Symptoms associated with a COVID-19 infection in the general population of Vienna BackgroundMost clinical studies report the symptoms experienced by those infected with Coronavirus disease 2019 (COVID-19) via patients already hospitalised. Here we analyse the symptoms experienced by the general population in Vienna.

MethodsThe Vienna Social Fund (FSW, Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Viennas official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV-2 nucleic acid amplification test (NAAT) was performed. In this publication, we analysed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.

ResultsBetween the 2nd of November 2020 and the 18th of November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an Area Under the Curve (AUC) of 0.74.

ConclusionThis study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.
ISARIC COVID-19 Clinical Data Report: 14 July 2021 ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.

The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.

For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/
Poor neutralization and rapid decay of antibodies to SARS-CoV-2 variants in vaccinated dialysis patients Patients on dialysis are at risk of severe course of SARS-CoV-2 infection. Understanding the neutralizing activity and coverage of SARS-CoV-2 variants of vaccine-elicited antibodies is required to guide prophylactic and therapeutic COVID-19 interventions in this frail population. By analyzing plasma samples from 130 hemodialysis and 13 peritoneal dialysis patients after two doses of BNT162b2 or mRNA-1273 vaccines, we found that 35% of the patients had low-level or undetectable IgG antibodies to SARS-CoV-2 Spike (S). Neutralizing antibodies against the vaccine-matched SARS-CoV-2 and Delta variant were low or undetectable in 49% and 77% of patients, respectively, and were further reduced against other emerging variants. The fraction of non-responding patients was higher in SARS-CoV-2-naive hemodialysis patients immunized with BNT162b2 (66%) than those immunized with mRNA-1273 (23%). The reduced neutralizing activity correlated with low antibody avidity. Patients followed up to 7 months after vaccination showed a rapid decay of the antibody response with an average 21- and 10-fold reduction of neutralizing antibodies to vaccine-matched SARS-CoV-2 and Delta variant, which increased the fraction of non-responders to 84% and 90%, respectively. These data indicate that dialysis patients should be prioritized for additional vaccination boosts. Nevertheless, their antibody response to SARS-CoV-2 must be continuously monitored to adopt the best prophylactic and therapeutic strategy. PBPK-led guidance for cystic fibrosis patients taking elexacaftor-tezacaftor-ivacaftor with nirmatrelvir-ritonavir for the treatment of COVID-19 BackgroundCystic fibrosis transmembrane conductance regulator (CFTR) modulating therapies including elexacaftor, tezacaftor, and ivacaftor (ETI) are primarily eliminated through cytochrome P450 (CYP) 3A-mediated metabolism. This creates a therapeutic challenge to the treatment of COVID-19 with nirmatrelvir-ritonavir in people with cystic fibrosis (pwCF) due to the potential for significant drug-drug interactions (DDI). However, pwCF are more at risk of serious illness following COVID-19 infection and hence it is important to manage the DDI risk and provide treatment options.

MethodsCYP3A-mediated DDI of ETI was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach. Modeling was performed incorporating physiological information and drug dependent parameters of ETI to predict the effect of ritonavir (the CYP3A4 inhibiting component of the combination) on pharmacokinetics of ETI. The ETI models were verified using independent clinical pharmacokinetic and DDI data of ETI with a range of CYP3A modulators.

ResultsWhen ritonavir was administered on day 1 through 5, the predicted AUC ratio of ivacaftor (the most sensitive CYP3A substrate) on day 6 was 9.31, indicating that its metabolism was strongly inhibited. Based on the predicted DDI, the dose of ETI should be reduced when co-administered with nirmatrelvir-ritonavir to elexacaftor 200mg-tezacaftor 100mg-ivacaftor 150mg on days 1 and 5, with resumption of full dose ETI on day 9, considering the residual inhibitory effect of ritonavir as a mechanism-based inhibitor.

ConclusionsCoadministration of nirmatrelvir-ritonavir requires a significant reduction in the ETI dosing frequency with delayed resumption of full dose due to the mechanism-based inhibition with ritonavir. High gamma activity distinguishes frontal cognitive control regions from adjacent cortical networks Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70-250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language, cingulo-opercular and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12-30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries. Macronutrient mass intake explains deferential weight and fat loss in isocaloric diets Differential weight and fat losses under isocaloric diets of distinct macronutrient composition are well-documented findings in obesity research.1-6 Such data are considered a result of inadequate methodology as it disagree with the energy balance theory.7 A recent mathematical analysis of this paradigm has found, however, serious analytical contradictions in its foundations.8 As an alternative, a mass balance model was proposed to explain the aforesaid body composition alterations. Here, we expand on this observation by contrasting both models. We show that mass balance explains a wide range of fending experiments including those concurring with the energy balance principle. The latter, however, is less flexible and results in poor forecasts in settings consistent with mass balance. The energy balance theory is thus an unsatisfactory model of body composition changes. Consequently, by shifting to a mass balance paradigm of obesity a much deeper understanding of this disease may follow in the near future. Declines and pronounced state disparities in prescription opioid distribution in the United States BackgroundThe United States (US) opioid epidemic is a persistent and pervasive public health emergency as it continues to claim the lives of Americans through addiction and overdose. There have been sustained efforts to reverse this iatrogenic crisis over the past decade. This study analyzed the changes in prescription opioid distribution for pain and identified regional differences between 2010 and 2019.

Methods and FindingsOpioid production data was obtained from the DEAs annual production quotas. Total opioid production has decreased 41.5% from 2013 (87.6 morphine mg equivalent metric tons) to 2019 (51.3). Opioid distribution from 2010 to 2019 was collected for ten prescription opioids (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol) from the US Drug Enforcement Administrations Automation of Reports and Consolidated Orders System. Regional variance was expressed as the ratio of the 95th to 5th percentiles of opioid distribution per state, corrected for population. The peak year for all ten prescription opioids was identified, individually, as between 2010 and 2013, except for codeine (2015). There was a 51.96% overall decrease in opioid distribution per capita with the largest decrease in Florida (-61.61%) and smallest in Texas (-18.64%). The largest quantities of opioid distribution were observed in Tennessee (520.70 morphine mg equivalent or MME per person) and Delaware (251.45) in 2011 and 2019. The smallest was Nebraska (153.39) in 2011 and Minnesota (90.49) in 2019. The highest to lowest state ratio of total opioid use, corrected for population, was sizable in 2011 (5.25) and 2019 (2.78). Similarly, the mean 95th/5th ratio was relatively stable in from 2011 (4.78 +0.70) and 2019 (5.64+0.98). The 95th/5th ratio in 2019 was greatest for methadone (10.23) and oxymorphone (10.09) and smallest for morphine (2.20) and fentanyl (2.12). Southern states (e.g MS, TN, AL, AK, DE, and NC) had the highest per capita distribution for eight of the ten opioids in 2019. The strength of the correlation between per capita hydrocodone and codeine, and hydromorphone and codeine increased significantly from 2011 to 2019

ConclusionsThis study found a decline in total production and distribution for ten prescription opioids during the last half-decade. However, distribution was non-homogeneous at a state level. Analysis of regional differences revealed a three-fold difference in the 95th:5th percentile ratio between states which was unchanged over the past decade. Future research focused on identifying factors contributing to the observed regional variability could prove valuable to understanding, and potentially remediating, the pronounced disparities in prescription opioid use in the US. Remote exposure to secondhand tobacco smoke is associated with lower exercise capacity through effects on oxygen pulse, a proxy of cardiac stroke volume BackgroundPast exposure to secondhand tobacco smoke (SHS) is associated with exercise limitation. Pulmonary factors including air trapping contribute to this limitation but the contribution of cardiovascular factors is unclear.

ObjectiveTo determine contribution of cardiovascular mechanisms to SHS-associated exercise limitation.

MethodsWe examined the cardiovascular responses to maximum effort exercise in 245 never-smokers with remote, prolonged occupational exposure to SHS and no known history of cardiovascular disease. We estimated the contribution of oxygen-pulse (proxy for cardiac stroke volume) and changes in systolic (SBP) and diastolic blood pressures (DBP) and heart rate (HR) towards exercise capacity, and examined whether the association of SHS with exercise capacity was mediated through these variables.

ResultsAt peak exercise (highest workload completed [WattsPeak]=156{+/-}46 watts [135{+/-}33 %predicted]), oxygen consumption (VO2Peak) and oxygen-pulse (O2-PulsePeak) were 1,557{+/-}476 mL/min (100{+/-}24 %predicted) and 11.0{+/-}3.0 mL/beat (116{+/-}25 %predicted), respectively, with 29% and 3% participants not achieving their predicted normal range. Oxygen saturation at peak exercise was 98{+/-}1% and remained >93% in all participants. Sixty-six percent showed hypertensive response to exercise. In models adjusted for covariates, WattsPeak was associated directly with O2-PulsePeak, HRPeak, and SBPPeak and inversely with SHS, air trapping (RV/TLC), and rise of SBP over workload (all P<0.01). Moreover, SHS exposure association with WattsPeak was substantially (41%) mediated through its effect on O2-PulsePeak (P<0.038). Although not statistically significant, a considerable proportion (36%) of air trapping effect on WattsPeak seemed to be mediated through O2-PulsePeak (P=0.078). The likelihood of having baseline respiratory symptoms (modified Medical Research Council score [&ge;]1) was associated with steeper rise in SBP over workload (P<0.01).

ConclusionIn a never-smoker population with remote exposure to SHS, abnormal escalation of afterload and an SHS-associated reduction in cardiac output contributed to lower exercise capacity.

Key messagesO_ST_ABSWhat is the key question?C_ST_ABSWhat are the cardiovascular health effects of past exposure to secondhand tobacco smoke in never-smokers? How do pulmonary and cardiovascular systems interact in this setting?

What is the bottom line?Healthy never-smokers with history of remote past exposure to secondhand tobacco smoke have an abnormal cardiovascular response to exercise, which is characterized by a stroke volume and thus an exercise capacity that are reduced proportional to their years of exposure to secondhand tobacco smoke.

Why read on?The abnormal cardiovascular response to exercise in this population reveals the presence of an occult or subclinical pathology that impairs the cardiopulmonary functional reserve and reduces the efficiency of bodys oxygen delivery machinery, which could be disadvantageous during the times of increased cardiopulmonary output demands as in physiological distress or disease. Variability in proteoglycan biosynthetic genes reveals new facets of heparan sulfates diversity. A systematic review and analysis Proteoglycans are complex macromolecules formed of glycosaminoglycan chains covalently linked to core proteins through a linker tetrasaccharide common to heparan sulfate proteoglycans (HSPG) and chondroitin sulfate proteoglycans (CSPG). Biosynthesis of a single proteoglycan requires the expression of dozens of genes, which together create the large structural and functional diversity reflected by the numerous diseases or syndromes associated to their genetic variability. Among proteoglycans, HSPG are the most structurally and functionally complex. To decrease this complexity, we retrieved and linked information on pathogenic variants, polymorphism, expression, and literature databases for 50 genes involved in the biosynthesis of HSPG core proteins, heparan sulfate (HS) chains, and their linker tetrasaccharide. This resulted in a new gene organization and biosynthetic pathway representation in which the phenotypic continuum of disorders as linkeropathies and other pathologies could be predictable. Moreover, ubiquitous NDST1, GLCE, HS2ST1, and HS6ST1 appeared to generate ubiquitous heparan sulfate (HS) sequences essential for normal development and homeostasis, whereas the tissue restricted NDST2-4, HS6ST2-3, and HS3ST1-6 appeared to generate specialized HS sequences mainly involved in responsiveness to stimuli. Supported by data on genetic polymorphism and clinical variants, we afford a new vision of HSPG involvement in homeostasis, disease, vulnerability to disease, and behavioral disorders. Patient attrition in Molecular Tumour Boards: A Systematic Review Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision making within precision medicine. Though these meetings are in use globally reporting often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 54 papers were reviewed spanning a 6 year period from 11 different countries. 20% of patients received treatment through the MTB process. Of those that did not receive treatment the main reasons were no mutations identified (26%), no actionable mutations (22%) and clinical deterioration (15%). However, the data was often incomplete due to inconsistent reporting of MTBs with only 53% reporting on patients having no mutations, 48% reporting on presence of actionable mutations with no treatment options and 57% reporting on clinical deterioration. As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data. Genetic factors affecting immune phenotypes in type 1 diabetes Large inter-individual variability in immunological cell composition and function determines immune responses in general and susceptibility to immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes, the pathophysiological mechanisms through which these variations exert their effects are unknown. In this study, we characterize the genetic factors influencing immune responses in patients with type 1 diabetes. Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTL) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D. Among them, 12 have never been reported in healthy population studies, implying a disease-specific genetic regulation. Altogether this study provides new insights into the genetic factors that affect immunological responses in T1D. COMPARISON OF sPLA2-IIA PERFORMANCE WITH HIGH-SENSITIVE CRP, NEUTROPHIL PERCENTAGE, PCT AND LACTATE TO IDENTIFY BACTERIAL INFECTION: A PROSPECTIVE STUDY Early bacterial infection (BI) identification in resource-limiting Emergency Departments (ED) is challenging especially in low- and middle-income counties (LMIC). Misdiagnosis predisposes to antibiotic overuse and propagates antimicrobial resistance. This study evaluates new emerging biomarkers, secretory phospholipase A2 group IIA (sPLA2-IIA), and compares with other biomarkers on their performance characteristic of BI detection in Malaysia, an LMIC. A prospective cohort study was conducted involving 151 consecutive patients admitted to the ED. A single measurement was taken upon patient arrival in ED and was analysed for serum levels of sPLA2-IIA, high-sensitive C-reactive protein (CRP), procalcitonin (PCT), neutrophil percentage (N%), and lactate. All biomarkers performance was compared for the outcomes using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The performance of sPLA2-IIA (AUROC 0.93 [95% CI: 0.89-0.97]; Sn 80% [95% CI: 72-87] Sp 94% [95% CI: 81-89]) was the highest among all. It was comparable with high-sensitive CRP (AUROC 0.93 [95% CI: 0.88-0.97]; Sn 75% [95% CI: 66-83]; Sp 91 [95% CI: 77-98]) but had a higher Sn and Sp. The sPLA2-IIA was also found superior to N%, PCT, and lactate. This finding suggested sPLA2-IIA was recommended biomarkers for BI detection in LMIC. Integrating Health Behavior Theories to Predict COVID-19 Vaccine Acceptance: Differences between Medical Students and Nursing Students BackgroundThis study aimed to explore behavioral-related factors predicting the intention of getting a COVID-19 vaccine among medical and nursing students using an integrative model combining the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB).

MethodsA cross-sectional online survey was conducted among medical and nursing students aged > 18 years in their clinical years in Israel between 27 August and 28 September 2020. Hierarchical logistic regression was used to predict acceptance of a COVID-19 vaccine.

ResultsA total number of 628 participants completed the survey. Medical students expressed higher intentions of getting vaccinated against COVID-19 than nursing students (88.1% vs. 76.2%, p < 0.01). The integrated model based on HBM and TPB was able to explain 66% of the variance (adjusted R2 = 0.66). Participants were more likely to be willing to get vaccinated if they reported higher levels of perceived susceptibility, benefits, barriers, cues to action, attitude, self-efficacy and anticipated regret. Two interaction effects revealed that male nurses had a higher intention of getting vaccinated than did female nurses and that susceptibility is a predictor of the intention of getting vaccinated only among nurses.

ConclusionsThis study demonstrates that both models considered (i.e., HBM and TPB) are important for predicting the intention of getting a COVID-19 vaccine among medical and nursing students, and can help better guide intervention programs, based on components from both models. Our findings also highlight the importance of paying attention to a targeted group of female nurses, who expressed low vaccine acceptance. Comparison of in-hospital mortality and clinical outcomes among patients greater than and less than 80 years undergoing TAVR BackgroundTranscatheter aortic valve replacement (TAVR) procedure has been increasingly utilized in the management of aortic stenosis in the elderly. We sought to assess the hospital outcomes and major adverse events of TAVR in patients [&ge;]80 years old compared to those <80 years.

MethodsWe performed a retrospective observational study using the National Inpatient Sample - 2018. We divided TAVR patients into two cohorts based on age being [&ge;]80 years and <80 years old. The primary outcomes were the comparison of in-hospital mortality and major adverse events (MAE) in the cohorts.

ResultsWe identified 63,630 patients who underwent TAVR procedures from January 1 to December 31, 2018. Among them, 35, 115(55%) were [&ge;]80 years and 28,515(45%) were <80 years of age. There was a higher rate of post-procedure in-hospital mortality rate in patients [&ge;]80 years (1.6% vs. 1.1%, aOR=1.56, [CI 1.13 -2.16], p=0.006). They also had higher rates of pacemaker insertion compared to those <80 years old (7.4 vs 6.5%, aOR=1.17 [CI 1-1.35], p=0.03). On subgroup analysis, the rates of MAE were not different between the two cohorts (23.8 vs 23.4, p=0.09) but patients [&ge;]80 years who experienced MAE had higher in-hospital mortality (5.7 vs 4.3%, aOR=1.58 [CI=1.08-2.32], p=0.01) and shorter length of hospital stay (7.2 vs 8.7 days, p=0.03) compared to those <80. Anemia, liver disease, chronic kidney disease and previous stroke were associated with higher odds of in-hospital MAE in both groups.

ConclusionThis study shows that in patients undergoing TAVR, those [&ge;]80 years old had higher in-hospital mortality and higher rates of pacemaker insertion compared to those < 80 years. The rates of MAE were not significantly different between the two groups. Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing BackgroundAs investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology.

MethodsWe modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 - 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints.

ResultsWe could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk.

ConclusionContinuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times. Precision MRI Phenotyping Enables Detection of Small Changes in Body Composition for Longitudinal Cohorts Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3,088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom. A Randomised Trial of a Wearable UV dosimeter for Skin Cancer Prevention BackgroundNon-melanoma skin cancer (NMSC) is the most prevalent cancer in the United States, affecting 5 million people and costing $8.1 billion per year. Despite well-defined guidelines on ultraviolet radiation (UVR) avoidance, it remains difficult for people to assess their individual exposure, as UVR is invisible and the onset of symptoms due to UVR damage are delayed.

MethodsIn a prospective, randomized-controlled trial, 97 patients with a history of actinic keratoses were enrolled from March 2018 to July 2018 and followed over 6 months. 50 patients were given a wearable device that measured UV exposure and a smartphone application that provided both real-time and cumulative UV exposure information, and 47 patients were provided with UV protection counseling by a dermatologist.

ResultsWe observed a significant decrease in the incidence rate of NMSC in the intervention group compared to the control group over 3 months (p = 0.02). We did not observe a significant decrease in the incidence rate of actinic keratoses (AK) in the intervention group compared to the control group. The observed clinical benefit was not accompanied by psychological side effects such as anxiety and depression.

ConclusionsThis study suggests that providing real-time UV exposure data using a wearable UV dosimeter is a safe and effective behavioral change strategy to prevent NMSC. (Funded by the National Cancer Institute, contract HHSN261201700005C; ClinicalTrials.gov number NCT03315286.) Evaluating a first fully automated interview grounded in Multiple Mini Interview (MMI) methodology: results from a feasibility study ObjectivesGlobal, Covid-driven restrictions around face-to-face interviews for healthcare student selection have forced admissions staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first fully automated interview grounded in Multiple Mini-Interview (MMI) methodology. This study aimed to explore test re-test reliability, acceptability and usability of the system.

Design, setting and participantsmixed-methods feasibility study in Physician Associate (PA) programmes from two UK and one US university during 2019 - 2020.

Primary, secondary outcomesFeasibility measures (test retest reliability acceptability and usability) were assessed using intra-class correlation (ICC), descriptive statistics, thematic and content analysis.

MethodsVolunteers took (T1), then repeated (T2), the automated MMI, with a seven-day interval (+/- 2) then completed an evaluation questionnaire. Admissions staff participated in focus group discussions.

ResultsSixty-two students and seven admission staff participated; 34 students and four staff from UK and 28 students and three staff from US universities.

Good-excellent test-retest reliability was observed with T1 and T2 ICC between 0.62-0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005, individual site (all ICC[&ge;] 0.76 p<0.001) and mean test retest across sites 0.82 p<0.001 (95% CI 0.7-0.9).

Admissions staff reported potential to reduce resource costs and bias through a more objective screening tool for pre-selection or to replace some MMI stations in a  hybrid model. Maintaining human interaction through  touch points was considered essential.

Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored.

ConclusionThese preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end-users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection processes. Strain-resolved analysis in a randomized trial of antibiotic pretreatment and maintenance dose delivery mode with fecal microbiota transplant for ulcerative colitis Fecal microbiota transplant is a promising therapy for ulcerative colitis. Parameters maximizing effectiveness and tolerability are not yet clear, and it is not known how import the transmission of donor microbes to patients is. Here (clinicaltrails.gov: NCT03006809) we have tested the effects of antibiotic pretreatment and compared two modes of maintenance dose delivery, capsules versus enema, in a randomized, pilot, open-label, 2x2 factorial design with 22 patients analyzed with mild to moderate UC. Clinically, the treatment was well-tolerated with favorable safety profile. Of patients who received antibiotic pretreatment, 6 of 11 experienced remission after six weeks of treatment, versus 2 of 11 non-pretreated patients (odds ratio: 1.69, 95% confidence interval: -0.25 to 3.62). No significant differences were found between maintenance dosing via capsules versus enema. In exploratory analyses, microbiome turnover at both the species and strain levels was extensive and significantly more pronounced in the pretreated patients. Associations were also revealed between taxonomic turnover and changes in the composition of primary and secondary bile acids. Together these findings suggest that antibiotic pretreatment contributes to microbiome engraftment and possibly clinical effectiveness, and validate longitudinal strain tracking as a powerful way to monitor the dynamics and impact of microbiota transfer. Adaptation of HL7 FHIR for the exchange of patients' gene expression profiles High-throughput technologies, especially gene expression analyses can accurately capture the molecular state in patients under different conditions. Thus, their application in clinical routine gains increasing relevance and fosters patient stratification towards individualized treatment decisions. Electronic health records already evolved to capture genomic data within clinical systems and standards like FHIR enable sharing within, and even between institutions. However, FHIR only provides profiles tailored to variations in the molecular sequence, while expression patterns are neglected although being equally important for decision making. Here we provide an exemplary implementation of gene expression profiles of a microarray analysis of patients with acute myeloid leukemia using an adaptation of the FHIR genomics extension. Our results demonstrate how FHIR resources can be facilitated in clinical systems and thereby pave the way for usage for the aggregation and exchange of transcriptomic data in multi-center studies. Public-private partnership to strengthening COVID-19 response in Kisumu, Kenya INTRODUCTIONIn Kenya approximately half of healthcare services are delivered through private sector providers. In line with our experience building rapid responses to HIV and to support and accelerate the public sectors COVID-19 response, we initiated a PPP project in Kisumu County, Kenya. In this manuscript we demonstrate this PPPs performance using digital tools as a replicable model for scaling the response to COVID-19 in similar resource-poor settings.

METHODSCOVID-19 diagnostic testing formed the basis for a PPP between KEMRI, Department of Health Kisumu County, PharmAccess Foundation, and local faith-based and private healthcare facilities, thus naming our PPP: COVID-Dx. COVID-Dx was implemented from June 01, 2020, to March 31, 2021, in Kisumu County, Kenya. Trained laboratory technologists in participating healthcare facilities collected nasopharyngeal and oropharyngeal samples from patients meeting the Kenyan MoH COVID-19 case definition. Samples were tested using RT-PCR at the central reference laboratory in KEMRI. Healthcare workers in participating facilities collected patient clinical data using a digitized MoH COVID-19 Case Identification Form. We shared aggregated results from these data via (semi-) live dashboard to all relevant stakeholders. We did descriptive statistical analyses using Stata 16 to inform project processes.

ResultsNine facilities participated in the project. A detailed collaboration schedule was developed, supported by a semi-real time digital dashboard. A total of 4,324 PCR tests for SARS-CoV-2 were added to the public response, with 425 positives. Geo-mapped and time-tagged information on incident cases was collected and fed back to policymakers. At the beginning of the pandemic, there a heavy focus on health workers within Kisumu County, with a total of 1009 tested consisting about 43% of all County health workforce.

CONCLUSIONWe demonstrate feasibility to rapidly increase the public health sector response to a COVID-19 epidemic outbreak in a resource-poor setting. Our complementary public-private intervention in Kisumu, Kenya was based on a joint testing strategy and demonstrated that digitalization of the healthcare system can gain efficiencies, link public and private healthcare efforts, increase transparency and inform local policy makers with semi-real time data supporting targeting interventions. The Kisumu model is currently being scaled to 13 additional western Kenyan counties. Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes. Endometriosis symptoms are heterogeneous with controversy on whether it constitutes a single disease or multiple distinct types. Our previous work found recurrent somatic cancer-driver alterations in endometriosis; however, these have not been found ubiquitously. A handful of cases spread across studies also suggest mutations might be shared (clonal) between lesions of the same type. As current classification systems correlate poorly with symptoms or outcomes, somatic genomics may improve the current system. Here, we investigate heterogeneity of somatic cancer-driver mutations within patients and across endometriosis types. We examined anatomically distinct types of endometriosis (ovarian, deep infiltrating, and superficial endometriosis) in 27 individual patients all of whom had at least two types of endometriosis. Specimens were analyzed using high-sensitivity targeted sequencing with orthogonal validation from droplet digital PCR and mutation-surrogate immunohistochemistry. Results found 13/27 patients had informative somatic driver mutation in endometriosis, 9/13 had identical mutations across distinct lesions. Endometriomas tended to have a higher mutational complexity, with functionally redundant driver mutations in same gene and within the same lesions.

Our data are consistent with clonality across endometriosis lesions regardless of subtype. Further the finding of redundancy in mutations with the same gene and lesions is also consistent with endometriosis representing an oligoclonal disease with dissemination likely to consist of multiple epithelial clones travelling together. This suggests the current anatomically defined classification of endometriosis does not fully recognize the etiology of the disease. A novel classification should take into account genomic and other molecular features. These findings could further contribute to development of a more personalized endometriosis diagnosis and care. Estimating the impact of Angelman syndrome on parental productivity in Australia using productivity-adjusted life years Angelman syndrome (AS) is a severe neurodevelopmental condition associated with a significant socioeconomic impact. This is the first study to estimate the impact of AS on parental productivity in Australia using cost-of-illness modelling with simulated follow-up over a 10-year period using 2019 as the baseline year. Key data were obtained from a prospective cohort of AS families, peer-reviewed literature, and publicly-available sources. The base-case productivity burden borne by the estimated 330 living parents of the 428 prevalent-persons with AS totalled AUD$1.25 million, corresponding to a loss of 38.42% of PALYs per-parent. This shows that caring for a child with AS has a significant impact on the productivity of affected parents, with a large associated impact on the broader Australian economy. Neutrophils and triglycerides predict critical illness in COVID-19 and reveal CDK6 inhibitors as potential preventive treatment Despite the recent development of vaccines and monoclonal antibodies preventing SARS-CoV-2 infection, treating critically ill COVID-19 patients still remains a top goal. In principle, drug repurposing - the use of an already existing drug for a new indication - could provide a shortcut to a treatment. However, drug repurposing is often very speculative due to lack of clinical evidence. We report here on a methodology to find and test drug target candidates for drug repurposing. Using UK Biobank data, we screened for significant differences in 33 blood cell types, 30 blood biochemistries, and body mass index between an infectious disease phenotype and healthy controls. We then matched critically ill COVID-19 cases with controls that exhibited mild or no symptoms after SARS-CoV-2 infection. Using data from the UK Biobank, we describe a workflow to find evidence for high neutrophil cell count and high concentrations of blood triglycerides as predictors of the immune overreaction in critical illness due to COVID-19. Based on these findings, we identified the enzyme CDK6 as a potential drug target to prevent in high risk individuals with high neutrophil cell count the immune overreaction in critical illness due to COVID-19. Three existing CDK4/6 inhibitors -- abemaciclib, ribociclib, and palbociclib -- have been approved for the treatment of breast cancer. Clinical evidence for CDK4/6 inhibitors in treating critically ill COVID-19 patients has been reported. Further clinical investigations are ongoing. Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study) Psychiatric and psychosomatic diseases are an increasingly cumbersome burden for the medical system. Indeed, hospital costs associated with mental health conditions have been constantly on the rise in recent years. Moreover, psychiatric conditions are likely to have a negative effect on the treatment of other medical conditions and surgical outcomes, in addition to their direct effects on the overall quality of life. Our study aims to investigate the impact of preoperative Risk factor, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and length of hospital stay via predictive modeling techniques.

MethodPatient data will be collected from several participating national and international surgical centers The machine learning models will be calculated and coded, but also published according to the TRIPOD guidelines (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).

ResultIt is conceivable to arrive at generalizable models predicting the abovementioned endpoints through large amounts of data from several centers. The models will be subsequently deployed as a free-to-use web-based prediction tool using the Shiny environment [47]. The cost for the hosting server and digital infrastructure will be covered by Dr. Anas Taha. The sponsor will store the data for ten years.