Intertrochanteric (IT) femur fractures are the most common fractures in elderly people, and they lead to significant morbidity, mortality, and reduced quality of life. The different types of fractures require a careful definition to ensure accurate surgical planning and reduce the operation time, healing time, and number of surgical failures. In this study, a deep learning-based automatic multi-class IT fracture detection model was developed using computed tomography (CT) images and based on the AO/OTA classification method. The original CT image was resized and rearranged according to the fracture location and an unsharp masking filter was applied. A multi-class classification of nine different types of IT fractures and no fracture was performed using the faster regional-convolutional neural network (R-CNN). Bayesian optimization was also implemented to determine the optimal hyperparameter values for the faster R-CNN algorithm. In our proposed model, IT fractures classified into two classes showed an average accuracy of 0.97 ± 0.02, which was 0.90 ± 0.02 when classified into ten classes. Additionally, the detected region of interest from our proposed model showed minimum root mean square error and intersection over union values of 16.34 ± 47.01 pixels and 0.87 ± 0.12, respectively. In the future, our proposed automatic multi-class IT femur fracture detection model could allow clinicians to identify the fracture region and diagnose different types of femur fractures faster and more accurately. This will increase the probability of correct surgical treatment and minimize postoperative complications.

• MeSH
• Deep Learning MeSH
• Hip Fractures * diagnostic imaging   classification   MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Tomography, X-Ray Computed * methods   MeSH
• Statistics as Topic MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Clinical Study MeSH
• Research Support, Non-U.S. Gov't MeSH

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

• MeSH
• Liver Cirrhosis MeSH
• Liver pathology   MeSH
• Mice, Inbred C57BL MeSH
• Mice MeSH
• Non-alcoholic Fatty Liver Disease * diagnosis   pathology   MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

Objectives: The goals of this study were to examine relationships among health literacy and outcomes for sub-populations identified within a large, multi-dimensional Omaha System dataset. Specific aims were to extract sub-populations from the data using Latent Class Analysis (LCA); and quantify the change in knowledge score from pre- to post-intervention for common sub-populations. Design: Data-driven retrospective study using statistical modeling methods. Sample: A set of admission and discharge cases, captured in the Omaha System, representing 65,468 cases from various health care providers. Measures: Demographic information and the Omaha System terms including problems, signs/symptoms, and interventions were used as the features describing cases used for this study. Development of a mapping of demographics across health care systems enabled the integration of data from these different systems. Results: Knowledge scores increased for all five sub-populations identified by latent class analysis. Effect sizes of interventions related to health literacy outcomes varied from low to high, with the greatest effect size in populations of young at-risk adults. The most significant knowledge gains were seen for problems including Pregnancy, Postpartum, Family planning, Mental health, and Substance use. Conclusions: This is the first study to demonstrate positive relationships between interventions and health literacy outcomes for a very large sample. A deeper analysis of the results, focusing on specific problems and relevant interventions and their impact on health literacy is required to guide resource allocation in community-based care. As such, future work will focus on determining correlations between interventions for specific problems and knowledge change post-intervention.

• Keywords
• Omaha systém,
• MeSH
• Retrospective Studies MeSH
• Standardized Nursing Terminology MeSH
• Patient Education as Topic MeSH
• Health Literacy MeSH
• Health Education MeSH

• MeSH
• Mathematics MeSH
• Statistics as Topic MeSH

• Conspectus
• Statistika
• NML Fields
• statistika, zdravotnická statistika

AIM: It remains unclear, why only some patients form alloantibodies against foreign RBC antigens. Transfusion of red blood cell (RBC) products and pregnancy are the most relevant causes of immunization against RBC alloantigens. Here we investigated the relationship between RBC alloantibodies, Rh phenotype, and HLA phenotype among patients with multiple RBC alloantibodies METHODS: In a group of 124 multi-responders ‒ including both pregnant women and transplant recipients ‒ we analysed the distribution of HLA-Class II variants in subgroups of multi-responders to RBC alloantigens according to their Rh status. RESULTS: As expected, the RhD-negative phenotype was overrepresented in our alloimmunized group (49.2 %) compared to in the general population. Importantly, HLA-DRB1*15 carriers were significantly overrepresented among D-negative multi-responders compared to D-positive multi-responders (Pc = 0.045). Furthermore, the linked HLA-DRB1*13, HLA-DQB1*06, and HLA-DQA1*01 variants were more frequent in individuals with the DCCee phenotype than in other RhD-positive phenotypes. CONCLUSION: Our present findings showed that RBC multispecific alloimmunization was associated with particular HLA-Class II variants based on Rh status (Tab. 3, Ref. 22).

• Keywords
• aloimunizace, HLA,
• MeSH
• Antigens immunology   MeSH
• Erythrocytes * immunology   MeSH
• Phenotype MeSH
• Immune System Phenomena * MeSH
• Immunologic Techniques methods   MeSH
• Isoantibodies immunology   MeSH
• Blood Transfusion MeSH
• Humans MeSH
• Antibodies immunology   MeSH
• Statistics as Topic MeSH
• Pregnancy immunology   statistics & numerical data   MeSH
• Check Tag
• Humans MeSH
• Pregnancy immunology   statistics & numerical data   MeSH
• Publication type
• Clinical Study MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND AND OBJECTIVES: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. METHODS: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. RESULTS: The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. CONCLUSIONS: A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.

• MeSH
• Histological Techniques MeSH
• Malaria * MeSH
• Plasmodium vivax * MeSH
• Life Cycle Stages MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

• Publication type
• Journal Article MeSH

The publication focuses on viruses of medical importance and on viral infections. Intended for professionals.

• MeSH
• Virus Diseases MeSH
• Viruses MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Virologie
• NML Fields
• virologie
• infekční lékařství

MikroRNA (miRNA) tvoří velkou skupinu krátkých nekódujících RNA post-transkripčně regulujících genovou expresi. Schopnost miRNA inhibovat translaci onkogenů a nádorových supresorů dává předpoklad jejich zapojení do procesů kancerogeneze. Důkazů o funkcích miRNA v regulaci apoptózy, buněčné proliferace a diferenciace neustále přibývá. Zajímavá je rovněž skutečnost, že přibližně 50 % miRNA genů se nachází na fragilních částech chromozómů, jejichž ztráta nebo amplifikace je často detekována u nádorových onemocnění. Z tohoto pohledu jsou i nekódující miRNA nositelkami důležité genetické informace, jejíž regulace je narušena, nebo dochází k její ztrátě v průběhu nádorové transformace. Analýza expresních profilů miRNA je proto stále častěji využívána pro účely molekulární diagnostiky nádorových onemocnění, analogicky, jako je tomu u studií založených na DNA čipech a profilování kódujících transkriptů. Tato přehledová práce přináší základní poznatky o biogenezi a biologických funkcích miRNA a přehledně shrnuje dosavadní znalosti o významu miRNA nejen v oblasti nádorové biologie, ale také diagnostické a prediktivní onkologie.

MicroRNAs (miRNAs) are large class of non-coding RNAs that post-transcriptionally regulate gene expression. Their ability of translational repression applied for example on oncogenes or tumor-suppressor genes indicates involvement of miRNAs in multi-step carcinogenesis. Evidences of miRNAs linkage to biological processes like apoptosis, proliferation, differentiation and cell survival are rapidly accumulating. Approximately 50% of miRNAs are located at fragile sites of chromosomes or regions known to be amplified or deleted in human cancer. That is why, non-coding miRNAs seem to be another level of genetic information which regulation is altered or lost during neoplastic growth. Expression profiles of miRNAs are successfully used for molecular classification, more exact diagnosis and prognosis of human cancers and reached analogical analytical characteristics like studies based on DNA micro-arrays technology and profiling of coding transcripts. In this review we attempt to introduce basic knowledge of miRNAs biogenesis and biological functions and in particular summarise reports focused on miRNAs in oncology research area.

• MeSH
• Apoptosis immunology   MeSH
• Gene Expression genetics   MeSH
• Research Support as Topic MeSH
• MicroRNAs genetics   MeSH
• Cell Transformation, Neoplastic genetics   MeSH
• Neoplasms diagnosis   genetics   MeSH
• Predictive Value of Tests MeSH

Introduction: Transcatheter aortic valve implantation (TAVI) has evolved as an alternative method for surgical valve replacement in high-risk patients. Initially the transfemoral (TF) approach was used, later the transapical (TA) approach was adopted as an option for selected patients. The aim of our study was to compare the safety and anatomical and functional success of TAVI procedures with surgical aortic valve replacement (SAVR). Material and methods: The study included 45 consecutive high-risk patients with symptomatic severe aortic stenosis indicated for aortic valve intervention who met the entry criteria (age >75 years; logistic Euroscore >15%). The patients were allocated to one of three groups according the type of procedure: SAVR (n=15), TAVI TA (n=15) and TAVI TF (n=15). The groups did not differ in their preoperative characteristics except for myocardial infarction, which was more common in the TAVI groups. The Edwards Sapien valve was implanted in the TAVI patients and Edwards Perimount bioprosthesis was used in the SAVR patients. The TA approach was used in patients who were not eligible for the TF approach. Results: All procedures were technically successful. The prostheses used in the SAVR group were smaller in size than those implanted in the TA and TF groups (SAVR, 22.2(21.7;22.8); TA, 24.0(23.6;24.3); TF, 25.0(24.6;25.3)). The TA group patients were exposed to radiation for a shorter period and received a larger amount of contrast medium (TA, 9.7(9.0;10.5)min and 278.3(238.5;318.1)ml; TF, (15.0(13.7;16.4)min, 200.7(179.2;222.1)ml) in TF group). There were no statistically significant differences in the duration of procedures, stay in the intensive care unit and in the hospital, and intra- and post-operative complications among the groups. Early mortality (30 days) was 2.2%. One patient died of clostridium sepsis on day 12 (early mortality, 2.2%). Another patient died due to the multi-organ failure on the 58th day of hospital stay. Five other patients died during one-year follow-up (one-year survival rate, 86.3%). The functional class highly improved in all the patients, of whom 80% were with NYHA classes I or II. Conclusion: Our results show that TAVI is a safe method for treatment of aortic stenosis in high-risk patients and its early results are comparable with surgical aortic valve replacement. The TF and TA approaches are equally efficient, with similar outcomes and complication rates. Provided these results are confirmed at long-term follow-up, it can be assumed that the indication criteria for TAVI approaches will expand.

• Keywords
• TAVI, transapikální přístup, transfemorální přístup,
• MeSH
• Aortic Valve Stenosis physiopathology   pathology   therapy   MeSH
• Heart Valve Prosthesis Implantation history   methods   trends   MeSH
• Diagnostic Techniques, Cardiovascular utilization   MeSH
• Financing, Organized MeSH
• Cardiovascular Surgical Procedures contraindications   methods   utilization   MeSH
• Humans MeSH
• Meta-Analysis as Topic MeSH
• Interdisciplinary Communication MeSH
• Intraoperative Complications MeSH
• Postoperative Complications MeSH
• Risk Factors MeSH
• Cardiac Catheterization history   methods   trends   MeSH
• Statistics as Topic MeSH
• Age Factors MeSH
• Outcome and Process Assessment, Health Care MeSH
• Check Tag
• Humans MeSH