During the last decades several new drug formulations were developed to target the central nervous system (CNS) from the nasal cavity. However, in these studies less attention was paid to the possible drug-drug interactions in case of multi-drug therapy. In our pilot study first we compared a nasal solution and a nasal gel to demonstrate their distribution in the nasal cavity (3D printed rat skull model and histology). Due to the aspiration induced high mortality at administration of nasal solution the study was continued only with the gel formulation of quinidine. The aim of our experiments was to identify the possible functional role of P-glycoprotein (P-gp) in the drug absorption in nasal cavity and to test drug-drug interactions at nose-to-brain delivery. Therefore, a P-gp substrate model drug, quinidine was tested by intranasal (IN) administration in presence of PSC-833 (specific P-gp inhibitor) given intravenously (IV) or IN and adrenaline (IN) at low (50 ng) or high (20 μg) dose. In control animals the brain penetration of quinidine was at the level of detection limit, but in combination therapy with IV PSC-833 the brain levels increased dramatically, similarly to high dose IN adrenalin, where due to vasoconstriction peripheral distribution was blocked. These results indicate that P-gp has an important role in drug absorption and efflux at nasal cavity, while adrenaline is also able to modify the penetration profile of the P-gp substrate model drug at nasal application as it decreases nose-to-blood absorption, letting more quinidine to reach the brain along with the nasal nerves.

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.

Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Advances in deep-learning-based pipelines have led to breakthroughs in a variety of microscopy image diagnostics. However, a sufficiently big training data set is usually difficult to obtain due to high annotation costs. In the case of banded chromosome images, the creation of big enough libraries is difficult for multiple pathologies due to the rarity of certain genetic disorders. Generative Adversarial Networks (GANs) have proven to be effective in generating synthetic images and extending training data sets. In our work, we implement a conditional GAN (cGAN) that allows generation of realistic single chromosome images following user-defined banding patterns. To this end, an image-to-image translation approach based on automatically created 2D chromosome segmentation label maps is used. Our validation shows promising results when synthesizing chromosomes with seen as well as unseen banding patterns. We believe that this approach can be exploited for data augmentation of chromosome data sets with structural abnormalities. Therefore, the proposed method could help to tackle medical image analysis problems such as data simulation, segmentation, detection, or classification in the field of cytogenetics.