RATIONALE: Persistent respiratory symptoms following Coronavirus Disease 2019 (COVID-19) are associated with residual radiological changes in lung parenchyma, with a risk of development into lung fibrosis, and with impaired pulmonary function. Previous studies hinted at the possible efficacy of corticosteroids (CS) in facilitating the resolution of post-COVID residual changes in the lungs, but the available data is limited. AIM: To evaluate the effects of CS treatment in post-COVID respiratory syndrome patients. PATIENTS AND METHODS: Post-COVID patients were recruited into a prospective single-center observational study and scheduled for an initial (V1) and follow-up visit (V2) at the Department of Respiratory Medicine and Tuberculosis, University Hospital Olomouc, comprising of pulmonary function testing, chest x-ray, and complex clinical examination. The decision to administer CS or maintain watchful waiting (WW) was in line with Czech national guidelines. RESULTS: The study involved 2729 COVID-19 survivors (45.7% male; mean age: 54.6). From 2026 patients with complete V1 data, 131 patients were indicated for CS therapy. These patients showed significantly worse radiological and functional impairment at V1. Mean initial dose was 27.6 mg (SD ± 10,64), and the mean duration of CS therapy was 13.3 weeks (SD ± 10,06). Following therapy, significantly better improvement of static lung volumes and transfer factor for carbon monoxide (DLCO), and significantly better rates of good or complete radiological and subjective improvement were observed in the CS group compared to controls with available follow-up data (n = 894). CONCLUSION: Better improvement of pulmonary function, radiological findings and subjective symptoms were observed in patients CS compared to watchful waiting. Our findings suggest that glucocorticoid therapy could benefit selected patients with persistent dyspnea, significant radiological changes, and decreased DLCO.
- Publikační typ
- časopisecké články MeSH
AIMS: The study analysed post-acute COVID-19 symptoms and the pulmonary function test (PFT) results in patients surviving the native strain of the virus. METHODS: The study was prospective; the inclusion criteria were positive PCR test for SARS-CoV-2 and age 18-100. Exclusion criteria were active respiratory infection, known or suspicious pre-existing pulmonary disease, cardiac failure, recent or acute pulmonary embolism, anaemia, and neuromuscular diseases. The recruitment period was 1st March 2020 - 25th December 2020. The initial examination was performed 4-12 weeks after the disease onset. All subjects underwent physical examination, anamnesis, chest x-ray and PFT. RESULTS: The study involved 785 subjects (345 male) mean age 53.8 (SD 14.6). The disease severity groups were: mild (G1), moderate (G2) and severe/critical (G3). Anosmia was present in the acute disease phase in 45.2% of G1 patients, but only in 4.5% of G3 patients. Dyspnoea occurred frequently in more severe groups (40%, 51.8% and 63.7% for G1, G2 and G3 respectively), while cough and fatigue showed no relationship to disease severity. Females were more likely to experience persistent symptoms. PFT results were significantly decreased in more severe groups compared to the mild COVID-19 patients, diffusing capacity was 86.3%, 79% and 68% of predicted values in G1, G2 and G3 respectively. CONCLUSION: Anosmia during the acute phase was associated with mild disease, persisting dyspnoea was more frequent after more severe COVID-19. Females tended to have persisting symptoms in post-acute phase more frequently. PFT results showed decrease with disease severity.
- MeSH
- anosmie MeSH
- COVID-19 * komplikace diagnóza MeSH
- dospělí MeSH
- dyspnoe etiologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- prospektivní studie MeSH
- respirační funkční testy MeSH
- SARS-CoV-2 MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.
- Publikační typ
- časopisecké články 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
- histologické techniky MeSH
- malárie * MeSH
- Plasmodium vivax * MeSH
- stadia vývoje MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND AND OBJECTIVES: The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper. METHODS: The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases. The choice of Power Spectral Density (PSD) makes the model extract highly discriminatory features significant for the multi-classification of four common cardiac disorders. RESULTS: Data augmentation techniques are applied to make the model robust, and the model undergoes 10-fold cross-validation to yield an overall accuracy of 98.879% on the test dataset to diagnose multi heart diseases from PCG signals. CONCLUSION: The proposed model is completely automatic, where signal pre-processing and feature engineering are not required. The conversion time of power spectrogram from PCG signals is very low range from 0.10 s to 0.11 s. This reduces the complexity of the model, making it highly reliable and robust for real-time applications. The proposed architecture can be deployed on cloud and a low cost processor, desktop, android app leading to proper access to the dispensaries in remote areas.
U části pacientů po těžkém průběhu onemocnění covidem-19 nedochází ke spontánní resorpci (po) zánětlivých změn a je zde riziko rozvoje organizující se pneumonie a fibrotizace plicního parenchymu. Přes nedostatek důkazů o jejich efektivitě v této indikaci je použití glukokortikoidů v léčbě plicního postižení v rámci post-covidového syndromu běžnou praxí. Studií zabývajících se jejich použitím v pozdní fázi onemocnění je bohužel málo. Na základě dostupných prací se zdá, že v případě neregredujících infiltrátů s větším rozsahem než 15 % plicního parenchymu a za současné nepřítomnosti kontraindikace kortikoterapie je jejich použití opodstatněné. Článek shrnuje význam kortikoterapie v léčbě a prevenci plicního post-covidového postižení.
Prolonged resorption occurs in the small number of patients with severe course of COVID-19. There is the risk of development of organizing pneumonia and subsequent fibrotization of lung parenchyma. The use of glucocorticoids in the treatment of post-COVID lung involvement is common practice, despite the lack of evidence regarding their efficacy in this indication. The number of studies dealing with their use in late phase of the disease is unfortunately small. According to current scarce evidence their use may be justified in case of 15% or larger lung involvement without spontaneous resolution. Of course, the absence of contraindications for their use is required. The article focuses on the position of glucocorticoids in the prevention and therapy of lung involvement in post-COVID syndrome patients.
- MeSH
- COVID-19 * komplikace MeSH
- dexamethason * aplikace a dávkování terapeutické užití MeSH
- farmakoterapie COVID-19 MeSH
- glukokortikoidy aplikace a dávkování terapeutické užití MeSH
- lidé středního věku MeSH
- lidé MeSH
- syndrom dechové tísně etiologie farmakoterapie komplikace MeSH
- virová pneumonie diagnostické zobrazování etiologie komplikace MeSH
- výsledek terapie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
- práce podpořená grantem MeSH
The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.
- MeSH
- algoritmy * MeSH
- neuronové sítě (počítačové) * MeSH
- Publikační typ
- časopisecké články MeSH
Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.
- MeSH
- gliom * diagnóza MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- nádory mozku * diagnóza MeSH
- strojové učení * MeSH
- stupeň nádoru MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.
- MeSH
- COVID-19 * MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- pandemie MeSH
- reprodukovatelnost výsledků MeSH
- SARS-CoV-2 MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
Cervical cancer is still one of the most prevalent cancers in women and a significant cause of mortality. Cytokine gene variants and socio-demographic characteristics have been reported as biomarkers for determining the cervical cancer risk in the Indian population. This study was designed to apply a machine learning-based model using these risk factors for better prognosis and prediction of cervical cancer. This study includes the dataset of cytokine gene variants, clinical and socio-demographic characteristics of normal healthy control subjects, and cervical cancer cases. Different risk factors, including demographic details and cytokine gene variants, were analysed using different machine learning approaches. Various statistical parameters were used for evaluating the proposed method. After multi-step data processing and random splitting of the dataset, machine learning methods were applied and evaluated with 5-fold cross-validation and also tested on the unseen data records of a collected dataset for proper evaluation and analysis. The proposed approaches were verified after analysing various performance metrics. The logistic regression technique achieved the highest average accuracy of 82.25% and the highest average F1-score of 82.58% among all the methods. Ridge classifiers and the Gaussian Naïve Bayes classifier achieved the highest sensitivity-85%. The ridge classifier surpasses most of the machine learning classifiers with 84.78% accuracy and 97.83% sensitivity. The risk factors analysed in this study can be taken as biomarkers in developing a cervical cancer diagnosis system. The outcomes demonstrate that the machine learning assisted analysis of cytokine gene variants and socio-demographic characteristics can be utilised effectively for predicting the risk of developing cervical cancer.
- MeSH
- Bayesova věta MeSH
- cytokiny genetika MeSH
- demografie MeSH
- lidé MeSH
- nádory děložního čípku * epidemiologie genetika MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
