An interesting article posted at MIT Technology Review portal: “If this controversial idea gains acceptance, it could radically change the way we treat getting old.” – Please click on this link to read the full article by David Adam.
DEMONSTRATE team has made significant progress on data and sample collection and is currently working on isolation of circulating micro-RNAs. The next important phase of the study has the objective to use the RT-qPCR approach to determine differences in expression patterns of selected micro-RNA species between the group of participants with colon cancer and normal cognitive function, and the group with cognitive impairment, but no history of cancer.
The Green Deal Call worth €1 billion has been launched on the 18th of September. The call is the last within the H2020 Programme and just ahead of the launch of Horizon Europe, the next research and innovation programme kicking-off in 2021. Read more at: link.
The Clinical Center of Montenegro recently purchased a microperimeter MAIA (Macular Integrity Assessment) device from CenterVue. This device was purchased for the implementation of the clinical study RECOGNISED.
This diagnostic modality allows analysis of the structural and functional elements of retina by measuring sensitivity mapping and fixation analysis with fundus image in one exam. It is used to diagnose and follow the progression of pathological processes affecting the macular area.
The MAIA device has auto focus, it is quick, and simple to use, as it does not require pupil dilatation. Each eye can be examined in less than 3 minutes.
Most importantly, recent studies showed that in people with type 2 diabetes, retinal sensitivity assessed by microperimetry directly correlates with neurodegeneration of the brain. Therefore, this diagnostic method and could be used as a biomarker for detection of patients at risk for developing dementia.
Dr. Isidora Rovčanin Dragović successfully presented her PhD thesis research proposal in front of the following Committee:
- Miodrag Radunović, MD, PhD, Dean of the Faculty of Medicine, University of Montenegro
- Nataša Popović, MD, PhD, Mentor, Associate Professor at the Department of Medical Physiology, Faculty of Medicine, University of Montenegro
- Milica Martinović, MD, PhD, Co-mentor, Full Professor at the Department of Pathophysiology, Faculty of Medicine, University of Montenegro
- Elka Stefanova, MD, PhD, Full Professor of Neurology, Faculty of Medicine, University of Belgrade, Serbia
- Appolonia Tullo, PhD, Biomedical researcher at the Institute of Biomembranes and Bioenergetics, Bari, Italy
The title of the proposed research was: „ A New Method for Stratification of the Risk for Alzheimer‘s Disease in Patients in Montenegro“. This research is conducted as a part of the project DEMONSTRATE.
The PhD candidate demonstrated the competency on the subject, the potential for independent research, and passed the preliminary examination with an excellent grade.
Project DEMONSTRATE team members from Montenegro organized an online conference on November 6th, 2020 in order to organize available facilities and resources, and plan activities for the 2nd year of the project in accordance with the changes related to COVID-19 pandemic.
DEMONSTRATE team members presented their research at the conference 14th World Congress on Controversies in Neurology – CONy 2020, held from October 29th to November 1st 2020. The poster titled “Improving the Diagnosis of Cognitive Impairment in Montenegro- on the Path of Learning” by Isidora Rovčanin Dragović, Ljiljana Radulović, Jevto Eraković, Miodrag Radunović, Goran Popivoda, Tijana Vuković and Nataša Popović was presented at the conference.
Due to on-going developments with COVID-19 pandemic, this year the conference was organized completely online.
For more information on the conference click the following link: http://cony.comtecmed.com/
You can access the zoomable poster by clicking on the following link: https://simul-europe.com/2020/cony/Files/220953.pdf
The Ministry of Science of Montenegro accepted the 12-month progress and financial report for the project DEMONSTRATE. The overall score of the report is 96.5 out of 100 points. The second year of the project and continuation of the financial support has been approved.
Taking into account the new circumstances that arose as a result of the Covid-19 pandemic, the Ministry approved the extension of the project for six months so the planned activities could be adequately implemented. The new contractual deadline for the realization of the project is September 30, 2021.
DEMONSTRATE project was presented in local newspaper Dnevne Novine today. Prof. Nataša Popović gave an interview discussing the project results and new Horizon project called RECOGNISED that started this year (link).
Project DEMONSTRATE was featured in the paper “Lacunarity Analysis of Microvascular Morphology in Human Retina” by I. Konatar, N. Popovic, T. Popovic, M. Radunovic, and B. Vukcevic was presented at the 2020 IcETRAN conference today. More about the conference at the following link.
ABSTRACT – Fractal analysis provides means for the quantitative assessment of geometric patterns in one, two, and three dimensions. It is aimed at analysis of graphical shapes that belong to a class of fractal objects that are characterized by the self-similarity over different scales. Various structures in nature are fractals and fractal analysis techniques are widely used for analysis of biomedical images. One such example of application is analyzing blood vessel structure in the human retina that can be extracted from digital images captured by fundus camera. The most commonly used fractal analysis is estimation of fractal dimension using various box-counting methods for mono-and multi-fractals. Although two fractal images can have the same fractal dimension they can have very different appearance and structure. One can appear as a structure that fills most of the available space, while the other can have a lot of empty areas. These differences can be quantified by lacunarity parameter, which has greater value in images with less space-filling properties. This paper focuses on the estimation of the lacunarity parameter implemented in the Python programming language, which is aimed at lacunarity analysis of microvaculae morphology in human retina. The implementation is validated by comparison with the results obtained by ImageJ, a commonly used software for analysis of biomedical images. The value of the lacunarity analysis is demonstrated on a set of actual images of human retina associated with different medical conditions.