Aortic regurgitation is associated with African American and Asian race, smoking, renal disease, and numerous autoimmune diseases in addition to traditional cardiovascular risk factors but has lower risk with alcohol intake.

BACKGROUND: Aortic regurgitation (AR) is associated with increasing age, rheumatic heart disease, and a bicuspid aortic valve, but its association with other comorbidities and race is less known. The purpose of this study was to investigate any association between AR and comorbid conditions in older adults above 40. METHOD: The large Nationwide Inpatient Sample database was utilized for our study using uni- and multivariate analysis. Data were extracted from available ICD-10 codes for the years of 2016-2020. RESULTS: The NIS data included 112,982,565 patients. A total of 660,730 were found to have AR. AR was found to be associated with male gender (OR 1.15, CI 1.14-1.16, P < 0.001), smoking (OR 1.04, CI 1.02-1.05, P < 0.001), hypertension (OR 1.65, CI 1.62-1.68, P < 0.001), hyperlipidemia (OR 1.36, CI 1.34-1.37, P < 0.001), chronic kidney disease (OR 1.22, CI 1.21-1.24, P < 0.001), antiphospholipid antibody syndrome (OR 1.56, CI 1.33-1.83, P < 0.001), rheumatoid arthritis (OR 1.1, CI 1.06-1.14, P < 0.001), scleroderma (OR 1.49, CI 1.31-1.7, P < 0.001), systemic connective tissue disorders (OR 1.32, CI 1.25-1.4, P < 0.001), Raynaud's syndrome (OR 1.62, CI 1.47-1.77, P < 0.001), and systemic lupus erythematosus (OR 1.44, CI 1.34-1.54, P < 0.001) in add to known bicuspid aortic valve. CONCLUSION: Using a very large database, we found new associations between AR and many comorbid conditions, including many inflammatory and chronic degenerative diseases in addition to the known risk factors.

Independent association of aortic stenosis with many known cardiovascular risk factors and many inflammatory diseases.

BACKGROUND: Aortic valve stenosis is associated with age, rheumatic fever and bicuspid aortic valve, but its association with other co-morbidities, such as inflammatory disease and race/ethnicity, is less known. AIM: To investigate any association between aortic stenosis and many co-morbidities. METHODS: We used the large Nationwide Inpatient Sample database to evaluate any association between aortic stenosis and risk factors. We performed univariate and multivariable analyses, adjusting for co-morbid conditions. RESULTS: Data were extracted from the first available database that used the International Classification of Diseases, Tenth Revision codes specifically coding for aortic stenosis alone, spanning from 2016 to 2020 (n=112,982,565). A total of 2,322,649 patients had aortic stenosis; the remaining 110,659,916 served as controls. We found a strong and independent significant association between aortic stenosis and coronary artery disease (odds ratio [OR]: 2.11, 95% confidence interval [CI]: 2.09-2.13), smoking (OR: 1.08, 95% CI: 1.07-1.08), diabetes mellitus (OR: 1.15, 95% CI: 1.14-1.16), hypertension (OR: 1.41, 95% CI: 1.4-1.42), hyperlipidaemia (OR: 1.31, 95% CI: 1.3-1.32), renal disease (OR: 1.3, 95% CI: 1.29-1.31), chronic obstructive pulmonary disease (OR: 1.05, 95% CI: 1.04-1.05), obesity (OR: 1.3, 95% CI: 1.29-1.32), white race/ethnicity (OR: 1.47, 95% CI: 1.42-1.52), rheumatoid arthritis (OR: 1.13, 95% CI: 1.11-1.15), scleroderma (OR: 1.93, 95% CI: 1.79-2.09), systemic connective tissue disease (OR: 1.24, 95% CI: 1.2-1.27), polyarteritis nodosa (OR: 1.5, CI: 1.24-1.81) and Raynaud's syndrome (OR: 1.16, 95% CI: 1.09-1.24) (all P<0.001), in addition to known factors, such as age, male sex and bicuspid aortic valve. CONCLUSION: Using a very large database, we found many new associations with aortic valve stenosis, including race/ethnicity, renal disease, several inflammatory diseases, chronic obstructive pulmonary disease and obesity, in addition to many other known cardiovascular risk factors.

Coarse-grained simulation of PEGylated and tethered protein devices at all experimentally accessible surface residues on ß-lactamase for stability analysis and comparison.

PEGylated and surface-tethered proteins are used in a variety of biotechnological applications, but traditional methods offer little control over the placement of the functionalization sites on the protein. Fortunately, recent experimental methods functionalize the protein at any location on the amino acid sequence, so the question becomes one of selecting the site that will result in the best protein function. This work shows how molecular simulation can be used to screen potential attachment sites for surface tethering or PEGylation. Previous simulation work has shown promise in this regard for a model protein, but these studies are limited to screening only a few of the surface-accessible sites or only considered surface tethering or PEGylation separately rather than their combined effects. This work is done to overcome these limitations by screening all surface-accessible functionalization sites on a protein of industrial and therapeutic importance (TEM-1) and to evaluate the effects of tethering and PEGylation simultaneously in an effort to create a more accurate screen. The results show that functionalization site effectiveness appears to be a function of super-secondary and tertiary structures rather than the primary structure, as is often currently assumed. Moreover, sites in the middle of secondary structure elements, and not only those in loops regions, are shown to be good options for functionalization-a fact not appreciated in current practice. Taken as a whole, the results show how rigorous molecular simulation can be done to identify candidate amino acids for functionalization on a protein to facilitate the rational design of protein devices.