grafts are prepared with different lengths and fixed by specific fixations in knee joints. However, there are incomplete studies to investigate both the joint kinematics and graft biomechanics in the ACL reconstructions with different HT graft lengths. In this paper, three different graft lengths (i.e., 30, 50, and 70 mm) were developed in the ACL reconstruction and analyzed using finite element method under two usual clinical test loads (i.e., 134 N anterior tibial drawer and pivot shift test load). The different mechanical properties of the corresponding fixations were also considered for each graft length. It was revealed that the change in HT graft length would cause different strain and stress results in the grafts, but did not greatly influence joint stabilities under the two clinical test loads. The graft reaction force at the femoral fixation was always greatly lower than that at the tibial fixation regardless of load and graft length. The comparison of stress and strain results also indicated that more graft tissues inside the femoral and tibial tunnels could decrease the stress and strain values at the femoral and tibial fixation sites, respectively. [ABSTRACT FROM AUTHOR]

Irreversible osteoporosis may occur in astronauts during long-term space flight. The flow field of tissue fluid in the lacunar-canalicular system (LCS) of osteon and the mechanical response of osteocytes to the flow field under different gravity fields were studied by numerical simulation. This study is expected to explain how the decrease in liquid transmission within microgravity can be a cause of osteoporosis in astronauts from the perspective of biomechanics using a fundamental research approach. A 3D axisymmetric fluid-solid coupling finite element model of an osteon with a two-stage pore structure (Haversian canals and lacunar-canalicular network) and osteocytes was established. The model compared the influence of differences in pulsating pressure of arterioles in Haversian canal, from 33 mmHg to 45 mmHg within a microgravity field (0 g), Earth's gravity field (1 g), and a high G gravitational fields (2-8 g). The liquid flow velocity in the LCS within a microgravity field was less than that within a normal gravitational field, and the flow velocity increased with gravitational acceleration. There was a significant liquid pressure gradient in the osteocytes within a normal and higher gravitational field compared with in microgravity. A reduction in the fluid flow velocity and fluid shear stress on osteocytes in different zones in microgravity compared with Earth's gravitational field. For these reasons, possibly causing a decrease in mechanical conduction and biochemical function, even cell death, leads to increased osteoclast activity, eventually causing the loss of a large quantity of bone. Graphical abstract A 3D axisymmetric fluid-solid coupling finite element model of an osteon with a two-stage pore structure was established. The model compared the influence of magnitudes of gravity on liquid transmission in LCS and mechanical response of osteocytes. The mean flow velocity of liquid in various layers (shallow, middle, and deep) increased linearly as acceleration due to gravity increased, and there was a significant liquid pressure gradient in osteocytes within a normal gravitational field compared with in microgravity. In microgravity environment, the osteocytes were unable to experience the pressure difference compared to that of Earth, possibly causing a decrease in mechanical conduction and biochemical function, even cell death, leading to increased osteoclast activity, eventually causing the loss of a large quantity of bone. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test-retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies. [ABSTRACT FROM AUTHOR]

Inserting a titanium implant in the bone tissue may modify its physiological loading and therefore cause bone resorption, via a phenomenon called stress-shielding. The local stress field around the bone-implant interphase (BII) created under shear loading may be influenced by different parameters such as the bone-implant contact (BIC) ratio, the bone Young's modulus, the implant roughness and the implant material. A 2-D finite element model was developed to model the BII and evaluate the impact of the aforementioned parameters. The implant roughness was described by a sinusoidal function (height 2Δ, wavelength λ), and different values of the BIC ratio were simulated. A heterogeneous distribution of the maximum shear stress was evidenced in the periprosthetic bone tissue, with high interfacial stress for low BIC ratios and low implant roughness and underloaded regions near the roughness valleys. Both phenomena may lead to stress-shielding-related effects, which were concentrated within a distance lower than 0.8λ from the implant surface. Choosing an implant material with mechanical properties matching those of bone tissue leads to a homogenized shear stress field and could help to prevent stress-shielding effects. Finally, the equivalent shear modulus of the BII was derived to replace its complex behavior with a simpler analytical model in future studies. Schematic illustrations of the 2-D finite element model used in the present study and spatial variation of the maximal shear stress in the periprosthetic bone tissue for different implant roughness and bone-implant contact ratios. [ABSTRACT FROM AUTHOR]

Atrial fibrosis plays a key role in the initiation and progression of atrial fibrillation (AF). Atrial fibrosis is typically identified by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets. Nevertheless, this approach disregards signal spatiotemporal information and b-EGM sensitivity to catheter orientation. To overcome these limitations, we propose the dominant-to-remaining eigenvalue dominance ratio (EIGDR) of unipolar electrograms (u-EGMs) within neighbor electrode cliques as a waveform dispersion measure, hypothesizing that it is correlated with the presence of fibrosis. A simulated 2D tissue with a fibrosis patch was used for validation. We computed EIGDR maps from both original and time-aligned u-EGMs, denoted as [Formula: see text] and [Formula: see text], respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, [Formula: see text]. The performance of each map in detecting fibrosis was evaluated in scenarios including noise and variable electrode-tissue distance. Best results were achieved by [Formula: see text], reaching 94% detection accuracy, versus the 86% of b-EGMs voltage maps. The proposed strategy was also tested in real u-EGMs from fibrotic and non-fibrotic areas over 3D electroanatomical maps, supporting the ability of the EIGDRs as fibrosis markers, encouraging further studies to confirm their translation to clinical settings. Upper panels: map of [Formula: see text] from 3×3 cliques for Ψ= 0∘ and bipolar voltage map Vb-m, performed assuming a variable electrode-to-tissue distance and noisy u-EGMs (noise level σv = 46.4 μV ). Lower panels: detected fibrotic areas (brown), using the thresholds that maximize detection accuracy of each map. [ABSTRACT FROM AUTHOR]

The complex structure and composition of articular cartilage make its performance show depth-dependent characteristics, but its related parameters are not complete at present. In this study, porcine articular cartilage was divided into three zones along the thickness direction, and the cartilage tissue in each zone was tested for electrical impedance, compression relaxation, and permeability to obtain their mechanical and electrical impedance parameters. The results showed that there were significant differences in mechanical and electrical properties of cartilage tissue in different zones in which resistivity, elastic modulus, relaxation time, and final relaxation rate increased gradually from superficial zone to deep zone along the direction of cartilage thickness while the permeability decreased gradually. Bioimpedance analysis can capture the phenomenon of very slight histological changes, which is expected to provide information for predicting cartilage degeneration, but the electrical impedance parameters of cartilage are still very lacking. These data are expected to provide reference for the treatment of clinical osteoarthritis and the research of cartilage repair materials. [ABSTRACT FROM AUTHOR]

High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images, that can reflect important physical processes in a statistical sense are accumulated. However, traditional imaging techniques cannot make full use of them. Recently, deep learning, in which the large quantity of imaging data can be utilized and patterns can be learned by a hierarchical structure, has provided new ideas for CT image quality improvement. Many researchers have proposed a large number of deep learning algorithms to improve CT image quality, especially in the field of image postprocessing. This survey reviews these algorithms and identifies future directions. [ABSTRACT FROM AUTHOR]

Brain functional network (FN) has emerged as a potential tool for identifying mental and neurological diseases. Traditional FN estimation methods such as Pearson's correlation (PC) and sparse representation (SR), despite their popularity, can only model low-order relationships between brain regions (i.e., nodes of FN), thus failing to capture more complex interaction in the brain. Recently, researchers proposed to estimate high-order FN (HoFN) and successfully used them in the early diagnosis of neurological diseases. In practice, however, such HoFN is constructed by directly considering the columns (or rows) of the adjacency matrix of low-order FN (LoFN) as node feature vectors that may contain some redundant or noisy information. In addition, it is not really reflected whether the original low-order relationship is maintained during the construction of the HoFN. To address these problems, we propose correlation-preserving embedding (COPE) to re-code the LoFN prior to constructing HoFN. Specifically, we first use SR to construct traditional LoFN. Then, we embed the LoFN via COPE to generate the new node representation for removing the potentially redundant/noisy information in original node feature vectors and simultaneously maintaining the low-order relationship between brain regions. Finally, the expected HoFN is estimated by SR based on the new node representation. To verify the effectiveness of the proposed scheme, we conduct experiments on 137 subjects from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database to identify subjects with mild cognitive impairment (MCI) from normal controls. Experimental results show that the proposed scheme can achieve better performance than the baseline method. Brain functional network (FN) has emerged as a potential tool for identifying mental and neurological diseases. Traditional FN estimation methods such as Pearson's correlation (PC) and sparse representation (SR), despite their popularity, can only model low-order relationships between brain regions (i.e., nodes of FN), thus failing to capture more complex interaction in the brain. Recently, researchers proposed to estimate high-order FN (HoFN) and successfully used them in the early diagnosis of neurological diseases. In practice, however, such HoFN is constructed by directly considering the columns (or rows) of the adjacency matrix of low-order FN (LoFN) as node feature vectors that may contain some redundant or noisy information. In addition, it is not really reflected whether the original low-order relationship is maintained during the construction of the HoFN. To address these problems, we propose correlation-preserving embedding (COPE) to re-code the LoFN prior to constructing HoFN. Specifically, we first use SR to construct traditional LoFN. Then, we embed the LoFN via COPE to generate the new node representation for removing the potentially redundant/noisy information in original node feature vectors and simultaneously maintaining the low-order relationship between brain regions. Finally, the expected HoFN is estimated by SR based on the new node representation. To verify the effectiveness of the proposed scheme, we conduct experiments on 137 subjects from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database to identify subjects with mild cognitive impairment (MCI) from normal controls. Experimental results show that the proposed scheme can achieve better performance than the baseline method. [ABSTRACT FROM AUTHOR]

The process of identifying cardiac adipose tissue (CAT) from volumetric magnetic resonance imaging of the heart is tedious, time-consuming, and often dependent on observer interpretation. Many 2-dimensional (2D) convolutional neural networks (CNNs) have been implemented to automate the cardiac segmentation process, but none have attempted to identify CAT. Furthermore, the results from automatic segmentation of other cardiac structures leave room for improvement. This study investigated the viability of a 3-dimensional (3D) CNN in comparison to a similar 2D CNN. Both models used a U-Net architecture to simultaneously classify CAT, left myocardium, left ventricle, and right myocardium. The multi-phase model trained with multiple observers' segmentations reached a whole-volume Dice similarity coefficient (DSC) of 0.925 across all classes and 0.640 for CAT specifically; the corresponding 2D model's DSC across all classes was 0.902 and 0.590 for CAT specifically. This 3D model also achieved a higher level of CAT-specific DSC agreement with a group of observers with a Williams Index score of 0.973 in comparison to the 2D model's score of 0.822. [ABSTRACT FROM AUTHOR]

Adolescent idiopathic scoliosis (AIS) is a musculoskeletal disorder characterized as three-dimensional (3D) deformity, and bracing is a common conservative treatment for AIS. Finite element analysis (FEA) is a technique for numerically solving the differential equations arising in engineering and mathematical modeling and has been widely used in biomechanical studies. Recently, FEA has been under intensive focus to improve the clinical outcomes of brace treatment. This review focuses on using FEA to assist brace treatment for AIS and technique choices that may be encountered during the construction of the finite element model (FEM). The construction of geometric models, the mechanical property, element type, the boundary condition, and the observation items of FEA have been summarized while establishing FEM. In each technical aspect, different fields and limitations of FEA are discussed. The observation items based on FEA are collected in addition to the biomechanical value in clinical research. We also summarized the technical aspects of brace treatment by FEA and observation items and provided guidance and directions to improve the brace treatment. [ABSTRACT FROM AUTHOR]

Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)-based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI1, adSQI2), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI1, adSQI2, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pulse wave propagation model of haemodynamics. Four different machine learning (ML) methods are used to train and test a series of classifiers-both binary and multiclass-to distinguish between healthy and unhealthy virtual patients (VPs) using different combinations of pressure and flow-rate measurements. It is found that the ML classifiers achieve specificities larger than 80% and sensitivities ranging from 50 to 75%. The most balanced classifier also achieves an area under the receiver operative characteristic curve of 0.75, outperforming approximately 20 methods used in clinical practice, and thus placing the method as moderately accurate. Other important observations from this study are that (i) few measurements can provide similar classification accuracies compared to the case when more/all the measurements are used; (ii) some measurements are more informative than others for classification; and (iii) a modification of standard methods can result in detection of not only the presence of stenosis, but also the stenosed vessel. Graphical Abstract An overview of methodology fo the creation of virtual patients and their classification. [ABSTRACT FROM AUTHOR]

We present a new, automatic, correlation-based method for measuring the duration of motor unit action potentials (MUAPs). The method seeks to replicate the way an expert elctromyographer uses his or her eyes, calculating the start and end of the MUAP waveform on the basis of the degree of similarity of non-excluded discharges. We analysed 68 potentials from normal deltoid muscles during slight contraction. For each MUAP, two experienced electromyographers manually determined start and end marker positions, which were used as gold standard duration positions (GSP) in our subsequent tests. The novel method was compared with Nandedkar's method and a wavelet transform-based method. To compare the three methods, the differences between the automatic marker positions and GSPs were statistically evaluated using one-factor ANOVA, the estimated mean square error, and a Chi-square test on the numbers of automatic marker placements with gross errors. All these parameters showed smaller values for the novel method and in most of the cases were statistically significant. In addition, the parameters of the new method were subjected to a sensitivity study, showing its good performance within a range of clinically useful parameter values. The new automatic method determined start and end markers in a more accurate and reliable manner than both of the acknowledged state-of-the art methods used in our comparison study. Graphical abstract The description of a new automatic duration measurement algorithm based on the similarity among discharges of the same MUAP. This method gave better results than the Nandedkar method and a highly regarded wavelet-based method. The new correlation-based method also had the lowest rate of gross aberrant errors in automatic placements. [ABSTRACT FROM AUTHOR]

Intraoperative fluoroscopic images, as one of the most important input data for computer-assisted orthopedic surgery (CAOS) systems, have a significant influence on the positioning accuracy of CAOS system. In this study, we proposed to use multi-angle intraoperative fluoroscopy images as input based on real clinical scenario, and the aim was to analyze the positioning accuracy and the error propagation rules with multi-angle input images compared with traditional two input images. In the experiment, the positioning accuracy of the C-arm calibration-based algorithm was studied, respectively, using two, three, four, five, and six intraoperative fluoroscopic images as input data. Moreover, the error propagation rules of the positioning error were analyzed by the Monte Carlo method. The experiment result showed that increasing the number of multi-angle input fluoroscopic images could reduce the positioning error of CAOS system, which has dropped from 1.01 to 0.61 mm. The Monte Carlo simulation analysis showed that for random input errors subject to normal distribution (μ = 0, σ = 1), the image positioning error dropped from 0.29 to 0.23 mm, and the staff gauge positioning error dropped from 1.36 to 1.19 mm, while the tracking device positioning error dropped from 3.41 to 2.13 mm. In addition, the results showed that image positioning error and staff gauge positioning error were all nonlinear error for the whole system, but tracker device positioning error was a strictly linear error. In conclusion, using multi-angle fluoroscopy images was helpful for clinic, which could improve the positioning accuracy of the CAOS system by nearly 30%. Graphical abstract The experiment process and Monte Carlo analysis of spatial positioning accuracy (A: Setup for the experiment; B: The process of Monte Carlo analysis; C: Results). [ABSTRACT FROM AUTHOR]

The cognitive processing and detection of errors is important in the adaptation of the behavioral and learning processes. This brain activity is often reflected as distinct patterns of event-related potentials (ERPs) that can be employed in the detection and interpretation of the cerebral responses to erroneous stimuli. However, high-accuracy cross-condition classification is challenging due to the significant variations of the error-related ERP components (ErrPs) between complexity conditions, thus hindering the development of error recognition systems. In this study, we employed support vector machines (SVM) classification methods, based on waveform characteristics of ErrPs from different time windows, to detect correct and incorrect responses in an audio identification task with two conditions of different complexity. Since the performance of the classifiers usually depends on the salience of the features employed, a combination of the sequential forward floating feature selection (SFFS) and sequential forward feature selection (SFS) methods was implemented to detect condition-independent and condition-specific feature subsets. Our framework achieved high accuracy using a small subset of the available features both for cross- and within-condition classification, hence supporting the notion that machine learning techniques can detect hidden patterns of ErrP-based features, irrespective of task complexity while additionally elucidating complexity-related error processing variations. Graphical abstract A schematic of the proposed approach. (a) EEG recordings in an auditory experiment in two conditions of different complexity. (b) Characteristic event related activity feature extraction. (c) Selection of feature vector subsets for easy and hard conditions corresponding to correct (Class1) and incorrect (Class2) responses. (d) Performance for individual and cross-condition classification. [ABSTRACT FROM AUTHOR]

Sudden hearing loss can be treated noninvasively by administering drugs to the middle ear (≈1 ml) via the eustachian tube. The nasopharyngeal cavity requires high dexterity manipulation as it is restricted by the nasal vestibule, and precise drug delivery through the small cavity can allow previously unreachable areas to be reconsidered for localized delivery. Nitinol has shape memory capabilities and can be used for distal actuation accessed from small lumen and a tortuous path. The drug delivery device (DDD) is a soft and needle-sized (2 mm) and comprises of Nitinol, ribbon spring, and a drug delivery tube. By controlling the input voltage to the Nitinol, bending of the device at different angles could be achieved, and the ribbon spring works antagonistically to the Nitinol to revert to the initial position once deactivated. The actuation of the device and its corresponding bending are calculated in vitro and found to have a bending angle ranging between 36.2 and 66.8° for applied voltages of 1.2-2.0 V, with surface temperature of 45.6-154 °C. The DDD is able to actuate 200 cycles with ≈91-76% retention of bending performance, with a temperature increase of ≈8.5-9% when 1.2-2.0 V is applied. Addition of soft insulating material shows ≈34-62% reduction in the surface temperature in the first cycle and ≈37-59% over 200 cycles when actuated at 1.2-2.0 V. The active steering and navigation capabilities of the DDD are demonstrated in simulated environments (based on the eustachian tube dimensions of adult and infant). Preclinical testing in human cadavers is demonstrated and suggests the developed DDD controlled by varying the input voltages for bending, and mechanically varied drug delivery may be a feasible option for localized drug delivery in eustachian tube. Graphical abstract. [ABSTRACT FROM AUTHOR]

Taking advantage of motion sensing technology, a quantitative assessment method for lower limbs motor function of cerebral palsy (CP) based on the gross motor function measurement (GMFM)-24 scale was explored in this study. According to the motion analysis on GMFM-24 scale, we translated the assessment problem of GMFM-24 scale into a detection problem of different motion modes including static state, fall, step, turning, alternating gait, walking, running, lifting legs, kicking balls, and jumping. The surface electromyography (sEMG) electrodes and inertial sensors were adopted to capture motion data, and a framework integrating a series of detection algorithms was presented for the assessment of lower limbs gross motor function. Two groups of participants including 8 healthy adults and 14 CP children were recruited. A self-developed data acquisition equipment integrating 24 sEMG electrodes and 9 inertial units was adopted for data acquisition. A platform based on two laser beam sensors was used to perform cross-border detection. The parameters/thresholds of motion detection algorithms were determined by the data from healthy adults, and the lower limbs gross motor function evaluation was conducted on 14 CP children. The experimental results verified the feasibility and effectiveness of the proposed quantitative assessment method. Compared to the clinical assessment score based on GMFM-24 scale, 90.1% accuracy was obtained for evaluation of 303 tasks in 14 CP children. The objective motor function assessment method proposed has potential application value for the quantitative assessment of lower limbs motor function of CP children in clinical practice. Graphical abstract The algorithm framework for the assessment of lower limbs gross motor function. Using the GMFM-24 scale as the evaluation standard, a quantitative evaluation program for the lower limbs gross motor function of CP children based on motion sensing technology was proposed. [ABSTRACT FROM AUTHOR]

Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants-18-25-year-old university students-to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01-30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group. Graphical abstract. [ABSTRACT FROM AUTHOR]

Appropriate management of hypertensive patients relies on the accurate identification of clinically relevant features. However, traditional statistical methods may ignore important information in datasets or overlook possible interactions among features. Machine learning may improve the prediction accuracy and interpretability of regression models by identifying the most relevant features in hypertensive patients. We sought the most relevant features for prediction of cardiovascular (CV) events in a hypertensive population. We used the penalized regression models least absolute shrinkage and selection operator (LASSO) and elastic net (EN) to obtain the most parsimonious and accurate models. The clinical parameters and laboratory biomarkers were collected from the clinical records of 1,471 patients receiving care at Mostoles University Hospital. The outcome was the development of major adverse CV events. Cox proportional hazards regression was performed alone and with penalized regression analyses (LASSO and EN), producing three models. The modeling was performed using 10-fold cross-validation to fit the penalized models. The three predictive models were compared and statistically analyzed to assess their classification accuracy, sensitivity, specificity, discriminative power, and calibration accuracy. The standard Cox model identified five relevant features, while LASSO and EN identified only three (age, LDL cholesterol, and kidney function). The accuracies of the models (prediction vs. observation) were 0.767 (Cox model), 0.754 (LASSO), and 0.764 (EN), and the areas under the curve were 0.694, 0.670, and 0.673, respectively. However, pairwise comparison of performance yielded no statistically significant differences. All three calibration curves showed close agreement between the predicted and observed probabilities of the development of a CV event. Although the performance was similar for all three models, both penalized regression analyses produced models with good fit and fewer features than the Cox regression predictive model but with the same accuracy. This case study of predictive models using penalized regression analyses shows that penalized regularization techniques can provide predictive models for CV risk assessment that are parsimonious, highly interpretable, and generalizable and that have good fit. For clinicians, a parsimonious model can be useful where available data are limited, as such a model can offer a simple but efficient way to model the impact of the different features on the prediction of CV events. Management of these features may lower the risk for a CV event. Graphical Abstract In a clinical setting, with numerous biological and laboratory features and incomplete datasets, traditional statistical methods may ignore important information and overlook possible interactions among features. Our aim was to identify the most relevant features to predict cardiovascular events in a hypertensive population, using three different regression approaches for feature selection, to improve the prediction accuracy and interpretability of regression models by identifying the relevant features in these patients. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a non-invasive and real-time imaging method that has the potential to be used for monitoring intracerebral hemorrhage (ICH). Recent studies have proposed that ischemia secondary to ICH occurs simultaneously in the brain. Real-time monitoring of the development of hemorrhage and risk of secondary ischemia is crucial for clinical intervention. However, few studies have explored the performance of EIT monitoring in cases where hemorrhage and secondary ischemia exist. When these lesions get close to each other, or their conductivity and volume changes differ greatly, it becomes challenging for dynamic EIT algorithms to simultaneously reconstruct subtle injuries. To address this, an iterative damped least-squares (IDLS) algorithm is proposed in this study. The quality of the IDLS algorithm was assessed using blur radius and temporal response during computer simulation and a phantom 3D head-shaped model where bidirectional disturbance targets were simulated. The results showed that the IDLS algorithm enhanced contrast and concurrently reconstructed bidirectional disturbance targets in images. Moreover, it showed superior performance in decreasing the blur radius and was time cost-effective. With further improvement, the IDLS algorithm has the potential to be used for monitoring the development of hemorrhage and risk of ischemia secondary to ICH. Graphical abstract (a) and (b) are simulation images of bidirectional disturbance targets with different change ratios of volume (Vr) and conductivity (σr) based on the damped least-squares (DLS) algorithm and iterative damped least-squared (IDLS) algorithm, respectively. (c) shows the performance metrics of blur radius and temporal response with different volume ratio (corresponding to Vr). (d) shows the performance metrics of blur radius and temporal response with different conductivity change percentage (corresponding to σr). [ABSTRACT FROM AUTHOR]

Studies have reported the benefits of sensory noise in motor performance, but it is not clear if this phenomenon is influenced by muscle contraction intensity. Additionally, most of the studies investigated the role of a stochastic noise on the improvement of motor control and there is no evidence that a sinusoidal vibrotactile stimulation could also enhance motor performance. Eleven participants performed a sensorimotor task while sinusoidal vibrations were applied to the finger skin. The effects of an optimal vibration (OV) on force steadiness were evaluated in different contraction intensities. We assessed the standard deviation (SD) and coefficient of variation (CoV) of force signals. OV significantly decreased force SD irrespective of contraction intensity, but the decrease in force CoV was significantly higher for low-intensity contraction. To the best of our knowledge, our findings are the first evidence that sinusoidal vibrotactile stimulation can enhance force steadiness in a motor task. Also, the significant improvement caused by OV during low-intensity contractions is probably due to the higher sensitivity of the motor system to the synaptic noise. These results add to the current knowledge on the effects of vibrotactile stimulation in motor control and have potential implications for the development of wearable haptic devices. Graphical abstract In this work the effects of a sinusoidal vibrotactile stimulation on force steadiness was investigated. Index finger sensorimotor tasks were performed in three levels of isometric contraction of the FDI muscle: 5, 10 and 15 %MVC. An optimal level of vibration significantly improved force steadiness, but the decrease in force CoV caused by vibration was more pronounced in contractions at 5 %MVC. [ABSTRACT FROM AUTHOR]

In this study, an electrochemical impedance biosensor was developed as a simple and fast method for real-time monitoring of biofilm binding properties via continuous impedance spectroscopy. To prepare the sensing membrane, cells were immobilized onto gold electrodes with nitrocellulose membranes. Different cell growth features were measured with the impedance instrument and analyzed using an equivalent model for data fitting and support vector regression (SVR) for data processing. The collected impedance spectra revealed that the binding attachment areas of cells differ depending on the cell density. Our results demonstrate the usefulness and feasibility of training our impedance-based sensor with a small amount of data to predict the effective area of different biofilms (GE, NGE, and CNGE), with a prediction error of 9.8%. Graphical abstract. [ABSTRACT FROM AUTHOR]

The Markovian model has generally been used for cardiac electrophysiological simulations. However, the Markovian model is so stiff that speeding up the computation of the algorithms with variable time-steps always results in simulation instability. In particular, the unstable simulations always occur at a low voltage rate or current change, while transition rates in the Markovian model are changing markedly. The uniformization (UNI) method allows for a Markovian model simulation with high stability but also a high computation cost. To save computation costs with variable time-steps, we propose a speed increasing idea that is a compromise to the trade-off between stability and acceleration by combining Chen-Chen-Luo's "quadratic adaptive algorithm" (CCL) method with "hybrid operator splitting" (HOS) into the solver (CCL + HOS solver). The computation cost of this CCL + HOS solver is approximately 24 times lower than the CCL + UNI solver, and the CCL + HOS solver can function 295 times faster in comparison to the HOS solver with a fixed time-step (DT). The suggested optimal solver should be CCL + HOS solver with a maximum time-step at 0.1 ms due to its high speed with low error. Additionally, the CCL method has much better performance and stability than the hybrid method in this single-cell model simulation. [ABSTRACT FROM AUTHOR]

In this survey, we review the field of human shoulder functional kinematic representations. The central question of this review is to evaluate whether the current approaches in shoulder kinematics can meet the high-reliability computational challenge. This challenge is posed by applications such as robot-assisted rehabilitation. Currently, the role of kinematic representations in such applications has been mostly overlooked. Therefore, we have systematically searched and summarised the existing literature on shoulder kinematics. The shoulder is an important functional joint, and its large range of motion (ROM) poses several mathematical and practical challenges. Frequently, in kinematic analysis, the role of the shoulder articulation is approximated to a ball-and-socket joint. Following the high-reliability computational challenge, our review challenges this inappropriate use of reductionism. Therefore, we propose that this challenge could be met by kinematic representations, that are redundant, that use an active interpretation and that emphasise on functional understanding. [ABSTRACT FROM AUTHOR]

Infant posture and motor pattern development are normally analyzed by clinical assessment scales. Lately, this approach is combined with the use of sensor-supported systems, such as optical, inertial, and electromagnetic measurement systems, as well as novel assessment devices, such as CareToy. CareToy is a modular device for assessment and rehabilitation of preterm infants, comprising pressure mattresses, inertial and magnetic measurement units, and sensorized toys. Since such integrated sensor system combination is new to the field of sensor-supported infant behavior assessment and rehabilitation, dedicated methods for data analysis were developed and presented. These comprise trunk rotation, arm movement, forearm orientation, and head movement analysis, along with toy play and trunk posture stability evaluation. Methods were tested on case study data, evaluating suitability of developed algorithms for infant posture and activity analysis, regardless of behavioral responses. Obtained results demonstrate suitability of the proposed methods for successful use in studies of different motor pattern subfields. This represents an important step on the course towards objective, accurate, sensor-supported infant motor development assessment. Graphical abstract Posture and movement assessment of infants using analysis of sensory data, obtained with a dedicated sensorized gym with toys. [ABSTRACT FROM AUTHOR]

As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (< 7 days) in women with threatened preterm labor undergoing tocolytic therapy, using both EHG-burst and whole EHG window analyses, by calculating temporal, spectral, and non-linear parameters. Only two non-linear EHG-burst parameters and four whole EHG window analysis parameters were able to distinguish the women who delivered < 7 days from the others, showing that EHG can provide relevant information on the approach of labor, even in women with threatened preterm labor under the effects of tocolytic therapy. The whole EHG window outperformed the EHG-burst analysis and is seen as a step forward in the development of real-time EHG systems able to predict imminent labor in clinical praxis. Graphical abstract The ability of EHG recordings to predict imminent labor (< 7 days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7 days from those who did not. [ABSTRACT FROM AUTHOR]

Osteoporosis is one of the most common diseases that leads to bone fractures. Dual-energy X-ray absorptiometry is currently employed to measure the bone mineral density and to diagnose osteoporosis. Alternatively, the dielectric properties of bones are found to be influenced by bone mineral density; hence, dielectric properties of bones may potentially be used to diagnose osteoporosis. Microwave tomographic imaging is currently in development to potentially measure in vivo dielectric properties of bone. Therefore, the foci of this work are to summarize all available dielectric data of bone in the microwave frequency range and to analyze the confounders that may have resulted in variations in reported data. This study also compares the relationship between the dielectric properties and bone quality reported across different studies. The review suggests that variations exist in the dielectric properties of bone and the relationship between bone volume fraction and dielectric properties is in agreement across all studies. Conversely, the evidence of a relationship between bone mineral density and dielectric properties is inconsistent across the studies. This summary of dielectric data of bone along with a comparison of the relationship between the dielectric properties and bone quality will accelerate the development of microwave tomographic imaging devices for the monitoring of osteoporosis. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]