Burnout and quality of life among healthcare professionals during the COVID-19 pandemic in Saudi Arabia

Background and Objectives. Healthcare professionals (HCPs) have had to deal with large numbers of confirmed or suspected cases of COVID-19 and were at a high risk of burnout and dissatisfaction regarding their work-life integration. This article aims to assess burnout, the work-life balance (WLB), and quality of life (QoL) among healthcare workers and the relationship between these aspects in Saudi Arabia. Methods. An analytical cross-sectional study was conducted among 491 HCPs from five secondary hospitals in Jazan, Saudi Arabia. Three standardized questionnaires were used to gather data, including WLB, burnout, and the WHO Quality of Life-BREF. Results. Healthcare professionals struggled to balance their work and personal lives during COVID-19 and reported many burnout symptoms and a low level of QoL. Two-thirds (68.8%) of HCPs arrived home late from work and (56.6%) skipped a meal. HCPs who worked through a shift without any breaks were found in 57.8%. It was reported that 39.3% of HCPs felt frustrated by technology while being exhausted from their work (60.5%). The correlation coefficients between the WLB and health-related QoL (HRQoL) showed a significant negative correlation for all items, which ranged from (-.099 to -.403, P<0.05). The WLB and burnout scores were successful predictors of low levels of HRQoL (P<0.001 for both explanatory variables). Conclusions. Work-life imbalances, high levels of burnout, and low QoL levels are common among healthcare professionals in Saudi Arabia during COVID-19. Hospital administration should address the WLB and reduce burnout symptoms among HCPs to increase satisfaction and improve the quality of care

Suppression of Ribosomal Function Triggers Innate Immune Signaling through Activation of the NLRP3 Inflammasome

Some inflammatory stimuli trigger activation of the NLRP3 inflammasome by inducing efflux of cellular potassium. Loss of cellular potassium is known to potently suppress protein synthesis, leading us to test whether the inhibition of protein synthesis itself serves as an activating signal for the NLRP3 inflammasome. Murine bone marrow-derived macrophages, either primed by LPS or unprimed, were exposed to a panel of inhibitors of ribosomal function: ricin, cycloheximide, puromycin, pactamycin, and anisomycin. Macrophages were also exposed to nigericin, ATP, monosodium urate (MSU), and poly I:C. Synthesis of pro-IL-ß and release of IL-1ß from cells in response to these agents was detected by immunoblotting and ELISA. Release of intracellular potassium was measured by mass spectrometry. Inhibition of translation by each of the tested translation inhibitors led to processing of IL-1ß, which was released from cells. Processing and release of IL-1ß was reduced or absent from cells deficient in NLRP3, ASC, or caspase-1, demonstrating the role of the NLRP3 inflammasome. Despite the inability of these inhibitors to trigger efflux of intracellular potassium, the addition of high extracellular potassium suppressed activation of the NLRP3 inflammasome. MSU and double-stranded RNA, which are known to activate the NLRP3 inflammasome, also substantially inhibited protein translation, supporting a close association between inhibition of translation and inflammasome activation. These data demonstrate that translational inhibition itself constitutes a heretofore-unrecognized mechanism underlying IL-1ß dependent inflammatory signaling and that other physical, chemical, or pathogen-associated agents that impair translation may lead to IL-1ß-dependent inflammation through activation of the NLRP3 inflammasome. For agents that inhibit translation through decreased cellular potassium, the application of high extracellular potassium restores protein translation and suppresses activation of the NLRP inflammasome. For agents that inhibit translation through mechanisms that do not involve loss of potassium, high extracellular potassium suppresses IL-1ß processing through a mechanism that remains undefined

Understanding the retinal basis of vision across species

The vertebrate retina first evolved some 500 million years ago in ancestral marine chordates. Since then, the eyes of different species have been tuned to best support their unique visuoecological lifestyles. Visual specializations in eye designs, large-scale inhomogeneities across the retinal surface and local circuit motifs mean that all species' retinas are unique. Computational theories, such as the efficient coding hypothesis, have come a long way towards an explanation of the basic features of retinal organization and function; however, they cannot explain the full extent of retinal diversity within and across species. To build a truly general understanding of vertebrate vision and the retina's computational purpose, it is therefore important to more quantitatively relate different species' retinal functions to their specific natural environments and behavioural requirements. Ultimately, the goal of such efforts should be to build up to a more general theory of vision

Reliability Constrained Optimal Sizing of Renewable Energy Resources and Capacity Credit Evaluation

Renewable energy sources (RES) have become of paramount significance in mitigating global gas emissions as they are now increasingly used instead of conventional generation. The growing penetration of RES is changing its role from supplementary to alternative energy resources. If not properly planned, this transformation can significantly increase uncertainty due to the intermittent and non-dispatchable nature of resources such as solar irradiation and wind speed, potentially jeopardizing the reliability of the power supply. Unlike RES, conventional power plants are dispatchable. Consequently, RES is usually considered an energy rather than a capacity source. Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning the integration of these resources with the grid. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. In chapter 2 of this dissertation, a multi-objective approach is introduced to simultaneously optimize reliability and cost. Also, to deal with multiple types of RESs, a new concept, cost credit, is proposed as a supplement or alternative to capacity credit. Cost credit is a parameter that can be used to quantify the cost during planning and increase the reliability of the system. The over-all objective is to combine and size the RESs, i.e., photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS), to meet the customer demand based on the total cost and reliability of the system. Two optimization methods, multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II), are explored for grid connected and stand-alone systems. The best combined size that gives optimal reliability and cost, is then obtained from their outputs utilizing a Fuzzy technique. Then, capacity credit and cost credit are estimated for the obtained optimal solution. Finally, sensitivity analysis is conducted to examine the impact of changing different parameters, purchasing/selling price, capacity of grid-connected, and swept area of RES, on the system size. Chapter 3 presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used. Then, the two techniques have been compared, and the superior performance of the optimization approach has been demonstrated. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems contain photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this chapter, three different scenarios, system as a whole, only wind, and no batteries, are adopted to test the CC. Finally, sensitivity analysis is carried out to examine the impact of adjusting the wind speed, solar irradiation, and load. It is illustrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability. Chapter 4 aims to interconnect the RES with a battery energy storage system (BESS) to assist the system’s balancing. In case BESS could not handle the balancing, the demand response (DR) has been regarded as a virtual power plant to mitigate loss of load events. Moreover, multi-objective particle swarm optimization (MOPSO) is introduced to optimize the reliability and cost of the combined RESs such as PV, WT, BESS, and DR. Stand-alone systems with and without DR have been explored in this work, and the optimal solution is obtained using fuzzy logic. Since system planning is carried out based on the size of the combined system, capacity credit (CC) analysis of the optimal solution is obtained using genetic algorithm optimization (GA). Two approaches to estimate the CC, equivalent firm capacity (EFC) and effective load carrying capability (ELCC), are proposed. Different reliability indices are employed, namely daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS), to examine their impact on the CC