To develop proper drug formulations and to optimize the delivery of their active ingredients through the dermal barrier, the Franz diffusion cell system is the most widely used in vitro/ex vivo technique. However, different providers and manufacturers make various types of this equipment (horizontal, vertical, static, flow-through, smaller and larger chambers, etc.) with high variability and not fully comparable and consistent data. Furthermore, a high amount of test drug formulations and large size of diffusion skin surface and membranes are important requirements for the application of these methods. The aim of our study was to develop a novel Microfluidic Diffusion Chamber device and compare it with the traditional techniques. Here the design, fabrication, and a pilot testing of a microfluidic skin-on-a chip device are described. Based on this chip, further developments can also be implemented for industrial purposes to assist the characterization and optimization of drug formulations, dermal pharmacokinetics, and pharmacodynamic studies. The advantages of our device, beside the low costs, are the small drug and skin consumption, low sample volumes, dynamic arrangement with continuous flow mimicking the dermal circulation, as well as rapid and reproducible results.

There is increasing interest in miniaturized technologies in diagnostics, therapeutic testing, and biomedicinal fundamental research. The same is true for the dermal studies in topical drug development, dermatological disease pathology testing, and cosmetic science. This review aims to collect the recent scientific literature and knowledge about the application of skin-on-a-chip technology in drug diffusion studies, in pharmacological and toxicological experiments, in wound healing, and in fields of cosmetic science (ageing or repair). The basic mathematical models are also presented in the article to predict physical phenomena, such as fluid movement, drug diffusion, and heat transfer taking place across the dermal layers in the chip using Computational Fluid Dynamics techniques. Soon, it can be envisioned that animal studies might be at least in part replaced with skin-on-a-chip technology leading to more reliable results close to study on humans. The new technology is a cost-effective alternative to traditional methods used in research institutes, university labs, and industry. With this article, the authors would like to call attention to a new investigational family of platforms to refresh the researchers’ theranostics and preclinical, experimental toolbox.

The efficacy of transdermal absorption of drugs and the irritation or corrosion potential of topically applied formulations are important areas of investigation in pharmaceutical, military and cosmetic research. The aim of the present experiments is to test the role of P-glycoprotein in dermal drug delivery in various ex vivo and in vitro platforms, including a novel microchip technology developed by Pázmány Péter Catholic University. A further question is whether the freezing of excised skin and age have any influence on P-glycoprotein-mediated dermal drug absorption. Two P-glycoprotein substrate model drugs (quinidine and erythromycin) were investigated via topical administration in diffusion cells, a skin-on-a-chip device and transdermal microdialysis in rat skin. The transdermal absorption of both model drugs was reduced by P-glycoprotein inhibition, and both aging and freezing increased the permeability of the tissues. Based on our findings, it is concluded that the process of freezing leads to reduced function of efflux transporters, and increases the porosity of skin. P-glycoprotein has an absorptive orientation in the skin, and topical inhibitors can modify its action. The defensive role of the skin seems to be diminished in aged individuals, partly due to reduced thickness of the dermis. The novel microfluidic microchip seems to be an appropriate tool to investigate dermal drug delivery.

This paper presents and compares two different strategies in the numerical simulation of passive microfluidic mixers based on chaotic advection. In addition to flow velocity field calculations, concentration distributions of molecules and trajectories of microscale particles were determined and compared to evaluate the performance of the applied modeling approaches in the proposed geometries. A staggered herringbone type micromixer (SHM) was selected and studied in order to demonstrate finite element modeling issues. The selected microstructures were fabricated by a soft lithography technique, utilizing multilayer SU-8 epoxy-based photoresist as a molding replica for polydimethylsiloxane (PDMS) casting. The mixing processes in the microfluidic systems were characterized by applying molecular and particle (cell) solutions and adequate microscopic visualization techniques. We proved that modeling of the molecular concentration field is more costly, in regards to computational time, than the particle trajectory based method. However, both approaches showed adequate qualitative agreement with the experimental results.

A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one of the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number of subjects, typically less than or equal to 10. Furthermore, the algorithms usually include only bandpass filtering as a means of reducing noise and increasing signal quality. In this study, we conducted a comparative analysis of five renowned neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, and MI-EEGNet) utilizing open-access databases with a larger subject pool in conjunction with the BCI Competition IV 2a dataset to obtain statistically significant results. We employed the FASTER algorithm to eliminate artifacts from the EEG as a signal processing step and explored the potential for transfer learning to enhance classification results on artifact-filtered data. Our objective was to rank the neural networks; hence, in addition to classification accuracy, we introduced two supplementary metrics: accuracy improvement from chance level and the effect of transfer learning. The former is applicable to databases with varying numbers of classes, while the latter can underscore neural networks with robust generalization capabilities. Our metrics indicated that researchers should not disregard Shallow ConvNet and Deep ConvNet as they can outperform later published members of the EEGNet family.

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation frameworkPanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution ofPanSimallows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.