A novel measurement approach to dynamic change of limb length discrepancy using deep learning and wearable sensors.

The accurate identification of dynamic change of limb length discrepancy (LLD) in non-clinical settings is of great significance for monitoring gait function change in people's everyday lives. How to search for advanced techniques to measure LLD changes in non-clinical settings has always been a challenging endeavor in recent related research. In this study, we have proposed a novel approach to accurately measure the dynamic change of LLD outdoors by using deep learning and wearable sensors. The basic idea is that the measurement of dynamic change of LLD was considered as a multiple gait classification task based on LLD change that is clearly associated with its gait pattern. A hybrid deep learning model of convolutional neural network and long short-term memory (CNN-LSTM) was developed to precisely classify LLD gait patterns by discovering the most representative spatial-temporal LLD dynamic change features. Twenty-three healthy subjects were recruited to simulate four levels of LLD by wearing a shoe lift with different heights. The Delsys TrignoTM system was implemented to simultaneously acquire gait data from six sensors positioned on the hip, knee and ankle joint of two lower limbs respectively. The experimental results showed that the developed CNN-LSTM model could reach a higher accuracy of 93.24% and F1-score of 93.48% to classify four different LLD gait patterns when compared with CNN, LSTM, and CNN-gated recurrent unit(CNN-GRU), and gain better recall and precision (more than 92%) to detect each LLD gait pattern accurately. Our model could achieve excellent learning ability to discover the most representative LLD dynamic change features for classifying LLD gait patterns accurately. Our technical solution would help not only to accurately measure LLD dynamic change in non-clinical settings, but also to potentially find out lower limb joints with more abnormal compensatory change caused by LLD.

Conductive Hydrogel Dressing with High Mechanical Strength for Joint Wound Healing.

Hydrogel wound dressing can accelerate angiogenesis to achieve rapid wound healing, but traditional hydrogel dressings are difficult to meet the repair of joint sites due to their low mechanical strength. Therefore, we constructed the gel system by designing the chemical-physical interpenetrating network structure to achieve high strength and high toughness of the hydrogel. The high-strength double-network hydrogels were synthesized by simple free radical polymerization and low-temperature physicochemical cross-linking in our experiments. The suspension was obtained by green reduction of graphene oxide with carboxymethyl chitosan, followed by the introduction of acrylamide (AM) to form a covalent cross-linked network, which was immersed in ferric chloride solution to form metal ligand bonds, and finally the chemical-physical dual cross-linked network hydrogel wound dressing was prepared. Here, reduced graphene oxide can enhance electrical conductivity and excellent near-infrared photothermal effect to the hydrogel. The cell viability of this novel wound dressing was above 90.0%, its hemolysis rate was below 2.0%, and the electrical conductivity could reach (6.89 ± 0.07 (mS/cm)). In addition, the stress-strain curve demonstrated that the double cross-linked network hydrogel could reach a stress of more than 0.8 MPa at 82.0% strain, and the cyclic compression experiment shows that it can still recover its original shape after five times of repeated compression. This work can provide a reference for the exploitation of high mechanical strength hydrogel wound dressings with good electrical conductivity and near-infrared photothermal effect. This article is protected by copyright. All rights reserved.

PEG-α-CD/AM/liposome @amoxicillin double network hydrogel wound dressing-Multiple barriers for long-term drug release.

Wound infection and poor wound healing are the major challenges of wound treatment. Antibiotic drug treatment is the effective way to inhibit wound infection. It is necessary to achieve sustained release of antibiotics to get a longer treatment for wound infection. The double network hydrogels based on liposome, polyethylene glycol (PEG), α- cyclodextrin (α-CD) and acrylamide (AM) were developed, in which liposome acts as amoxicillin repository. Because the drug would release from the multiple barriers including two cavities of liposome and α-CD, as well as polyethylene glycol -α- cyclodextrin/acrylamide (PEG-CD/AM) double network, the PEG-α-CD/AM/liposome @amoxicillin double network hydrogels could achieve sustained drug release. The drug release assay showed that the dressing could release amoxicillin continuously until 12 days, than that of 8th day for single-network hydrogel releasing. The antibacterial ratio of the hydrogel could reach above 80%. What's more, the hydrogels present adjustable mechanical strength by changing the ratio of the components. The swelling ratio proved that the hydrogel had potential ability to absorb wound exudates. The cytotoxicity test of the hydrogels demonstrated excellent biocompatibility. These results indicated that this study can provide a new thought for antibacterial wound dressing and has a broad application prospect.

Photocatalytic antibacterial agent incorporated double-network hydrogel for wound healing.

A novel antibacterial hydrogel was prepared through the addition of IT to a chitin (CT) and polyvinyl alcohol (PVA) hydrogel, creating a promising material for wound dressings. The addition of nano particles IT endowed the anti-bacterial activity of hydrogel as well as had a positive impact on the mechanical properties of the hydrogels. The structure of the prepared hydrogel dressing was characterized by FTIR, XPS, XRD, SEM and TEM. The composite hydrogel exhibited excellent anti-bacterial activity under the visible light. Cytotoxicity tests (L929 fibroblast cells) showed all samples achieving up to 80% cell viability. Furthermore, compared with conventional dressings, wound healing test revealed that CT/PVA/IT hydrogel could accelerated wound healing in vivo, wound closure rates reached 95.5% after 10 days. This study suggests that the novel hydrogel has considerable potential for applications in wound dressings.

Listeriosis in two twin pregnancies after in vitro fertilization with differential outcome and literature review.

PURPOSE: In vitro fertilization (IVF) has become a very common procedure in the infertility practice due to its accessibility. The study is aiming at presenting two twin pregnancy-related infections caused by Listeria monocytogenes and reviewing the reported cases of listeriosis in multiple pregnancies. MATERIALS AND METHODS: Two listeriosis cases with twin pregnancy after IVF were described and the literature on pregnancy-associated listeriosis was reviewed. RESULTS: The risk of listeriosis should be underscored in pregnant women after IVF, and timely diagnosis and rational treatment might result in a better outcome. CONCLUSIONS: The current study highlights that the infections due to L. monocytogenes should be noted in multiple pregnancies after IVF.

Mussel-inspired in situ forming adhesive hydrogels with anti-microbial and hemostatic capacities for wound healing.

All kinds of commercially available wound dressings are clinically used as fleshly obstacles and therapeutic materials in opposition to microbial incursion. Few researches focused on effective-bleeding and anti-bacteria at the same time. In order to better solve this problem, two hydrogels were synthetized in this study. One is phosphate buffer solution-activated dopamine-modified-γ-poly glutamic acid (PBS-PD) hydrogel, the other one is cirsium setosum extracts-activated dopamine-modified-γ-poly glutamic acid (CSE-PD) hydrogel. The two hydrogels are prepared by applying an enzyme-catalyzed crosslinking means in the presence of horseradish peroxidase (HRP) and hydrogen peroxide (H2O2). The chemical structures were characterized through 1H-NMR and FT-IR. In conclusion, both PBS-PD and CSE-PD hydrogels exhibit superior tissue adhesion properties, and remarkable anti-infection quality. In addition, these two hydrogels manifest prominent hemostatic efficiency. The bio adhesion performance can achieve 30 kPa, meanwhile the CSE-PD hydrogels show good germicidal properties, and the antibacterial rate can reach 98%. The hydrogels could reduce blood loss without any obvious side effect, and present a new prospect in the field of hemostasis rapidly.

Microbiological and clinical characteristics of Group B Streptococcus isolates causing materno-neonatal infections: high prevalence of CC17/PI-1 and PI-2b sublineage in neonatal infections.

PURPOSE: Group B Streptococcus (GBS) is one of the major pathogens in severe materno-neonatal infections. We aimed to describe the clinical and molecular characteristics of GBS isolates causing infections in 45 maternal and 50 neonatal subjects, collected from eight healthcare centres in mainland China over the period 2010- 2017. METHODOLOGY: The phenotypic and genotypic features of the GBS isolates, including capsular polysaccharide (cps) serotypes, pilus island (PI) genes and antibiotic resistance profiles and genes, were characterized by both conventional and molecular methods. The clonal relationship between these strains was investigated using multilocus sequence typing (MLST). RESULTS: Of the 95 isolates, the predominant serotypes were III (51, 53.7 %), V (13, 13.7 %) and Ib (13, 13.7 %). All GBS strains carried at least one pilus island, with 32.6 % carrying PI-2b and 67.4 % PI-2a, singly or in combination. The most frequently-detected pilus island pattern was the combination of PI-1 and PI-2a, accounting for 56.8 % (54 isolates), followed by PI-1 combined with PI-2b (28, 29.5 %), PI-2a (10, 10.5 %) and PI-2b (3, 3.2 %). The strains were classified into 17 individual sequence types, and further clustered into six clonal complexes (CCs). A high prevalence of CC17/PI-1 and PI-2b (17, 34.0 %) was detected in 50 GBS isolates causing neonatal infections. No strain was resistant to penicillin, ampicillin, ceftriaxone or vancomycin, whereas 78.9, 76.8 and 81.5 % were resistant to erythromycin, clindamycin and tetracycline, respectively. CONCLUSION: Our study highlights the high genotypic diversity of GBS strains causing materno-neonatal infections, and the CC17/PI-1 and PI-2b sublineages should be noted in neonatal infections.

Kinetic, isotherm, and thermodynamic studies of the adsorption of dyes from aqueous solution by cellulose-based adsorbents.

In this study, a highly efficient and eco-friendly porous cellulose-based aerogel was synthesized by grafting polyethyleneimine onto quaternized cellulose (PQC) to remove the anionic dye Congo Red (CR). The prepared aerogel had a good flexibility and formability. The adsorbents were characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and elemental analysis. The results showed that there were many amino groups on CE/PQC aerogel and the structure was porous, which increased the adsorption capacity. The effects of initial concentration, adsorbent dose, contact time, temperature, and pH on the dye sorption were all investigated. The adsorption mechanism was also explored, including adsorption kinetics, adsorption isotherms and thermodynamic studies of adsorption. The results showed that the adsorption kinetics and isotherms fitted the pseudo-second-order kinetic model and Langmuir isotherm, respectively. The Langmuir isotherm revealed that the maximum theoretical adsorption capacity of the aerogels for CR was 518.403 mg g-1. The thermodynamic parameters including Gibbs free energy change (ΔG0), enthalpy change (ΔH0) and entropy change (ΔS0), showed the adsorption process was exothermic and spontaneous. These results imply that this new absorbent can be universally and effectively used for the removal of dyes from industrial textile wastewater.

Advanced bi-functional CoPi co-catalyst-decorated g-C3N4 nanosheets coupled with ZnO nanorod arrays as integrated photoanodes.

In this work, a CoPi-decorated type II heterojunction composed of one-dimensional (1D) ZnO nanorod arrays (NRAs) coated with two-dimensional (2D) carbon nitride (g-C3N4) was successfully prepared and used as photoanode. CoPi has been speculated to be a mixture of amorphous compound and two cobalt-based compounds: Co3(PO3)6·14H2O and Co(H2PO2)2·6H2O. The advanced bi-functional CoPi acts like a shelter, effectively inhibiting the photocorrosion of CNNS/ZnO NRAs and providing a faster hole transfer channel. Synergistic effects at the interface of the heterojunction efficiently improve the separation of charge carriers from photoexcited g-C3N4 nanosheets to the ZnO nanorods. Photocurrent density is also greatly enhanced by loading CoPi on CNNS/ZnO NRAs heterojunction. The maximum photocurrent density (2.45 mA cm-2 at 1.23 V vs. RHE) generated from CoPi(10)-CNNS(600)/ZnO nanorods is about 10.2 times greater than that of pristine ZnO nanorods (0.24 mA cm-2 at 1.23 V vs. RHE) and 2.5 times higher than that of CNNS(800)/ZnO (0.95 mA cm-2 at 1.23 V vs. RHE). The further increase of photoelectrocatalytic performance may be attributed to CoPi relieving the charge accumulation at the semiconductor/electrolyte, which decreases the electron-hole recombination rate.

An Advanced Hybrid Technique of DCS and JSRC for Telemonitoring of Multi-Sensor Gait Pattern.

The jointly quantitative analysis of multi-sensor gait data for the best gait-classification performance has been a challenging endeavor in wireless body area networks (WBANs)-based gait telemonitoring applications. In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique of distributed compressed sensing (DCS) and joint sparse representation classification (JSRC) for multi-sensor gait classification. Firstly, the DCS technique is utilized to simultaneously compress multi-sensor gait data for capturing spatio-temporal correlation information about gait while the energy efficiency of the sensors is available. Then, the jointly compressed gait data are directly used to develop a novel neighboring sample-based JSRC model by defining the sparse representation coefficients-inducing criterion (SRCC), in order to yield the best classification performance as well as a lower computational time cost. The multi-sensor gait data were selected from an open wearable action recognition database (WARD) to validate the feasibility of our proposed method. The results showed that when the comparison ratio and the number of neighboring samples are selected as 70% and 40%, respectively, the best accuracy (95%) can be reached while the lowest computational time spends only 60 ms. Moreover, the best accuracy and the computational time can increase by 5% and decrease by 40 ms, respectively, when compared with the traditional JSRC techniques. Our proposed hybrid technique can take advantage of the joint sparsity of data for jointly processing multi-sensor gait data, which greatly contributes to the best gait-classification performance. This has great potential for energy-efficient telemonitoring of multi-sensor gait.

Synthesis of cellulose-based double-network hydrogels demonstrating high strength, self-healing, and antibacterial properties.

Novel antibacterial double-network (DN) hydrogels with superior mechanical and self-healing properties are developed via the UV-initiated copolymerization of polyacrylic acid (PAA)-grafted quaternized cellulose (QCE) and polyvinyl alcohol (PVA). The QCE functioned as an antibacterial agent, resulting in excellent antibacterial capability (antibacterial rate >93%). The hydrogels are thus protected from microbial attack in natural environments, prolonging their lifetime. The PVA functioned as a physical cross-linker, resulting in superior mechanical properties. At PVA and QCE contents of 8% and 1.5%, respectively, the strain and stress at break of hydrogel were 465.37% and 1.13MPa, respectively. The hydrogel maintained good self-healing properties owing to ionic bonding between the ferric ions and carboxylic groups, and hydrogen bonding between the PVA molecules. The hydrogel was responsive to pH; its water-holding ability could be controlled by changing the pH. The material is simply prepared and used. Hydrogels with such excellent properties could be applied in various biomedical fields.

An advanced scheme of compressed sensing of acceleration data for telemonintoring of human gait.

BACKGROUND: The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues. METHODS: In our scheme, the sparse binary matrix is firstly designed as an optimal measurement matrix only containing a smallest number of nonzero entries. And then the block sparse Bayesian learning (BSBL) algorithm is introduced to reconstruct acceleration data with high fidelity by exploiting block sparsity. Finally, some commonly used gait classification models such as multilayer perceptron (MLP), support vector machine (SVM) and KStar are applied to further validate the feasibility of our scheme for gait telemonitoring application. RESULTS: The acceleration data were selected from open Human Activity Dataset of Southern California University (USC-HAD). The optimal sparse binary matrix (a smallest number of nonzero entries is 8) is as strong as the full optimal measurement matrix such as Gaussian random matrix. Moreover, BSBL algorithm significantly outperforms existing conventional CS reconstruction algorithms, and reaches the maximal signal-to-noise ratio value (70 dB). In comparison, MLP is best for gait classification, and it can classify upstairs and downstairs patterns with best accuracy of 95 % and seven gait patterns with maximal accuracy of 92 %, respectively. CONCLUSIONS: These results show that sparse binary matrix and BSBL algorithm are feasibly applied in compressive sensing of acceleration data to achieve the perfect compression and reconstruction performance, which has a great potential for gait telemonitoring application.

The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

PCA-based SVM for automatic recognition of gait patterns.

In this technical note, we investigate a combination PCA with SVM to classify gait pattern based on kinetic data. The gait data of 30 young and 30 elderly participants were recorded using a strain gauge force platform during normal walking. The gait features were first extracted from the recorded vertical directional foot- ground reaction forces curve using PCA, and then these extracted features were adopted to develop the SVM gait classifier. The test results indicated that the performance of PCA-based SVM was on average 90% to recognize young- elderly gait patterns, resulting in a markedly improved performance over an artificial neural network-based classifier. The classification ability of the SVM with polynomial and radial basis function kernels was superior to that of the SVM with linear kernel. These results suggest that the proposed technique could provide an effective tool for gait classification in future clinical applications.

Feature extraction via KPCA for classification of gait patterns.

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

[Supernumerary teeth: report of a rare case and review of the literature].

Supernumerary teeth is a well-recognized clinical phenomenon. Multiple supernumerary teeth are commonly associated with variable syndromes. However the multiple supernumerary teeth reported in mandibular and maxilla premolar region of an 14-year-old female patient without any associated systemic condition/syndrome is a rare case. A review of the literature relating to supernumerary teeth is presented including possible presentations, diagnostic features and treatment options.