Background and objective The anti-inflammatory properties of vitamin C (VC) and the promising results it has shown in the treatment for common cold have prompted clinicians to use it as adjuvant therapy in the treatment of COVID-19. The purpose of this study was to find out the role of VC as adjunctive therapy in coronavirus disease 2019 (COVID-19). Methodology This study was conducted from March to July 2020 in the COVID-19 unit of a tertiary care hospital in Karachi. In this randomized controlled trial (RCT), one group received the intervention [50 mg/kg/day of intravenous (IV) VC] along with the standard therapy, and the other group received standard therapy only. Data such as age, gender, vitals, and biochemical values as well as outcomes including the number of days required for treatment, hospital stay, need for ventilation, and mortality were compared between the two groups and recorded using a self-structured questionnaire. Results COVID-19 patients who received IV VC became symptom-free earlier (7.1 ± 1.8 vs. 9.6 ± 2.1 days, p-value: <0.0001) and spent fewer days in the hospital (8.1 ± 1.8 vs. 10.7 ± 2.2 days, p-value: <0.0001) compared to those who received standard therapy only. However, there was no significant difference in the need for mechanical ventilation (p-value: 0.406) and mortality (p-value: 0.31) between the two groups. Conclusion VC can significantly improve clinical symptoms in patients affected with COVID-19; however, it had no impact on mortality and the need for mechanical ventilation. More large-scale studies are required to further assess the role of VC in the treatment of COVID-19.

COVID-19 is a global pandemic that has emerged and it is rapidly spreading throughout the world and subsequently causing great damage to the global economy and health-care. Patients with diabetes or other comorbidities are at a greater risk of developing severe illness. Knowledge and awareness are key elements to stimulate practice of preventive measures. The present study evaluated the level of knowledge and awareness about COVID-19 among individuals with diabetes and their compliance with the preventive measures against it. A total of 242 individuals who were diagnosed with diabetes mellitus and were 18 years or older participated in the study. The data was collected using an interview based questionnaire. Data was analyzed

Atrial fibrillation (AF) is characterized by abnormal heart rhythm. Among other well-known associations, recent studies suggest an association of AF with height. Height is related to 50 diseases spanning different body systems, AF is one of them. Since AF, a heterogeneous disease process, is influenced by structural, neural, electrical, and hemodynamic factors, height alters this process through its contribution to increasing atrial and ventricular size, leading to altered conduction patterns, autonomic dysregulation, and development of AF. Multiple underlying mechanisms associate height with AF. Apart from these indirect mechanisms, genome-wide association studies suggest the involvement of the same genes in AF and growth pathways. Tall stature is independently associated with a higher risk of AF development in healthy individuals. Since adult height is achieved much earlier than the onset of AF, protective measures can be taken in individuals with increased height to monitor, manage, and prevent the progression of AF.

Online Search (OLS) is a key component of many popular Internet services. Datacenters running OLS consume significant amounts of energy. However, reducing their energy is challenging due to their tight response time requirements. A key aspect of OLS is that each user query goes to all or many of the nodes in the cluster, so that the overall time budget is dictated by the tail of the replies' latency distribution; replies see latency variations both in the network and compute. Previous work proposes to achieve load-proportional energy by slowing down the computation at lower datacenter loads based directly on response times (i.e., at lower loads, the proposal exploits the average slack in the time budget provisioned for the peak load). In contrast, we propose TimeTrader to reduce energy by exploiting the latency slack in the sub-critical replies which arrive before the deadline (e.g., 80% of replies are 3-4x faster than the tail). This slack is present at all loads and subsumes the previous work's load-related slack. While the previous work shifts the leaves' response time distribution to consume the slack at lower loads, TimeTrader reshapes the distribution at all loads by slowing down individual sub-critical nodes without increasing missed deadlines. TimeTrader exploits slack in both the network and compute budgets. Further, TimeTrader leverages Earliest Deadline First scheduling to largely decouple critical requests from the queuing delays of subcritical requests which can then be slowed down without hurting critical requests. A combination of real-system measurements and at-scale simulations shows that without adding to missed deadlines, TimeTrader saves 15% and 40% energy at 90% and 30% loading, respectively, in a datacenter with 512 nodes, whereas previous work saves 0% and 30%. Further, as a proof-of-concept, we build a small-scale real implementation to evaluate TimeTrader and show 10-30% energy savings.

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