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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Killing bacteria, eliminating biofilm and building soft tissue integration are very important for percutaneous implants which service in a complicated environment. In order to endow Ti implants with above abilities, multifunctional coatings consisted of Fe2O3–FeOOH nanograins as an outer layer and Zn doped microporous TiO2 as an inner layer were fabricated by micro-arc oxidation, hydrothermal treatment and annealing treatment. The microstructures, physicochemical properties and photothermal response of the coatings were observed; their antibacterial efficiencies and cell response in vitro as well as biofilm elimination and soft tissue integration in vivo were evaluated. The results show that with the increased annealing temperature, coating morphologies didn't change obviously, but lattices of β-FeOOH gradually disorganized into amorphous state and rearranged to form Fe2O3. The coating annealed at 450 °C (MA450) had nanocrystallized Fe2O3 and β-FeOOH. With a proper NIR irradiation strategy, MA450 killed adhered bacteria efficiently and increased fibroblast behaviors via up-regulating fibrogenic-related genes in vitro; in an infected model, MA450 eliminated biofilm, reduced inflammatory response and improved biointegration with soft tissue. The good performance of MA450 was due to a synergic effect of photothermal response and released ions (Zn2+ and Fe3+).

Tissue engineered cardiac patches have great potential as a therapeutic treatment for myocardial infarction (MI). However, for successful integration with the native tissue and proper function of the cells comprising the patch, it is crucial for these patches to mimic the ordered structure of the native extracellular matrix and the electroconductivity of the human heart. In this study, a new composite construct that can provide both conductive and topographical cues for human induced pluripotent stem cell derived cardiomyocytes (iCMs) is developed for cardiac tissue engineering applications. The constructs are fabricated by 3D printing conductive titanium carbide (Ti3C2Tx) MXene in pre-designed patterns on polyethylene glycol (PEG) hydrogels, using aerosol jet printing, at a cell-level resolution and then seeded with iCMs and cultured for one week with no signs of cytotoxicity. The results presented in this work illustrate the vital role of 3D-printed Ti3C2Tx MXene on aligning iCMs with a significant increase in MYH7, SERCA2, and TNNT2 expressions, and with an improved synchronous beating as well as conduction velocity. This study demonstrates that 3D printed Ti3C2Tx MXene can potentially be used to create physiologically relevant cardiac patches for the treatment of MI.

Sulfated glycosaminoglycans (sGAG) show interaction with biological mediator proteins. Although collagen-based biomaterials are widely used in clinics, their combination with high-sulfated hyaluronan (sHA3) is unexplored. This study aims to functionalize a collagen-based scaffold (Mucograft®) with sHA3 via electrostatic (sHA3/PBS) or covalent binding to collagen fibrils (sHA3+EDC/NHS). Crosslinking without sHA3 was used as a control (EDC/NHS Ctrl). The properties of the sHA3-functionalized materials were characterized. In vitro growth factor and cytokine release after culturing with liquid platelet-rich fibrin was performed by means of ELISA. The cellular reaction to the biomaterials was analyzed in a subcutaneous rat model. The study revealed that covalent linking of sHA3 to collagen allowed only a marginal release of sHA3 over 28 days in contrast to electrostatically bound sHA3. sHA3+EDC/NHS scaffolds showed reduced vascular endothelial growth factor (VEGF), transforming growth factor beta 1 (TGF-β1) and enhanced interleukin-8 (IL-8) and epithelial growth factor (EGF) release in vitro compared to the other scaffolds. Both sHA3/PBS and EDC/NHS Ctrl scaffolds showed a high proinflammatory reaction (M1: CD-68+/CCR7+) and induced multinucleated giant cell (MNGC) formation in vivo. Only sHA3+EDC/NHS scaffolds reduced the proinflammatory macrophage M1 response and did not induce MNGC formation during the 30 days. SHA3+EDC/NHS scaffolds had a stable structure in vivo and showed sufficient integration into the implantation region after 30 days, whereas EDC/NHS Ctrl scaffolds underwent marked disintegration and lost their initial structure. In summary, functionalized collagen (sHA3+EDC/NHS) modulates the inflammatory response and is a promising biomaterial as a stable scaffold for full-thickness skin regeneration in the future., Graphical abstract Image 1, Highlights • Covalent linking of high-sulfated hyaluronan (sHA3) to collagen allows a sustained release of sHA3. • Covalent linking of sHA3 to collagen modulates the release of growth factor and cytokines in vitro. • Covalent linking of sHA3 to collagen suppresses the induction of multinucleated giant cells in vivo. • Covalent linking of sHA3 to collagen reduces the proinflammatory macrophage M1 response in vivo. • Functionalized collagen with sHA3 is promising for full-thickness skin regeneration.

Purpose Scanning switch keypads are commonly used as augmentative and alternative communication aids for people with complex communication needs. Low communication rates and high error rates are some of the problems that users of scanned keyboards encounter in daily use, which could lead to the abandonment of this type of technology. Thus, this study presents a new configuration system for scanned keyboards using visual feedback recovery time to help the user have a better performance and experience when using this type of alternative communication. Methods The system was developed in C#. The control switch is realized by means of a wireless USB mouse button. In order to evaluate the system, 42 participants transcribed sentences using a system based on our recovery delay approach, as well as a second time-delay-free scanning system for comparison purposes. We analyzed the input rate, success rate, and probability of error occurrence. These data were used to compare the performance of the two types of systems. Results The results showed that synchronization and selection errors were lower in the group that used the system with a recovery delay than in the group that used the layout without. Conclusion The error reduction provided by the recovery delay system, allows for a more stable and enjoyable user experience when using scanned keyboards to communicate. This contribution could improve the use of augmentative and alternative communication devices and therefore contribute to reducing the abandonment of this type of technology.

Preclinical studies involving stem cells require efficient physiochemical regulations on the fate of such cells. Because of their unique planar structure, metallic conductivity, and flexible surface functionalization, MXenes show potential for modulating stem cell fate. Here, the Ti3C2TxMXenenanosheets are dispersed on tissue culture polystyrene (TCPS). When primary mouse neural stem cells (NSCs) are cultured on laminin-coated Ti3C2TxMXene film, they form stable adhesion, retain their proliferative ability, and show extensive spreading of terminal extensions. With respect to their functional activity, NSCs cultured on Ti3C2TxMXene films form more active and synchronous network activity than those cultured on TCPS substrates. Moreover, Ti3C2TxMXene film significantly promotes the neural differentiation and the neurons have longer neurites and greater numbers of branch points and branch tips. NSC-derived neurons grown on the Ti3C2Tx MXene film preserved normal synapse development. Finally, electrical stimulation coupled with Ti3C2TxMXene film significantly enhances the proliferation of NSCs. These results indicate that Ti3C2TxMXene is an efficient interface for the proliferation and neural differentiation of NSC and the maturation of NSC-derived neurons, which expands the potential uses of the MXene family of materials and provides new strategies for stem cell studies.

Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.

Background The prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes. Dataset We used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCA Methods In this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance. Results The results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.

We demonstrate a simple, effective and feasible method to address the shrinkage of Poly (lactic-co-glycolic acid) (PLGA) through a core-shell structure fiber strategy. The results revealed that introducing size-stable poly-caprolactone (PCL) as the core fiber significantly improved the PLGA-based fibrous scaffold's dimensional maintenance. We further utilized fish collagen to modify the PLGA shell layer (PFC) of coaxial fibers and loaded baicalin (BA) into the PCL core layer (PCL-BA) to endow fibrous scaffold with more functional biological cues. The PFC/PCL-BA fibrous scaffold promoted the osteogenic differentiation of bone mesenchymal stem cells and stimulated the RAW264.7 cells to polarize into a pro-reparative phenotype. Importantly, the in vivo study demonstrated that the PFC/PCL-BA scaffold could regulate inflammation and osteoclast differentiation, favor neovascularization and bone formation. This work tactfully combined PLGA and PCL to establish a drug release platform based on the core-shell fibrous scaffold for vascularized bone regeneration., Graphical abstract A multifunctional baicalin-loaded coaxial nanofiber scaffold prepared by electrospinning technology, which can effectively resist the shrinkage of PLGA, alleviate the inflammatory response after implantation, and more importantly, can achieve vascularized bone regeneration. This is a simple and feasible strategy for bone tissue regeneration, and the PFC/PCL-BA scaffold is expected to be an alternative for bone grafts.Image 1, Highlights • A multifunctional baicalin-loaded coaxial fiber scaffold prepared by electrospinning. • The coaxial nanofiber can effectively resist the shrinkage of PLGA. • Baicalin endow the nanofibrous scaffold with excellent biological properties. • The scaffold can alleviate the inflammation and achieve vascularized bone regeneration.

"Learning from nature" provides endless inspiration for scientists to invent new materials and devices. Here, we review state-of-the-art technologies in flexible electronics, with a focus on bio-inspired smart skins. This review focuses on the development of E-skin for sensing a variety of parameters such as mechanical loads, temperature, light, and biochemical cues, with a trend of increased integration of multiple functions. It highlights the most recent advances in flexible electronics inspired by animals such as chameleons, squids, and octopi whose bodies have remarkable camouflage, mimicry, or self-healing attributes. Implantable devices, being overlapped with smart E-skin in a broad sense, are included in this review. This review outlines the remaining challenges in flexible electronics and the prospects for future development for biomedical applications. STATEMENT OF SIGNIFICANCE: This article reviews the state-of-the-art technologies of bio-inspired smart electronic skin (E-skin) developed in a "learning-mimicking-creating" (LMC) cycle. We emphasize the most recent innovations in the development of E-skin for sensing physical changes and biochemical cues, and for integrating multiple sensing modalities. We discuss the achievements in implantable materials, wireless communication, and device design pertaining to implantable flexible electronics. This review will provide prospective insights integrating material, electronics, and mechanical engineering viewpoints to foster new ideas for next-generation smart E-skin.

Objectives This study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF. Materials and Methods This assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted. Results Among the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups. Conclusion The combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder.

Glioblastoma multiforme (GBM), also known as grade IV astrocytoma, represents the most aggressive primary brain tumor. The complex genetic heterogeneity, the acquired drug resistance, and the presence of the blood-brain barrier (BBB) limit the efficacy of the current therapies, with effectiveness demonstrated only in a small subset of patients. To overcome these issues, here we propose an anticancer approach based on ultrasound-responsive drug-loaded organic piezoelectric nanoparticles. This anticancer nanoplatform consists of nutlin-3a-loaded ApoE-functionalized P(VDF-TrFE) nanoparticles, that can be remotely activated with ultrasound-based mechanical stimulations to induce drug release and to locally deliver anticancer electric cues. The combination of chemotherapy treatment with chronic piezoelectric stimulation resulted in activation of cell apoptosis and anti-proliferation pathways, induction of cell necrosis, inhibition of cancer migration, and reduction of cell invasiveness in drug-resistant GBM cells. Obtained results pave the way for the use of innovative multifunctional nanomaterials in less invasive and more focused anticancer treatments, able to reduce drug resistance in GBM. Statement of significance Piezoelectric hybrid lipid-polymeric nanoparticles, efficiently encapsulating a non-genotoxic drug (nutlin-3a) and functionalized with a peptide (ApoE) that enhances their passage through the BBB, are proposed. Upon ultrasound stimulation, nanovectors resulted able to reduce cell migration, actin polymerization, and invasion ability of glioma cells, while fostering apoptotic and necrotic events. This wireless activation of anticancer action paves the way to a less invasive, more focused and efficient therapeutic strategy.

Vascular calcification (VC) is a common characteristic of aging, diabetes, chronic renal failure, and atherosclerosis. The basic component of VC is hydroxyapatite (HAp). Nano-sized HAp (nHAp) has been identified to play an essential role in the development of pathological calcification of vasculature. However, whether nHAp can induce calcification in vivo and the mechanism of nHAp in the progression of VC remains unclear. We discovered that nHAp existed both in vascular smooth muscle cells (VSMCs) and their extracellular matrix (ECM) in the calcified arteries from patients. Synthetic nHAp had similar morphological and chemical properties as natural nHAp recovered from calcified artery. nHAp stimulated osteogenic differentiation and accelerated mineralization of VSMCs in vitro. Synthetic nHAp could also directly induce VC in vivo. Mechanistically, nHAp was internalized into lysosome, which impaired lysosome vacuolar H+-ATPase for its acidification, therefore blocked autophagic flux in VSMCs. Lysosomal re-acidification by cyclic-3′,5′-adenosine monophosphate (cAMP) significantly enhanced autophagic degradation and attenuated nHAp-induced calcification. The accumulated autophagosomes and autolysosomes were converted into calcium-containing exosomes which were secreted into ECM and accelerated vascular calcium deposit. Inhibition of exosome release in VSMCs decreased calcium deposition. Altogether, our results demonstrated a repressive effect of nHAp on lysosomal acidification, which inhibited autophagic degradation and promoted a conversion of the accumulated autophagic vacuoles into exosomes that were loaded with undissolved nHAp, Ca2+, Pi and ALP. These exosomes bud off the plasma membrane, deposit within ECM, and form calcium nodules. Vascular calcification was thus accelerated by nHAP through blockage of autophagic flux in VSMCs., Graphical abstract The basic component of vascular calcification is hydroxyapatite (HAp). Nano-sized HAp (nHAp, nano-HAp) has been identified to play a role in development of vascular calcification. Nano-HAp was found in the calcified vasculature. Here we demonstrate the possible roles of nano-HAp in vascular calcification. When nHAp was endocytosed by VSMCs and located in early endosomes, osteogenic trans-differentiation was enhanced with more Runx2 and ALP expression. Lysosomal vacuolar H+-ATPase was damaged by nHAp when endosomes fused with lysosomes, therefore lysosomal acidification was impaired. However, nHAp did not affect autophagosome-lysosome fusion. Such formed autolysosome could not be degraded. The blockage of autophagic flux promoted a conversion of the accumulated autophagic vacuoles into exosomes that were loaded with undissolved nHAp, Ca2+, Pi and ALP. These exosomes buded off the plasma membrane, deposited within ECM, and formed calcium nodules. Vascular calcification was thus accelerated by nHAp through blockage of autophagic flux in VSMCs.Image 1, Highlights • We first demonstrated that nHAp was internalized into the vascular cell in human calcified aorta. • Nano-HAp impairs lysosomal acidification and degradation, and causesblockage of autophagy flux in VSMCs. • The accumulated autophagosomes and autolysosomes induced by nHAp in VSMCs are converted into exosomes which promote calcification development.

Myocardial infarction (MI) is one of the fatal diseases in humans. Its incidence is constantly increasing annually all over the world. The problem is accompanied by the limited regenerative capacity of cardiomyocytes, yielding fibrous scar tissue formation. The propagation of electrical impulses in such tissue is severely hampered, negatively influencing the normal heart pumping function. Thus, reconstruction of the internal cardiac electrical connection is currently a major concern of myocardial repair. Conductive biomaterials with or without cell loading were extensively investigated to address this problem. This article introduces a detailed overview of the recent progress in conductive biomaterials and fabrication methods of conductive scaffolds for cardiac repair. After that, the advances in myocardial tissue construction in vitro by the restoration of intercellular communication and simulation of the dynamic electrophysiological environment are systematically reviewed. Furthermore, the latest trend in the study of cardiac repair in vivo using various conductive patches is summarized. Finally, we discuss the achievements and shortcomings of the existing conductive biomaterials and the properties of an ideal conductive patch for myocardial repair. We hope this review will help readers understand the importance and usefulness of conductive biomaterials in cardiac repair and inspire researchers to design and develop new conductive patches to meet the clinical requirements. Statement of significance After myocardial infarction, the infarcted myocardial area is gradually replaced by heterogeneous fibrous tissue with inferior conduction properties, resulting in arrhythmia and heart remodeling. Conductive biomaterials have been extensively adopted to solve the problem. Summarizing the relevant literature, this review presents an overview of the types and fabrication methods of conductive biomaterials, and focally discusses the recent advances in myocardial tissue construction in vitro and myocardial repair in vivo, which is rarely covered in previous reviews. As well, the deficiencies of the existing conductive patches and their construction strategies for myocardial repair are discussed as well as the improving directions. Confidently, the readers of this review would appreciate advantages and current limitations of conductive biomaterials/patches in cardiac repair.

Although with the good biological properties, silk fibroin (SF) is immensely restrained in long-distance vascular defect repair due to its relatively fast degradation and inferior mechanical properties. It is necessary to construct a multifunctional composite scaffold based on SF. In this study, a novel magnetic SF scaffold (MSFCs) was prepared by an improved infiltration method. Compared with SF scaffold (SFC), MSFCs were found to have better crystallinity, magnetocaloric properties, and mechanical strength, which was ascribed to the rational introduction of iron-based magnetic nanoparticles (MNPs). Moreover, in vivo and in vitro experiments demonstrated that the degradation of MSFCs was significantly extended. The mechanism of delayed degradation was correlated with the dual effect that was the newly formed hydrogen bonds between SFC and MNPs and the complexing to tyrosine (Try) to inhibit hydrolase by internal iron atoms. Besides, the β-crystallization of protein in MSFCs was increased with the rise of iron concentration, proving the beneficial effect after MNPS doped. Furthermore, although macrophages could phagocytose the released MNPs, it did not affect their function, and even a reasonable level might cause some cytokines to be upregulated. Finally, in vitro and in vivo studies demonstrated that MSFCs showed excellent biocompatibility and the growth promotion effect on CD34-labeled vascular endothelial cells (VECs). In conclusion, we confirm that the doping of MNPs can significantly reduce the degradation of SFC and thus provide an innovative perspective of multifunctional biocomposites for tissue engineering., Graphical abstract SFC's degradation mechanism: Tyrosine binding SFC for degradation and the hydrogen bonding interaction between SFC and MNPs/iron atoms competes for the active site of the protease (tyrosine) so that the two cooperate to delay the degradation of SFC.Image 1, Highlights • A high-performance magnetic composite scaffold (MSFC) for vascular repair was successfully prepared. • The mechanical strength of the MSFC was improved, and the degradation of the MSFC was delayed. • The mechanism of the delayed degradation was explained. • The phagocytosis of MNPs released by MSFC could promote macrophages to secrete cytokines that help vascular repair. • The MSFC could significantly promote the growth of VECs, showing the potential for long-distance vascular repair.

The combination of tumor ablation and immunotherapy is a promising strategy against tumor relapse and metastasis. Photothermal therapy (PTT) triggers the release of tumor-specific antigens and damage associated molecular patterns (DAMPs) in-situ. However, the immunosuppressive tumor microenvironment restrains the activity of the effector immune cells. Therefore, systematic immunomodulation is critical to stimulate the tumor microenvironment and augment the anti-tumor therapeutic effect. To this end, polyethylene glycol (PEG)-stabilized platinum (Pt) nanoparticles (Pt NPs) conjugated with a PD-L1 inhibitor (BMS-1) through a thermo-sensitive linkage were constructed. Upon near-infrared (NIR) exposure, BMS-1 was released and maleimide (Mal) was exposed on the surface of Pt NPs, which captured the antigens released from the ablated tumor cells, resulting in the enhanced antigen internalization and presentation. In addition, the Pt NPs acted as immune adjuvants by stimulating dendritic cells (DCs) maturation. Furthermore, BMS-1 relieved T cell exhaustion and induced the infiltration of effector T cells into the tumor tissues. Thus, Pt NPs can ablate tumors through PTT, and augment the anti-tumor immune response through enhanced antigen presentation and T cells infiltration, thereby preventing tumor relapse and metastasis.

Thanks to their biocompatibility, biodegradability, injectability and self-setting properties, calcium phosphate cements (CPCs) have been the most economical and effective biomaterials of choice for use as bone void fillers. They have also been extensively used as drug delivery carriers owing to their ability to provide for a steady release of various organic molecules aiding the regeneration of defective bone, including primarily antibiotics and growth factors. This review provides a systematic compilation of studies that reported on the controlled release of drugs from CPCs in the last 25 years. The chemical, compositional and microstructural characteristics of these systems through which the control of the release rates and mechanisms could be achieved have been discussed. In doing so, the effects of (i) the chemistry of the matrix, (ii) porosity, (iii) additives, (iv) drug types, (v) drug concentrations, (vi) drug loading methods and (vii) release media have been distinguished and discussed individually. Kinetic specificities of in vivo release of drugs from CPCs have been reviewed, too. Understanding the kinetic and mechanistic correlations between the CPC properties and the drug release is a prerequisite for the design of bone void fillers with drug release profiles precisely tailored to the application area and the clinical picture. The goal of this review has been to shed light on these fundamental correlations., Graphical abstract Image 1, Highlights • Drug release from calcium phosphate cements (CPCs) for bone healing and regeneration. • Release of antibiotics, anti-inflammatories, chemotherapeutics, growth factors, and metallic ions. • Influence of different CPC formulations, matrix porosity, structure, and crystallinity. • Influence of matrix additives and the release medium. • Influence of the drug type, concentration and loading method.

Commercially pure Fe, Fe35Mn, and (Fe35Mn)5Ag alloys were prepared by uniaxial pressing of the mixture of individual powders, followed by sintering. The influence of the alloying elements Mn and Ag on the corrosion behaviour of these Fe-based alloys was investigated in Hanks’ Balanced Salt Solution (HBSS). Furthermore, the role of the components in HBSS, particularly Ca2+ ions during alloys degradation was studied. Distribution of local pH and dissolved oxygen concentration was measured 50 μm above the interface of the degrading alloys. The results revealed that 5 wt% Ag addition to Fe35Mn alloy triggered micro-galvanic corrosion, while uniform corrosion dominated in pure Fe and Fe35Mn. Fast precipitation of Ca–P-containing products on the surface of these Fe-based alloys buffered local pH at the metal interface, and blocked oxygen diffusion at the initial stages of immersion. In the (Fe35Mn)5Ag, the detachment or structural changes of Ca–P-containing products gradually diminished their barrier property. These findings provided valuable insights into the degradation mechanism of promising biodegradable Fe-based alloys., Graphical abstract Image 1, Highlights • Local pH and O2 concentration were monitored at Fe-alloy interfaces in flowing HBSS at 37 °C. • Local pH at metal interface was more neutral (7.1–7.8) in HBSS + Ca than HBSS (6.4–8.3). • Dissolved oxygen was depleted at metal interface, in both electrolytes, HBSS and HBSS + Ca. • Oxygen reduction reaction and oxidation of ferrous products consumed O2 at a high rate. • The barrier property of Ca–P-containing products diminished as immersion time elapsed.

Cardiovascular diseases such as myocardial infarction (MI) are among the major causes of death worldwide. Although intramyocardial injection of hydrogels can effectively enhance the ventricular wall, this approach is limited because of its restriction to the poor vascularization in the infarcted myocardium. Here, we reported a new type of hydrogel composed of alginate (ALG) and hyaluronic acid (HA) with lyophilized platelet-rich fibrin (Ly-PRF) for releasing abundant growth factors to realize their respective functions. The results of in vitro studies demonstrated favorable mechanical property and release ability of ALG-HA with Ly-PRF. When injected into the infarcted myocardium, this composite hydrogel preserved heart function and the Ly-PRF within the hydrogel promoted angiogenesis and increased vascular density in both infarcted and border zone, which rescued the ischemic myocardium. These beneficial effects were also accompanied by macrophage polarization and regulation of myocardial fibrosis. Moreover, the autologous origin of Ly-PRF with ALG-HA hydrogel offers myriad advantages including safety profile, easiness to obtain and cost-effectiveness. Overall, this study demonstrated the versatile therapeutic effects of a novel composite hydrogel ALG-HA with Ly-PRF, which optimizes a promising vascularized substitution strategy for improving cardiac function after MI., Graphical abstract Image 1, Highlights • Alginate (ALG) alone has a limited vascularized ability in vivo. • ALG with 20% hyaluronic acid (HA) could attenuate LV remodeling and promote angiogenesis. • ALG-HA with lyophilized platelet-rich fibrin (Ly-PRF) could make further improvement in angiogenesis. • The Ly-PRF is promising for further clinical application because of autologous source and definite therapeutic effects.

Injectable biomaterial-based treatment is a promising strategy to enhance tissue repair after traumatic spinal cord injury (SCI) by bridging cavity spaces. However, there are limited reports of injectable, electroconductive hydrogels with self-healing properties being employed for the treatment of traumatic SCI. Hence, a natural extracellular matrix (ECM) biopolymer (chondroitin sulphate and gelatin)-based hydrogel containing polypyrrole, which imparted electroconductive properties, is developed for traumatic SCI repair. The resulting hydrogels showed mechanical (~928 Pa) and conductive properties (4.49 mS/cm) similar to natural spinal cord tissues. Moreover, the hydrogels exhibited shear-thinning and self-healing abilities, which allows it to be effectively injected into the injury site and to fill the lesion cavity to accelerate the tissue repair of traumatic SCI. In vitro, electroconductive ECM hydrogels promoted neuronal differentiation, enhanced axon outgrowth, and inhibited astrocyte differentiation. The electroconductive ECM hydrogel activated endogenous neural stem cell neurogenesis in vivo (n = 6), and induced myelinated axon regeneration into the lesion site via activation of the PI3K/AKT and MEK/ERK pathways, thereby achieving significant locomotor function restoration in rats with spinal cord injury (p, Graphical abstract An injectable, electroconductive hydrogels with self-healing properties based on natural extracellular matrix biopolymer is developed. The hydrogel can effectively be injected into the injury site and to fill the lesion cavity and promote new endogenous neurogenesis and induce myelinated axon regeneration in vivo, thus achieving significant locomotor function restoration in rats with spinal cord injury.Image 1, Highlights • Injectable, electroconductive hydrogels with self-healing properties was designed. • The injectable hydrogel was adhesive, biocompatible and biodegradable. • The injectable hydrogel promoted neuronal differentiation and axon outgrowth in vivo. • Injection of the hydrogel enhanced endogenous neurogenesis and function restoration.

Zinc is generally considered to be one of the most promising materials to be used in biodegradable implants, and many zinc alloys have been optimized to improve implant biocompatibility, degradation, and mechanical properties. However, long-term degradation leads to the prolonged presence of degradation products, which risks foreign body reactions. Herein, we investigated the in vivo biocompatibility and degradation of a biodegradable Zn–Mg–Fe alloy osteosynthesis system in the frontal bone, mandible, and femur in beagles for 1 year. Results of the routine blood, biochemical, trace element, and histological analyses of multiple organs, peripheral blood CD4/CD8a levels, and serum interleukin 2 and 4 levels showed good biocompatibility of the Zn–Mg–Fe alloy. Zinc content analysis revealed zinc accumulation in adjacent bone tissue, but not in the liver, kidney, and spleen, which was related to the degradation of the Zn–Mg–Fe alloy. The alloy demonstrated a uniform slowing degradation rate in vivo. No degradation differences in the frontal bone, mandible, and femur were observed. The degradation products included zinc oxide [ZnO], zinc hydroxide [Zn(OH)2], hydrozincite [Zn5(OH)6(CO3)2], and hopeite [Zn3(PO4)2·4H2O]. The good biocompatibility and degradation properties of the Zn–Mg–Fe alloy render it a very attractive osteosynthesis system for clinical applications., Graphical abstract Image 1, Highlights • Zn–Mg–Fe alloy possesses good biocompatibility. • Zn–Mg–Fe alloy demonstrates a uniform slowing-down degradation rate in vivo. • No degradation differences are observed in different sites, which supports the design of degradable osteosynthesis systems. • The degradation products are zinc oxide, zinc hydroxide, hydrozincite, and hopeite. • Zn–Mg–Fe alloy is a promising candidate for an osteosynthesis system.

In this study, a new series of zinc oxide (ZnO) with high specific surface area and narrow energy band gap are prepared using a facile microwave-induced method. The corresponding formation mechanism is also discussed for the first time. Due to the introduction of C, these ZnO can be excited by long wave temperature light without harmful short wave radiation, and play an efficient photocatalytic activity. This valuable property fundamentally improves the biological safety of its photocatalytic application. Herein, taking teeth whitening as an example, the photocatalytic performance of ZnO is evaluated. The “pure” yellow light-emitting diode (PYLED) with high biological safety is used as the excitation source. It is found that this method could effectively remove pigment on the tooth surface through physical adsorption. In addition, these ZnO could generate active oxygen to degrade the pigment on the tooth surface under the irradiation of yellow light. Some further optimization of these “warm light” responsive ZnO is also discussed in this systematical study, which could open up new opportunities in biomedical field., Graphical abstract Image 1, Highlights • A series of ZnO (PZCs, ZnO-BC) with high specific surface area and low band gap were synthesized by simple microwave-induced synthesis. • The introduction of C or BC can effectively reduce the band gap of ZnO. • Long wavelength warm light can effectively stimulate the photocatalytic activity of PZCs and ZnO-BC. • Under the yellow light, ZnO can effectively decompose the pigment on the tooth surface. • Warm light whitening, from the light source and material two levels to reduce the stimulation of teeth, and no obvious damage to enamel.

A biodegradable metallic ureteral stent with suitable mechanical properties and antibacterial activity remains a challenge. Here we reveal the scientific significance of a biodegradable Mg-Sr-Ag alloy with a favorable combination of balanced mechanical properties, adjustable indwelling time in urinary tract and evident antibacterial activity via in vivo experiments in a swine model. Attributed to the rheo-solidification process, equiaxial microstructure and significantly refined grains (average grain size: 27.1 μm) were achieved. Mg17Sr2 and Mg4Ag were found as the primary precipitates in the matrix, due to which the alloy obtained ca. 111% increase in ultimate tensile strength in comparison to pure magnesium. Both the in vitro and in vivo results demonstrated the satisfactory biocompatibility of the alloy. Histological evaluation and bioindicators analysis suggested that there was no tissue damage, inflammation and lesions in the urinary system caused by the degradation process. The stent also improved the post-operative bladder functions viewed from the urodynamic results. Our findings highlight the potential of this alloy as antibacterial biodegradable urinary implant material., Graphical abstract Image 1, Highlights • Innovative biodegradable antibacterial Mg-Sr-Ag alloy. • In vivo study in pig ureter models. • Enhanced mechanical properties and adjustable indwelling time. • Outstanding urinary compatibility and evident antibacterial activity.