Search

This review identifies the three-dimensional knee loads that have the highest risk of injuring the anterior cruciate ligament (ACL) in the athlete. It is the combination of the muscular resistance to a large knee flexion moment, an external reaction force generating knee compression, an internal tibial torque, and a knee abduction moment during a single-leg athletic manoeuvre such as landing from a jump, abruptly changing direction, or rapidly decelerating that results in the greatest ACL loads. While there is consensus that an anterior tibial shear force is the primary ACL loading mechanism, controversy exists regarding the secondary order of importance of transverse-plane and frontal-plane loading in ACL injury scenarios. Large knee compression forces combined with a posteriorly and inferiorly sloped tibial plateau, especially the lateral plateau-an important ACL injury risk factor-causes anterior tibial translation and internal tibial rotation, which increases ACL loading. Furthermore, while the ACL can fail under a single supramaximal loading cycle, recent evidence shows that it can also fail following repeated submaximal loading cycles due to microdamage accumulating in the ligament with each cycle. This challenges the existing dogma that non-contact ACL injuries are predominantly due to a single manoeuvre that catastrophically overloads the ACL.

The aim of the study was to investigate whether jerk cost (JC) can discriminate between swimming levels. Nine elite and nine non-elite swimmers swam a 50-m front-crawl sprint wearing a 3D accelerometer on their back between the inferior angles of the scapulae. Lap times and JC were calculated from the acceleration signal and compared between groups and between swimmers within a group. The elite swimmers swam significantly faster lap times than the non-elite swimmers (p < 0.001). They did so with significantly lower levels of JC compared to the non-elite swimmers (p = 0.005). Furthermore, a stepwise multiple linear regression showed JC accounted for 32.9% of the variation in lap time of the elite swimmers. These results indicate that it is possible to discriminate elite from non-elite swimmers using JC: elite swimmers swim with lower JCs than non-elite swimmers. Additionally, swimming at higher speed is associated with more accelerations and decelerations in both elite and non-elite swimmers, which is reflected by higher JCs and lower smoothness. In sum, JC provides an index of swimming technique that is easy to use in training practice.

Background and objective Heart murmur characterization is a crucial part of cardiac auscultation for determining the potential etiology and severity of heart diseases. One such helpful murmur characterization is the sonic qualities, which reflect both structural and hemodynamical states of the heart. Therefore, the objective is to develop a machine learning based solution for classifying murmur qualities. Methods Four medically defined murmur qualities, namely the musical quality, blowing-like quality, coarse quality, and soft quality were examined. Feature was extracted from heart murmurs signals in their time domain, frequency domain, time-frequency domain, and phase space domain. Sequential forward floating selection (SFFS) was implemented along with three classifiers, including k-nearest neighbor (KNN), Naive-Bayes (NB), and linear support vector machine (SVM). Results It was found that multi-domain features are suited for better classification results and linear SVM was able to achieve a better balance between performance and the size of feature subsets among tested classifiers. Using the derived features, classification accuracies of 86%, 91%, 90%, and 84% were achieved for musical quality, blowing-like quality, coarse quality, and soft quality classifications respectively. Conclusions The study demonstrated that it is possible to effectively characterize heart murmur through its diagnostic characteristics instead of drawing direct conclusions, which is helpful for retaining versatility and generality found in the conventional cardiac auscultation.

Objectives Magnesium alloys are the potential candidate for metallic implants due to their excellent mechanical characteristics, biodegradable nature, and properties similar to human bone. However, a high degradation rate is primary obstacle in implementing these alloys as biodegradable orthopedic implants. Powder-mixed electric discharge machining (PMEDM) is an emerging method of surface modification of metallic alloys that can be implemented to improve the corrosion resistance of Mg alloys. Therefore, PMEDM using zirconium (Zr) and manganese (Mn) powder particles has been proposed to modify the surface characteristics of Mg-4Zn alloy. Materials and Methods In the present work, Zr and Mn powders have been used in varying concentrations during PMEDM of Mg-4Zn alloy. Experiments were conducted as per mixed design L18 orthogonal array (OA). Taguchi and Grey Relational Analysis (GRA) have been used to optimize the process parameters. Analysis of response characteristics, namely material removal rate (MRR), surface roughness (SR), and thickness of the alloyed layer (TAL), has been carried out at different values of input variables (like powder additives (Pa), powder concentration (Cp), peak current (Ip), pulse on time (Ton) and duty cycle (DC)). The corrosion analysis was carried out by immersing the specimen (machined at an optimized setting) in simulated body fluid (SBF). Results It is observed from the analysis that Cp, Ip, and Ton play a pivotal role in evaluating response characteristics. The favorable setting suggested by the gray approach is Pa: Zr; Cp: 2 g/l; Ip: 4A; Ton: 50 μs; DC: 80%, while responses at this setting are confirmed by confirmation experiments with MRR: 32.14 mm3/min; SR: 5.578 μm and TAL: 8.28 μm. The immersion test signifies that the corrosion rate (CR) of PMEDMed sample (3.20 mm/year) is 40.74% lesser than the corrosion rate of polished sample (5.40 mm/year). Conclusion Zr powder shows better performance in terms of higher MRR, lower SR and higher TAL as compared to Mn powder during the PMEDM process. The corroded surface of polished sample exhibited larger size micro pits and cracks than the machined sample, which concluded that surface modification of MZ-4Zn alloy via PMEDM is a powerful tool to enhance its corrosion resistance.

Background: Osteotomies during rhinoplasty are usually based on surgeon's proprioception to determine the number, energy, and trajectory of impacts. Objective: The first objective was to detect the...

Objective The initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms. Materials and methods In this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naive Bayes, and Decision Tree, have been used to classify the features. Results The best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods. Conclusion The comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.

Objectives This study aims to evaluate the fatigue stress around custom-made all-on-4 implants system to find out which type of implants have a better performance under different graded multidirectional occlusal forces. Material and methods 3D normal and implanted models simulating the “All-on-4” concept were created and analyzed under three different conditions of occlusal loadings. Two types of static and fatigue were applied. Stress distribution was analyzed based on von Mises and Goodman theories in ANSYS environment in addition to the safety factor. Statistical tests were performed to assess the significance of the results as well as the reproducibility of the results. Results The results showed stress increasing reaching a value of 48%, 29% in tilted implants compared to vertical implants and normal cases respectively. In contrast, tilted implants appeared to be less stable (safety factor may reach 0.7) and they may fail during the application of occlusal forces. The safety factor of cortical bone decreased by about 91% in the implanted model compared to the normal model, indicating a higher possibility of bone remodeling around the bone. Conclusion The orientation and position of occlusal forces had an important influence on stress distribution between the implant and the surrounding bone, and fatigue loading caused greater stresses in comparison with static loading. Lower amounts of stress were found in the vertical implants, ensuring a higher safety factor and a longer clinical service. In contrast, the critical safety factor values are observed in tilted implants, which may fail under the influence of applied occlusal forces.

Purpose Leukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques. Procedures The study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification. Findings A classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms. Conclusions The paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.

Background Pulse diagnosis (wrist pulse signal) is a well-known traditional technique used for a health examination. It has the potential to detect cardiac and non-cardiac diseases. Objective A study was conducted to investigate human emotions using wrist pulse signal assessment. The aim was to categorize anxiety, boredom, physical pain, and reference state by processing and analysis of acquired signals. Method A protocol was designed to induce emotions. Data were acquired from 24 healthy volunteers. Signals were processed and further analyzed using paired t-test and Analysis of Variance (ANOVA). Machine learning algorithms, Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF), and Support Vector Machine using kernel Gaussian radial basis function (RBF-SVM) were used to evaluate significant features and classify the emotions. Results Computing significant plus ranked features performed better over randomly selected features for pairwise emotion classification. Here, the QDF classifier outperforms LDF. Additionally, ANOVA validated the effectiveness of statistically prominent features to classify emotional states. Ratio_Pulse_Strength, total_power, Spectral Entropy, and meancd5 came out as the four most significant features to classify the emotion “Anxiety”, “Boredom”, “Pain”, and “Reference” with positive prediction rate of 100%, 73%, 100%, and 86% respectively using RBF-SVM in the user-independent model. Conclusion Previously, WPS has been used mainly to detect physical abnormality in the human body. The results endorse the potential of user-independent human emotion detection using a wrist pulse signal. The present work was focusing on a few emotional states. Results are encouraging and may be well applied to many more states.

Objectives With the rapid evolution and technology advancement, the healthcare sector is evolving day by day. It is taking advantage of different technologies such as Internet of things and Blockchain. Several applications related to daily healthcare activities are adopting the use of these technologies. In this paper, we present a review in which we group different healthcare applications that integrate the Internet of things and Blockchain in their systems. Material and methods A review study about the integration of IoT and Blockchain in healthcare systems was conducted. We searched the databases ScienceDirect, IEEE Xplore, Google Scholar and ACM Digital Library. Results This review focuses on categorizing the use cases of IoT and Blockchain in the healthcare sector. The study listed 6 applications in medical services, namely, remote patient monitoring, electronic medical records management, disease prediction, patient tracking, drug traceability and fighting infectious disease especially COVID-19. The paper also investigates the challenges associated with the adoption of the Blockchain technology in healthcare IoT-based systems and some of the existing solutions. It also introduces some future research directions. Conclusion The survey of the use cases of IoT and Blockchain in the healthcare sector will serve as a state of the art for future researches. In addition, the paper gives some directions to new possible researches that could help to revolutionize the healthcare sector by using other technologies such as artificial intelligence, big data, fog and cloud computing.

Objectives The deviation in gait cycle due to trunk acceleration and muscle activity on even and uneven inclined planes should be analyzed for the design of lower limb exoskeletons. This study compares the gait variability of gastrocnemius and medial hamstring muscle activity variation of twenty young male adults on inclined even and uneven planes. Material and methods The individuals walked on a long, 10° inclined even and uneven plane in both up-the-plane and down-the-plane directions at their preferred speed (average speed is 1.2 m/s). Gait variability during walking was calculated using an average standard deviation of trunk acceleration and the significance of change was calculated using two-way-ANOVA. For studying the difference between integrated electromyography (IEMG) values of walking on even and uneven planes, two parameters Normalized IEMG Percentage (NIP) and IEMG Variation Percentage (IVP) were chosen for the analysis. Results The results strongly agree with the hypothesis that gait variability hikes in the vertical direction of subject with a p-value of 0.04. The IEMG range of medial-hamstring muscle while walking on even and uneven plane is not highly significant for swing (0.44) as well as stance phase (0.47). While walking on an inclined uneven plane, the response of gastrocnemius muscle indicated the variation of NIP between 14.31% to 64.63%. It was observed that NIP and IEMG values of medial-hamstring muscles during backward walking have a resemblance. Conclusion Trunk variability had a significant change in the vertical direction (V) and was insignificant in medial-lateral (ML) and anterior-posterior (AP) orientations for both even and uneven inclined planes during forward and reverse walking. The muscle activity of gastrocnemius and medial-hamstring muscles does not have sound variations while walking on the inclined uneven plane.

Chromatin immunoprecipitation (ChIP-Seq) has emerged as a superior alternative to microarray technology as it provides higher resolution, less noise, greater coverage and wider dynamic range. While ChIP-Seq enables probing of DNA-protein interaction over the entire genome, it requires the use of sophisticated tools to recognize hidden patterns and extract meaningful data. Over the years, various attempts have resulted in several algorithms making use of different heuristics to accurately determine individual peaks corresponding to unique DNA-protein. However, finding all the significant peaks with high accuracy in a reasonable time is still a challenge. In this work, we propose the use of Multi-level thresholding algorithm, which we call LinMLTBS, used to identify the enriched regions on ChIP-Seq data. Although various suboptimal heuristics have been proposed for multi-level thresholding, we emphasize on the use of an algorithm capable of obtaining an optimal solution, while maintaining linear-time complexity. Testing various algorithm on various ENCODE project datasets shows that our approach attains higher accuracy relative to previously proposed peak finders while retaining a reasonable processing speed.

The glaucoma is an eye disease that causes blindness when it progresses in an advanced stage. Early glaucoma diagnosis is essential to prevent the vision loss. However, early detection is not covered due to the lack of ophthalmologists and the limited accessibility to retinal image capture devices. In this paper, we present an automated method for glaucoma screening dedicated for Smartphone Captured Fundus Images (SCFIs). The implementation of the method into a smartphone associated to an optical lens for retina capturing leads to a mobile aided screening system for glaucoma. The challenge consists in insuring higher performance detection despite the moderate quality of SCFIs, with a reduced execution time to be adequate for the clinical use. The main idea consists in deducing glaucoma based on the vessel displacement inside the Optic Disk (OD), where the vessel tree remains sufficiently modeled on SCFIs. Within this objective, our major contribution consists in proposing: (1) a robust processing for locating vessel centroids in order to adequately model the vessel distribution, and (2) a feature vector that relevantly reflect two main glaucoma biomarkers in terms of vessel displacement. Furthermore, all processing steps are carefully chosen based on lower complexity, to be suitable for fast clinical screening. A first evaluation of our method is performed using the two public DRISHTI-DB and DRIONS-DB databases, where 99% and 95% accuracy, 96.77% and 97,5% specificity and 100% and 95% sensitivity are respectively achieved. Thereafter, the method is evaluated using two fundus image databases respectively captured through a smartphone and retinograph for the same persons. We achieve 100% accuracy using both databases which assesses the robustness of our method. In addition, the detection is performed on 0.027 and 0.029 second when executed respectively on the Samsung-M51 on the Samsung-A70 smartphone devices. Our proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.

Objective In this research paper, the brain MRI images are going to classify by considering the excellence of CNN on a public dataset to classify Benign and Malignant tumors. Materials and Methods Deep learning (DL) methods due to good performance in the last few years have become more popular for Image classification. Convolution Neural Network (CNN), with several methods, can extract features without using handcrafted models, and eventually, show better accuracy of classification. The proposed hybrid model combined CNN and support vector machine (SVM) in terms of classification and with threshold-based segmentation in terms of detection. Result The findings of previous studies are based on different models with their accuracy as Rough Extreme Learning Machine (RELM)-94.233%, Deep CNN (DCNN)-95%, Deep Neural Network (DNN) and Discrete Wavelet Autoencoder (DWA)-96%, k-nearest neighbors (kNN)-96.6%, CNN-97.5%. The overall accuracy of the hybrid CNN-SVM is obtained as 98.4959%. Conclusion In today's world, brain cancer is one of the most dangerous diseases with the highest death rate, detection and classification of brain tumors due to abnormal growth of cells, shapes, orientation, and the location is a challengeable task in medical imaging. Magnetic resonance imaging (MRI) is a typical method of medical imaging for brain tumor analysis. Conventional machine learning (ML) techniques categorize brain cancer based on some handicraft property with the radiologist specialist choice. That can lead to failure in the execution and also decrease the effectiveness of an Algorithm. With a brief look came to know that the proposed hybrid model provides more effective and improvement techniques for classification.

Objectives This study aimed to investigate whether DistEn was capable of identifying complexity or irregularity for gait data and whether having low parameter-dependency sensitivity by comparing with the Approximate Entropy (ApEn) and Sample Entropy (SampEn). Material and methods The data were divided into three groups according to gait maturation. Firstly, the mean amplitude histogram, standard deviation (SD), and the power spectrum were calculated for each group. Secondly, ApEn, SampEn, and DistEn algorithms were calculated. Statistical analyses were then performed to compare groups. Results For m=3 with M= 256 and M=512 parameters, DistEn showed a statistically significant difference between in pairwise comparisons between all groups (Pa, Pb, and Pc Conclusion DistEn showed the best performance in capture the complexity changes in gain patterns with growth.

( Early Access ) Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker

In this paper, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of features extracted from sensor EEG signals are used for emotion recognition. However, they cannot unambiguously describe the location of emotions associated with neural activities and information propagation or the interaction between brain regions. In addition, most current studies use low-density sensor EEG signals. Moreover, source signals estimated from high-density sensor EEG signal have not been employed for emotion recognition to date. We designed a BDC network-based framework using EEG source signals to investigate emotion recognition. The global cortex factor-based multivariate autoregressive (GCF-MVAR) method was utilized to extract emotion-related BDC features. Our study revealed that the combined BDC and DE features facilitated a recognition accuracy of up to 89.58 %, which is higher than the rate obtained from BDC features and DE features alone. The sensor features derived from high-density EEG signals also exhibited higher recognition accuracy compared to low-density EEG signals. These findings suggest that BDC features derived from EEG source signals can better characterize human emotional states and are meaningful for emotion recognition.

Recently, deep learning models have been successfully employed in video-based affective computing applications. One key application is automatic depression recognition from facial expressions. State-of-the-art approaches to recognize depression typically explore spatial and temporal information individually, by using convolutional neural networks (CNNs) to analyze appearance information, and then by either mapping feature variations or averaging the depression level over video frames. This approach has limitations to represent dynamic information that can help to discriminate between depression levels. In contrast, 3D CNN-based models can directly encode the spatio-temporal relationships, although these models rely on fixed-range temporal information and single receptive field. This approach limits the ability to capture facial expression variations with diverse ranges, and the exploitation of diverse facial areas. In this paper, a novel 3D CNN architecture the Multiscale Spatiotemporal Network (MSN) is introduced to effectively represent facial information related to depressive behaviours. The basic structure of the model is composed of parallel convolutional layers with different temporal depths and sizes of receptive field, which allows the MSN to explore a wide range of spatio-temporal variations in facial expressions. Experimental results on two benchmark datasets show that our MSN is effective, outperforming state-of-the-art methods in automatic depression recognition.

To investigate the feasibility of developing an acoustic measurement library for non-invasive trans-rodent skull ultrasonic focusing at high frequency.A fiber-optic hydrophone (FOH) was positioned at the geometric focus of a spherically-curved phased array (64 elements, 25 mm diameter, 20 mm radius of curvature). Elements were driven sequentially (3.3 MHz driving frequency) and FOH waveforms were recorded with and without intervening ex-vivo rodent skullcaps. Measurements were carried out on 15 skullcaps (Sprague-Dawley rats, 182-209 g) across 3 fixed transmission regions per specimen. An element-wise measurement library of skull-induced phase differences was constructed using mean values across all specimens for each transmission region. Library-based transcranial phase differences were compared with direct FOH-based measurements across 5 additional skullcaps not included in the library.Library-based phase corrections deviated less from FOH-based trans-skull phase difference values than those calculated for the water-path case, and restored partial transcranial focal quality relative to that recovered using invasive hydrophone-based corrections. Retrospective analysis suggests comparable performance can be obtained using smaller library sizes.An acoustic measurement library can facilitate non-invasive transcranial aberration correction in rodents at high frequency.Library-based focusing represents a practical approach for delivering high-frequency ultrasound brain treatments in small animals.

The cornea is an important barrier to consider when developing ophthalmic formulations, but proper modeling of this multilayered tissue remains a challenge. This is due to the varying properties associated with each layer in addition to the dynamics of the tear film. Hence, the most representative models to date rely on animals. Animal models, however, differ from humans in several aspects and are subject to ethical limitations. Consequently, in vitro approaches are being developed to address these issues. This review focuses on the barrier properties of the cornea and evaluates the most advanced three-dimensional cultures of human corneal equivalents in literature. Their application potential is subsequently assessed and discussed in the context of preclinical testing along with our perspective toward the future. Impact statement Most ocular drugs are applied topically, with the transcorneal pathway as the main administration route. Animal corneas are currently the only advanced models available, contributing to the drug attrition rate. Anatomical and physiological interspecies differences might account for a poor translatability of preclinical results to clinical trials, urging researchers to devise better corneal equivalents. This review elaborates on the emerging generation of three-dimensional in vitro models, which comprises spheroids, organoids, and organs-on-chips, which can serve as a stepping stone for advancements in this field.

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.

Objective Calculation of body fat percentage (BFP) is a frequently encountered problem in the literature. BFP is one of the most significant parameters which should be processed in body weight control programs. Anthropometric measurements and statistical methods are being used generally in the literature for BFP estimation. Artificial intelligence and gender-based models with a photoplethysmography signal (PPG) were proposed for BFP estimation in this study. Material and Methods In the study, the PPG signal is divided into lower frequency bands, and 25 features are taken out from each frequency band. Artificial intelligence algorithms were created by reducing the extracted features with the help of a feature selection algorithm. Results According to the results obtained, models with performance values of R M S E = 0.35 , R = 1 for men, R M S E = 0.87 , R = 1 for women were created. Conclusions In the best performing models, the PPG signal's high-frequency components are used for men, whereas the low-frequency band of the PPG signal is used for women. As a result, the proposed model in this study is considered to be used for BFP measurement.

Objectives To improve the hydrophilicity of cyclic olefin copolymer, a simple and rapid method using two-stage with ultraviolet irradiation was developed in order to graft a bioactive polymer on the surface of these polymers. Materials and Methods A bioactive polymer, poly(sodium styrene sulfonate) was grafting in two steps on the cyclic olefin copolymer surface. The process consists to activate the surface with ozone and grafting to under UV irradiation in presence of sodium styrene sulfonate. The presence of polymer on the surfaces was characterized by water contact angle, Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive spectroscopy and the quantity of polymer grafted was determined by a colorimetric method. Results First, the time of UV irradiation for the grafting was studied. The results showed that the maximum grafting rate is reached after 60 minutes of reaction. Second, the influence of the presence of additive on the grafting was investigated. The degree of grafting is significantly reduced compared to a sample without additive. Conclusion We have developed a simple and fast method to graft a hydrophilic and bioactive polymer covalently to a COC surface.

Background Frequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques. Method In this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification. Results An experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods. Conclusion Achieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.

Background Accurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images. Methods The proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour. Results Experimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy. Conclusions The geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.

Biodegradable materials offer many advantages over the conventionally used non-biodegradable biomaterials. A number of biodegradable material-based (BMB) alloys have been developed for biomedical applications. Such alloys are increasingly used for manufacturing orthopaedic implants. The elimination of secondary surgery depends on the efficacy of the BMB implant which needs to fulfill two essential requirements. While such an implant has to be strong enough to provide adequate support to the damaged structure in skeleton, it should also be capable of maintaining just the right balance between rate of healing of the damaged structure and rate of degradation of the implant. The complexities involved in striking a balance between these two apparently conflicting requirements makes it imperative to optimize the design and preparation strategy of the BMB alloy. Many biodegradable metals, ceramics and polymers have been investigated for this application and the Magnesium (Mg)-based alloys have been found to be amongst the most suited ones. This review focuses on preparation techniques employed in development of Mg – alloys for biodegradable orthopaedic implants (fracture fixation implants). The process can essentially be split into: material optimization, processing techniques, and surface modifications.

A magnesium alloy containing essential, non-toxic, biodegradable elements such as Ca and Zn has been fabricated using a novel twin-roll casting process (TRC). Microstructure, mechanical properties, in vivo corrosion and biocompatibility have been assessed and compared to the properties of the rare earth (RE) element containing WE43 alloy. TRC Mg-0.5 wt% Zn- 0.5 wt% Ca exhibited fine grains with an average grain size ranging from 70 to 150 μm. Mechanical properties of a TRC Mg-0.5Zn-0.5Ca alloy showed an ultimate tensile strength of 220 MPa and ductility of 9.3%. The TRC Mg-0.5Zn-0.5Ca alloy showed a degradation rate of 0.51 ± 0.07 mm/y similar to that of the WE43 alloy (0.47 ± 0.09 mm/y) in the rat model after 1 week of implantation. By week 4 the biodegradation rates of both alloys studied were lowered and stabilized with fewer gas pockets around the implant. The histological analysis shows that both WE43 and TRC Mg-0.5Zn-0.5Ca alloy triggered comparable tissue healing responses at respective times of implantation. The presence of more organized scarring tissue around the TRC Mg-0.5Zn-0.5Ca alloys suggests that the biodegradation of the RE-free alloy may be more conducive to the tissue proliferation and remodelling process., Graphical abstract Image 1, Highlights • Mg-0.5Zn-0.5Ca alloy plates were fabricated by a twin-roll casting (TRC) process. • TRC alloy showed an ultimate strength and elongation of 221 ± 2 MPa and 9 ± 2%. • Gas development during in vivo degradation was analysed using μ-CT techniques. • Histological analysis revealed a good biocompatibility and promoted healing.

Rho-associated protein kinases (ROCKs) affect a variety of cellular functions, including cell attachment, migration, and proliferation. ROCK inhibitors therefore have potential as tools for optimizing cell behavior in tissue engineering applications, including the manufacturing of cultivated epithelial autografts (CEAs) used in the treatment of burn patients. For example, ROCK inhibitors may facilitate earlier engraftment of CEA sheets by increasing the proliferation of skin keratinocytes ex vivo. Nevertheless, the current understanding of ROCK inhibitor action on epidermal keratinocytes is unclear owing to multiple drug formulations, drug concentrations, and cellular function assays having been used. The aim of this review article therefore is to identify consistent patterns of ROCK inhibitor action on human keratinocytes, as well as revealing key knowledge gaps. In doing so, we propose a clearer course of action for pursuing the potential benefits of ROCK inhibitors for the future treatment of burn patients. Impact statement The properties of Rho-associated protein kinase (ROCK) inhibitors are already used clinically within the fields of cardiology, neurology, and ophthalmology. These results encourage the broadening of ROCK inhibitor uses for other clinical applications. With respect to burn patients, ROCK inhibitors may facilitate improvements in patient survival and healing by reducing the time required for generating cultivated epithelial autograft (CEA) sheets from patient biopsies. Nevertheless, varying approaches to studying the effects of ROCK inhibitors on skin cells in vitro have complicated the development of improved protocols. Our review aims to clarify a diverse and growing body of literature as to the potential benefits for burn patients.

In the regenerative medicine and tissue engineering (TE) field, it is often an overlooked and challenging aspect to fathom how TE-related basic research could be translated to applied and translational research, ultimately aiming to develop and market a product or service. The aim of the present article is to investigate the patents in the field of TE and to look up relevant patents, type of applicants, and outcomes of relevant patents over the last 10 years. Besides referencing these patents, it was also the aim to collect data on companies related to the relevant patents to investigate the current commercial status of the product or service that the patent relates to.

GOAL: To develop a low-cost magnetic resonance imaging (MRI)-free transcranial focused ultrasound (FUS) system for microbubble-mediated therapy. METHODS: A 128-element 11 MHz array for skull localization was integrated within a 256-module multi-frequency (306/612/1224 kHz) dual-mode phased array. The system’s transcranial transmit and receive performance was evaluated with ex-vivo human skullcaps using phase aberration corrections calculated from computed tomography (CT)-based simulations via ultrasound-based (USCT) and landmark-based (LMCT) registrations, and a gold-standard fixed source emitter (FSE)-based method. RESULTS: Displacement and rotation registration errors of 1.4 ± 0.4 mm and 2.1 ± 0.2° were obtained using USCT, resulting in sub-millimeter transmit targeting errors driven at 306 kHz (0.9 ± 0.2 mm) and 612 kHz (0.9 ± 0.3 mm), and source localization errors of 1.0 ± 0.3 mm and 0.6 ± 0.2 mm at receive frequencies of 306 kHz and 612 kHz, respectively (mean ± SD). Similar errors were obtained using LMCT and no significant differences between these two approaches were found on either transmit (p = 0.64/0.99) or receive (p = 0.45/0.36) at 306 kHz/612kHz. During volumetric multi-point exposures, approximately 70% and 60% of the transmit frames in which microbubble activity was detected via FSE were recovered using USCT when imaging at the second-harmonic and half-harmonic, respectively, compared to 60% and 69% using LMCT. CONCLUSION: This low-cost ultrasound-guided transcranial FUS system affords USCT skull registration with accuracy comparable to LMCT methods. SIGNIFICANCE: Such systems have great potential to advance the adoption of microbubble-mediated FUS brain therapy by improving access to the technology.

Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs) while moving. The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that these JAEs are attributed to tribological origins. Methods: JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from 10 healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed with standard inverse dynamic analysis through musculoskeletal modeling, and a tribological parameter, lubrication coefficient, was calculated from these signals. Besides, JAEs were divided into short windows, and 64 time-frequency features were extracted. The lubrication coefficients and joint sound features of the two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features. Then, the classifier was used to predict the label of one-leg squat JAE windows. To evaluate these results, the predicted joint sound labels were directly compared against the associated lubrication coefficients. Results: The trained classifier achieves a high test-accuracy of 84% distinguishing lubrication modes of the one-leg squat JAE windows. The Pearson correlation coefficient between the estimated friction coefficient and the predicted JAE scores was 0.830.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, was calculated from joint sound labels, and it decreased by half from two-leg to one-leg squats. This result was consistent with the tribological changes in the knee load as it was inversely doubled in one-leg squats. These results verify that JAEs contain salient information on knee tribology. Significance: This study supports the potential use of JAEs as a quantitative digital biomarker to extract tribological information about joint lubrication modes and loading conditions. Since arthritis and many other conditions impact the roughness of cartilage and other surfaces within the knee, the use of JAEs in clinical applications could thereby have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.

Introduction Obstructive sleep apnea syndrome is a high-prevalence disorder found in the population. Studies have shown a possible association between nasal obstruction and obstructive sleep apnea syndrome, but the existence of a association between the degree of nasal obstruction and obstructive sleep apnea syndrome severity has not yet been proven. Objective To evaluate the internal nasal dimensions of adults with primary snoring and obstructive sleep apnea syndrome by acoustic rhinometry and to correlate the findings with obstructive sleep apnea severity. Methods Twenty-one male Caucasian subjects with complaints of snoring and/or respiratory pauses during sleep, aged between 18 and 60 years of age, were evaluated. After clinical evaluation, otorhinolaryngological examination and flexible nasopharyngolaryngoscopy, all patients underwent type III polysomnography. The participants were divided into two groups according to symptom severity: group 1, primary snoring and/or mild obstructive sleep apnea syndrome(n = 9) and group 2, moderate/severe obstructive sleep apnea syndrome (n = 12). Internal nasal dimensions were measured by acoustic rhinometry, analyzing minimum cross sectional area (CSA) and three nasal segment volumes. Results The respiratory event index corresponded to 8.1 ± 4.0 in group 1 and 47.5 ± 19.1 in group 2. In group 1, the cross-sectional areas values, in cm2, corresponded to: CSA 1 = 1.1 ± 0.4; CSA 2 = 2.1 ± 0.9; CSA 3 = 3.5 ± 1.8 and, in group 2: CSA 1 = 1.2 ± 0.3, CSA 2 = 2.0 ± 0.5; CSA 3 = 2.8 ± 0.7. In group 1, volumes (V), in cm3, corresponded to: V1 = 3.5 ± 1.0; V2 = 9.3 ± 5.0; V3 = 40.2 ± 21.5 and in group 2 a: V1 = 3.6 ± 0.5; V2 = 7.6 ± 1.5; V3 = 31.5 ± 6.7. Cross-sectional area and volume did not differ between groups. Conclusion There were no significant differences in the cross-sectional areas and nasal volumes between individuals with primary snoring-mild obstructive sleep apnea syndrome and moderate-severe obstructive sleep apnea syndrome. Differently to the raised hypothesis, our results suggest that there is no association between internal nasal dimensions and severity of obstructive sleep apnea syndrome. Resumo Introdução A síndrome da apneia obstrutiva do sono é uma desordem de elevada prevalência na população. Estudos constataram possível associação entre obstrução nasal e síndrome da apneia obstrutiva do sono, porém a existência de uma relação entre a intensidade da obstrução nasal e a gravidade da síndrome da apneia obstrutiva do sono ainda não foi comprovada. Objetivo Avaliar as dimensões internas nasais de adultos com ronco primário e síndrome da apneia obstrutiva do sono por meio de rinometria acústica e correlacionar os achados com a gravidade da síndrome da apneia obstrutiva do sono. Método Foram avaliados 21 indivíduos com queixas de ronco e/ou pausas respiratórias durante o sono, gênero masculino, entre 18 e 60 anos, brancos. Após avaliação clínica, exame físico otorrinolaringológico e nasofaringolaringoscopia flexível, todos foram submetidos à polissonografia tipo III. Os participantes foram divididos em dois grupos de acordo com a gravidade dos sintomas: grupo 1, ronco primário e/ou síndrome da apneia obstrutiva do sono leve (n = 9) e grupo 2, síndrome da apneia obstrutiva do sono moderada/grave (n = 12). Dimensões internas nasais foram aferidas por rinometria acústica, foram consideradas para análise as áreas de secção transversa mínima e os volumes de três diferentes segmentos nasais. Resultados O índice de eventos respiratórios correspondeu a 8,1 ± 4,0 no grupo 1 e 47,5 ± 19,1 no grupo 2. No grupo 1, os valores de área de secção transversa, em cm2, corresponderam a: área de secção transversa 1 = 1,1 ± 0,4; área de secção transversa 2 = 2,1 ± 0,9; área de secção transversa 3 = 3,5 ± 1,8. No grupo 2: área de secção transversa 1 = 1,2 ± 0,3, área de secção transversa 2 = 2,0 ± 0,5; áre de secção transversa 3 = 2,8 ± 0,7. No grupo 1 os valores do volume, em cm3, corresponderam a: volume 1 = 3,5 ± 1,0; volume 2 = 9,3 ± 5,0; volume 3 = 40,2 ± 21,5 e no grupo 2 a: volume 1 = 3,6 ± 0,5; V2 = 7,6 ± 1,5; volume 3 = 31,5 ± 6,7. Os valores de área de secção transversa e volume não diferiram entre os grupos. Conclusão Não foram demonstradas diferenças significantes quanto às áreas seccionais transversas e os volumes nasais entre indivíduos com ronco primário e síndrome da apneia obstrutiva do sono leve e síndrome da apneia obstrutiva do sono moderada-grave. Contrariamente à hipótese levantada, os resultados sugerem não existir relação entre as dimensões internas nasais e o nível de gravidade da síndrome da apneia obstrutiva do sono.

OBJECTIVE: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG potential over the parieto-occipital scalp area called the motion-onset visual evoked potential (mVEP). METHODS: We recruited 21 healthy subjects for an experiment in which motion-onset stimulations translating leftwards (LT) or rightwards (RT) were encoding 9 displayed targets. We propose a novel algorithm that exploits the phase-shift between EEG electrodes to improve target decoding performance. We hereto extend the spatiotemporal beamformer (stBF) with a phase extracting procedure, leading to the phase-spatial beamformer (psBF). RESULTS: we show that psBF performs significantly better than the stBF (p < 0.001 for 1 and 2 stimulus repetitions and p < 0.01 for 3 to 5 stimulus repetitions), as well as the previously validated linear support-vector machines (p < 0.001 for 5 stimulus repetitions and p < 0.01 for 1,2 and 6 stimulus repetitions) and stepwise linear discriminant analysis decoders (p < 0.001 for all repetitions) when simultaneously addressing timing and translation direction. CONCLUSION: We provide evidence of decodability of joint direction and target in mVEP responses. SIGNIFICANCE: the described methods can aid in the development of a faster and more comfortable BCI based on mVEPs. ispartof: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING vol:69 issue:5 pages:1802-1812 ispartof: location:United States status: published

It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.

It is well known that the biomechanical and tribological performance of articular cartilage is inextricably linked to its extracellular matrix (ECM) structure and zonal heterogeneity. Furthermore, it is understood that the presence of native ECM components, such as collagen II and aggrecan, promote healthy homeostasis in the resident chondrocytes. What is less frequently discussed is how chondrocyte metabolism is related to the extracellular mechanical environment, at both the macro and microscale. The chondrocyte is in immediate contact with the pericellular matrix of the chondron, which acts as a mechanocoupler, transmitting external applied loads from the ECM to the chondrocyte. Therefore, components of the pericellular matrix also play essential roles in chondrocyte mechanotransduction and metabolism. Recreating the biomechanical environment through tuning material properties of a scaffold and/or the use of external cyclic loading can induce biosynthetic responses in chondrocytes. Decellularized scaffolds, which retain the native tissue macro- and microstructure also represent an effective means of recapitulating such an environment. The use of such techniques in tissue engineering applications can ensure the regeneration of skeletally mature articular cartilage with appropriate biomechanical and tribological properties to restore joint function. Despite the pivotal role in graft maturation and performance, biomechanical and tribological properties of such interventions is often underrepresented. This review outlines the role of biomechanics in relation to native cartilage performance and chondrocyte metabolism, and how application of this theory can enhance the future development and successful translation of biomechanically relevant tissue engineering interventions. Impact statement Physiological cartilage function is a key criterion in the success of a cartilage tissue engineering solution. The

Evaporation of phosphate species during thermal treatment (> 400 °C) of calcium phosphates leads to the formation of an alkaline layer on their surface. The aim of this study was to evaluate the hypothesis that the biological response of thermally treated calcium phosphates is modified by the presence of such an alkaline layer on their surface. For this purpose, 0.125-0.180 mm α- and β-tricalcium phosphate (TCP) granules were obtained by crushing and size classification, with some being subjected to thermal treatment at 500 °C. The four types of granules (α-TCP, β-TCP, α-TCP-500 °C, and β-TCP-500 °C) were implanted subcutaneously and orthotopically in rats. Sham operations served as control. Subcutaneously, α-TCP and β-TCP induced significantly more multinucleated giant cells (MNGCs) than calcined granules. Most of the induced MNGCs were TRAP-negative, CD-68 positive and cathepsin K-negative, reflecting a typical indication of a reaction with a foreign body. The vessel density was significantly higher in the α-TCP and β-TCP groups than it was in the α-TCP-500 °C and β-TCP-500 °C groups. In the femur model, β-TCP-500 °C induced significantly more new bone formation than that induced by β-TCP. The granule size was also significantly larger in the β-TCP-500 °C group, making it more resistant to degradation than β-TCP. The MNGC density was higher in the α-TCP and β-TCP groups than in the α-TCP-500 °C and β-TCP-500 °C groups, including cathepsin-positive, CD-68 positive, TRAP-positive and TRAP-negative MNGCs. In conclusion, this study confirms that the biological response of calcium phosphates was affected by the presence of an alkaline layer on their surface. Thermally-treated α-TCP and β-TCP granules produced significantly fewer MNGCs and were significantly less degraded than non-thermally-treated α-TCP and β-TCP granules. Thermally treating α-TCP and β-TCP granules shifts the reaction from a foreign body reaction towards a physiological reaction by downregulating the number of induced MNGCs and enhancing degradation resistance.

Antioxidant proteins have been discovered closely associated with disease control due to its capability to eradicate excess free radicals. The accurate identification of antioxidant proteins is on the upsurge owing to their therapeutic significance. However, observing the rapid increases of this toxic disease in the human body, several machine learning algorithms have been applied and performed inadequately to identify antioxidant proteins. Therefore, measuring the effectiveness of antioxidant proteins on the human body, a reliable intelligent model is indispensable for the researchers. In this study, primary protein sequences are formulated using evolutionary and sequence-based numerical descriptors. Whereas, evolutionary features are collected using a bigram Position-specific scoring matrix, besides, K-space amino acid pair (KSAAP) and dipeptide composition are utilized to extract sequential information. Furthermore, in order to reduce the computational time and to eradicate irreverent and noisy features, the Sequential forward selection and Support vector machine (SFS-SVM) based ensemble approach is applied to select optimal features. At last, several distinct nature classification learning methods are applied to choose a suitable operational engine for our model. After evaluating the empirical results, SVM using optimal features achieved an accuracy of 97.54%, 93.71% using the training and independent dataset, respectively. It was found that our proposed model outperformed and reported the highest performance than the existing computational models. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. The source code and all datasets are publicly available at https://github.com/salman-khan-mrd/Antioxident_proteins .

Graphical abstract, Highlights • COVID-19 reveals radiological signatures that can be detected using chest X-rays. • The evaluation of radiological signatures is a time-consuming and error-prone task. • Therefore, there is a need to automate the analysis of chest X-rays. • An automatic analysis of chest X-rays is achieved using deep learning models., The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.

Craniofacial tissue defects caused by trauma, developmental malformation, or surgery are critical issues of high incidence, which are harmful to physical and psychological health. Transplantation of engineered tissues or biomaterials is a potential method to repair defects and regenerate the craniofacial tissues. Revascularization is essential to ensure the survival and regeneration of the grafts. Since microvessels play a critical role in blood circulation and substance exchange, the pre-establishment of the microvascular network in transplants provides a technical basis for the successful regeneration of the tissue defect. In this study, we reviewed the recent development of strategies and applications of prevascularization in tissue engineering and regeneration of craniofacial tissues. We focused on the cellular foundation of the

Background and Objective As is known, point clouds representing the objects are frequently used in object registration. Although the objects can be registered by using all the points in the corresponding point clouds of the objects, the registration process can also be achieved with a smaller number of the landmark points selected from the entire point clouds of the objects. This paper introduces a research study focusing on the fast and accurate rigid registration of the bilateral proximal femurs in bilateral hip joint images by using the random sub-sample points. For this purpose, Random Point Sub-sampling (RPS) was analyzed and the reduced point sets were used for an accurate registration of the bilateral proximal femurs in coronal hip joint magnetic resonance imaging (MRI) slices. Methods In registration, bilateral proximal femurs in MRI slices were registered rigidly by performing a process consisting of three main phases named as MR image preprocessing, proximal femur registration over the random sub-sample points and MR image postprocessing. In the stage of the MR image preprocessing, segmentation maps of the bilateral proximal femurs are obtained as region of interest (RoI) images from the entire MRI slices and then, the edge maps of the segmented proximal femurs are extracted. In the registration phase, the edge maps describing the proximal femur surfaces are represented as point clouds initially. Thereafter, the RPS is performed on the proximal femur point clouds and the number of points representing the proximal femurs is reduced at different ratios. For the registration of the point clouds, the Iterative Closest Point (ICP) algorithm is performed on the reduced sets of points. Finally, the registration procedures are completed by performing MR image postprocessing on the registered proximal femur images. Results In performance evaluation tests performed on healthy and pathological proximal femurs in 13 bilateral coronal hip joint MRI slices of 13 Legg-Calve-Perthes disease (LCPD) patients, bilateral proximal femurs were successfully registered with very small error rates by using the reduced set of points obtained via the RPS and promising results were achieved. The minimum error rate was observed at RPS rate of 30% as the value of 0.41 (±0.31)% on all over the bilateral proximal femurs evaluated. When the range of RPS rate of 20-30% is considered as the reference, the elapsed time in registration can be reduced by almost 30-40% compared to the case where all the proximal femur points were included in registration. Additionally, it was observed that the RPS rate should be selected as at least 25% to achieve a successful registration with an error rate below 1%. Conclusion It was concluded from the observed results that a more successful and faster registration can be accomplished by selecting fewer points randomly from the point sets of proximal femurs instead of using all the points describing the proximal femurs. Not only an accurate registration with low error rates was performed, but also a faster registration process was performed by means of the limited number of points that are sub-sampled randomly from the whole point sets.

Background and objective An important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot. Methods In order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically. Results The experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs. Conclusions Further analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.

Treatment for children born with vaginal agenesis remains difficult, without a clear gold standard for tissue replacement. An autologous-engineered vaginal replacement would significantly improve quality of life for people born with this condition. The aim of this study was to critically review literature on the current state of tissue engineering for vaginal reconstruction in a pediatric population. An electronic literature search was conducted using PubMed for articles describing pediatric vaginal tissue engineering from January 2003 to December 2020. Nine studies met inclusion criteria and were reviewed. The model, methods, cell type and source, scaffold type, and time of analysis and evaluation were compared. Three studies used

This study tested whether osseous integration into poly (ε-caprolactone) (PCL) bioplastic scaffolds with fully-interconnecting 155 ± 8 μm pores is enhanced by an adhesive, non-inflammatory 99% degree of deacetylation (DDA) chitosan coating (99-PCL), or further incorporation of pro-inflammatory 83% DDA chitosan microparticles (83-99-PCL) to accelerate angiogenesis. New Zealand White rabbit osteochondral knee defects were press-fit with PCL, 99-PCL, 83-99-PCL, or allowed to bleed (drill-only). Between day 1 and 6 weeks of repair, drill-only defects repaired by endochondral ossification, with an 8-fold higher bone volume fraction (BVF) versus initial defects, compared to a 2-fold (99-PCL), 1.1-fold (PCL), or 0.4-fold (83-99-PCL) change in BVF. Hematoma innate immune cells swarmed to 83-99-PCL, elicited angiogenesis throughout the pores and induced slight bone resorption. PCL and 99-PCL pores were variably filled with cartilage or avascular mesenchyme near the bone plate, or angiogenic mesenchyme into which repairing trabecular bone infiltrated up to 1 mm deep. More repair cartilage covered the 99-PCL scaffold (65%) than PCL (18%) or 83-99-PCL (0%) (p < 0.005). We report the novel finding that non-inflammatory chitosan coatings promoted cartilage infiltration into and over a bioplastic scaffold, and were compatible with trabecular bone integration. This study also revealed that in vitro osteogenesis assays have limited ability to predict osseous integration into porous scaffolds, because (1) in vivo, woven bone integrates from the leading edge of regenerating trabecular bone and not from mesenchymal cells adhering to scaffold surfaces, and (2) bioactive coatings that attract inflammatory cells induce bone resorption., Graphical abstract Image 1, Highlights • Porous polycaprolactone scaffolds elicited drawn-out osteochondral wound repair. • Regenerating trabecular bone only infiltrated angiogenic mesenchyme free of inflammatory cells. • 83% DDA chitosan stimulated sterile inflammatory angiogenesis and trabecular bone resorption. • 99% DDA chitosan coatings promoted chondrogenesis inside and over the PCL articular surface.

The main focus of the paper is to propose an artificial immune systems-based classification model for code-mixed social media data. The artificial immune systems are computational models inspired by the biological immune system. In this paper, artificial immune systems are used to optimize the initial parameters of Long short-term memory (LSTM) model. The proposed artificial immune systems-based LSTM model is then used for the classification of code-mixed data. The classification of Hindi-English code-mixed data into Hindi, English, and ambiguous words is done. Popular word embedding features were used for the representation of each word. The word embedding features and character embedding features have been used. The proposed method helps in identifying the word context by extracting the intent of user for using the ambiguous word in code-mixed sentence. Extensive experiments reveal that the artificial immune systems-based classification model outperforms competitive models especially when there are some ambiguous words in the social media data.

Injury to the facial nerve can occur after different etiologies and range from simple transection of the branches to varying degrees of segmental loss. Management depends on the extent of injury and options include primary repair for simple transections and using autografts, allografts, or conduits for larger gaps. Tissue engineering plays an important role to create artificial materials that are able to mimic the nerve itself without extra morbidity in the patients. The use of neurotrophic factors or stem cells inside the conduits or around the repair site is being increasingly studied to enhance neural recovery to a greater extent. Preclinical studies remain the hallmark for development of these novel approaches and translation into clinical practice. This review will focus on preclinical models of repair after facial nerve injury to help researchers establish an appropriate model to quantify recovery and analyze functional outcomes. Different bioengineered materials, including conduits and nerve grafts, will be discussed based on the experimental animals that were used and the defects introduced. Future directions to extend the applications of processed nerve allografts, bioengineered conduits, and cues inside the conduits to induce neural recovery after facial nerve injury will be highlighted. IMPACT STATEMENT: Recovery after facial nerve injury is a complex process, which involves different management options such as primary repair or the use of nerve grafts or conduits. Various tissue-engineered approaches are increasingly studied on preclinical models with limited, but promising, translation to the clinical setting. Herein, preclinical models focusing on different recovery methods after facial nerve injury are comprehensively reviewed based on the experimental animals used. The review provides key insights into current developments and future directions on this highly relevant topic to help researchers further expand the field of tissue engineering and facial nerve recovery.

Objectives This study aimed to evaluate and compare the stress distribution and primary stability of three different designs of dental implants. Material and Methods 24 implants Neodent® were used ( n = 8 ): G1 – Alvim CM; G2 – Drive CM; G3 – Facility, submitted to insertion torque (IT) and pullout test, on 20 PCF (0.24 g/cm3) and 40 PCF (0.64 g/cm3) polyurethane blocks. For the stress distribution, by means of photoelasticity, axial and oblique loads (model at 30° inclination) of 100 N were performed, for reading and quantifying the fringe orders. According to the distribution data, a parametric or non-parametric analysis was performed ( α = 0.05 ). Results The IT was lower in G3 ( p 0.05 ) compared to G1 and G2, in 20 and 40 PCF polyurethanes. In the pullout, no difference ( p > 0.05 ) was observed between G1 and G2, in both 20 and 40 PCF polyurethanes. In the comparisons between polyurethanes, higher values ( p 0.05 ) were obtained in the 40 PCF for IT and pullout. In the axial loading, lower stresses were observed in the cervical third and higher stresses in the middle and apical third of the implants. With the oblique inclination of the models, higher stresses were generated in the opposite side of the load, in the cervical third of G1, followed by G3 and G2. Conclusion The results allow to affirm that G1 and G2 exhibit high primary stability and satisfactory stress distribution. Although G3 generates stresses comparable to other implants, its indication is limited in low density bones.

BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 [Formula: see text] N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.

Egocentric augmented reality (AR) interfaces are quickly becoming a key asset for assisting high precision activities in the peripersonal space in several application fields. In these applications, accurate and robust registration of computer-generated information to the real scene is hard to achieve with traditional Optical See-Through (OST) displays given that it relies on the accurate calibration of the combined eye-display projection model. The calibration is required to efficiently estimate the projection parameters of the pinhole model that encapsulate the optical features of the display and whose values vary according to the position of the user's eye. In this article, we describe an approach that prevents any parallax-related AR misregistration at a pre-defined working distance in OST displays with infinity focus; our strategy relies on the use of a magnifier placed in front of the OST display, and features a proper parameterization of the virtual rendering camera achieved through a dedicated calibration procedure that accounts for the contribution of the magnifier. We model the registration error due to the viewpoint parallax outside the ideal working distance. Finally, we validate our strategy on a OST display, and we show that sub-millimetric registration accuracy can be achieved for working distances of ±100 mm around the focal length of the magnifier.

Macrophages and osteoclasts are both derived from monocyte/macrophage lineage, which plays as the osteoclastic part of bone metabolism. Although they are regulated by bone implant surface nanoarchitecture and involved in osseointegration, the beneath mechanism has not been simultaneously analyzed in a given surface model and their communication with osteoblasts is also blurring. Here, the effect of implant surface topography on monocyte/macrophage lineage osteoclastogenesis and the subsequent effect on osteogenesis are systematically investigated. The nanoporous surface is fabricated on titanium implant by etching and anodizing to get the nanotubes structure. The early bone formation around implant is significantly accelerated by the nanoporous surface in vivo. Meanwhile, the macrophage recruitment and osteoclast formation are increased and decreased respectively. Mechanistically, the integrin mediated FAK phosphorylation and its downstream MAPK pathway (p-p38) are significantly downregulated by the nanoporous surface, which account for the inhibition of osteoclastogenesis. In addition, the nanoporous surface can alleviate the inhibition of osteoclasts on osteogenesis by changing the secretion of clastokines, and accelerate bone regeneration by macrophage cytokine profiles. In conclusion, these data indicate that physical topography of implant surface is a critical factor modulating monocyte/macrophage lineage commitment, which provides theoretical guidance and mechanism basis for promoting osseointegration by coupling the osteogenesis and osteoclastogenesis., Graphical abstract This paper investigates the modulation of osteogenesis by monocyte/macrophage lineage on titanium surface nanoarchitecture. The nanoporous surface promotes bone regeneration by changing the secretion profiles of macrophage (Mϕ) cytokines and clastokines. Mechanistically, the nanoporous surface inhibit the differentiation of macrophages into osteoclasts by blocking integrin mediated FAK phosphorylation and downstream MAPK pathway.Image 1, Highlights • Nanoporous implant inhibits osteoclastogenesis via integrin β1/FAKpY397/MAPK. • Nanoporous implant with larger diameter inhibits osteoclastogenesis more strongly. • Nanoporous implant increases osteogenic cytokines of macrophages/osteoclasts.