Pikemcfarland0818

Drug nanocrystals have been used for a wide range of drug delivery platforms in the pharmaceutical industry, especially for bioavailability enhancement of poorly water-soluble drugs. Wet stirred media milling (WSMM) is the most widely used process for producing dense, stable suspensions of drug nanoparticles, also referred to as nanosuspensions. Despite a plethora of review papers on the production and applications of drug nanosuspensions, modeling of WSMM has not been thoroughly covered in any review paper before. The aim of this review paper is to briefly expose the pharmaceutical scientists and engineers to various modeling approaches, mostly mechanistic, including computational fluid dynamics (CFD), discrete element method (DEM), population balance modeling (PBM), coupled methods, the stress intensity-number model (SI-SN model), and the microhydrodynamic (MHD) model with a main focus on the MHD model for studying the WSMM process. A total of 71 studies, 30 on drugs and 41 on other materials, were reviewed. Analysis of the pharmaceutics literature reveals that WSMM modeling is largely based on empirical, statistically based modeling approaches, and mechanistic modeling could help pharmaceutical engineers develop a fundamental process understanding. After a review of the salient features and various pros-cons of each modeling approach, recent advances in microhydrodynamic modeling and process insights gained therefrom were highlighted. The SI-SN and MHD models were analyzed and critiqued objectively. Finally, the review points out potential research directions such as more mechanistic and accurate CFD-DEM-PBM simulations and the coupling of the MHD-PBM models with the CFD.Suicidal behaviors during pregnancy are prevalent and have the potential to adversely affect a woman's health and her developing infant. The purpose of this study was to examine prevalence and correlates of suicidal behaviors in a national sample of pregnant women. Using data from the 2009-2018 National Survey on Drug Use and Health, a sample of 7479 pregnant women was analyzed. Multiple logistic regression was used to examine associations between sample characteristics and suicidal behaviors overall and by pregnancy trimester. In this sample, 3.4% of women exhibited suicidal behaviors such as ideation, planning, and attempt. Suicidal behaviors were more prevalent at 4.4% among women in the first trimester compared to the second/third trimesters (2.9%). Of those exhibiting suicidal behavior, 63.0% were ideators, 18.9% planned suicide, and 18.1% attempted suicide. Logistic regression analyses revealed that all racial/ethnic groups of women in the third trimester were less likely to be suicidal relative to black non-Hispanic women. S63845 in vivo Alcohol abuse (OR 3.70, 95% CI 1.97, 6.81) and major depressive episode (OR 4.91, 95% CI 3.10, 7.84) in the past year significantly increased the odds of suicidality for all pregnant women. Perceived unmet need for treatment increased the likelihood (OR 5.64, 95% CI 3.55, 8.97) of suicidal behavior regardless of trimester. These findings underscore the importance of screening for suicidal behaviors in the first trimester, especially among those with existing mood disorders and substance abuse. Racial/ethnic differences should be considered in targeted interventions for suicide prevention.The highly organized transverse T-tubule membrane system represents the ultrastructural substrate for excitation-contraction coupling in ventricular myocytes. While the architecture and function of T-tubules have been well described in animal models, there is limited morpho-functional data on T-tubules in human myocardium. Hypertrophic cardiomyopathy (HCM) is a primary disease of the heart muscle, characterized by different clinical presentations at the various stages of its progression. Most HCM patients, indeed, show a compensated hypertrophic disease ("non-failing hypertrophic phase"), with preserved left ventricular function, and only a small subset of individuals evolves into heart failure ("end stage HCM"). In terms of T-tubule remodeling, the "end-stage" disease does not differ from other forms of heart failure. In this review we aim to recapitulate the main structural features of T-tubules during the "non-failing hypertrophic stage" of human HCM by revisiting data obtained from human myectomy samples. Moreover, by comparing pathological changes observed in myectomy samples with those introduced by acute (experimentally induced) detubulation, we discuss the role of T-tubular disruption as a part of the complex excitation-contraction coupling remodeling process that occurs during disease progression. Lastly, we highlight how T-tubule morpho-functional changes may be related to patient genotype and we discuss the possibility of a primitive remodeling of the T-tubule system in rare HCM forms associated with genes coding for proteins implicated in T-tubule structural integrity, formation and maintenance.Enzymes production by solid-state cultivation in packed-bed bioreactor needs to be improved by mathematical modeling and also by experimentation. In this work, a mixture of sugarcane bagasse and wheat bran was used for the growth of the fungus Myceliophthora thermophila I-1D3b, able to secrete endoglucanase and xylanase, enzymes of interest in the second-generation ethanol production. Bench and pilot-scale bioreactors were used for the experiments, while critical parameters as bed porosity and airflow distribution were evaluated. Results showed enzymes with higher activities for the most porous medium, even though the less substrate amount to be cultivated. For the pilot-scale bioreactor, only the most porous medium was evaluated using different airflow distribution techniques. Using an inner tube for air supply resulted in more homogeneous enzyme production, with higher activities. The results here presented will be helpful for the scale-up of this class of bioreactor into industrial apparatuses.This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics.