Tedizolid (TZD) and daptomycin (DAP) were assessed in a rat endocarditis model against Enterococcus faecalis, Enterococcus faecium (resistant to vancomycin and ampicillin), and Staphylococcus aureus. As a monotherapy, TZD for 5 days was not effective in a comparison with no-treatment controls, while DAP for 5 days was significantly effective against these bacteria. Step-down therapy (DAP for 3 days followed by TZD for 2 days) was as effective as DAP for 5 days and was comparable to 3 days of DAP plus ceftriaxone against all bacteria and to 3 days of DAP plus gentamicin against E. faecalis OG1RF.

We investigated dose-fractionated polymyxin B (PB) on acute kidney injury (AKI). PB at 12 mg of drug/kg of body weight per day (once, twice, and thrice daily) was administered in rats over 72 h. The thrice-daily group demonstrated the highest KIM-1 increase (P = 0.018) versus that of the controls (P = 0.99) and histopathological damage (P = 0.013). A three-compartment model best described the data (bias, 0.129 mg/liter; imprecision, 0.729 mg²/liter²; R², 0.652,). Area under the concentration-time curve at 24 h (AUC₂₄) values were similar (P = 0.87). The thrice-daily dosing scheme resulted in the most PB-associated AKI in a rat model.

Animal models of bacterial infection have been widely used to explore the in vivo activity of antibacterial drugs. These data are often submitted to the U.S. Food and Drug Administration to support human use in an investigational new drug application (IND). To better understand the range and scientific use of animal models in regulatory submissions, a database was created surveying recent pneumonia models submitted as part of IND application packages. The IND studies were compared to animal models of bacterial pneumonia published in the scientific literature over the same period of time. In this review, we analyze the key experimental design elements, such as animal species, immune status, pathogens selected, and route of administration, and study endpoints.

Enterococcus faecium strains are commonly resistant to vancomycin and β-lactams. In addition, E. faecium often causes biofilm-associated infections and these infections are difficult to treat. In this context, we investigated the activity of dosing regimens using daptomycin (DAP) (8, 10, 12, and 14 mg/kg of body weight/day) alone and in combination with ceftaroline (CPT), ampicillin (AMP), ertapenem (ERT), and rifampin (RIF) against 2 clinical strains of biofilm-producing vancomycin-resistant Enterococcus faecium (VREfm), namely, strains S447 and HOU503, in an in vitro biofilm model. HOU503 harbors common LiaS and LiaR substitutions, whereas S447 lacks mutations associated with the LiaFSR pathway. MIC results demonstrated that both strains were susceptible to DAP and resistant to CPT, AMP, ERT, and RIF. The 168-h pharmacokinetic/pharmacodynamic (PK/PD) CDC biofilm reactor models (simulating human antibiotic exposures) were used with titanium and polyurethane coupons to evaluate the efficacy of antibiotic combinations. DAP 12 and 14 achieved bactericidal activity against S447 but lacked such effect against HOU503. Addition of ERT and RIF enhanced DAP activity, allowing DAP 8 and 10 plus ERT or RIF to produce bactericidal activity against both strains at 168 h. While DAP 8 and 10 plus CPT improved killing, they did not reach bactericidal reduction against S447. Combination of AMP, CPT, ERT, or RIF resulted in enhanced and bactericidal activity for DAP against HOU503 at 168 h. Our data provide further support for the use of combinations of DAP with AMP, ERT, CPT, and RIF in infections caused by biofilm producing VREfm. Further research involving DAP combinations against biofilm-producing enterococci is warranted.

Fluoroquinolones are reported to possess immunomodulatory activity; hence, a novel benzoquinolizine fluoroquinolone, levonadifloxacin, was evaluated in lipopolysaccharide-stimulated human whole-blood (HWB) and mouse acute lung injury (ALI) models. Levonadifloxacin significantly mitigated the inflammatory responses in an HWB assay through inhibition of proinflammatory cytokines and in the ALI model by lowering lung total white blood cell count, myeloperoxidase, and cytokine levels. The immunomodulatory effect of levonadifloxacin, along with promising antibacterial activity, is expected to provide clinical benefits in the treatment of infections.

A chambered heart is common to all vertebrates, but reptiles show unparalleled variation in ventricular septation, ranging from almost absent in tuataras to full in crocodilians. Because mammals and birds evolved independently from reptile lineages, studies on reptile development may yield insight into the evolution and development of the full ventricular septum. Compared with reptiles, mammals and birds have evolved several other adaptations, including compact chamber walls and a specialized conduction system. These adaptations appear to have evolved from precursor structures that can be studied in present-day reptiles. The increase in the number of studies on reptile heart development has been greatly facilitated by sequencing of several genomes and the availability of good staging systems. Here, we place reptiles in their phylogenetic context with a focus on features that are primitive when compared with the homologous features of mammals. Further, an outline of major developmental events is given, and variation between reptile species is discussed.

Hepatitis C virus (HCV) is an important and underreported infectious disease, causing chronic infection in ~71 million people worldwide. The limited host range of HCV, which robustly infects only humans and chimpanzees, has made studying this virus in vivo challenging and hampered the development of a desperately needed vaccine. The restrictions and ethical concerns surrounding biomedical research in chimpanzees has made the search for an animal model all the more important. In this review, we discuss different approaches that are being pursued toward creating small animal models for HCV infection. Although efforts to use a nonhuman primate species besides chimpanzees have proven challenging, important advances have been achieved in a variety of humanized mouse models. However, such models still fall short of the overarching goal to have an immunocompetent, inheritably susceptible in vivo platform in which the immunopathology of HCV could be studied and putative vaccines development. Alternatives to overcome this include virus adaptation, such as murine-tropic HCV strains, or the use of related hepaciviruses, of which many have been recently identified. Of the latter, the rodent/rat hepacivirus from Rattus norvegicus species-1 (RHV-rn1) holds promise as a surrogate virus in fully immunocompetent rats that can inform our understanding of the interaction between the immune response and viral outcomes (i.e., clearance vs. persistence). However, further characterization of these animal models is necessary before their use for gaining new insights into the immunopathogenesis of HCV and for conceptualizing HCV vaccines.

BACKGROUND AND PURPOSE:

Brain metastases are a common finding on brain MRI. However, the factors that dictate their size and distribution are incompletely understood. Our aim was to discover a statistical model that can account for the size distribution of parenchymal metastases in the brain as measured on contrast-enhanced MR imaging.

A program that combines artificial intelligence and geometrical modelling can create an accurate 3D model of your face from a single 20-second video

Shannon's said: “OCRO HLI RGWR NMIELWIS”

Hindsight is 20/20, even if you’re smizing.

Asos has been making more use of augmented reality (AR)...

The post Coronavirus: Asos Scales Up Usage Of AR Technology To Model Products appeared first on Man's World India.

A new process to harness multiple disease models for outbreak management has been developed by an international team of researchers. The team will immediately implement the process to help inform policy decisions for the COVID-19 outbreak.

Whirlpool has announced they will recall thousands more washing machines which could risk causing fires in people's homes.

First and last names, email addresses, gender and sexual orientation, and credit card information of models and users was left on an insecure server

https://www.bestbuy.com/site/apple-ipad-latest-model-with-wi-fi-128gb-space-gray/5985613.p?skuId=5985613

I'm thinking about it, but I am uniformed about the current ipads.

Airpods are $139.99

Statistical models used by IMD will still be used for monsoon forecast, but the ministry of earth sciences is putting more emphasis on dynamic models.

Tyra Banks agrees that America's Next Top Model has aged, well, poorly. The Sports Illustrated covergirl and host of ANTM came under fire this week when resurfaced clips from the...

In the COVID-19 pandemic, numerous models are being used to predict the future. But as helpful as they are, they cannot make sense of themselves. They rely on epidemiologists and other modelers to interpret them. Trouble is, making predictions in a pandemic is also a philosophical exercise. We need to think about hypothetical worlds, causation, evidence, and the relationship between models and reality.1,2

The value of philosophy in this crisis is that although the pandemic is unique, many of the challenges of prediction, evidence, and modeling are general problems. Philosophers like myself are trained to see the most general contours of problems—the view from the clouds. They can help interpret scientific results and claims and offer clarity in times of uncertainty, bringing their insights down to Earth. When it comes to predicting in an outbreak, building a model is only half the battle. The other half is making sense of what it shows, what it leaves out, and what else we need to know to predict the future of COVID-19.

Prediction is about forecasting the future, or, when comparing scenarios, projecting several hypothetical futures. Because epidemiology informs public health directives, predicting is central to the field. Epidemiologists compare hypothetical worlds to help governments decide whether to implement lockdowns and social distancing measures—and when to lift them. To make this comparison, they use models to predict the evolution of the outbreak under various simulated scenarios. However, some of these simulated worlds may turn out to misrepresent the real world, and then our prediction might be off.

In his book Philosophy of Epidemiology, Alex Broadbent, a philosopher at the University of Johannesburg, argues that good epidemiological prediction requires asking, “What could possibly go wrong?” He elaborated in an interview with Nautilus, “To predict well is to be able to explain why what you predict will happen rather than the most likely hypothetical alternatives. You consider the way the world would have to be for your prediction to be true, then consider worlds in which the prediction is false.” By ruling out hypothetical worlds in which they are wrong, epidemiologists can increase their confidence that they are right. For instance, by using antibody tests to estimate previous infections in the population, public health authorities could rule out the hypothetical possibility (modeled by a team at Oxford) that the coronavirus has circulated much more widely than we think.3

One reason the dynamics of an outbreak are often more complicated than a traditional model can predict is that they result from human behavior and not just biology.

Broadbent is concerned that governments across Africa are not thinking carefully enough about what could possibly go wrong, having for the most part implemented coronavirus policies in line with the rest of the world. He believes a one-size-fits-all approach to the pandemic could prove fatal.4 The same interventions that might have worked elsewhere could have very different effects in the African context. For instance, the economic impacts of social distancing policies on all-cause mortality might be worse because so many people on the continent suffer increased food insecurity and malnutrition in an economic downturn.5 Epidemic models only represent the spread of the infection. They leave out important elements of the social world.

Another limitation of epidemic models is that they model the effect of behaviors on the spread of infection, but not the effect of a public health policy on behaviors. The latter requires understanding how a policy works. Nancy Cartwright, a philosopher at Durham University and the University of California, San Diego, suggests that “the road from ‘It works somewhere’ to ‘It will work for us’ is often long and tortuous.”6 The kinds of causal principles that make policies effective, she says, “are both local and fragile.” Principles can break in transit from one place to the other. Take the principle, “Stay-at-home policies reduce the number of social interactions.” This might be true in Wuhan, China, but might not be true in a South African township in which the policies are infeasible or in which homes are crowded. Simple extrapolation from one context to another is risky. A pandemic is global, but prediction should be local.

Predictions require assumptions that in turn require evidence. Cartwright and Jeremy Hardie, an economist and research associate at the Center for Philosophy of Natural and Social Science at the London School of Economics, represent evidence-based policy predictions using a pyramid, where each assumption is a building block.7 If evidence for any assumption is missing, the pyramid might topple. I have represented evidence-based medicine predictions using a chain of inferences, where each link in the chain is made of an alloy containing assumptions.8 If any assumption comes apart, the chain might break.

An assumption can involve, for example, the various factors supporting an intervention. Cartwright writes that “policy variables are rarely sufficient to produce a contribution [to some outcome]; they need an appropriate support team if they are to act at all.” A policy is only one slice of a complete causal pie.9 Take age, an important support factor in causal principles of social distancing. If social distancing prevents deaths primarily by preventing infections among older individuals, wherever there are fewer older individuals there may be fewer deaths to prevent—and social distancing will be less effective. This matters because South Africa and other African countries have younger populations than do Italy or China.10

The lesson that assumptions need evidence can sound obvious, but it is especially important to bear in mind when modeling. Most epidemic modeling makes assumptions about the reproductive number, the size of the susceptible population, and the infection-fatality ratio, among other parameters. The evidence for these assumptions comes from data that, in a pandemic, is often rough, especially in early days. It has been argued that nonrepresentative diagnostic testing early in the COVID-19 pandemic led to unreliable estimates of important inputs in our epidemic modeling.11

Epidemic models also don’t model all the influences of the pathogen and of our policy interventions on health and survival. For example, what matters most when comparing deaths among hypothetical worlds is how different the death toll is overall, not just the difference in deaths due to the direct physiological effects of a virus. The new coronavirus can overwhelm health systems and consume health resources needed to save non-COVID-19 patients if left unchecked. On the other hand, our policies have independent effects on financial welfare and access to regular healthcare that might in turn influence survival.

A surprising difficulty with predicting in a pandemic is that the same pathogen can behave differently in different settings. Infection fatality ratios and outbreak dynamics are not intrinsic properties of a pathogen; these things emerge from the three-way interaction among pathogen, population, and place. Understanding more about each point in this triangle can help in predicting the local trajectory of an outbreak.

In April, an influential data-driven model, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, which uses a curve-fitting approach, came under criticism for its volatile projections and questionable assumption that the trajectory of COVID-19 deaths in American states can be extrapolated from curves in other countries.12,13 In a curve-fitting approach, the infection curve representing a local outbreak is extrapolated from data collected locally along with data regarding the trajectory of the outbreak elsewhere. The curve is drawn to fit the data. However, the true trajectory of the local outbreak, including the number of infections and deaths, depends upon characteristics of the local population as well as policies and behaviors adopted locally, not just upon the virus.

Predictions require assumptions that in turn require evidence.

Many of the other epidemic models in the coronavirus pandemic are SIR-type models, a more traditional modelling approach for infectious-disease epidemiology. SIR-type models represent the dynamics of an outbreak, the transition of individuals in the population from a state of being susceptible to infection (S) to one of being infectious to others (I) and, finally, recovered from infection (R). These models simulate the real world. In contrast to the data-driven approach, SIR models are more theory-driven. The theory that underwrites them includes the mathematical theory of outbreaks developed in the 1920s and 1930s, and the qualitative germ theory pioneered in the 1800s. Epidemiologic theories impart SIR-type models with the know-how to make good predictions in different contexts.

For instance, they represent the transmission of the virus as a factor of patterns of social contact as well as viral transmissibility, which depend on local behaviors and local infection control measures, respectively. The drawback of these more theoretical models is that without good data to support their assumptions they might misrepresent reality and make unreliable projections for the future.

One reason why the dynamics of an outbreak are often more complicated than a traditional model can predict, or an infectious-disease epidemiology theory can explain, is that the dynamics of an outbreak result from human behavior and not just human biology. Yet more sophisticated disease-behavior models can represent the behavioral dynamics of an outbreak by modeling the spread of opinions or the choices individuals make.14,15 Individual behaviors are influenced by the trajectory of the epidemic, which is in turn influenced by individual behaviors.

“There are important feedback loops that are readily represented by disease-behavior models,” Bert Baumgartner, a philosopher who has helped develop some of these models, explains. “As a very simple example, people may start to socially distance as disease spreads, then as disease consequently declines people may stop social distancing, which leads to the disease increasing again.” These looping effects of disease-behavior models are yet another challenge to predicting.

It is a highly complex and daunting challenge we face. That’s nothing unusual for doctors and public health experts, who are used to grappling with uncertainty. I remember what that uncertainty felt like when I was training in medicine. It can be discomforting, especially when confronted with a deadly disease. However, uncertainty need not be paralyzing. By spotting the gaps in our models and understanding, we can often narrow those gaps or at least navigate around them. Doing so requires clarifying and questioning our ideas and assumptions. In other words, we must think like a philosopher.

Jonathan Fuller is an assistant professor in the Department of History and Philosophy of Science at the University of Pittsburgh. He draws on his dual training in philosophy and in medicine to answer fundamental questions about the nature of contemporary disease, evidence, and reasoning in healthcare, and theory and methods in epidemiology and medical science.

References

1. Walker, P., et al. The global impact of COVID-19 and strategies for mitigation and suppression. Imperial College London (2020).

2. Flaxman, S., et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020).

3. Lourenco, J., et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. medRxiv:10.1101/2020.03.24.20042291 (2020).

4. Broadbent, A., & Smart, B. Why a one-size-fits-all approach to COVID-19 could have lethal consequences. TheConversation.com (2020).

5. United Nations. Global recession increases malnutrition for the most vulnerable people in developing countries. United Nations Standing Committee on Nutrition (2009).

6. Cartwright, N. Will this policy work for you? Predicting effectiveness better: How philosophy helps. Philosophy of Science 79, 973-989 (2012).

7. Cartwright, N. & Hardie, J. Evidence-Based Policy: A Practical Guide to Doing it Better Oxford University Press, New York, New York (2012).

8. Fuller, J., & Flores, L. The Risk GP Model: The standard model of prediction in medicine. Studies in History and Philosophy of Biological and Biomedical Sciences 54, 49-61 (2015).

9. Rothman, K., & Greenland, S. Causation and causal inference in epidemiology. American Journal Public Health 95, S144-S50 (2005).

10. Dowd, J. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences 117, 9696-9698 (2020).

11. Ioannidis, J. Coronavirus disease 2019: The harms of exaggerated information and non‐evidence‐based measures. European Journal of Clinical Investigation 50, e13222 (2020).

12. COVID-19 Projections. Healthdata.org. https://covid19.healthdata.org/united-states-of-america.

13. Jewell, N., et al. Caution warranted: Using the Institute for Health metrics and evaluation model for predicting the course of the COVID-19 pandemic. Annals of Internal Medicine (2020).

14. Nardin, L., et al. Planning horizon affects prophylactic decision-making and epidemic dynamics. PeerJ 4:e2678 (2016).

15. Tyson, R., et al. The timing and nature of behavioural responses affect the course of an epidemic. Bulletin of Mathematical Biology 82, 14 (2020).

Lead image: yucelyilmaz / Shutterstock

Arizona's Republican Gov. Doug Ducey's administration disbanded a panel of university scientists who had warned that reopening the state now would be dangerous.

(Family Hour/Thirty Tigers)
Sean and Sara Watkins are back and in reflective mood

California’s Sean and Sara Watkins are akin to royalty in American folk circles, firstly as founding members of the hugely successful Nickel Creek, and secondly as hosts of an 18-year residency at LA’s Largo club, where they perform alongside invited guests. Brother Sister draws on both strands of their history. Like its self-titled 2015 predecessor, the album sets aside the pizzazz of Nickel Creek for a down-home approach, but instead of boisterous, star-studded cover versions come five original songs and a minimal musical palette.

Alternating on lead, the pair’s vocals remain a model of sibling harmony, while the interplay between Sean’s intricate guitar picking and Sara’s elegant fiddle is similarly impressive – the breakneck bluegrass instrumental Bella and Ivan is a case in point. Mostly, however, the mood is reflective. Lafayette and Miles of Desert Sand chronicle the search for a better life, and Fake Badge, Real Gun is an artful snipe at Trump – “Throw your tantrums but the truth will be waiting”. Warren Zevon’s forlorn Accidentally Like a Martyr fits in neatly, while Charley Jordan’s ribald Keep It Clean is a gleeful example of a Largo session.

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The first ever set of specific recommendations to support transgender autistic young people was co-created by these youth and their families working hand-in-hand with clinical experts. The resulting model offers clinicians a set of concrete ways to provide this unique population the support they need.

Lipids are essential building blocks of cell membranes, which control the exchange of substances and energy between a cell and its environment. Developed at the Moscow Institute of Physics and Technology, a new open-source software tool PCAlipids aims to analyze lipid behavior.

The modelling predicts more than a quarter of a million jobs could be lost in Victoria due to the coronavirus pandemic in what Premier Daniel Andrews says is the perhaps the "biggest economic and employment challenge" in the state's history.

New Delhi (Sputnik) Apr 22, 2020
Due to the unavailability of a reliable model to predict the electron density of the ionosphere, navigation errors remain, creating technological hurdles. A new model developed by Indian researchers has potential applications in calculating these Global Navigation Satellite System (GNSS) positioning errors. In a major development that could have a wider impact, ranging from accurate aiming

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The Department of Justice filed a civil antitrust lawsuit today challenging Anheuser-Busch InBev’s (ABI) proposed acquisition of total ownership and control of Grupo Modelo.

The Department of Justice announced today that it has reached a settlement with Anheuser-Busch InBev SA/NV (ABI) and Grupo Modelo S.A.B. de C.V. that requires the companies to divest Modelo’s entire U.S. business to Constellation Brands Inc., in order to go forward with their merger.

The Department of Justice’s Office of Justice Programs (OJP), in partnership with the International Association of Chiefs of Police (IACP), today released a seminal model policy regarding police interaction with children who are impacted when a parent is arrested and law enforcement carries out its investigative and arrest responsibilities. Reflecting the collective input of a wide range of subject-matter experts and stakeholders, and understanding that interactions between children and law enforcement create lasting impressions, the resulting model policy, Safeguarding Children of Arrested Parents , provides strategies for law enforcement to improve their procedures and positively impact the communities they serve