Many Applications In Healthcare Research Articles

Electrospun polarity-controlled molecular orientation for synergistic performance of an artifact-free piezoelectric anisotropic sensor.

Anisotropy in mechanical, optical and thermal sensors in a spatial direction has many applications in health care, robotics, aerospace, and tissue engineering. In particular, wearable and implantable sensors respond to stretching and bending strains that probe mechanical energy and track physiological signals. Hence, the development of anisotropic pressure sensors with true piezoelectric (PE) signals is of utmost importance to achieve efficient devices. Herein, a simple and efficient method is developed for high longitudinal and transverse responses, with an approach to isolating a true piezoelectric signal. The electrospun (ES) polarity of oriented dipoles inside flexible fibers gives rise to a high longitudinal/transverse PE response of both lateral and transverse strains. Nanofibers of poly(vinylidene-chlorotrifluoroethylene) copolymers contain poled dipoles, up to 86%, that promote an enhanced PE coefficient of 42 pm V-1 in the case of negative polarity-based electrospinning. It is 40% higher in composition than the commonly adopted positive polarity-biased electrospinning process. We demonstrated the advantage of such a high PE coefficient by the enhanced sensitivity of the longitudinal (VLs = 0.3 V kPa-1, ILs = 0.07 μA kPa-1) as well as transverse (VTs = 1.0 V kPa-1, ITs = 0.8 μA kPa-1) PE response. To counter the ambiguity of high transverse response as compared to longitudinal in electrospun fiber-based devices, a facile method is proposed to isolate the ferroelectret, triboelectric and piezoelectric signals in a fiber-based hybrid device with their independent charge generation mechanisms.

Plasmonic and Photothermal Properties of Silica-Capped Gold Nanoparticle Aggregates.

Owing to their biocompatibility, gold nanoparticles have many applications in healthcare, notably for targeted drug delivery and the photothermal therapy of tumors. The addition of a silica shell to the nanoparticles can help to minimize the aggregation of the nanoparticles upon exposure to harsh environments and protect any Raman reporters adsorbed onto the metal surface. Here, we report the effects of the addition of a silica shell on the photothermal properties of a series of gold nanostructures, including gold nanoparticle aggregates. The presence of a Raman reporter at the surface of the gold nanoparticles also allows the structures to be evaluated by surface-enhanced Raman scattering (SERS). In this work, we explore the relationship between the degree of aggregation and the position and the extinction of the near-infrared plasmon on the observed SERS intensity and in the increase in bulk temperature upon near-infrared excitation. By tailoring the concentration of the silane and the thickness of the silica shell, it is possible to improve the photothermal heating capabilities of the structures without sacrificing the SERS intensity or changing the optical properties of the gold nanoparticle aggregates.

Progress in copper-based materials for wound healing.

Chronic wounds have become the leading cause of death, particularly among diabetic patients. Chronic wounds affect ~6.5 million patients each year, according to statistics, and wound care and management incur significant financial costs. The rising prevalence of chronic wounds, combined with the limitations of current treatments, necessitates the development of new and innovative approaches to accelerate wound healing. Copper has been extensively studied for its antibacterial and anti-inflammatory activities. Copper in its nanoparticle form could have better biological properties and many applications in health care.

The Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis

Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare, space exploration, banking and finance. This rapid progress in AI has brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national and international organisations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependency of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analysed and summarized existing AI principles and guidelines but did not provide findings on principle-to-practice gaps nor how to mitigate them. These findings are particularly important to ensure that AI practical guidances are aligned with ethical principles and values. In this paper, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current practical guidances of ethical AI principles in different countries, to identify gaps in the practical guidance of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-to-practice gaps.

Recent Advances in Nanomaterials Used for RFID Technology and Their Applications

This study reviews RFID technology and its applications in the real world. RFID technology has been around for a long time. Yet, it hasn't been implemented on a large scale due to numerous reasons in the past. Now, RFID technology is considered as a means to handle supply chains during the pandemic situation. RFID is a subpart of printed electronics that use organic and inorganic materials to create electronic ink. The area of nanoparticles has contributed immensely to RFID as carbon nanotubes are being used extensively in RFID systems. RFID systems have many applications in healthcare and medical fields, agriculture management, food processing, library management, highway toll system, and supply chain management. This paper describes some of the nanoparticle inks and methods of printing. It also discusses some of the applications of RFID technology in the fields mentioned above. Aftereffects of the survey and bibliometric examination reveal that RFID research has attracted significant attention from the SCM and logistics community, medical community, and food processing community. To the best of the authors' information, the current review is one of the few attempts to investigate materials used in RFID technology and its applications using an exhaustive set of articles published during the past decade.

Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT.

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.

A High-Fidelity Wearable System for Measuring Lower-Limb Kinetics and Kinematics

There are many important challenges in gait analysis, which has many applications in healthcare, rehabilitation, therapy, and exercise training. However, gait analysis is typically performed in a gait laboratory, which is inaccessible to the general population and is not available in natural gait environments (e.g., outdoors). In this paper, we discuss the development of a high-fidelity, cost-effective, wireless sensor network to address the challenge of efficient gait monitoring in real-world walking scenarios. The sensor network is designed in a modular way to capture plantar forces and knee angle, angular velocity, and angular acceleration. A force module called a smart insole is designed to measure the plantar forces. The module is comprised of force sensitive resistors (FSRs) and a signal conditioning circuit. Various signal conditioning techniques, including a novel technique called transfrequency, are investigated to provide a linear mapping for FSR measurements and to provide data acquisition fidelity. The motion module includes a low-cost inertial measurement unit (IMU) augmented with a Kalman filter to provide filtered knee kinematics. A qualitative evaluation of the sensor network communication module is achieved by considering the internal communication protocols between the modules and the external wireless transmission protocol used to deliver data to an end-point terminal PC. Experiments are conducted to validate the motion and force modules. Then, the overall, integrated system is compared to gold standard laboratory results, demonstrating a successful application for gait identification. The results show that the sensor network accurately captures important gait parameters and features.

Blockchain in Healthcare: A Patient-Centered Model

A blockchain is a system for storing and sharing information that is secure because of its transparency. Each block in the chain is both its own independent unit containing its own information, and a dependent link in the collective chain, and this duality creates a network regulated by participants who store and share the information, rather than a third party. Blockchain has many applications in healthcare, and can improve mobile health applications, monitoring devices, sharing and storing of electronic medical records, clinical trial data, and insurance information storage. Research about blockchain and healthcare is currently limited, but blockchain is on the brink of transforming the healthcare system; through its decentralized principles, blockchain can improve accessibility and security of patient information, and can therefore overturn the healthcare hierarchy and build a new system in which patients manage their own care.

A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes

Research on the microbiome is an emerging and crucial science that finds many applications in healthcare, food safety, precision agriculture and environmental studies. Huge amounts of DNA from microbial communities are being sequenced and analyzed by scientists interested in extracting meaningful biological information from this big data. Analyzing massive microbiome sequencing datasets, which embed the functions and interactions of thousands of different bacterial, fungal and viral species, is a significant computational challenge. Artificial intelligence has the potential for building predictive models that can provide insights for specific cutting edge applications such as guiding diagnostics and developing personalised treatments, as well as maintaining soil health and fertility. Current machine learning workflows that predict traits of host organisms from their commensal microbiome do not take into account the whole genetic material constituting the microbiome, instead basing the analysis on specific marker genes. In this paper, to the best of our knowledge, we introduce the first machine learning workflow that efficiently performs host phenotype prediction from whole shotgun metagenomes by computing similaritypreserving compact representations of the genetic material. Our workflow enables prediction tasks, such as classification and regression, from Terabytes of raw sequencing data that do not necessitate any pre-prossessing through expensive bioinformatics pipelines. We compare the performance in terms of time, accuracy and uncertainty of predictions for four different classifiers. More precisely, we demonstrate that our ML workflow can efficiently classify real data with high accuracy, using examples from dog and human metagenomic studies, representing a step forward towards real time diagnostics and a potential for cloud applications.

The optimal calculation formula for glomerular filtration rate estimation in patients with acute kidney injury in the early course of acute pancreatitis

Inductively coupled plasma-mass spectrometry (ICP-MS) is currently the reference method for the determination of inorganic elements, and has many applications in healthcare and the environmental field. The objective of the present study was to develop and validate a high-resolution ICP-MS method for the simultaneous quantification of 38 elements in samples of human whole blood, urine, hair and tissues after microwave mineralization. The samples were incubated with nitric acid, hydrogen peroxide and internal standards prior to microwave mineralization for 25 min. The analysis was performed with an Element XR ICP-MS and validated using commercial reference standards (whole blood, urine, and hair) and in-house quality control samples. The 38 elements were detected in low-, medium- or high-resolution mode, depending on interferences and sensitivity. The lower and upper limits of quantification were adjusted for each element. The method was linear for all elements (correlation coefficient >0.996), and the inter- and intraday precision values (coefficient of variation) were below 15%. Samples from a clinical trial were used to confirm the high-resolution ICP-MS method’s suitability for the assessment of patient samples.

A high-resolution ICP-MS method for the determination of 38 inorganic elements in human whole blood, urine, hair and tissues after microwave digestion

Inductively coupled plasma-mass spectrometry (ICP-MS) is currently the reference method for the determination of inorganic elements, and has many applications in healthcare and the environmental field. The objective of the present study was to develop and validate a high-resolution ICP-MS method for the simultaneous quantification of 38 elements in samples of human whole blood, urine, hair and tissues after microwave mineralization. The samples were incubated with nitric acid, hydrogen peroxide and internal standards prior to microwave mineralization for 25 min. The analysis was performed with an Element XR ICP-MS and validated using commercial reference standards (whole blood, urine, and hair) and in-house quality control samples. The 38 elements were detected in low-, medium- or high-resolution mode, depending on interferences and sensitivity. The lower and upper limits of quantification were adjusted for each element. The method was linear for all elements (correlation coefficient >0.996), and the inter- and intraday precision values (coefficient of variation) were below 15%. Samples from a clinical trial were used to confirm the high-resolution ICP-MS method’s suitability for the assessment of patient samples.

Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life

Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile)-based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.

Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection

Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than $30$ percent energy savings while achieving $96.7$ percent classification accuracy.

DEVELOPMENT OF AN ULTRASONIC TECHNIQUE TO ASSESS OIL AND GREASE CONTENTS IN BIOFUEL WASTEWATER

Ultrasound as a metrology tool has many applications in health care, industrial, and chemical analyses. Ultrasonic techniques are rapid, low-cost, non-invasive, and highly repeatable. Although ultrasound can be used to measure emulsions, no effort had been made thus far to optimize its sensitivity for metrological analysis. In this work, a technique for analyzing oil in water was validated. The wave velocity and attenuation were chosen as the ultrasonic parameters. The technique was implemented in the boundary region established by law for effluents from industrial plants involved with biofuel manufacturing. A technical effort of this study was to establish stable emulsions in concentrations close to the desired limit of study. The phase behaviours of pseudo-ternary oil, sodium chloride, and sodium lauryl sulphate were studied. The composition in the widest region of the diagram allowed for the formation of a stable emulsion, from which the ultrasound measurement was carried out. An analytical curve was obtained using ultrasonic attenuation to determine the content of oils and greases in wastewater ranging 15–240 ppm. The speed of sound did not appear to be an applicable parameter for this application. The technique was demonstrated to be an important alternative solution for the continuous monitoring of wastewater with regard to oil concentrations.

Biosynthesis and wound healing activity of copper nanoparticles.

Nanotechnologies reinvented the utilities of various substances in healthcare. Copper in its native form (copper ion) has been well studied for its antimicrobial and anti-inflammatory activities. Copper in its nano form could have better biological profile and finds many applications in healthcare. There were reports on synthesis of copper nanoparticles by physical and chemical methods and their biological activities, although these methods have limitations. Biosynthesis of nanoparticles using microbes is an ecofriendly approach helping in the synthesis of biocompatible and stable nanoparticles. With this background in mind, the present study was designed to synthesise copper nanoparticles by Pseudomonas aeruginosa and testing their efficacy in enhancing the pace of wound healing. Culture supernatant was used to synthesise copper nanoparticles. Optimum conditions were selected to maximise the biosynthesis of nanoparticles. Biosynthesised copper nanoparticles (BNCPs) were characterised by Malvern zeta sizer and scanning electron microscopy. Average particle size, polydispersivity index and zeta potential of BNCPs were found to be 110.9 nm, 0.312 and (-) 18.3 mV, respectively. BNCPs was evaluated for its wound healing activity by excision wound model in rat. The pace of wound healing was enhanced by BNCPs compared with copper in native form.

A systems engineering perspective on process integration in industrial biotechnology

AbstractBiotechnology has many applications in health care, agriculture, industry and the environment. By using renewable raw materials, biotechnology contributes to lowering greenhouse gas emissions and moving away from a petro‐based towards a circular sustainable economy. However, major developments are still needed to make industrial biotechnology an economic alternative to conventional processes for fuels, specialty and/or bulk chemicals production.Process integration is a holistic approach to process design, which emphasizes the unity of the process and considers the interactions between different unit operations from the outset, rather than optimizing them separately. Furthermore, it also involves the substitution of two or more unit operations by one single novel unit capable of achieving the same process goal.Conversely, process systems engineering (PSE) deals with the analysis, design, optimization, operation and control of complex process systems, as well as the development of model‐based methods and tools that allow the systematic development of processes and products across a wide range of systems involving physical and chemical change. Mature tools and applications are available for chemical technology and steps have been taken to apply PSE principles also to bioprocess technology. This perspective paper argues that an interdisciplinary approach is needed towards integrated bio‐processing in order to link basic developments in biosciences with possible industrial applications. PSE can foster the application of existing and the development of new methods and tools for bioprocess integration that could promote the sustainable production of bio‐/chemical products. The inclusion of PSE principles and methods in biochemical engineering curricula and research is essential to achieve such goals. © 2014 Society of Chemical Industry

The shift minimisation personnel task scheduling problem: A new hybrid approach and computational insights

Assigning scheduled tasks to a multi-skilled workforce is a known NP-complete problem with many applications in health care, services, logistics and manufacturing. Optimising the use and composition of costly and scarce resources such as staff has major implications on any organisation׳s health. The present paper introduces a new, versatile two-phase matheuristic approach to the shift minimisation personnel task scheduling problem, which considers assigning tasks to a set of multi-skilled employees, whose working times have been determined beforehand. Computational results show that the new hybrid method is capable of finding, for the first time, optimal solutions for all benchmark instances from the literature, in very limited computation time. The influence of a set of problem instance features on the performance of different algorithms is investigated in order to discover what makes particular problem instances harder than others. These insights are useful when deciding on organisational policies to better manage various operational aspects related to workforce. The empirical hardness results enable to generate hard problem instances. A set of new challenging instances is now available to the academic community.

In situ bioconversion of compactin to pravastatin by Actinomadura species in fermentation broth of Penicillium citrinum

AbstractThe biocatalytic production of pravastatin from compactin by hydroxylation has found many applications in health care and pharmaceuticals. Actinomadura macra, Actinomadura madurae, and Actinomadura livida can efficiently bioconvert compactin to pravastatin. The fermentation broth (Penicillium citrinum fermented media) harvested on the eighth day contained 388.90 mg L−1 of compactin and an undetectable level of mycotoxin (citrinin). Bioconversion by A. macra was highest (87 %) in the yeast extract-amended medium. The anti-actinomadura effects of citrinin reduce the bioconversion capacity of Actinomadura. The in situ hydroxylation of compactin produced by P. citrinum represents a preferable alternative for the use of purified compactin, as a way to reduce cost and time processing.

Real-time locating systems (RTLS) in healthcare: a condensed primer

Real-time locating systems (RTLS, also known as real-time location systems) have become an important component of many existing ubiquitous location aware systems. While GPS (global positioning system) has been quite successful as an outdoor real-time locating solution, it fails to repeat this success indoors. A number of RTLS technologies have been used to solve indoor tracking problems. The ability to accurately track the location of assets and individuals indoors has many applications in healthcare. This paper provides a condensed primer of RTLS in healthcare, briefly covering the many options and technologies that are involved, as well as the various possible applications of RTLS in healthcare facilities and their potential benefits, including capital expenditure reduction and workflow and patient throughput improvements. The key to a successful RTLS deployment lies in picking the right RTLS option(s) and solution(s) for the application(s) or problem(s) at hand. Where this application-technology match has not been carefully thought of, any technology will be doomed to failure or to achieving less than optimal results.

Stable Flexible Electrodes With Enzyme Cluster Decorated Carbon Nanotubes for Glucose-Driven Power Source in Biosensing Applications

Over the years, implantable sensor technology has found many applications in healthcare. Research projects have aimed at improving power supply lifetime for longevity of an implanted sensor system. Miniature power sources, inspired from the biofuel cell principle, can utilize enzymes (proteins) as catalysts to produce energy from fuel(s) that are perennial in the human body. Bio-nanocatalytic hierarchical structures, clusters made of enzyme molecules, can be covalently linked to the electrode’s surface to provide better enzyme loading and sustained activity. Carbon nanotube base electrodes, with high surface area for direct electron transfer, and enzyme clusters can achieve efficient enzymatic redox reaction. A redox pair of such bioelectrodes can make up a power source with improved performance. In this study, we have investigated high throughput processes for coupling enzyme catalysts with power harvesting mechanisms via a screen printing process and solution processing. The process incorporates enzyme (glucosse oxidase and catalase) micro-/nanocluster immobilization on the surface of carboxylated (functionalized) carbon nanotubes with screen printed electrodes. The 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide and N-hydroxysulfosuccinimide amide linkage chemistries were used to bind the enzyme molecules to nanotube surface, and bis[sulfosuccinimidyl] suberate (BS3) was used as the cross-linker between enzymes. Optimized enzyme cross-linking was obtained after 25 min at room temperature with 0.07 mmol BS3/nmol of enzymes, with which 44% of enzymes were immobilized onto the surface of the bioelectrode with only 24% enzyme activity lost. A cell, redox pair of bioelectrodes, was tested under continuous operation. It was able to maintain most of the enzyme activity for 7 days before complete deactivation at 16 days. Thus, the power harvesting mechanism was able to produce power continuously for 7 days. The results were also analyzed to identify impeding factors such as competitive inhibition by reaction byproduct and cathode design, and methods to rectify them have been discussed. Coupling this new and improved nanobiopower cell with a product removal mechanism and enzyme mutagenesis should provide enzyme protection and longevity. This would bring the research one step closer to development of compatible implantable battery technology for medical applications.