UK higher education staff's mental health and wellbeing during Covid-19

Purpose: This paper examines the impact of the COVID-19 pandemic on the mental health and wellbeing of academic and professional Higher Education (HE) staff in the UK. Design/methodology/approach: A mixed-method survey questionnaire was sent to almost 300 UK HE staff to secure qualitative and quantitative data to enable data triangulation. Findings: The study found an adverse impact on academic and professional staff's mental health and wellbeing, further resulting in stress and anxiety. Several reasons for the increased stress and anxiety levels were identified, but social isolation and the increased workload were the most commonly reported. The most affected groups by the pandemic were females, younger staff, full-timers and those with disabilities or caring responsibilities. Practical implications: This study offers a range of strategies to support staff's mental health and wellbeing; as such, it is of great interest to policymakers to inform their decisions of similar crisis events in the future. It also addresses some of the COVID-19 areas of research interest for the UK parliament. Originality/value: The study's originality derives from exploring the pandemic's impact on UK HE staff's mental health and wellbeing by including professional staff's experiences alongside those of academics. It also expands the scant evidence concerning the pandemic's impact on HE staff in the UK

Sentiment Analysis and Classifying Hashtags in Social Media Using Data Mining Techniques

Big data is one of the important topics which is still open for a wide range of applications for extracting useful information and knowledge for supporting organizations by planning and decision-making. Social media as a technology is an important resource of data, especially because it has been widely used in the last years. A Hashtag is recently one of the most popular features provided by Social media and is used by most social media users to express, share, and retrieve opinions and feelings regarding a specific theme. Hashtag features in social media are used more and more in recent years to discuss and debate important current events by public audience. This paper sheds light on how business can use such sources of information and how needed technical processes can be implemented accordingly. The paper demonstrates sentiment analysis as a scenario for such implementation. The main innovation in this paper is not limited to the technical method used, but rather to focus on the idea of using hashtags as information source in business, which is still rarely addressed in science. This paper will provide a novel model based on text mining techniques to provide a sentiment analysis for classifying business-related Hashtags posted on social media from the customers. The results will be presented and verified through samples of positive, and negative classified comments extracted from the Hashtags for supporting the organization by planning and decision making for generating completive advantages

Molecular Modeling of Disease Causing Mutations in Domain C1 of cMyBP-C

Cardiac myosin binding protein-C (cMyBP-C) is a multi-domain (C0-C10) protein that regulates heart muscle contraction through interaction with myosin, actin and other sarcomeric proteins. Several mutations of this protein cause familial hypertrophic cardiomyopathy (HCM). Domain C1 of cMyBP-C plays a central role in protein interactions with actin and myosin. Here, we studied structure-function relationship of three disease causing mutations, Arg177His, Ala216Thr and Glu258Lys of the domain C1 using computational biology techniques with its available X-ray crystal structure. The results suggest that each mutation could affect structural properties of the domain C1, and hence it's structural integrity through modifying intra-molecular arrangements in a distinct mode. The mutations also change surface charge distributions, which could impact the binding of C1 with other sarcomeric proteins thereby affecting contractile function. These structural consequences of the C1 mutants could be valuable to understand the molecular mechanisms for the disease

Assessing the in vivo biocompatibility of molecularly imprinted polymer nanoparticles

Molecularly imprinted polymer nanoparticles (nanoMIPs) are high affinity synthetic receptors which show promise as imaging and therapeutic agents. Comprehensive analysis of the in vivo behaviour of nanoMIPs must be performed before they can be considered for clinical applications. This work reports the solid-phase synthesis of nanoMIPs and an investigation of their biodistribution, clearance and cytotoxicity in a rat model following both intravenous and oral administration. These nanoMIPs were found in each harvested tissue type, including brain tissue, implying their ability to cross the blood brain barrier. The nanoMIPs were cleared from the body via both faeces and urine. Furthermore, we describe an immunogenicity study in mice, demonstrating that nanoMIPs specific for a cell surface protein showed moderate adjuvant properties, whilst those imprinted for a scrambled peptide showed no such behaviour. Given their ability to access all tissue types and their relatively low cytotoxicity, these results pave the way for in vivo applications of nanoMIPs

Greater Weight Gain in Treatment-naive Persons Starting Dolutegravir-based Antiretroviral Therapy

Background Recent studies have reported weight gain in virologically suppressed persons living with human immunodeficiency virus (PLWH) switched from older antiretroviral therapy (ART) to newer integrase strand transfer inhibitor (INSTI)–based regimens. In this study, we investigated whether weight gain differs among treatment-naive PLWH starting INSTI-based regimens compared to other ART regimens. Methods Adult, treatment-naive PLWH in the Vanderbilt Comprehensive Care Clinic cohort initiating INSTI-, protease inhibitor (PI)–, and nonnucleoside reverse transcriptase inhibitor (NNRTI)–based ART between January 2007 and June 2016 were included. We used multivariable linear mixed-effects models to generate marginal predictions of weights over time, adjusting for baseline clinical and demographic characteristics. We used restricted cubic splines to relax linearity assumptions and bootstrapping to generate 95% confidence intervals. Results Among 1152 ART-naive PLWH, 351 initiated INSTI-based regimens (135 dolutegravir, 153 elvitegravir, and 63 raltegravir), 86% were male, and 49% were white. At ART initiation, median age was 35 years, body mass index was 25.1 kg/m2, and CD4+ T-cell count was 318 cells/μL. Virologic suppression at 18 months was similar between different ART classes. At all examined study time points, weight gain was highest among PLWH starting dolutegravir. At 18 months, PLWH on dolutegravir gained 6.0 kg, compared to 2.6 kg for NNRTIs (P < .05), and 0.5 kg for elvitegravir (P < .05). PLWH starting dolutegravir also gained more weight at 18 months compared to raltegravir (3.4 kg) and PIs (4.1 kg), though these differences were not statistically significant. Conclusions Treatment-naive PLWH starting dolutegravir-based regimens gained significantly more weight at 18 months than those starting NNRTI-based and elvitegravir-based regimens

Development of Intrinsic Seismic Vulnerability Index (ISVI) for assessing roadway system and its assets framework

Roadway systems and their assets are the backbone of the transport sector and are vital for social and economic prosperity. Hence, it is important to design and develop transportation networks that can withstand natural hazards such as earthquakes. In recent decades, research concerning disaster risk management for roadway systems has received a lot of attention, particularly via the use of seismic vulnerability assessment methods. The majority of those models focus on a single criterion e.g., physical degradation of road assets, traffic disturbance, and/or functionality loss of the network, rather than considering how different criteria interact, such as association between asset damage, functionality, and network traffic. The main purpose of this study is to provide an integrated methodology for evaluating the seismic vulnerability of road networks to inform decision-making for risk mitigation. The proposed framework correlates the Intrinsic Seismic Vulnerability Index (ISVI) scores with the variation of accessibility rates to critical service centers. The methodology is demonstrated through an application to a part of a road network for specific seismic scenarios. The ISVI quantifies the impact caused by a parameter’s physical performance on the road behaviour using Non-Linear Dynamic Analysis (NLDA) technique, which can reduce or limit the role of the studies that are based on expert opinion decisions. The validated results shows that the embankment height is considered the most effective parameter in the physical assessment approach, followed by the number of lanes, while the soil type and pavement strength are the least effective parameters with a better effectiveness for soil type compared to pavement strength. Additionally, the integration between the physical assessment approach and the analysed accessibility rates is clearly showing compatibility between the vulnerability and accessibility approach, demonstrating more precise assessment tool by considering the correlation between the vulnerability rates and the reduced accessibility levels. The proposed approach can assist infrastructure owners and operators to reduce risk and boost emergency accessibility. • Conducting ISVI for roadway and its assets based on physical damage approach. • Assessing road networks accessibility rates by introducing an accessibility index (AI) using different geographical aspects • Formulating an integrated model between physical damage and traffic accessibility through building transport performance maps.This research was supported by Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2020/TK02/USM/02/

Novel Diagnostic Biomarkers in Liver Disease-a Metabolomic Review

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A Validated UPLC-PDA Method for Simultaneous Determination of 3 Biologically Active Isoflavans in Trigonella stellata Extract

© The Author(s) 2020. In this study, an ultra-performance liquid chromatography (UPLC)/photodiode array method was developed for the simultaneous determination of trigonellan glucoside (1), isotrigonellan (2), and methoxy-isotrigonellan (3) in Trigonella stellata extract using an external standard method. The extract was prepared using a standardized method by maceration of the dried plant material in ethanol. The 3 isoflavans (1-3) were separated on an Acquity UPLC C18 column using gradient elution with a mobile phase consisting of 0.1% (v/v) formic acid aqueous solution and 0.1% (v/v) formic acid in acetonitrile, and ultraviolet detection. The method provides a linear correlation for all analytes over the investigated ranges with all correlation coefficients greater than 0.998. The validated lower limits of quantitation were 53, 127, and 5 μg/mL for isoflavans 1, 2, and 3, respectively. Intraday and interday precisions (percent relative SD [RSD%]) were less than 8.3% and accuracy (RE%) ranged from 90% to 100%. The method’s capability to remain unaffected by small, but deliberate variations in method parameters (method’s reliability during normal usage) described by the robustness showed RSD% less than 4.6% measured by varying 3 different parameters. The validated method was successfully applied to simultaneously determine the concentration of the 3 new isoflavans having anti-inflammatory and antidiabetic activities. The results revealed that the validated method can be used for quality control of herbal preparations containing these or similar isoflavans that are marketed for the prevention of inflammation and as antidiabetics