Východiska: V roce 2007 byl Českou myelomovou skupinou založen Registr monoklonálních gamapatií (RMG). RMG je registr určený pro sběr klinických dat týkajících se diagnózy, léčby, jejích výsledků a přežití u pacientů s monoklonálními gamapatiemi. V registru jsou sbírána data pacientů s monoklonální gamapatií nejasného významu (monoclonal gammopathy of undetermined significance – MGUS), Waldenströmovou makroglobulinemií (WM), mnohočetným myelomem (MM) nebo primární AL („amyloid light-chain“) amyloidózou. Data: V současné době do registru přispívá 19 českých a 4 slovenská centra. V registru je v současnosti evidováno více než 5 000 pacientů s MM, téměř 3 000 pacientů s MGUS, 170 pacientů s WM a 26 pacientů s primární AL amyloidózou; registr tak disponuje více než 8 000 pacienty s monoklonálními gamapatiemi. Výsledky: Článek je věnován popisu technologií využitých pro sběr, uložení dat a jejich následné online vizualizace. Představena je platforma CLADE-IS jako nový systém pro sběr a uchování dat z registru. Obecně pro všechny diagnózy je popsána struktura formulářů a funkcionality nového systému, které usnadňují zadávání nových dat do registru a minimalizují chybovost v datech. Představena je veřejně dostupná online vizualizace dat pacientů s MGUS, WM, MM nebo primární AL amyloidózou pro všechna česká nebo slovenská centra a autentizovaná vizualizace dat pacientů s MM z vybraných center. Závěr: RMG představuje datovou základnu, díky které lze monitorovat průběh onemocnění u pacientů s monoklonálními gamapatiemi na populační úrovni.
Background: The Registry of Monoclonal Gammopathies (RMG) was established by the Czech Myeloma Group in 2007. RMG is a registry designed for the collection of clinical data concerning diagnosis, treatment, treatment results and survival of patients with monoclonal gammopathies. Data on patients with monoclonal gammopathy of undetermined significance (MGUS), Waldenström macroglobulinaemia (WM), multiple myeloma (MM) or primary AL (“amyloid light-chain”) amyloidosis are collected in the registry. Data: Nineteen Czech centres and four Slovak centres currently contribute to the registry. The registry currently contains records on more than 5,000 patients with MM, almost 3,000 patients with MGUS, 170 patients with WM and 26 patients with primary AL amyloidosis, i.e. more than 8,000 records on patients with monoclonal gammopathies altogether. Results: This paper describes technology employed for the collection, storage and subsequent online visualisation of data. The CLADE-IS platform is introduced as a new system for the collection and storage of data from the registry. The form structure and functions of the new system are described for all diagnoses in general; these functions facilitate data entry to the registry and minimise the error rate in data. Publicly available online visualisations of data on patients with MGUS, WM, MM or primary AL amyloidosis from all Czech or Slovak centres are introduced, together with authenticated visualisations of data on patients with MM from selected centres. Conclusion: The RMG represents a data basis that makes it possible to monitor the disease course in patients with monoclonal gammopathies on the population level.
Uvádíme přehled metod lícování neboli registrace 2D (obrazových) a 3D (objemových) diskrétních dat. Registrací rozumíme nalezení geometrické transformace mezi dvěma soubory diskrétních dat, která ztotožní pozici, orientaci a velikost korespondujících objektů obou souborů. V biomedicíněje aktuální při srovnávání objektů v čase (např. sledování léčby nádoru) nebo při jejich sledování různými senzory (např. integrace dat z různých lékařských zobrazovacích zařízeni) nebo při rekonstrukci 3D objektů ze sériových řezů v mikroskopii a podobně.
We present a short survey of image/volume registration techniques. Registration represents determination of coefficients of geometrical transformation between two images/volumes in order to get corresponding objects into the same position, orientation and scale. In biomedicine this is actual when one compares object(s) during a time period (e.g. tumour treatment observation) or by the use of different sensors (e.g. different modality data fusion). Also, registration is a prerequisite for 3D reconstruction and visualisation of objects from serial optical slices captured by a microscope, etc.
3D imaging approaches based on X-ray microcomputed tomography (microCT) have become increasingly accessible with advancements in methods, instruments and expertise. The synergy of material and life sciences has impacted biomedical research by proposing new tools for investigation. However, data sharing remains challenging as microCT files are usually in the range of gigabytes and require specific and expensive software for rendering and interpretation. Here, we provide an advanced method for visualisation and interpretation of microCT data with small file formats, readable on all operating systems, using freely available Portable Document Format (PDF) software. Our method is based on the conversion of volumetric data into interactive 3D PDF, allowing rotation, movement, magnification and setting modifications of objects, thus providing an intuitive approach to analyse structures in a 3D context. We describe the complete pipeline from data acquisition, data processing and compression, to 3D PDF formatting on an example of craniofacial anatomical morphology in the mouse embryo. Our procedure is widely applicable in biological research and can be used as a framework to analyse volumetric data from any research field relying on 3D rendering and CT-biomedical imaging.
- MeSH
- Models, Anatomic MeSH
- Electronic Data Processing MeSH
- Data Compression statistics & numerical data MeSH
- Skull anatomy & histology embryology MeSH
- Mice MeSH
- Facial Bones anatomy & histology embryology MeSH
- X-Ray Microtomography statistics & numerical data MeSH
- Radiographic Image Interpretation, Computer-Assisted MeSH
- Information Dissemination methods MeSH
- Software * MeSH
- Imaging, Three-Dimensional statistics & numerical data MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Inactivating germline mutations in the tumour suppressor gene BRCA1 are associated with a significantly increased risk of developing breast and ovarian cancer. A large number (>1500) of unique BRCA1 variants have been identified in the population and can be classified as pathogenic, non-pathogenic or as variants of unknown significance (VUS). Many VUS are rare missense variants leading to single amino acid changes. Their impact on protein function cannot be directly inferred from sequence information, precluding assessment of their pathogenicity. Thus, functional assays are critical to assess the impact of these VUS on protein activity. BRCA1 is a multifunctional protein and different assays have been used to assess the impact of variants on different biochemical activities and biological processes. METHODS AND RESULTS: To facilitate VUS analysis, we have developed a visualisation resource that compiles and displays functional data on all documented BRCA1 missense variants. BRCA1 Circos is a web-based visualisation tool based on the freely available Circos software package. The BRCA1 Circos web tool (http://research.nhgri.nih.gov/bic/circos/) aggregates data from all published BRCA1 missense variants for functional studies, harmonises their results and presents various functionalities to search and interpret individual-level functional information for each BRCA1 missense variant. CONCLUSIONS: This research visualisation tool will serve as a quick one-stop publically available reference for all the BRCA1 missense variants that have been functionally assessed. It will facilitate meta-analysis of functional data and improve assessment of pathogenicity of VUS.
- MeSH
- Databases, Genetic MeSH
- Datasets as Topic MeSH
- Genetic Predisposition to Disease MeSH
- Genetic Testing MeSH
- Internet * MeSH
- Humans MeSH
- Mutation, Missense * MeSH
- DNA Mutational Analysis MeSH
- Breast Neoplasms genetics MeSH
- Ovarian Neoplasms genetics MeSH
- Computer Graphics * MeSH
- BRCA1 Protein genetics MeSH
- Software * MeSH
- Database Management Systems MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- MeSH
- Algorithms MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods instrumentation utilization MeSH
- Image Processing, Computer-Assisted methods instrumentation MeSH
- Statistics as Topic methods MeSH
- Models, Theoretical MeSH
- Image Enhancement methods instrumentation MeSH
- Imaging, Three-Dimensional methods instrumentation utilization MeSH
- Check Tag
- Humans MeSH
- Keywords
- USA, ČR,
- MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Personality Inventory statistics & numerical data MeSH
- Surveys and Questionnaires MeSH
- Psychological Tests statistics & numerical data MeSH
- Statistics as Topic MeSH
- Research statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Comparative Study MeSH
- Geographicals
- Czech Republic MeSH
- United States MeSH
Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.
- MeSH
- Reading MeSH
- Child MeSH
- Dyslexia * physiopathology diagnosis classification MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Fixation, Ocular * physiology MeSH
- Eye Movements physiology MeSH
- Eye-Tracking Technology * MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Based on the fact that tremors display some distinct 3D spatial characteristics, we decided to visualise tremor planes in 3D space. We obtained 3-axial linear accelerometer signals of hand tremors from 58 patients with Parkinson´s disease (PD), 37 with isolated resting tremor (iRT), 75 with essential tremor (ET), and 44 healthy volunteers with physiological tremor (Ph). For each group analysis was done with subsequent spatial 3D regression of the input data i.e. along the x, y and z axes; the projected vector lengths in the individual (vertical transversal XY, vertical longitudinal XZ and horizontal YZ) reference frame planes and their angles. Most meaningful and statistically significant differences were found in the analyses of the 3D vector lengths. The tremor of the PD and the iRT group was oriented mainly in the horizontal YZ plane. The tremors of the patients with ET and Ph were oriented approximately in the midway between the all three referential planes with less tilt toward the vertical longitudinal XZ plane.
- MeSH
- Accelerometry methods MeSH
- Adult MeSH
- Electromyography methods MeSH
- Essential Tremor diagnosis physiopathology MeSH
- Middle Aged MeSH
- Humans MeSH
- Parkinson Disease diagnosis physiopathology MeSH
- Aged MeSH
- Tremor diagnosis physiopathology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
