Contacts
Intranet
SK
| ITAGES – Identification of stress-induced alterations in expression of NRF2 target genes in rat models of prehypertension: the effect of comorbid hypertriglyceridemia and dimethyl fumarate treatment | |
| Identifikácia stresom vyvolaných zmien v expresii cieľových génov NRF2 v potkaních modeloch prehypertenzie: vplyv komorbidnej hypertriglyceridémie a liečby dimetylfumarátom | |
| Program: | SRDA |
| Project ID: | APVV-22-0296 |
| Duration: | 1.7.2023 – 30.6.2027 |
| Project leader: | Ing. Maňka Ján, CSc. |
| Annotation: | The nuclear transcription factor erythroid 2-related factor 2 (NRF2) is a key molecular link between several non-communicable diseases, as it regulates the expression of approximately 250 target genes, including those involvedin maintenance of redox balance, the development of metabolic disorders, cardiovascular and liver diseases, aswell as in immune responses. Borderline elevated blood pressure (prehypertension) is a common cardiovasculardisorder in humans, and elevated blood pressure has been found to be positively correlated with triglyceride levels.In addition, chronic stress is an etiological factor in the development of non-communicable diseases, includingelevated blood pressure and hypertriglyceridemia (HTG). In experimental studies, borderline hypertensive rats(BHR) and hypertriglyceridemic rats (HTGR) are suitable models of prehypertension without and with comorbidhypertriglyceridemia. These models are relevant for investigating the effects of stress as well as for investigatingthe role of changes in expression of NRF2 target genes in the development of hypertension associated withmetabolic diseases. To understand better the role of NFR2 as well as the impact of chronic social stress on thementioned diseased states, the aims of this project are: 1) to identify differences in expression of NRF2 targetgenes in two experimental models of prehypertension – without (in BHR) and with (in HTGR) comorbid HTG – incontrol conditions and during chronic social stress, 2) to determine if NRF2 activator dimethyl fumarate can reducestress-induced pathologies in prehypertensive rats, especially in those with comorbid HTG, and 3) to specify a setof suitable whole blood RNA biomarkers for evaluation of changes in NRF2 target genes in prehypertension andHTG and those genes altered by chronic social stress. |
| METIM – Design of a Methodology and its Verification for the Measurement of Selected Parameters of Ti Implants in the Manufacturing Process | |
| Návrh metodiky a jej overenie pre meranie vybraných parametrov Ti implantátov vo výrobnom procese | |
| Program: | SRDA |
| Project ID: | APVV-22-0328 |
| Duration: | 1.7.2023 – 30.6.2027 |
| Project leader: | RNDr. Hain Miroslav, PhD. |
| Annotation: | The project focuses on the development and application of measurement and non-destructive testing methods inthe manufacturing of titanium dental implants. Dental implants are medical devices that have to comply with thetechnical requirements given by regulation of the European Parliament and Council EU 2017/745 from 5 Apr 2017.Under this regulation, among other obligations, the manufacturer must ensure that these devices are safe andeffective and do not compromise the clinical condition or patients safety. The dental implants should also meet ahigh level of health and safety protection, taking into account the generally accepted state of the art in science andtechnology. In this project we will address the requirements related to the design and manufacturing and inparticular: the compatibility of the different parts of the device, the influence of processes on the properti es of thematerials, the mechanical properties of the materials used such as strength, ductility, resistance to wear andfatigue, the properties of the surfaces, and confirmation that the device meets all defined physical specifications aswell as the identification of contaminants in the manufacturing process. To ensure these requirements, we intend touse state-of-the-art measurement methods such as X-ray microtomography (microCT), scanning electronmicroscopy (SEM), optical measurement of surface roughness, SQUID magnetometry. Since the abovemeasurement methods are time consuming and do not allow their full application in the production, the solution willalso include the design of effective methods of statistical quality control, which will be applied at the manufacturerof dental titanium implants MARTIKAN, s.r.o. The objectives of the proposed project correlate with the Researchand Innovation Strategy for Smart Specialisation of the Slovak Republic 2021-2027 (SK RIS3 2021+), while theyaffect two defined domains, namely Innovative Industry for the 21st Century and Healthy Society. |
| FERINO – Advanced diagnostics of neurodegenerative disorders using magnetic resonance techniques and artificial intelligence | |
| Pokročilá diagnostika neurodegenerat ívnych ochorení pomocou techník magnetickej rezonancie a umelej inteligencie | |
| Program: | SRDA |
| Project ID: | APVV-22-0122 |
| Duration: | 1.7.2023 – 30.6.2027 |
| Project leader: | Ing. Gogola Daniel, PhD. |
| Annotation: | Neurodegenerative diseases (ND) are becoming a severe problem in developed countries. Since we currently haveno effective therapies available, early diagnosis is critical to ensure a good quality of life for ND patients. ND arecharacterized by iron accumulation and magnetite mineralization in brain tissue, with ferritin as a precursor. Due toits low relaxivity, physiological ferritin is at the edge of visibility using magnetic resonance imaging (MRI)techniques. On the contrary, "pathological" ferritin causes a significant shortening of MRI relaxation times. Thiscreates hypointense artifacts, which theoretically allow the distinguishability of both proteins. Since ironaccumulation precedes the clinical symptoms of the disease, MRI has the potential to become a non -invasivediagnostic method for the early stages of ND. At present, however, this is limited by the insufficient characterizationof the relaxation properties of biogenic iron and the uncertainty in the interpretation of clinical data. Therefore, ourbasic goal (application output) is the development of a comprehensive methodology (FERINO software tool) for theunequivocal diagnosis of the early stages of ND. To reach our goal, we will use a combination of several diagnostictechniques and artificial intelligence tools. The diagnostic techniques include in-vitro, in-silico, and in-vivocharacteristics of ferritin relaxation, structural MRI, magnetic resonance spectroscopy (MRS), neurological tests,and clinical biochemistry biomarkers. The cornerstone of the methodology will be the FerroQuant software tool,which was proposed by the principal investigator within the APVV 2012. It enables the analysis and quantificationof iron-related clinical MRI data but lacks new findings in iron MRI (false-positive artifacts, ferritin\’s mineral phases).FerroQuant also does not use artificial intelligence and does not combine different diagnostic data, whic h, however,will be an integral part of the FERINO tool. |
| MRCartilage – Automatic data evaluation tool from the longitudinal quantitative MRI studies of articular cartilage | |
| Automatický softvérový nástroj na výhodnocovanie kvantitatívnych MRI štúdií artikulárných chrupaviek v čase | |
| Program: | SRDA |
| Project ID: | APVV-21-0299 |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Dr. Szomolányi Pavol, (PhD.) |
| Annotation: | The aim of the project is to design a comprehensive tool for automatic evaluation of human articular cartilage data from quantitative MRI. Data obtained from the Osteoarthritis Initiative database, and measured at Institute of Measurement Science and Medical University of Vienna will be segmented using an automated segmentation tool based on convolutional neural networks. The annotated data will then be registered on quantitative MRI data that will be available from the database (T2 and T1rho mapping, gagCEST, sodium MR) using automated or semiautomated tools developed within this project. The data obtained will be evaluated at multiple time points according to MR measurements that will be available. In addition to quantitative MR data, this will include volumetric data, cartilage thickness, and texture analysis of quantitative maps. Patient evaluation will be based on risk factor groups (transverse ligament rupture, meniscus rupture and menisectomy). The expected number of patients is approximately 4000 divided into individual groups in the ratio 40/30/30. The output of the project will be a compiled version of an automatic cartilage evaluation tool that will be available in a public source (such as website of Institute of Measurement). |
| TInVR – Trustworthy human–robot and therapist–patient interaction in virtual reality | |
| Dôveryhodná interakcia človek–robot a terapeut–pacient vo virtuálnej realite | |
| Program: | SRDA |
| Project ID: | APVV-21-0105 |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | We aim to study specific forms of social interaction using state-of-the-art technology – virtual reality (VR) which is motivated by its known benefits. The project has two main parts, human–robot interaction (HRI) and therapist–patient interaction (TPI). The interactions are enabled using head-mounted displays and controllers allowing the human to act in VR. We propose two research avenues going beyond the state-of-the-art in respective contexts. In HRI, we will develop scenarios allowing the humanoid robot to learn, understand and imitate human motor actions using flexible feedback. Next, we develop scenarios for testing and validating human trust in robot behavior based on multimodal signals. We will also investigate physical interaction with a humanoid robot NICO. In TPI with stroke patients, we develop a series of VR-based occupational therapy procedures for motor and cognitive impairment neurorehabilitation using an active and passive brain-computer interface, and we will validate these procedures. We expect observations from HRI experiments to be exploited in TPI. The proposed project is highly multidisciplinary, combining knowledge and research methods from psychology, social cognition, robotics, machine learning and neuroscience. We expect to identify features and mechanisms leading to trustworthy processes with a human in the loop, as a precondition of success, be it a collaborative task or treatment in VR. |
| MATHMER – Advanced mathematical and statistical methods for measurement and metrology | |
| Pokročilé matematické a štatistické metódy pre meranie a metrológiu | |
| Program: | SRDA |
| Project ID: | APVV-21-0216 |
| Duration: | 1.7.2022 – 31.12.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | Mathematical models and statistical methods for analysing measurement data, including the correct determination of measurement uncertainty, are key to expressing the reliability of measurements, which is a prerequisite for progress in science, industry, health, the environment and society in general. The aim of the project is to build on traditional metrological approaches and develop new alternative mathematical and statistical methods for modelling and analysing measurement data for technical and biomedical applications. The originality of the project lies in the application of modern mathematical methods for modelling and detecting dependence and causality, as well as statistical models, methods and algorithms for determining measurement uncertainty using advanced probabilistic and computational methods based on the use of the characteristic function approach (CFA). In contrast to traditional approximation and simulation methods, the proposed methods allow working with complex and at the same time accurate probabilistic measurement models and analytical methods. Particular emphasis is placed on stochastic methods for combining information from different independent sources, on modelling dependence and causality in dynamic processes, on accurate methods for determining the probability distribution of values that can be reasonably attributed to the measured quantity based on a combination of measurement results and expert knowledge, and on the development of methods for comparative calibration, including the probabilistic representation of measurement results with a calibrated instrument. An important part of the project is the development of advanced numerical methods and efficient algorithms for calculating complex probability distributions by combining and inverting characteristic functions. These methods are widely applicable in various fields of measurement and metrology. In this project they are applied to the calibration of temperature and pressure sensors. |
| ECMeNaM – Efficient computation methods for nanoscale material characterization | |
| Efektívne výpočtové metódy pre charakterizáciu materiálov v nano mierke | |
| Program: | SRDA |
| Project ID: | SK-CZ-RD-21-0109 |
| Duration: | 1.7.2022 – 30.6.2025 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The aim of the project is to design and implement effective calculation methods for evaluating the results of measuring the mechanical properties of materials at the nanoscale using instrumented indentation methods (IIT) and atomic force microscopy (AFM). Both of these methods are able to provide highly localized information on the mechanical properties of the material, such as Young\’s modulus of elasticity (both methods), hardness (IIT method), or point-to-surface adhesion (AFM method). The principle is the analysis of the recording of the position of the measuring tip and the force interaction between the tip and the sample surface. The determination of the resulting values on the basis of data recorded by the instrument in both of these methods is based on non-trivial mathematical-statistical methods and calculation procedures working with data subjected to relatively high uncertainty or random noise, where it is also necessary to quantify the uncertainty of the measurement result. Both of these methods work with data of a similar nature, but each has certain specifics. The results obtained for IIT can thus serve as a reference for AFM. The project partners are the Czech Metrology Ins titute (CMI is the national metrology institute of the Czech Republic with top infrastructure in the field), the Institute of Measurement Science SAS (IMS SAS), and the Mathematical Institute SAS (MI SAS), which are academic institutions with extensive experience in basic research and applications of mathematics statistics in the field of measurement and metrology. This combination of partners brings a natural synergy and a combination of the necessary competencies for this |