Óbuda University

Accurate, reliable, and efficient estimation of blood glucose dynamics from real-world data is challenging due to the time-varying nature, high uncertainty, and nonlinear interplay of complex processes. In this study, we propose and investigate a stochastic representation of a virtual population by fitting a hierarchical Bayesian model. In total, we use 500 24h-long sequences, 50 from each of the 10 patients with type 1 diabetes on multiple daily injection therapy. We model uncertainty on multiple levels, in physiology and in self-reported events, and take into account intra- and interday variability, and the effect of physical activity as well. The root-mean-square error between the glucose measurements and the mean of the posterior predictive distribution using the fitted low-rank multivariate normal guide is 12.44 mg/dL. We show that the posterior distributions can be used to simulate realistic intra-, and interday variability in terms of the investigated patient cohort.

Fungal-derived natural compounds serve as a crucial resource for new drug development and have witnessed significant progress in recent years. Researchers have screened multiple bioactive compounds from fungal extracts. Epilepsy, a neurological disorder affecting tens of millions of people worldwide, presents substantial therapeutic challenges due to its complex pathogenesis. Neuronal apoptosis plays a critical role in epileptogenesis and seizure generation. In this study, we screened a fungal-derived natural compound, chaetosemin J, with antiepileptic activity using zebrafish and cell models. Screening assays using the zebrafish model demonstrated that chaetosemin J alleviated epileptiform behavior and downregulated seizure-induced epilepsy marker genes. In parallel, cell-based assays showed that chaetosemin J enhanced cell viability. The anti-epileptic pathways and associated targets of chaetosemin J were predicted using transcriptomics and molecular docking techniques. Furthermore, its anti-epileptic mechanism was validated through real-time quantitative polymerase chain reaction experiments and calcium ion reflow assays. Collectively, our findings suggested that chaetosemin J may alleviate epileptic seizures by downregulating the expression of integrin-related genes in the extracellular matrix/focal adhesion signaling pathway, reversing the abnormal elevation of intracellular calcium ion concentration during epileptic seizures, and thereby reducing the activation of the apoptosis-related factor Calpain-2. This study provides a new candidate molecule for the development of antiepileptic drugs.

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO2 reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO2 substrates and additional Si-SiO2 wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures.

This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in 99mTc-macroagregated albumin (99mTc-MAA) SPECT imaging, with the goal of enhancing quantitative accuracy for improved treatment planning and pre-therapy dosimetry in 90Y-selctive internal radiation therapy (SIRT). Data from 222 patients who underwent 99mTc-MAA SPECT imaging prior to 90Y-SIRT were included in this study. Uncorrected SPECT images (without attenuation and/or scatter correction) were used as input to a modified 3D shifted-window UNet Transformer (Swin UNETR) architecture. Three separate models were trained to predict attenuation corrected (AC), scatter corrected (SC), and joint attenuation and scatter corrected (ASC) SPECT images. The dataset was split into a training set (~ 80%) and an independent test set (~ 20%). Model training was performed using a five-fold cross-validation framework, with final evaluation conducted on the blind test set. To clinically assess model performance, 3D voxel-wise dosimetry was performed on the test set using the local energy deposition method, assuming 99mTc-MAA as a surrogate for 90Y distribution. Quantitative evaluation included organ- and voxel-level metrics, along with Gamma analysis using three combinations of distance-to-agreement (DTA, mm) and dose-difference (DD, %) criteria. The average (± SD) of the voxel-wise mean error (ME) was ≤ 0.003Gy for all tasks. The Relative Error (RE (%)) for AC, SC, and ASC tasks were 4.64 ± 7.52%, 8.99 ± 26.35%, and 16.45 ± 25.83%, respectively. Voxel-level Gamma evaluations within the whole body using three different criteria sets, including "DTA: 4.79mm, DD: 1%"; "DTA: 10mm, DD: 5%"; and "DTA: 15mm, DD: 10%" yielded pass rates of over 99.60%. The mean absolute error (MAE) for lesions, normal liver and lungs across all tasks were 3.16 ± 3.39, 0.35 ± 0.36, 0.41 ± 0.47Gy for AC, 1.97 ± 2.79, 0.19 ± 0.16, 0.22 ± 0.20Gy, for SC and 5.16 ± 7.10, 0.45 ± 0.51, and 0.34 ± 0.37Gy for ASC, respectively. Multiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.

The aim of the present study was to assess the cost-effectiveness of bariatric surgery, non-surgical weight loss and pharmacological non-surgical weight loss (BS-WL, NS-WL, PNS-WL) to avoid or delay total knee arthroplasty (TKA) in subjects with end-stage knee osteoarthritis and obesity. The quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER) were computed using a Markov State Model simulating outcomes and costs in 4000 potential subjects followed for 25 years. Model inputs were derived from the literature and internet sources. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of the model assumptions. BS-WL and PNS-WL increased QALYs from 7.70 (TKA-only group) to 8.96 and 8.41, respectively, while NS-WL marginally increased QALY to 7.86. The BS-WL and PNS-WL ICERs were $27,127 and $33,461, respectively, far below the willingness-to-pay threshold of $50,000. However, the NS-WL ICER was $57,843. The percentage of subjects needing first revision was greater after TKA-only (26.4%) and lower with the other strategies (12.9% with BS-WL, 23.5% with NS-WL and 20.1% with PNS-WL). The most sensitive parameter was BS-WL success in weight loss; 20% of the key input parameter variations produced BS-WL ICERs that were always below the threshold, as for the PNS-WL strategy. Bariatric surgery is effective and cost-effective in delaying or preventing TKA in patients with severe obesity and reducing the need for primary and secondary surgical revision. Despite current limitations on the long-term effects of available antiobesity medications, semaglutide appears to also be cost-effective, while non-surgical weight loss is the least effective strategy. Clinicians and policy makers should consider the cost-effectiveness of losing weight prior to total knee replacement in patients with obesity.

A tanulmány az Ukrajnában található 8 meteoritkráter természeti és művi környezetének esetleges háborús károsodását vizsgálja. A kutatás célja volt feltárni, hogy a rendelkezésre álló primer és szekunder adatforrások alapján kimutatható-e környezetkárosítás. A vizsgálat során meteoritikai adatbázisokat, szakirodalmi forrásokat, valamint térképes műhold- és légifelvételeket elemeztek a konfliktus előtti és alatti állapot összehasonlítására. Az eredmények azt mutatják, hogy a kráterek eddig sértetlenek maradtak, háborús környezetkárosításra utaló jelek nem azonosíthatók. Ha nem is tudatos cselekvés folytán, de a vizsgálat eredményei összhangban állnak az igazságos háború elméletében tárgyalt környezeti károkozás etikai megfontolásaival, amelyek szerint a katonai cselekményeknek kerülniük kell a természeti értékek indokolatlan, aránytalan pusztítását. A jövőben a háborús következmények és az ország infrastrukturális állapota miatt helyszíni vizsgálatok nem valószínűek, ezért a további kutatások is elsősorban távoli elemzésekre támaszkodnak.

This study explores how a privately-owned Science Park (SP) redefines its value proposition in response to the experience economy megatrend. Based on a single-case study of Kilometro Rosso Science Park (KRSP) in Lombardy, Italy, we apply the Gioia Methodology to analyze interview and secondary data. The study offers a conceptual framework integrating experience economy marketing and the Triple Helix innovation model. It also addresses a population gap by focusing on a privately-owned SP-an underexplored area in the literature. Findings show that KRSP responds to regional challenges with distinct value propositions shaped by its private ownership, marked by charismatic leadership, managerial efficiency, and market orientation. These traits enable greater agility than public counterparts. The study highlights how physical assets are strategically used to foster tacit knowledge flows, critical mass, and talent attraction. Implications for SP managers include the importance of continuously assessing stakeholder needs and updating the park’s value proposition to remain consumerfocused and effective.

Nowadays, the digitization of writing is becoming more and more widespread, and image processing is an essential technique for this. In addition to the basic image correction methods, examining the data in the image is a much more complex task. For example, when discussing character recognition or face detection, it is necessary to build a more flexible and adaptive unit. In this paper, we introduce a new approach to building a written text recognition system based on deep learning. For training the system, we used “synthetic” handwriting words and also tested both synthetic and real handwritten words. The results are compared with two of the most commonly used systems.

This paper presents the measurement data acquisition device used for validating our Complex Heat Transfer Modeling Software (CHeTMoS), which simulates heat transport processes. The fully custom-developed device is based on an Arduino MKR Zero development board and collects data from sensors connected via an I2C bus. The measurements are performed using redundant and diverse sensors, which we installed at a key location within the Paks Spent Fuel Interim Storage Facility. The data gathered by our custom device revealed discrepancies in parallel measurements, with varying sensitivity to solar radiation. It was determined that the current placement of the device does not ensure interference-free measurements. Since the validation process requires the most accurate comparative data possible, the existing temperature readings must be corrected using solar irradiance data, considering the design (color) of the sensors. Before conducting further measurements, the device should be relocated—either partially or entirely—to a more protected area of the air intake, considering that ionizing radiation levels increase as the distance to the spent fuel assemblies decreases.

Push and pull strategies offer distinct advantages and limitations in marketing execution, particularly within the fast-moving consumer goods (FMCG) sector. This study investigates the benefits and challenges of these strategies through a qualitative case study of Oishi Green Tea by ThaiBev Plc. Using secondary data, the research explores how the brand applies a hybrid marketing approach to align supply chain efficiency with consumer responsiveness. Findings reveal that push strategies support wide availability and cost control, while pull strategies drive innovation and brand engagement. However, the hybrid model also presents operational challenges, including demand forecasting, supply chain agility, and campaign coordination.