Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants.

(MOTIVATION): 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. (METHOD): 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. (RESULTS): 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. (APPLICABILITY): The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network.

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system's accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.

Microsimulation based quantitative analysis of COVID-19 management strategies.

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 framework PanSim, 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 of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

Modulation of nose-to-brain delivery of a P-glycoprotein (MDR1) substrate model drug (quinidine) in rats.

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.

Towards chemically accurate QM/MM simulations on GPUs.

Computational chemistry simulations are extensively used to model natural phenomena. To maintain performance similar to molecular mechanics, but achieve comparable accuracy to quantum mechanical calculations, many researchers are using hybrid QM/MM methods. In this article we evaluate our GPU-accelerated ONIOM implementation by measurements on the crambin and HIV integrase proteins with different size QM model systems. We demonstrate that by using a larger QM region, a better energy accuracy can be achieved at the expense of simulation time. This trade-off is important to consider for the researcher running QM/MM calculations. Furthermore, we show that the ONIOM energy monotonically approaches the pure quantum mechanical energy of the whole system. The experiments are made feasible by utilizing the cutting-edge BrianQC quantum chemistry module for Hartree-Fock level SCF and our GPU-accelerated MMFF94 force field implementation for molecular mechanics calculations.

Robust Memristor Networks for Neuromorphic Computation Applications.

One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.

Comparison of a noninvasive 3D force sensor-based method and the invasive arterial cannula in postsurgery intensive care patients: a pilot study.

Improvement in sensing technologies is leading to new, accurate noninvasive monitoring devices. However, noninvasive continuous blood pressure (BP) monitoring still faces many challenges, such as: patient's movement, device accuracy and consistency. In this study, the accuracy of a novel noninvasive BP measuring system based on a three-axis force sensor is compared with the invasive arterial cannula taking 21 simultaneous measurements mostly on elderly, postsurgical participants. For the simultaneously recorded invasive and noninvasive signals, the similarity was high, the average correlation was 0.9001 ± 0.0588. The average differences (±SD) for simultaneously recorded systolic, diastolic and mean arterial pressures were: -9.53 ± 4.69, -0.26 ± 3.06 and 1.25 ± 2.26 mmHg, respectively. The results of diastolic and mean arterial pressure satisfy the criteria set by the Association for the Advancement of Medical Instrumentation. These results suggest that this noninvasive system could be a useful tool in continuous noninvasive BP monitoring, but still requires development.

Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU.

In this article, we present an effective approach to calculate quantum chemical two-electron integrals over basis sets consisting of Gaussian-type basis functions on graphical processing unit (GPU). Our framework generates several different variants called routes to the same integral problem with different integral algorithms (McMurchie-Davidson, Head-Gordon-Pople, and Rys) and precision. Each route is benchmarked on more GPU architectures, and with this data, a model is fitted to select the best available route for an integral task given a GPU architecture. Moreover, this approach supports the computation of high angular momentum orbitals up to g effectively on GPU, tested up to cc-pVQZ-sized basis sets. Rigorous analysis is shown regarding the effectiveness of our method. Molecule simulations with several basis sets are measured using NVIDIA GTX 1080 Ti, NVIDIA P100, and NVIDIA V100 cards.

Reliability and Validity of Non-invasive Blood Pressure Measurement System Using Three-Axis Tactile Force Sensor.

Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the data were collected every 2 min for the short-term experiment, and every 10 min for the long-term experiment. In addition, the effects on the BP measurement of external physical factors such as the tension to the radial artery on applying the device and wrist circumference were evaluated. A high correlation between the measured BP with the proposed system and with the cuff-based non-invasive blood pressure, and reproducibility, were demonstrated. All data satisfied the Association for the Advancement of Medical Instrumentation criteria. The external physical factors did not affect the measurement results. In addition to previous research indicating the high reliability of the arterial pulse waveforms, the present results have demonstrated the reliability of numerical BP values, and this implies that the three-axis force sensor can be used as a patient monitoring device.

A novel non-invasive blood pressure waveform measuring system compared to Millar applanation tonometry.

The accurate, non-invasive, measuring of the continuous arterial blood pressure waveform faces some difficulties and an innovative blood pressure measurement technology is urgently needed. However, the arterial blood pressure waveform plays an essential role in health care by providing diagnostic information and base for calculating several heart function parameters. The aim of this study is to introduce a novel non-invasive measuring system that can measure the arterial blood pressure waveform with high accuracy in comparison to an applanation tonometry system. The applied measuring device utilizes a new measurement strategy enabled by the OptoForce 3D force sensor, which is attached to the wrist at the radial artery. To validate the accuracy, 30 simultaneous measurements were taken with a Millar tonometer. For the simultaneously recorded non-invasive signals, the similarity was high (the average correlation was [Formula: see text]). The differences in the systolic and the diastolic blood pressure measured by the two systems are small. The average differences ([Formula: see text]) for simultaneously recorded systolic, diastolic, mean arterial and incisura pressures were: [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], respectively. These results satisfy the AAMI criteria. Based on our results, this new system requires further development and validation against invasive arterial blood pressure monitoring in order to prove its usefulness in patient monitoring, emergency care, and pulse diagnosis.

Fast DRR generation for 2D to 3D registration on GPUs.

PURPOSE: The generation of digitally reconstructed radiographs (DRRs) is the most time consuming step on the CPU in intensity based two-dimensional x-ray to three-dimensional (CT or 3D rotational x-ray) medical image registration, which has application in several image guided interventions. This work presents optimized DRR rendering on graphical processor units (GPUs) and compares performance achievable on four commercially available devices. METHODS: A ray-cast based DRR rendering was implemented for a 512 × 512 × 72 CT volume. The block size parameter was optimized for four different GPUs for a region of interest (ROI) of 400 × 225 pixels with different sampling ratios (1.1%-9.1% and 100%). Performance was statistically evaluated and compared for the four GPUs. The method and the block size dependence were validated on the latest GPU for several parameter settings with a public gold standard dataset (512 × 512 × 825 CT) for registration purposes. RESULTS: Depending on the GPU, the full ROI is rendered in 2.7-5.2 ms. If sampling ratio of 1.1%-9.1% is applied, execution time is in the range of 0.3-7.3 ms. On all GPUs, the mean of the execution time increased linearly with respect to the number of pixels if sampling was used. CONCLUSIONS: The presented results outperform other results from the literature. This indicates that automatic 2D to 3D registration, which typically requires a couple of hundred DRR renderings to converge, can be performed quasi on-line, in less than a second or depending on the application and hardware in less than a couple of seconds. Accordingly, a whole new field of applications is opened for image guided interventions, where the registration is continuously performed to match the real-time x-ray.