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Catalytic asymmetric α-alkylation of carbonyl compounds represents a long-standing challenge in synthetic organic chemistry. Herein, we advance a dual biocatalytic platform for the efficient asymmetric alkylation of α-keto acids. First, guided by our recently obtained crystal structures, we develop SgvMVAV as a general biocatalyst for the enantioselective methylation, ethylation, allylation and propargylation of a range of α-keto acids with total turnover numbers (TTNs) up to 4,600. Second, we mine a family of bacterial HMTs from Pseudomonas species sharing less than 50% sequence identities with known HMTs and evaluated their activities in SAM regeneration. Our best performing HMT from P. aeruginosa, PaHMT, displays the highest SAM regeneration efficiencies (TTN up to 7,700) among HMTs characterized to date. Together, the synergistic use of SgvMVAV and PaHMT affords a fully biocatalytic protocol for asymmetric methylation featuring a record turnover efficiency, providing a solution to the notorious problem of asymmetric alkylation.

We report novel empirical estimates of the race-specific effects of larger police forces in the United States. Each additional police officer abates approximately 0.1 homicides. In per capita terms, effects are twice as large for Black versus White victims. Larger police forces also make fewer arrests for serious crimes, with larger reductions for crimes with Black suspects, implying that police force growth does not increase racial disparities among the most serious charges. At the same time, larger police forces make more arrests for low-level "quality-of-life" offenses, with effects that imply a disproportionate impact for Black Americans.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

ObjectivesConcern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.MethodsWe performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Background: Variation in usual practice in fluid trials assessing lower versus higher volumes may affect overall comparisons. To address this, we will evaluate the effects of heterogeneity in treatment intensity in the Conservative versus Liberal Approach to Fluid Therapy of Septic Shock in Intensive Care trial. This will reflect the effects of differences in site‐specific intensities of standard fluid treatment due to local practice preferences while considering participant characteristics. Methods: We will assess the effects of heterogeneity in treatment intensity across one primary (all‐cause mortality) and three secondary outcomes (serious adverse events or reactions, days alive without life support and days alive out of hospital) after 90 days. We will classify sites based on the site‐specific intensity of standard fluid treatment, defined as the mean differences in observed versus predicted intravenous fluid volumes in the first 24 h in the standard‐fluid group while accounting for differences in participant characteristics. Predictions will be made using a machine learning model including 22 baseline predictors using the extreme gradient boosting algorithm. Subsequently, sites will be grouped into fluid treatment intensity subgroups containing at least 100 participants each. Subgroups differences will be assessed using hierarchical Bayesian regression models with weakly informative priors. We will present the full posterior distributions of relative (risk ratios and ratios of means) and absolute differences (risk differences and mean differences) in each subgroup. Discussion: This study will provide data on the effects of heterogeneity in treatment intensity while accounting for patient characteristics in critically ill adult patients with septic shock. Registrations: The European Clinical Trials Database (EudraCT): 2018‐000404‐42, ClinicalTrials. gov: NCT03668236. [ABSTRACT FROM AUTHOR]

This paper examines Christian responses to Emperor Julian's attempt to build a Jewish Temple in Jerusalem (ca. 363 CE) and compares these reactions to those elicited by early Islamic construction on the Temple Mount, especially ʿAbd al-Malik's construction of the Dome of the Rock (ca. 692 CE). Both projects sparked Christian rhetoric that was often "apocalyptic," combining a potent mix of biblical citations with a deep-seated fear of a Jewish alliance with a pagan tyrant. But how should we read this apocalyptic rhetoric? Was the late antique religious environment, as many have recently suggested, one of particular eschatological foment? This essay suggests otherwise. An analysis of contemporary responses to Julian and ʿAbd al-Malik reveals that Christian reactions to these highly charged architectural endeavors were often classical invective clothed in biblical garb. This scripturalized polemic was flexible and self-consciously contingent on the successes and failures of historical actors. Rather than arguing about the end of the age, Christians were arguing about how best to interpret contemporary history. They were arguing, moreover, how best to position religious competitors—Jewish or Muslim—within this shifting story. [ABSTRACT FROM AUTHOR]

The suicide rate in the United States has risen nearly 40% since 2000. This increase is puzzling because suicide rates had been falling for decades at the end of the 20th century. In this paper, we review important facts about the changing rate of suicide. General trends do not tell the story of important differences across groups—suicide rates rose substantially among middle‐aged persons between 2005 and 2015 but have fallen since. Among young people, suicide rates began a rapid rise after 2010 that has not abated. We review empirical evidence to assess potential causes for recent changes in suicide rates. The economic hardship caused by the Great Recession played an important role in rising suicide among prime‐aged Americans. Among those under 25, nearly all the increase in suicide mortality during the 2010s can be explained by an increase in the prevalence of depression. Bullying victimization of LGBTQ youth could also account for part of the rise in suicide. The evidence that access to firearms or opioids are major drivers of recent suicide trends is less clear. We end by summarizing evidence on the most promising policies to reduce suicide mortality. [ABSTRACT FROM AUTHOR]

Different combined outcome‐data lags (follow‐up durations plus data‐collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome‐data lags (0–105 days) on the performance of various multi‐stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response‐adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type‐1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome‐data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome‐data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome‐data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response‐adaptive randomisation. Increased outcome‐data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome‐data lags when planning adaptive trials. [ABSTRACT FROM AUTHOR]