Lessons learned from adapting a quantitative to an emergent mixed methods research design in Pakistan during COVID-19

This article delineates the process through which a quantitative study in the context of Pakistan was adapted into emergent mixed methods research due to COVID-19-related complexities. The in-process data collection was halted abruptly as schools were closed and lockdowns were imposed across Pakistan in the early 2020s due to COVID-19. In response, the quantitative research design was adapted to adjust the research design by adding further research questions and introducing qualitative interviews. COVID-19 increased the complexity in the research context. We argue that mixed methods offer adaptive approaches in disruptive situations which help to deal with the complexities. The paper further suggests that disruption during research occurs in various forms and adaptive procedures should be described as part of the research rather than ignoring them. This article provides a practical example for researchers on using adaptive approaches to mixed methods in a developing country context where the possibilities of disruptions are more rampant.

Label-free electrochemical aptasensor for the detection of SARS-CoV-2 spike protein based on carbon cloth sputtered gold nanoparticles.

The proliferation and transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or the (COVID-19) disease, has become a threat to worldwide biosecurity. Therefore, early diagnosis of COVID-19 is crucial to combat the ongoing infection spread. In this study we propose a flexible aptamer-based electrochemical sensor for the rapid, label-free detection of SARS-CoV-2 spike protein (SP). A platform made of a porous and flexible carbon cloth, coated with gold nanoparticles, to increase the conductivity and electrochemical performance of the material, was assembled with a thiol functionalized DNA aptamer via S-Au bonds, for the selective recognition of the SARS-CoV-2 SP. The various steps for the sensor preparation were followed by using scanning electron microscopy, cyclic voltammetry and differential pulse voltammetry (DPV). The proposed platform displayed good mechanical stability, revealing negligible changes on voltammetric responses to bending at various angles. Quantification of SARS-CoV-2 SP was performed by DPV and chronopotentiometry (CP), exploiting the changes of the electrical signals due the [Fe(CN)6]3-/4- redox probe, when SARS-CoV-2 SP binds to the aptamer immobilized on the electrode surface. Current density, in DPV, and square root of the transition time, in CP, varied linearly with the log[ SARS-CoV-2 SP], providing lower limits of detection (LOD) of 0.11 ng/mL and 37.8 ng/mL, respectively. The sensor displayed good selectivity, repeatability, and was tested in diluted human saliva, spiked with different SARS-CoV-2 SP concentrations, providing LODs of 0.167 ng/mL and 46.2 ng/mL for DPV and CP, respectively.

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.

The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems. Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider, easy mobility, easy access, consistent patient engagement, and cost-effectiveness. Any leakage or unauthorized access to users’ medical data can have serious consequences for any medical information system. The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoofing, replay, Masquerade, and stealing of stored templates. In this article, we propose a new cancelable biometric approach which has tentatively been named as “Expression Hash” for Telecare Medical Information Systems. The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code (expression hash). This code can then be serving as a template for verification. Different expressions would result in different sets of expression hash codes, which could be used in different applications and for different roles of each individual. The templates are stored on the server-side and the processing is also performed on the server-side. The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices. In the case of compromise, the existing code can be revoked and can be directly replaced by a new set of expression hash code. The well-known JAFFE (The Japanese Female Facial Expression) dataset has been for empirical testing and the results advocate for the efficacy of the proposed approach.