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.

Background: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades. Main body: Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a subdiscipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles. Conclusion: We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods. Background Clinical epigenetics is a promising field of research. There is evidence that DNA methylation changes at cytosinephosphate-guanine (CpG) sites are associated with disease development [1-3]. Beyond genetic background, DNA methylation may additionally reflect environmental exposures and could improve diagnostic accuracy and prognostic prediction of certain diseases and be targetable by personalised therapy in the future [4, 5]. The current medical environment is characterised by collection of vast amounts of patient, hospital, and administrative data [6, 7], which makes traditional approaches to investigating these data individually less ideal. Machine learning (ML), however, is able to integrate large and complex data sets [8]. These data sources have the potential to enhance patient care and outcomes. A personalised medicine approach is tightly connected to increases in omics-data. For example, DNA sequence databases double in size twice a year [9]. Indeed, the increases in computer processing coupled with the rapid reduction in the cost of genomic sequencing have outpaced the rate of computing hardware advances [10]. Whilst far from a panacea, ML may be a tool to assist physicians in interpreting information-rich clinical data, including those collected in epigenetic studies [11, 12].

medical licence despite recognition of systemic failings and extreme pressure that she was under, has eroded trust from health-care professionals that they will be adequately supported in the event of potential mistakes under mitigating factors. These points must be explicitly addressed and conveyed on national levels before any student is used within clinical practice.Governments, regulatory bodies, and medical schools have a responsibility to both current and future patients to ensure that our future doctors are sufficiently trained and supported to deliver essential patient care, even in crises. Medical students, alongside all health-care staff, are prepared to contribute to patient care in the COVID-19 pandemic, yet in these uncertain times, forethought and transparency are essential.We declare no competing interests.

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.

Aging is associated with a vasoconstrictive, pro-coagulant, and pro-inflammatory profile of arteries and a decline in the bioavailability of the endothelium-derived molecule nitric oxide. Dietary nitrate elicits vasodilatory, anti-coagulant and anti-inflammatory effects in younger individuals, but little is known about whether these benefits are evident in older adults. We investigated the effects of 140 mL of nitrate-rich (HI-NI; containing 12.9 mmol nitrate) versus nitrate-depleted beetroot juice (LO-NI; containing ≤0.04 mmol nitrate) on blood pressure, blood coagulation, vascular inflammation markers, plasma nitrate and nitrite before, and 3 h and 6 h after ingestion in healthy older adults (five males, seven females, mean age: 64 years, age range: 57–71 years) in a randomized, placebo-controlled, crossover study. Plasma nitrate and nitrite increased 3 and 6 h after HI-NI ingestion (p < 0.05). Systolic, diastolic and mean arterial blood pressure decreased 3 h relative to baseline after HI-NI ingestion only (p < 0.05). The number of blood monocyte-platelet aggregates decreased 3 h after HI-NI intake (p < 0.05), indicating reduced platelet activation. The number of blood CD11b-expressing granulocytes decreased 3 h following HI-NI beetroot juice intake (p < 0.05), suggesting a shift toward an anti-adhesive granulocyte phenotype. Numbers of blood CD14++CD16+ intermediate monocyte subtypes slightly increased 6 h after HI-NI beetroot juice ingestion (p < 0.05), but the clinical implications of this response are currently unclear. These findings provide new evidence for the acute effects of nitrate-rich beetroot juice on circulating immune cells and platelets. Further long-term research is warranted to determine if these effects reduce the risk of developing hypertension and vascular inflammation with aging.