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.

PEG-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.

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.

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.

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.

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.

Advanced bi-functional CoPi co-catalyst-decorated g-CN 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.

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.

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.

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.

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.

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.

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.

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.