Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.

Abstract. e Health sector in the Pakistan is facing many problems to provide the Health facilities to the masses spread over the country. e most challenging problem is the shortage of Doctor's as compared to the population. Most of the professional Doctor's prefers to serve in the abroad instead to serve in Pakistan. ere are the many determinants of turnover in the Health Department. e objective of this study is to investigate the factors such as Pay, Promotion, Job Safety and Security, Nature of the Work that e ect the job satisfaction level and that are the cause of turnover of employee's in the Autonomous Medical Health Institutions in the Pakistan. e factors of job satisfaction are such as Pay, Promotion, Job Safety and Security, Nature of the Work. e sample of the research is consist of 200 doctors, nurses, administrative and accounts sta working in Autonomous Medical Health institutions in the Punjab. Out of total 270 Questionnaires distributed in the Autonomous Medical Institutions of the Punjab 200 were received back and used for analysis. For data analysis/results the SPSS 20.0 is used.

No abstract

SARS-CoV-2 has been associated with an increased rate of venous thromboembolism in critically ill patients. Since surgical patients are already at higher risk of venous thromboembolism than general populations, this study aimed to determine if patients with peri-operative or prior SARS-CoV-2 were at further increased risk of venous thromboembolism. We conducted a planned sub-study and analysis from an international, multicentre, prospective cohort study of elective and emergency patients undergoing surgery during October 2020. Patients from all surgical specialties were included. The primary outcome measure was venous thromboembolism (pulmonary embolism or deep vein thrombosis) within 30 days of surgery. SARS-CoV-2 diagnosis was defined as peri-operative (7 days before to 30 days after surgery); recent (1-6 weeks before surgery); previous (≥7 weeks before surgery); or none. Information on prophylaxis regimens or pre-operative anti-coagulation for baseline comorbidities was not available. Postoperative venous thromboembolism rate was 0.5% (666/123,591) in patients without SARS-CoV-2; 2.2% (50/2317) in patients with peri-operative SARS-CoV-2; 1.6% (15/953) in patients with recent SARS-CoV-2; and 1.0% (11/1148) in patients with previous SARS-CoV-2. After adjustment for confounding factors, patients with peri-operative (adjusted odds ratio 1.5 (95%CI 1.1-2.0)) and recent SARS-CoV-2 (1.9 (95%CI 1.2-3.3)) remained at higher risk of venous thromboembolism, with a borderline finding in previous SARS-CoV-2 (1.7 (95%CI 0.9-3.0)). Overall, venous thromboembolism was independently associated with 30-day mortality ). In patients with SARS-CoV-2, mortality without venous thromboembolism was 7.4% (319/4342) and with venous thromboembolism was 40.8% (31/76). Patients undergoing surgery with peri-operative or recent SARS-CoV-2 appear to be at increased risk of postoperative venous thromboembolism compared with patients with no history of SARS-CoV-2 infection. Optimal venous thromboembolism prophylaxis and treatment are unknown in this cohort of patients, and these data should be interpreted accordingly.

Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmark their applications using an open-source benchmark. To meet these objectives, the contemporary state of the art has been scrutinized to explore a real workload behavior in Google cluster traces. Starting from smaller- to moderate-size cloud computing infrastructures, the dataset generation process is demonstrated using the Monte Carlo simulation method to produce a Google Cloud Jobs (GoCJ) dataset based on the analysis of Google cluster traces. With this article, the dataset is made publicly available to enable other researchers in the field to investigate and benchmark their scheduling and resource-allocation schemes for the cloud. The GoCJ dataset is archived and available on the Mendeley Data repository.