Stereo flexible synthesis of the C8-C23 fragment of antarlides, androgen receptor antagonists.

A practical and efficient synthesis of the C8-C23 fragment of antarlides A-H, incorporating six stereocenters and a conjugated diene, is reported. A strategic combination of synthetic methods, including CBS reduction, Evans' aldol reaction, Keck-Maruoka allylation, and enzymatic resolution, enabled the selective introduction of these stereocenters. Furthermore, the pivotal coupling of key fragments is successfully executed through a Julia-Kocienski olefination reaction, connecting the C8-C14 and C15-C23 subunits.

Transcriptome profiling of type VI secretion system core gene tssM mutant of Xanthomonas perforans highlights regulators controlling diverse functions ranging from virulence to metabolism.

IMPORTANCE: T6SS has received attention due to its significance in mediating interorganismal competition through contact-dependent release of effector molecules into prokaryotic and eukaryotic cells. Reverse-genetic studies have indicated the role of T6SS in virulence in a variety of plant pathogenic bacteria, including the one studied here, Xanthomonas. However, it is not clear whether such effect on virulence is merely due to a shift in the microbiome-mediated protection or if T6SS is involved in a complex virulence regulatory network. In this study, we conducted in vitro transcriptome profiling in minimal medium to decipher the signaling pathways regulated by tssM-i3* in X. perforans AL65. We show that TssM-i3* regulates the expression of a suite of genes associated with virulence and metabolism either directly or indirectly by altering the transcription of several regulators. These findings further expand our knowledge on the intricate molecular circuits regulated by T6SS in phytopathogenic bacteria.

Construction of Octahydro-4H-cyclopenta[b]pyridin-6-one Skeletons using Pot, Atom, and Step Economy (PASE) Synthesis.

Cascade aza-Piancatelli reaction and [3+3]/[4+2] cycloaddition reactions are carried out using the ideality principles of pot, atom, and step economy (PASE) synthesis. The reaction resulted in generation of octahydro-4H-cyclopenta[b]pyridin-6-one scaffolds. Moreover, octahydro-5,7a-epoxycyclopenta[cd]isoindol-4-one frameworks of gracilamine alkaloid and a novel decahydro-1H-dicyclopenta[cd,hi]isoindol-6-one were also realized in good yields with excellent regio- and diastereo-selectivities.

Flurbiprofen inhibits heme induced NLRP3 inflammasome in Berkeley sickle cell disease mice.

Sickle cell disease (SCD) is accompanied by several complications, which emanate from the sickling of erythrocytes due to a point mutation in the ß-globin chain of hemoglobin. Sickled erythrocytes are unable to move smoothly through small blood capillaries and therefore, cause vaso occlusion and severe pain. Apart from pain, continuous lysis of fragile sickled erythrocytes leads to the release of heme, which is a strong activator of the NLRP3 inflammasome, thus producing chronic inflammation in sickle cell disease. In this study, we identified flurbiprofen among other COX-2 inhibitors to be a potent inhibitor of heme-induced NLRP3 inflammasome. We found that apart from being a nociceptive agent, flurbiprofen exerts a strong anti-inflammatory effect by suppressing NF-κB signaling, which was evidenced by reduced levels of TNF-α and IL-6 in wild-type and sickle cell disease Berkeley mice models. Our data further demonstrated the protective effect of flurbiprofen on liver, lungs, and spleen in Berkeley mice. The current sickle cell disease pain management regime relies mainly on opiate drugs, which is accompanied by several side effects without modifying the sickle cell disease-related pathology. Considering the potent role of flurbiprofen in inhibiting NLRP3 inflammasome and other inflammatory cytokines in sickle cell disease, our data suggests that it can be explored further for better sickle cell disease pain management along with the possibility of disease modification.

A novel approach to assess dynamic treatment regimes embedded in a SMART with an ordinal outcome.

Sequential multiple assignment randomized trials (SMARTs) are used to construct data-driven optimal intervention strategies for subjects based on their intervention and covariate histories in different branches of health and behavioral sciences where a sequence of interventions is given to a participant. Sequential intervention strategies are often called dynamic treatment regimes (DTR). In the existing literature, the majority of the analysis methodologies for SMART data assume a continuous primary outcome. However, ordinal outcomes are also quite common in clinical practice. In this work, first, we introduce the notion of generalized odds ratio ( G O R $$ GOR $$ ) to compare two DTRs embedded in a SMART with an ordinal outcome and discuss some combinatorial properties of this measure. Next, we propose a likelihood-based approach to estimate G O R $$ GOR $$ from SMART data, and derive the asymptotic properties of its estimate. We discuss alternative ways to estimate G O R $$ GOR $$ using concordant-discordant pairs and two-sample U $$ U $$ -statistic. We derive the required sample size formula for designing SMARTs with ordinal outcomes based on G O R $$ GOR $$ . A simulation study shows the performance of the estimated G O R $$ GOR $$ in terms of the estimated power corresponding to the derived sample size. The methodology is applied to analyze data from the SMART+ study, conducted in the UK, to improve carbohydrate periodization behavior in athletes using a menu planner mobile application, Hexis Performance. A freely available Shiny web app using R is provided to make the proposed methodology accessible to other researchers and practitioners.

Jump-Drop Adjusted Prediction of Cumulative Infected Cases Using the Modified SIS Model.

Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources in India. Historically, epidemiological models have helped in controlling such epidemics. Models require accurate historical data to predict future outcomes. In our data, there were days exhibiting erratic, apparently anomalous jumps and drops in the number of daily reported COVID-19 infected cases that did not conform with the overall trend. Including those observations in the training data would most likely worsen model predictive accuracy. However, with existing epidemiological models it is not straightforward to determine, for a specific day, whether or not an outcome should be considered anomalous. In this work, we propose an algorithm to automatically identify anomalous 'jump' and 'drop' days, and then based upon the overall trend, the number of daily infected cases for those days is adjusted and the training data is amended using the adjusted observations. We applied the algorithm in conjunction with a recently proposed, modified Susceptible-Infected-Susceptible (SIS) model to demonstrate that prediction accuracy is improved after adjusting training data counts for apparent erratic anomalous jumps and drops.

Access to Spiroindanolactones/lactams through an Aryne Insertion/Spirocyclization Strategy.

An efficient and metal-free strategy for the synthesis of spiro-fused indanolactones/lactams has been developed for the reaction of arynes with α-chloroacetyl lactones/lactams. This strategy provides access to spiroindanone derivatives via aryne insertion/spirocyclization.

Total synthesis of remdesivir.

Remdesivir, the first drug approved by the FDA to treat COVID-19, is in high demand for patients infected with the SARS-CoV-2 virus. Herein, we report a facile approach minimizing the protecting group manipulations to afford remdesivir in good overall yield.

Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART).

The sequential multiple assignment randomized trial (SMART) is a design used to develop dynamic treatment regimes (DTRs). Given that DTRs are generally less well researched, pilot SMART studies are often necessary. One challenge in pilot SMART is to determine the sample size such that it is small yet meaningfully informative for future full-fledged SMART. Here, we develop a precision-based approach, where the calculated sample size confines the marginal mean outcome of a DTR within a prespecified margin of error. The sample size calculations will be presented for two-stage SMARTs, and for various common outcome types.

COVID-19 in India: Statewise Analysis and Prediction.

BACKGROUND: The highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast. OBJECTIVE: Most of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources. METHODS: Since predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled. RESULTS: We found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R2 values corresponding to the logistic and the exponential models are above 0.90, indicating a reasonable goodness of fit. We also provide a web application to see the forecast based on recent data that is updated regularly. CONCLUSIONS: States with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

Interaction of the Xanthomonas effectors XopQ and XopX results in induction of rice immune responses.

Xanthomonas oryzae pv. oryzae uses several type III secretion system (T3SS) secreted effectors, namely XopN, XopQ, XopX and XopZ, to suppress rice immune responses that are induced following treatment with cell wall degrading enzymes. Here we show that a T3SS secreted effector XopX interacts with two of the eight rice 14-3-3 proteins. Mutants of XopX that are defective in 14-3-3 binding are also defective in suppression of immune responses, suggesting that interaction with 14-3-3 proteins is required for suppression of host innate immunity. However, Agrobacterium-mediated delivery of both XopQ and XopX into rice cells results in induction of rice immune responses. These immune responses are not observed when either protein is individually delivered into rice cells. XopQ-XopX-induced rice immune responses are not observed with a XopX mutant that is defective in 14-3-3 binding. Yeast two-hybrid, bimolecular fluorescence complementation and co-immunoprecipitation assays indicate that XopQ and XopX interact with each other. A screen for Xanthomonas effectors that can suppress XopQ-XopX-induced rice immune responses led to the identification of five effectors, namely XopU, XopV, XopP, XopG and AvrBs2, that could individually suppress these immune responses. These results suggest a complex interplay of Xanthomonas T3SS effectors in suppression of both pathogen-triggered immunity and effector-triggered immunity to promote virulence on rice.

Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Effect of alternative intravenous lipid emulsion in clinical outcome in non-critically ill patients.

INTRODUCTION: Most studies on alternative intravenous lipid emulsion (IVLE) versus conventional IVLE have been conducted in the critically ill patients. The benefits of alternative IVLE in non-critically ill patients is uncertain. We aim to determine clinical outcome difference between alternative IVLE versus conventional IVLE in non-critically ill patients. METHOD: All patients on parenteral nutrition (PN) from July 2007 to September 2010 were identified. Patients were stratified into two groups: conventional IVLE (soybean oil-based) and alternative IVLEs, namely MCT oil-based, olive oil-based and fish oil-containing IVLE. RESULT: Three hundred and eighty-eight patients were included in the study. Ninety-one patients received soybean-based IVLE, 59 patients received MCT oil-based IVLE, 141 patients received olive oil-based IVLE and 97 patients received fish oil-containing IVLE. Adjusting the effect of baseline covariates in separate multiple linear/logistic regression models, there were no differences in mortality, readmission, length of stay and infection between conventional IVLE group and alternative IVLEs group, the adjusted p-value was 0.64, 0.06, 0.36 and 0.18 respectively. However, there was a significant change in day 5 CRP between these two groups (8.43 g/L (SD 112.2) vs -41.2 (SD 106.4); adjusted p-value = 0.01). There was no difference in day 5 albumin between these two group (-1.03 (SD 5.1) vs -0.1 (SD 5.3); adjusted p-value = 0.08). CONCLUSION: Our study showed that pertinent clinical outcomes in non-critically ill patients who received either conventional IVLE or alternative IVLEs were the same. However, there was significant reduction in day-5 CRP in alternative IVLE compared to conventional IVLE.

Exploring the underlying biology of intrinsic cardiorespiratory fitness through integrative analysis of genomic variants and muscle gene expression profiling.

Intrinsic cardiorespiratory fitness (CRF) is defined as the level of CRF in the sedentary state. There are large individual differences in intrinsic CRF among sedentary adults. The physiology of variability in CRF has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored in the present study by interrogating intrinsic CRF-associated DNA sequence variation and skeletal muscle gene expression data from the HERITAGE Family Study through an integrative bioinformatics guided approach. A combined analytic strategy involving genetic association, pathway enrichment, tissue-specific network structure, cis-regulatory genome effects, and expression quantitative trait loci was used to select and rank genes through a variation-adjusted weighted ranking scheme. Prioritized genes were further interrogated for corroborative evidence from knockout mouse phenotypes and relevant physiological traits from the HERITAGE cohort. The mean intrinsic V̇o2max was 33.1 ml O2·kg-1·min-1 (SD = 8.8) for the sample of 493 sedentary adults. Suggestive evidence was found for gene loci related to cardiovascular physiology (ATE1, CASQ2, NOTO, and SGCG), hematopoiesis (PICALM, SSB, CA9, and CASQ2), skeletal muscle phenotypes (SGCG, DMRT2, ADARB1, and CASQ2), and metabolism (ATE1, PICALM, RAB11FIP5, GBA2, SGCG, PRADC1, ARL6IP5, and CASQ2). Supportive evidence for a role of several of these loci was uncovered via association between DNA variants and muscle gene expression levels with exercise cardiovascular and muscle physiological traits. This initial effort to define the underlying molecular substrates of intrinsic CRF warrants further studies based on appropriate cohorts and study designs, complemented by functional investigations. NEW & NOTEWORTHY Intrinsic cardiorespiratory fitness (CRF) is measured in the sedentary state and is highly variable among sedentary adults. The physiology of variability in intrinsic cardiorespiratory fitness has received much attention, but little is known about the genetic and molecular mechanisms that impact intrinsic CRF. These issues were explored computationally in the present study, with further corroborative evidence obtained from analysis of phenotype data from knockout mouse models and human cardiovascular and skeletal muscle measurements.

Exploring the associations between systemic inflammation, obesity and healthy days: a health related quality of life (HRQOL) analysis of NHANES 2005-2008.

BACKGROUND: Obesity is positively associated with low-level chronic inflammation, and negatively associated with several indices of health-related quality of life (HRQOL). It is however not clear if obesity-associated inflammation is partly responsible for the observed negative associations between obesity and HRQOL, and also whether systemic inflammation independently affects HRQOL. We conducted an exploratory analysis to investigate the relationships between obesity, systemic inflammation and indices of HRQOL, using NHANES survey data. METHODS: Data for the variables of interest were available for 6325 adults (aged 20-75 years, BMI > 18.5 kg/m2). Demographic, body mass index (BMI), C-reactive protein (CRP), inflammatory disease status, medication use, smoking, and HRQOL data were obtained from NHANES (2005-2008) and analyzed using sampling-weighted generalized linear models. Data was subjected to multiple imputation in order to mitigate information loss from survey non-response. Both main effects and interaction effects were analyzed to evaluate possible mediation or moderation effects. Model robustness was ascertained via sensitivity analysis. Averaged results from the imputed datasets were reported in as odds ratios (OR) and confidence intervals (CI). RESULTS: Obesity was positively associated with poor physical healthy days (OR: 1.59, 95% CI: 1.15-2.21) in unadjusted models. 'Elevated' and 'clinically raised' levels of the inflammation marker CRP were also positively associated with poor physical healthy days (OR = 1.61, 95% CI: 1.23-2.12, and OR = 2.45, 95% CI: 1.84-3.26, respectively); additionally, 'clinically raised' CRP was positively associated with mental unhealthy days (OR = 1.66, 95% CI: 1.26-2.19). The association between obesity and physical HRQOL was rendered non-significant in models including CRP. Association between 'elevated' and 'clinically raised' CRP and physical unhealthy days remained significant even after adjustment for obesity or inflammation-modulating covariates (OR = 1.36, 95% CI: 1.02-1.82, and OR = 1.75, 95% CI: 1.21-2.54, respectively). CONCLUSIONS: Systemic inflammation appears to mediate the association between obesity and physical unhealthy days. Clinically raised inflammation is an independent determinant of physical and mental unhealthy days. Importantly, elevated (but sub-clinical) inflammation is also negatively associated with physical healthy days, and may warrant more attention from a population health perspective than currently appreciated.

Sample size of the reference sample in a case-augmented study.

The case-augmented study, in which a case sample is augmented with a reference (random) sample from the source population with only covariates information known, is becoming popular in different areas of applied science such as pharmacovigilance, ecology, and econometrics. In general, the case sample is available from some source (for example, hospital database, case registry, etc.); however, the reference sample is required to be drawn from the corresponding source population. The required minimum size of the reference sample is an important issue in this regard. In this work, we address the minimum sample size calculation and discuss related issues. Copyright © 2017 John Wiley & Sons, Ltd.

Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial.

A dynamic treatment regimen consists of decision rules that recommend how to individualize treatment to patients based on available treatment and covariate history. In many scientific domains, these decision rules are shared across stages of intervention. As an illustrative example, we discuss STAR*D, a multistage randomized clinical trial for treating major depression. Estimating these shared decision rules often amounts to estimating parameters indexing the decision rules that are shared across stages. In this article, we propose a novel simultaneous estimation procedure for the shared parameters based on Q-learning. We provide an extensive simulation study to illustrate the merit of the proposed method over simple competitors, in terms of the treatment allocation matching of the procedure with the "oracle" procedure, defined as the one that makes treatment recommendations based on the true parameter values as opposed to their estimates. We also look at bias and mean squared error of the individual parameter-estimates as secondary metrics. Finally, we analyze the STAR*D data using the proposed method.

Effect of reporting bias in the analysis of spontaneous reporting data.

It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities. This work incorporates the reporting probabilities into existing methodologies, specifically to Bayesian confidence propagation neural network and DuMouchel's empirical Bayesian methods, and shows how the two methods lead to biased results in the presence of under-reporting. Considering all the cases to be reported, the association measure for the source population can be estimated by using only exposure information through a reference sample from the source population.

Analysis of spontaneous adverse drug reaction (ADR) reports using supplementary information.

Assessment of safety of newly marketed drugs is an important public health issue. Once the drug is in the market, clinicians and/or health professionals are responsible for recognizing and reporting suspected side effects known as adverse drug reaction (ADR). Such reports are collected in a so-called spontaneous reporting (SR) system. The primary purpose of spontaneous ADR reporting is to provide early warnings or suspicions, which have not been recognized prior to marketing of a drug because of limitations of clinical trials. We shall discuss the existing work to analyze the SR database and their drawbacks and also suggest methodologies to tackle these drawbacks by defining a source population and considering the problem of under-reporting, with the help of supplementary data. Unbiased estimate of population odds-ratio has been obtained and the corresponding asymptotic results are derived.