Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

The research on the potential of cavitation exploitation is currently an extremely interesting topic. To reduce the costs and time of the cavitation reactor optimization, nowadays, experimental optimization is supplemented and even replaced using computational fluid dynamics (CFD). One of the approaches towards sustainable water treatment is the use of the cavitation reactor with bluff elements mounted on its stator and rotor. The experimental results show that, besides the rotational speed, the spacing of the rotor pins has the most significant effect on the cavitation intensity and effectiveness, while the pin diameter and the surface roughness are less significant design parameters. The present paper uses a simplified CFD approach to investigate the conditions in the reactor and to select the optimal among a number of geometry variations.

This article describes the impregnation of copper nanoparticles (CuNP) in a polyester fibre filter that can be used in solid-gas filtration to retain the spread of pathogen microorganisms in indoor environments. The impregnation of the CuNP was achieved by spraying the suspension on the surface of filter media. An acid pretreatment was also evaluated to increase the adhesion between fibre and nanoparticle. The synthesis of the CuNP was done by chemical reduction. The bacterial effect was measured through the contact method for and , and we demonstrate that the presence of CuNP to filter media reduced up to 99.99% of gram-negative and 99.98% of gram-positive bacteria. The pretreatment with HCl was a good alternative to filter modification due to the higher adhesion between CuNP and the fibre while the high efficiency against pathogen microorganisms was kept. The modification of filters with CuNP can improve the air quality of indoor environments, vanishing the pathogen microorganisms circulating in the air.

We discuss the impact of COVID-19, the journey towards developing vaccines against the disease, and how biomanufacturing should evolve in order to meet similar challenges in the future.

Chemical engineering involves a skill set that is transferrable to a broad range of other areas. A case in point is the work that is being done by chemical engineers to better understand and fight the COVID-19 epidemic. In this study, we consider a problem that has eluded the COVID-19 research community, which is nevertheless very tractable with a chemical engineering mindset: the true or intrinsic mortality rate of COVID-19, i.e., the fraction or percentage of COVID-19 infected people that die of the disease. We solve this problem in two locations (Spain and the state of New York) for the epidemic's first wave with a combination of daily death data, a fit of a computer simulation of an epidemiological model with adjustable parameters, and independent results of immunological blood testing on a random sample of the population. Parallels are drawn with the problem of determining the turnover frequency of a catalyst based on a similar combination of data and approaches. It is concluded from the study that the intrinsic mortality rate of COVID-19 was 1.45 ± 0.45 % during the first wave, a number that reflects OECD countries. By incorporating data on the age dependence of the mortality rate, a relationship = (3.0 ± 0.7)×10 exp(0.1), where is the age in years, is tentatively put forward for the mortality rate as a fraction. This article is protected by copyright. All rights reserved.

In the Quadrupole Magnetic Sorter (QMS) magnetic particles enter a vertical flow annulus and are separated from non-magnetic particles by radial deflection into an outer annulus where the purified magnetic particles are collected via a flow splitter. The purity of magnetically isolated particles in QMS is affected by the migration of nonmagnetic particles across transport lamina in the annular flow channel. Computational Fluid Dynamics (CFD) simulations were used to predict the flow patterns, pressure drop and nonspecific crossover in QMS flow channel for the isolation of pancreatic islets of Langerhans. Simulation results were compared with the experimental results to validate the CFD model. Results of the simulations were used to show that one design gives up to 10% less nonspecific crossover than another and this model can be used to optimise the flow channel design to achieve maximum purity of magnetic particles.

During tissue morphogenesis and homeostasis, cells experience various signals in their environments, including gradients of physical and chemical cues. Spatial and temporal gradients regulate various cell behaviours such as proliferation, migration, and differentiation during development, inflammation, wound healing, and cancer. One of the goals of functional tissue engineering is to create microenvironments that mimic the cellular and tissue complexity found in vivo by incorporating physical, chemical, temporal, and spatial gradients within engineered three-dimensional (3D) scaffolds. Hydrogels are ideal materials for 3D tissue scaffolds that mimic the extracellular matrix (ECM). Various techniques from material science, microscale engineering, and microfluidics are used to synthesise biomimetic hydrogels with encapsulated cells and tailored microenvironments. In particular, a host of methods exist to incorporate micrometer to centimetre scale chemical and physical gradients within hydrogels to mimic the cellular cues found in vivo. In this review, we draw on specific biological examples to motivate hydrogel gradients as tools for studying cell-material interactions. We provide a brief overview of techniques to generate gradient hydrogels and showcase their use to study particular cell behaviours in two-dimensional (2D) and 3D environments. We conclude by summarizing the current and future trends in gradient hydrogels and cell-material interactions in context with the long-term goals of tissue engineering.