2,253 research outputs found
Reduced Graphene Oxide and Carbon Nanotubes for the development of polyphenols amperometric biosensors
Full text linkBiosensors for sensitive, rapid and precise determination of phenolic compounds are attracting growing interest in environmental control and protection as well as in food industry.
Laccase (Lac) and tyrosinase (Tyr) are multicopper oxidase enzymes that catalyze the oxidation of phenol derivatives to the relevant quinones with the concomitant reduction of oxygen directly to water without the formation of reactive oxygen intermediates.
Here we present the development of amperometric biosensors based on Lac or Tyr physically adsorbed on glassy carbon electrodes modified with carbon-based nanomaterials.
Carbon-based nanomaterials are often employed in electrochemistry for their beneficial properties and in the last few years they have been frequently used in the development of biosensors to enhance the electron transfer between the electrode and the enzyme. Graphene represents an excellent material for sensing applications due to properties like fast heterogeneous electron transfer, large surface area, high mechanical strength, ease of functionalization, high conductivity and good biocompatibility. The electron transfer rates on graphene sheets obtained by electrochemical reduction of graphene oxide (GO) are similar to those observed for carbon nanotubes and higher than those of glassy carbon electrodes since the reduction of oxygen functional groups at edge and basal planes produces defect sites. The electrochemical reduction of GO is a versatile method to obtain a graphene layer (rGO) on the electrode surface which still displays some controllable oxygen-containing functionalities, usable for covalent or physical immobilization of enzymes. The enzyme immobilization represents a rather critical issue because the methods used for this procedure significantly influence the biosensor properties, operability and long-term stability. Among the methods available in the case of tyrosinase and laccase, the most commonly employed is the physical entrapment, obtained by a cross-linking with bovine serum albumin (BSA) and glutaraldehyde (GA)
sj-pdf-1-jao-10.1177_03913988241232315 – Supplemental material for Step by step enhancement of aesthetics for distal phalangeal prosthetic replacement using neoteric stamp technique: A case report
No full textSupplemental material, sj-pdf-1-jao-10.1177_03913988241232315 for Step by step enhancement of aesthetics for distal phalangeal prosthetic replacement using neoteric stamp technique: A case report by Komal Kaur Saroya, Vishal Kumar, Surbhi Sharma and Sonam Kalsi in The International Journal of Artificial Organs</p
Predicting Adverse Outcomes in At-Risk Populations with Machine Learning Methods
Full text linkPredicting Adverse Outcomes in At-Risk Populations with Machine Learning Methods
by
Vishal Sharma
A thesis submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in
Public Health - Epidemiology
School of Public Health
University of Alberta
© Vishal Sharma, 2022
Abstract
Canada has a publicly funded health system and heterogeneous population. There are segments of this population which account for substantial health care utilization and adverse outcomes. Machine learning (ML) approaches can assist public health intervention programs to mitigate health system costs and improve patient outcomes, particularly for segments within Canadian society that qualify as at-risk. The specific ones to be studied in this PhD are prescription opioid users, older adults taking benzodiazepines, and people with heart failure (HF). Identifying high risk individuals within these segments using ML methods trained on administrative health data as well as assessing prediction performance and value to inform health system planners are the objectives of this PhD program. This PhD studied outcomes related to admissions and deaths and presented findings on potential cost savings of ML assisted programs.
The main findings in this thesis were:
1. Machine-learning classifiers, especially incorporating hospitalization and physician claims data, have better predictive performance compared to guideline or prescription history only approaches when predicting 30-day risk of adverse outcomes pursuant to an opioid dispensation. Prescription monitoring programs and health departments with access to administrative data can use ML classifiers to effectively identify those at higher risk compared to current guideline-based approaches,
2. Despite predicting readmissions in patients with HF better than the LaCE, even the best ML model trained on administrative health data (XGBoost) did not provide substantially informative prediction performance as it only generated a moderate shift from pre to post-test probability. Health systems wishing to deploy such a tool should consider training ML models with additional data. Adding other techniques like Natural Language Processing, along with ML, to use other clinical information (like chart notes) might improve prediction performance,
3. Developing ML models using only administrative health data may not provide health regulators with sufficient informative predictions to use as decision aids for potential interventions, especially if considering daily or quarterly classifications of benzodiazepine risks in older adults. ML models may be informative for this context if yearly classifications are preferred. Health regulators should have access to other types of data to improve ML prediction, and
4. Prescription drug monitoring programs can use ML classifiers to identify patients at risk of adverse outcomes from opioids and potentially reduce health-care costs by intervening on high-ranked predictions. Better access to available administrative and clinical data could improve the prediction performance of ML classifiers, especially if probability thresholds are important, and thus expand opioid stewardship efforts and further reduce costs.
In conclusion, the findings suggest that ML methods may demonstrate value in opioid stewardship programs with limited benefits in predicting adverse outcomes in older adults taking benzodiazepines and readmissions in people with HF. Health systems wishing to integrate ML into their program planning may benefit from additional sources of data to train ML models. Data governance, bias and ML transparency are key issues requiring future research
Electrocatalytic determination of thiols using hybrid Copper Cobalt Hexacyanoferrate modified glassy carbon electrode
No full textAn electrochemical sensor based on a glassy carbon electrode (GCE) modified by a thin film of hybrid copper cobalt hexacyanoferrate (Cu-CoHCF) was prepared and tested for the determination of three thiols: l-cysteine (CySH), l-glutathione (GSH) and 1,4-butanedithiol (BdSH). Cyclic voltammetry (CV) measurements were carried out with the as prepared and thermally treated chemically modified electrode (CME) in phosphate buffer solution from pH 2 to 7. From the CV measurements, it was concluded that at pH higher than 5, the Cu-CoHCF layer was unstable and underwent significant fouling when biased at a potential at which the three thiols were electrocatalically oxidized. Following the preliminary CVs chronoamperometric measurements were carried out to determine the optimum conditions to develop an analytical method for the determination of thiols. Cysteine showed the lowest limit of detection (7.5 × 10-7 M), but very low values were displayed also by GSH (2.5 × 10-6 M) and BdSH (2.0 × 10-6 M). The range of linearity extended up to 6.0 × 10-5 M for CySH, 9.0 × 10-5 M for GSH and 1.2 × 10-4 M for BdSH without significant fouling of the CME. The analytical method was applied to the determination of GSH in a nutraceutical purchased from the local market. © 2015 Elsevier B.V. All rights reserved
An experimental study on mechanical properties and wear of A380/nB4C composites fabricated using two different liquid state processes
No full textIn the present work, fabrication of metal matrix composite of aluminium alloy A380 reinforced with 0.5, 1 and 1.5 weight percentages of nano boron carbide through mechanical stirring and ultrasonic treatment processes. Scanning Electron Microscopy (SEM), Back Scattered Electron Detector (BSE) and energy-dispersive X-ray spectroscopy (EDS) are used to characterize fabricated composites. Furthermore, tensile, hardness, and wear tests are performed to compare the mechanical and wear properties of prepared samples. It is found that UST is an efficient process as compared to MS for refining the microstructure of base alloy and fabricated composites. Also, the reinforced particles are efficiently and uniformly distributed in the ultrasonic treatment process. The improved properties of the ultimate tensile strength (UTS) and hardness observed at A380/1nB4C as 57%, and 35% respectively and wear of the fabricated composites samples is less as compared to the base alloy
Microstructure, Mechanical Properties and Tribological Behavior of A380/Nano-Hexagonal Boron Nitride Metal Matrix Composite
No full texthttps://link.springer.com/article/10.1007/s11665-021-06570-
Copper-cobalt hexacyanoferrate modified glassy carbon electrode for an indirect electrochemical determination of mercury
No full textAn electrochemical sensor based on a glassy carbon electrode modified with a hybrid film of copper
cobalt hexacyanoferrate was prepared for the indirect electrochemical determination of Hg2+. It exploits
the formation of a redox inactive complex with thiols. l-cysteine and 1,4-butanedithiol were used, with
the former giving more sensitive results. Interference studies were carried out which led to Cu2+ being
the major interferent. Exploiting the fact that the response of l-cysteine to Hg2+ is much more rapid
compared to Cu2+, the interference from Cu2+ was avoided or minimized by quick measurement of the
amperometric current after the addition of the analyte. The modified electrode showed promising results
in detecting Hg2+ in spiked mineral water samples
On-demand ultra-dense cloud drone networks: Opportunities, challenges and benefits
Full text linkThe paradigm of previous 4G cellular technology has led to an increase in requirements for high data rate demands of mobile users in 5G. In order to meet this demand, constant densification of communication networks was required. Ultra Dense Networks (UDNs) have been proposed as a promising 5G technology to fulfill these requirements by the efficient and dynamic distribution of the radio resources. However, for implementation of UDN, mobile operators have to face many challenges such as severe interference resulting in a limited capacity due to the dense deployment of small cells, site location and acquisition for the deployment of base stations, backhauling issues, energy consumption, etc. In this article, in order to alleviate these limitations, we propose a novel idea of Ultra Dense Cloud-Drone Network (UDCDN) architecture. This scheme is featured with "on-demand" quality and substantial flexibility in terms of deployment. This anchors the challenges of traditional UDN settings and offers numerous benefits. Through simulation results of cell coverage, we verify the genuineness of implementing the proposed scheme and offer a new paradigm shift for UDN
Investigation of tool geometry effect and penetration strategies on cutting forces during thread milling
Full text linkThe application of thread milling is increasing in industry because of its inherent advantages over other thread cutting techniques. The objective of this study is to investigate the effect of milling cutter tool geometry on cutting forces during thread milling. The proposed method can compare the performance of milling cutters in spite of the different number of tooth. The best thread milling cutter among the studied tools was determined from the cutting forces point of view. Furthermore, this study also pinpoints the best penetration strategy that provides minimum cutting forces. Lower cutting force variations will lead to fewer vibrations of the tool which in turn will produce accurate part.Postdoc de V Sharma financé par la région Bourgogn
Indian look at European classic : Vishal Bhardwaj as the adaptor of William Shakespeare's dramas
No full textArtykuł jest analizą adaptacji dramatów Williama Shakespeare'a autorstwa Vishala Bhardwaja. Autor opisuje inne adaptacje szekspirowskie pojawiające się w indyjskiej kinematografii, następnie opisuje adaptację Makbeta, Otella i Hamleta.The article is an analysis of the adaptation of dramas William Shakespeare's made by Vishal Bhardwaj. The author describes other Shakespearean adaptations appearing in the Indian cinematography. The next describes the adaptation of Macbeth, Othello and Hamlet
- …
