Woodrufftyson5429

ary interactions (e.g., in predator-prey interactions), yet the physical mechanisms underlying trait matching are understudied and rarely quantified. We summarize existing methods and present novel approaches that can be used to quantify key benefits to interacting partners in a variety of ecological systems.

To provide pharmacists with an overview of ocular viscosurgical devices (OVDs) and a comprehensive resource describing characteristics of commercially available agents.

OVDs are substances that are injected into the eye during ophthalmic procedures, such as cataract surgery, to reduce injury to the endothelium that may result from surgical manipulation. Currently available OVDs are composed of one or more of the following active ingredients sodium hyaluronate, sodium chondroitin sulfate, and hydroxypropylmethylcellulose. Rheologic properties of OVDs, such as viscosity, elasticity, pseudoplasticity, and cohesion, affect the products' function and performance. find more Based on rheologic properties, OVDs can be generally classified as cohesive or dispersive. Given each products' unique characteristics, OVDs are not interchangeable. An understanding of OVD characteristics and role in practice allows for improved product selection, which varies based on patient characteristics and procedure. Availability of OVD information and literature is generally lacking since OVDs are regulated by the US Food and Drug Administration (FDA) as medical devices. This primer includes an overview of relevant ophthalmic surgical practices and the landscape of comparative efficacy and safety literature to assist in formulary decision-making. This review also provides a comprehensive guide to commercially available OVDs and a discussion on practical considerations for the pharmacist.

Pharmacists may be tasked with handling OVDs in institutional settings. Knowledge about OVD rheologic properties, product characteristics, role in practice, and available literature is necessary for managing formularies and ensuring optimal product selection.

Pharmacists may be tasked with handling OVDs in institutional settings. Knowledge about OVD rheologic properties, product characteristics, role in practice, and available literature is necessary for managing formularies and ensuring optimal product selection.There are concerns about neutralizing antibodies (NAbs) potency against SARS-CoV-2 variants. Despite decreased NAb titers elicited by BNT162b2-vaccine against VOC202012/01 and 501Y.V2 strains, 28/29 healthcare workers (HCW) had a NAb titer ≥110. In contrast, six months after COVID-19 mild-forms, only 9/15 (60%) of HCW displayed detectable NAbs against 501Y.V2 strain.Mineralocorticoid receptor (MR) antagonists (MRA), also referred to as aldosterone blockers, are now well recognised for their clinical benefit in patients with heart failure with reduced ejection fraction (HFrEF). Recent studies have also shown MRA can improve outcomes in patients with 'HFpEF', where the ejection fraction is preserved but left ventriclar filling is reduced. While the MR is a steroid hormone receptor best known for anti-natriuretic actions on electrolyte homeostasis in the distal nephron, it is now estalished that the MR has many physiological and pathophysiological roles in the heart, vasculature and other non-epithelial tissue types. It is the impact of MR activation on these tissues that underpins the use of MRA in cardiovascular disease, in particular heart failure. This minireview will discuss the origins and the development of MRA and highlight how their use has evolved from the 'potassium-sparing diuretics' spironolactone and canrenone over 60 years ago, to the more receptor-selective eplerenone and most recently the emergence of new non-steroidal receptor antagonists esaxerenone and finerenone.

Based on the concept that adjacent CpG sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may improve cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks.

Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients' survival are given.

EnMCB is freely available for download at GitHub (https//github.com/whirlsyu/EnMCB/) and Bioconductor (http//bioconductor.org/packages/release/bioc/html/EnMCB.html).

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Currently, most genome-wide association studies (GWAS) are studies of a single disease against controls. However, an individual is often affected by more than one condition. For example, coronary artery disease (CAD) is often comorbid with type 2 diabetes (T2DM). Similarly, it is clinically meaningful to study patients with one disease but without a related comorbidity. For example, obese T2DM may have different pathophysiology from non-obese T2DM.

We developed a statistical framework (CombGWAS) to uncover susceptibility variants for comorbid disorders (or a disorder without comorbidity), using GWAS summary statistics only. In essence, we mimicked a case-control GWAS in which the cases are affected with comorbidities or a disease without comorbidity. We extended our methodology to analyze continuous traits with clinically meaningful categories (e.g. lipids), and combination of more than 2 traits.We verified the feasibility and validity of our method by applying it to simulated scenarios and four cardiometabolic (CM) traits. In total, we identified 384 and 587 genomic risk loci respectively for 6 comorbidities and 12 CM disease 'subtypes' without a relevant comorbidity. Genetic correlation analysis revealed that some subtypes may be biologically distinct from others. Further Mendelian randomization analysis showed differential causal effects of different subtypes to relevant complications. For example, we found that obese T2DM is causally related to increased risk of CAD (p=2.62E-11).

The R code is available at https//github.com/LiangyingYin/CombGWAS.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.