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SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

Cellulose nanofibrils (CNFs) can be used as an effective reinforcement material for biopolymer films intended for food packaging applications. The aim of this study was to improve the mechanical and barrier properties of xylan-alginate films by incorporating CNFs into the xylan-alginate matrix. CNFs was produced from maize stalk waste residues through a combination of chemical and mechanical treatment. The CNFs was incorporated into the xylan-alginate matrix between 1 and 10 wt%. The suitability of the CNFs reinforced composite films for food packaging applications was investigated by testing the mechanical, thermal and optical properties as well as the moisture sorption, solubility and water vapour permeability of the films. The CNFs produced had fibre diameters between 10 and 80 nm and transmission electron microscopy images showed that the CNFs were highly entangled hence forming a web like structure. It was found that the incorporation of CNFs into the xylan-alginate matrix increased the tensile strength and Young's modulus of the films. The incorporation of CNFs improved the WVP of the films but did not show any significant effect on the thermal properties of the films., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this study, xylan-alginate-based films were reinforced with nanoclays (bentonite or halloysite) by the solvent casting technique. The effect of the nanoclay loadings (1-5 wt %) on various properties-mechanical, optical, thermal, solubility, water sorption, and water vapor permeability (WVP)-of the xylan-alginate films were examined for their application as food packaging materials. A 5 wt % loading of either bentonite or halloysite resulted in a 49% decrease of the WVP due to the impermeable nature of the silicate layers that make up both bentonite and halloysite. Thermal stability and solubility of the nanocomposite films were not significantly influenced by the presence of the nanoclays, whereas the optical properties were significantly improved when compared to neat xylan-alginate blend. In general, films reinforced with bentonite exhibited superior mechanical and optical properties when compared to both halloysite-based nanocomposite and neat films.

Obtaining chemicals and materials in sustainable ways is of growing importance. A potential source of sustainable chemicals and materials is lignocellulosic biomass residues generated as waste from agriculture. Hemicellulose which is a large component in lignocellulosic biomass residues, provides many potential applications such as the generation of chemicals, packaging materials, drug delivery and biomedical applications. This review deals with the various techniques which can be used for the extraction of hemicellulose from biomass residues, purification and some potential applications of the extracted hemicellulose. The methods that have been used to further produce chemicals from extracted hemicellulose as well as their applications are discussed., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This MISO system model provides the estimated finger forces to be produced as input to the prosthetic hand. Optimal tracking method has been applied to track the estimated force profile of the Fusion based sEMG-force model. The simulation results show good agreement between reference force profile and the actual force.

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand.

Although among designs of prosthetics there have been some successes in the design of functional robotic implants, there remain many issues and challenges concerned with the failure to meet the 'ideal' requirements of a satisfactory prosthetic. These 'ideals' require the device to be easy to control, comfortable to wear, and cosmetically pleasing. Because the literature on prosthetics and robotic implants are voluminous, this review focuses on four topics to determine key challenges and opportunities underlying these interdisciplinary research areas: firstly, an artificial hand as a biomimetic; secondly, prosthetic implants (electromyography signals and control); thirdly, prosthetic implants and tissue reactions to the material(s) of implants; fourthly, how inflammatory responses of cells and tissues surrounding implanted sensors interfere with the signal transmission of such sensors. This review also notes the importance of the biological interfaces that robotic implants and other prosthetic devices are in contact with and how an improved knowledge of pathophysiological changes at such biological interfaces will lead to improved and more biocompatible designs of prosthetics. This review concludes with the vision that, to develop a design that satisfies the above 'ideals', an interdisciplinary team of biomedical and tissue engineers, and biomaterial and biomedical scientists is needed to work together holistically and synergistically.