EASI
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Atopický ekzém je časté chronické zánětlivé onemocnění, které může mít výrazný vliv na kvalitu života. Možnosti léčby zvláště těžkých forem onemocnění byly po dlouhou dobu omezené a zahrnovaly převážně širokospektré imunosupresivní léky jako cyklosporin, které však mají potenciální četné nežádoucí účinky. Nově máme k dispozici první biologickou terapii atopického ekzému a další nová léčiva budou v následujících letech následovat. Vzhledem k ekonomické nákladnosti nových moderních léků začíná být nutné zavedení standardizovaných skórovacích systémů k objektivnímu zhodnocení efektivity těchto léčiv. V současnosti existuje více než 25 různých hodnotících systémů pro atopickou dermatitidu, z nichž Eczema Area and Severity Index (EASI) je vyžadován při posuzování závažnosti a úspěšnosti biologické léčby atopické dermatitidy.
Atopic eczema is a common chronic inflammatory disease that can significantly affect a patient's quality of life. Treatment options, particularly in severe forms of the disease, have been limited for a long time and largely included broad-spectrum immunosuppressive agents, such as cyclosporine, which, however, possess potential numerous adverse effects. The first biological therapy for atopic eczema has recently become available and other new drugs will follow in the years to come. Given the economic costs of novel modern drugs, the introduction of standardized scoring systems to objectively evaluate the efficacy of these drugs is becoming necessary. Currently, there are more than 25 different scoring systems for atopic dermatitis of which the Eczema Area and Severity Index (EASI) is required to assess the severity of and success rate of biological therapy for atopic dermatitis.
- Klíčová slova
- EASI, Eczema Area and Severity Index,
- MeSH
- atopická dermatitida * diagnóza patologie MeSH
- lidé MeSH
- stupeň závažnosti nemoci * MeSH
- Check Tag
- lidé MeSH
- MeSH
- aplikace inhalační přístrojové vybavení MeSH
- bronchiální astma terapie MeSH
- dechová terapie MeSH
- lidé MeSH
- nebulizátory a vaporizátory MeSH
- vzdělávání pacientů jako téma MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- kongresy MeSH
V uplynulých 5 letech autoři zavedli celkem 24 permanentních kaválních filtrů. Nejčastějším důvodem byla opakovaná plieni embólie, věkový průměr pacientů byl 61 let. Použití TRAP EASY filtru shledáváme jako technicky jednoduchý výkon, v našem souboru bez průkazu komplikací.
In the last 5 years the authors introduced 24 permanent caval filters on the whole. Repeated pulmonary embolism was the most frequent reason (indication), the age of the patients being 61 years on the average. The use of TRAP EASY filter proved to be a simple intervention and no complications were encountered in the group.
Je prezentován případ pacientky s těžkou formou atopické dermatitidy. Před více než dvěma lety byla u ní zahájena léčba dupilumabem. Skóre EASI (Eczema Area and Severity Index) při zahájení terapie dosahovalo hodnoty 54 bodů. V 16. týdnu bylo splněno tzv. EASI 50, tedy zlepšení projevů oproti výchozímu stavu o více než 50 %. K dalšímu zmírnění příznaků došlo ve 24. týdnu, kdy byly projevy zlepšeny o více než 75 %. Při poslední kontrole v lednu 2022, tedy po více než dvou letech léčby, bylo EASI 5,2. Současně došlo i ke zmírnění astmatických obtíží.
A case of a patient with a severe form of atopic dermatitis is presented. Dupilumab therapy was started more than two years ago. The EASI (Eczema Area and Severity Index) score at the start of therapy was 54 points. The so-called EASI 50 was met in the 16th week, i.e., an improvement in symptoms more than 50%. Further reduction of symptoms occurred at week 24. At the last inspection on January 2022, after more than two years therapy, EASI was 5.2 points. At the same time asthmatic symptoms were reduced.
- Klíčová slova
- Dupixent,
- MeSH
- atopická dermatitida * farmakoterapie genetika klasifikace MeSH
- bronchiální astma etiologie farmakoterapie MeSH
- dospělí MeSH
- humanizované monoklonální protilátky aplikace a dávkování farmakologie MeSH
- injekce subkutánní MeSH
- interleukin-13 antagonisté a inhibitory MeSH
- interleukin-4 antagonisté a inhibitory MeSH
- lidé MeSH
- monoklonální protilátky aplikace a dávkování farmakologie MeSH
- stupeň závažnosti nemoci MeSH
- výsledek terapie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
Proper respiratory tract protection is the key factor to limiting the rate of COVID-19 spread and providing a safe environment for health care workers. Traditional N95 (FFP2) respirators are not easy to regenerate and thus create certain financial and ecological burdens; moreover, their quality may vary significantly. A solution that would overcome these disadvantages is desirable. In this study a commercially available knit polyester fleece fabric was selected as the filter material, and a total of 25 filters of different areas and thicknesses were prepared. Then, the size-resolved filtration efficiency (40-400 nm) and pressure drop were evaluated at a volumetric flow rate of 95 L/min. We showed the excellent synergistic effect of expanding the filtration area and increasing the number of filtering layers on the filtration efficiency; a filter cartridge with 8 layers of knit polyester fabric with a surface area of 900 cm2 and sized 25 × 14 × 8 cm achieved filtration efficiencies of 98% at 95 L/min and 99.5% at 30 L/min. The assembled filter kit consists of a filter cartridge (14 Pa) carried in a small backpack connected to a half mask with a total pressure drop of 84 Pa at 95 L/min. In addition, it is reusable, and the filter material can be regenerated at least ten times by simple methods, such as boiling. We have demonstrated a novel approach for creating high-quality and easy-to-breathe-through respiratory protective equipment that reduces operating costs and is a green solution because it is easy to regenerate.
BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.
- MeSH
- genomika MeSH
- neuronové sítě * MeSH
- sekundární struktura proteinů MeSH
- strojové učení * MeSH
- Publikační typ
- časopisecké články MeSH
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