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Background: Coronavirus Disease 2019 (COVID-19) is a highly contagious disease that resulted in 4533645 deaths until September first, 2021. Multiple Sclerosis (MS) patients receive immunosuppressive drugs. Thus, there is a concern that these drugs will reduce the patient's immune system resistance against COVID19. Objective(s): This study aimed to evaluate the epidemiology of COVID19 and its impact on MS patients in our university hospital in Tehran City, Iran. Material(s) and Method(s): A cross-sectional study was conducted based on hospital-based registry data from May 2020 to March 2021. Among more than 500 registered MS patients in Imam Khomeini Hospital in Tehran City, Iran, referring within our study period, 84 patients reported SARS-COV2 infection. The diagnosis of MS was confirmed by the McDonald criteria. Moreover, the diagnosis of COVID-19 in MS patients was established by the real-time-PCR technique and chest computed tomography. Result(s): Out of 84 MS patients with SARS-COV2 infection, 55(65.5%) were women, and their mean age was 37.48 years. The most commonly used medications by MS patients were Rituximab 20 (26.3%) and Dimethyl Fumarate 14(18.4%). Totally, 9(10.8%) of the patients needed to be hospitalized due to COVID-19, with a mean hospitalization duration of 5.88 days. A total of 1 (1.2%) death was reported. Conclusion(s): Compared to the healthy population, COVID-19 is not more serious in MS patients. Most MS patients with COVID-19 infection were not hospitalized and continued their medication during the infection.Copyright © 2022 The Authors. This is an open access article under the CC-By-NC license. All Rights Reserved.
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Convolutional neural network (CNN) is the most widely used structure-building technique for deep learning models. In order to classify chest x-ray pictures, this study examines a number of models, including VGG-13, AlexN ct, MobileNet, and Modified-DarkCovidNet, using both segmented image datasets and regular image datasets. Four types of chest X- images: normal chest image, Covid-19, pneumonia, and tuberculosis are used for classification. The experimental results demonstrate that the VGG offers the highest accuracy for segmented pictures and Modified Dark CovidN et performs best for multi class classification on segmented images. © 2023 Bharati Vidyapeeth, New Delhi.
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Computer-aided diagnosis of COVID-19 from lung medical images has received increasing attention in previous clinical practice and research. However, developing such automatic model is usually challenging due to the requirement of a large amount of data and sufficient computer power. With only 317 training images, this paper presents a Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for data synthetising. In order to take into account, the feature extraction ability and lightness of the model for lung CT images, the CACGAN network is mainly constructed by convolution blocks. During the training process, each iteration will update the discriminator's network parameters twice and the generator's network parameters once. For the evaluation of CACGAN. This paper organized multiple comparison between each pair from CACGAN synthetic data, classic augmented data, and original data. In this paper, 7 classifiers are built, ranging from simple to complex, and are trained for the three sets of data respectively. To control the variable, the three sets of data use the exact same classifier structure and the exact same validation dataset. The result shows the CACGAN successfully learned how to synthesize new lung CT images with specific labels. © 2023 SPIE.
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Considering the commonality of the pathogenetic links of the critical forms of COVID-19 and influenza AH1N1pdm09 (cytokine over-release syndrome), the question arises: will the predictors of an unfavorable outcome in patients on mechanical ventilation and, accordingly, the universal tactics of respiratory support in these diseases be identical? Objective. In a comparative aspect, to characterize patients with influenza AH1N1pdm09 and COVID-19 who were on mechanical ventilation, to identify additional clinical and laboratory risk factors for death, to determine the degree of influence of respiratory support (RP) tactics on an unfavorable outcome in the studied category of patients. Patients and methods. Patients treated on the basis of resuscitation and intensive care departments of the State Budgetary Healthcare Institution "SKIB" in Krasnodar and the State Budgetary Healthcare Institution "IB No 2" in Sochi were studied: group 1 - 31 people with influenza AH1N1pdm09 (21 people died - subgroup 1A;10 people survived - subgroup 1B) and group 2 - 50 people with COVID-19 (29 patients died - subgroup 2A;21 people survived - subgroup 2B). All patients developed hypoxemic ARF. All patients received step-by-step tactics of respiratory support, starting with oxygen therapy and ending with the use of "traditional" mechanical ventilation. Continuous variables were compared in subgroups of deceased and surviving patients for both nosologies at the stages: hospital admission;registration of hypoxemia and the use of various methods of respiratory therapy;development of multiple organ dysfunctions. With regard to the criteria for which a statistically significant difference was found (p < 0.05), we calculated a simple correlation, the relative risk of an event (RR [CI 25-75%]), the cut-off point, which corresponded to the best combination of sensitivity and specificity. Results. Risk factors for death of patients with influenza AH1N1pdm09 on mechanical ventilation: admission to the hospital later than the 8th day of illness;the fact of transfer from another hospital;leukocytosis >=10.0 x 109/l, granulocytosis >=5.5 x 109/l and LDH level >=700.0 U/l at admission;transfer of patients to mechanical ventilation on the 9th day of illness and later;SOFA score >=8;the need for pressor amines and replacement of kidney function. Predictors of poor outcome in ventilated COVID-19 patients: platelet count <=210 x 109/L on admission;the duration of oxygen therapy for more than 4.5 days;the use of HPNO and NIV as the 2nd step of RP for more than 2 days;transfer of patients to mechanical ventilation on the 14th day of illness and later;oxygenation index <=80;the need for pressors;SOFA score >=8. Conclusion. When comparing the identified predictors of death for patients with influenza and COVID-19 who needed mechanical ventilation, there are both some commonality and differences due to the peculiarities of the course of the disease. A step-by-step approach to the application of respiratory support methods is effective both in the case of patients with influenza AH1N1pdm09 and patients with COVID-19, provided that the respiratory support method used is consistent with the current state of the patient and his respiratory system, timely identification of markers of ineffectiveness of the respiratory support stage being carried out and determining the optimal moment escalation of respiratory therapy.Copyright © 2022, Dynasty Publishing House. All rights reserved.
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Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.
Subject(s)
COVID-19 , Humans , Spectrum Analysis, RamanABSTRACT
The aim of the work is to form the principles of a personalized approach to the management of patients with COVID-19 with a complicated comorbid background. Material and methods. The article describes a clinical case of successful recovery of an 87-year-old patient from a new coronavirus infection COVID-19, complicated by pneumonia involving 36% of the lung parenchyma area. Along with age, the situation was aggravated by the comorbid status of the patient: the presence of chronic lymphocytic leukemia, hypertension, mechanical prostheses of the mitral and aortic valves, postinfarction cardiosclerosis, paroxysmal atrial fibrillation, type 2 diabetes mellitus, stage 4 CKD, anemic syndrome, and subclinical hypothyroidism. Results. The C-reactive protein level at admission was 114.46 mg/L. The patient refused hospitalization. Baricitinib 4 mg, favipiravir according to the scheme, vitamin D 2000 units were prescribed for the previously taken therapy. Already after 3 days, C-reactive protein decreased by 4.6 times, and by the 8th day by 15.5 times and amounted to 7.38 mg/ml. The temperature returned to normal on day 2 from the start of baricitinib. In dynamics, a decrease in creatinine level to 177.0 mumol/l was noted, the glomerular filtration rate increased to 30 ml/min/1.73 m2, which corresponded to stage 3b of CKD (a pronounced decrease in glomerular filtration rate). Conclusion. Despite the age of the patient, many comorbidities, each of which could be fatal, the timely use of baricitinib on an outpatient basis made it possible to stop the progressive course of the disease.Copyright © Eco-Vector, 2023. All rights reserved.
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Objective To explore the application of ultrasound-guided arterial line placement in severe patients with COVID-19. Methods From February to April 2020, we retrospectively collected and analyzed the clinical data of critical patients with COVID-19 with an indwelling peripheral arterial catheter treated by the medical team of Peking Union Medical College Hospital. Patients with ultrasound-guided peripheral arterial catheterization were taken as the study group, while patients whose arterial catheter was placed by traditional palpation were taken as the control group. The puncture condition and complication rate were compared between the two groups. Results A total of 60 severe patients with COVID-19 who met the inclusion and exclusion criteria were enrolled in this study. There were 30 cases in the study group and 30 cases in the control group. In the study group, the success rate of the first catheterization of the peripheral artery (63.3% vs. 26.7%) and the total puncture success rate [(79.43+/- 25.79)% vs. (53.07+/-30.21)%] were higher than those in the control group (all P < 0.05), the puncture times(1.43+/-0.56 vs. 2.50+/-1.28) were less than those of the control group (P < 0.05). The rates of 24-hour disuse (6.7% vs. 30.0%), local hematoma (10.0% vs. 36.7%), occlusion, and tortuous (3.3% vs. 40.0%) in the study group were lower than those in the control group (all P < 0.05). Conclusion Under the three-level protection, ultrasound-guided arterial catheter placement for severe patients with COVID-19 can improve the success rate of catheter placement, reduce puncture times, and reduce the incidence of complications.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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A doctor of any specialty in his practice is faced with an infectious pathology, in connection with which the early diagnosis of infectious diseases is important both from a clinical and epidemiological standpoint. The aim - design and development of a unified practical guide to infectious diseases with elements of digitalization of content on pharmacotherapy. Material and methods. When developing the structure of the practical guide "Tactics of an infectious disease doctor", prototypes of educational and methodological materials were worked out. The structure of the practical guide included socially significant infectious diseases of viral, bacterial etiology, the most important helminthiases and protozoses, which practitioners of various specialties may encounter, both in inpatient and outpatient settings. Results and discussion. In the practical guide "Tactics of an infectious disease doctor" all nosologies are presented in a unified form: a brief definition of nosologies, characteristics of the etiological agent, epidemic process, clinical classification, examples of the formulation of a diagnosis, diagnosis, organization of medical care, treatment, pharmacotherapy, approximate terms of temporary disability, criteria recovery, rehabilitation, dispensary observation, recommendations for treatment and prevention. The practical guide contains a short guide to medicines. An innovation is the presentation of medicines via a QR code. Also, by means of a QR code, it is possible to switch to the electronic version of the practical guide. Recommendations for the prevention of infectious diseases are given in the form of pictographic diagrams. Conclusion. The practical guide "Tactics of an infectious disease doctor" allows primary care physicians and general practitioners in a short time period to make the optimal decision on the tactics of managing patients with infectious diseases, within the framework of modern clinical guidelines and approaches set out in national guidelines.Copyright © 2021 Infectious Diseases: News, Opinions, Training. All rights reserved.
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Detection of any disease in the early stage can save a life. There are many medical imaging modalities like MRI, FMRI, ultrasound, CT, and X-ray used in the detection of disease. In the last decades, neural network-based methods are effective in detecting and classifying the disease based on abnormalities present in the medical images. Acute laryngotracheobronchitis (croup) is one of the common diseases seen in children among the 0.5-3 years age group which infects the respiratory system that can cause the larynx, trachea, and bronchi. Prior detection can lower the risk of spreading and can be treated accurately by a pediatrician. Commonly this infection can be diagnosed though physical examination. But due to the similarity of Covid-19 symptoms urges the physicians to get accurate detection of this disease using X-ray and CT images of the infant's chest and throat. The proposed work aims to develop a croup diagnose system (CDS) which identify the Croup infection through post anterior (PA) view of pediatric X-ray using deep learning algorithm. We used the well-known transfer learning algorithm VGG19 and ResNet50. Data augmentation being adapted for reducing the overfitting and to improve the quantity of image samples. We show that the proposed transfer learning based CDS method can be used to classify the X-ray images into two classes namely, croup and normal. The experiment results confirm that VGG19 performs better than ResNet50 with promising classification accuracy (90.91%.). The results show that the proposed CDS models can be used for more pediatric medical image classification problem. © 2024 World Scientific Publishing Company.
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Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.Copyright © 2023 North Atlantic University Union NAUN. All rights reserved.
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Computational prediction of diseases is vital in medical research that contributes to computer-aided diagnostics and helps doctors and medical practitioners in critical decision-making for various diseases such as bacterial and viral kinds of disease, including COVID-19 of the current pandemic situation. Feature selection techniques function as a preprocessing phase for classification and prediction algorithms. For disease prediction, these features may be the patient's clinical profiles or genomic features such as gene expression profiles from microarray and read counts from RNA-Seq. The performance of a classifier depends primarily on the selected features. In addition, genomic features are too large in numbers, resulting in the curse of dimensionality problem. In the last few years, several feature selection algorithms have been developed to overcome the existing problems to get rid of eliminating chronic diseases, such as various cancers, Zika virus, Ebola virus, and the COVID-19 pandemic. In this review article, we systematically associate soft computing-based approaches for feature selection and disease prediction by applying three data types: patients' clinical profiles, microarray gene expression profiles, and RNA-Seq sample profiles. According to related work, when the discussion took place, the percentage of medical data types highlighted through pictorial representation and the respective ratio of percentages mentioned were 52%, 27%, 9% and 12% for clinical symptoms, gene expression, MRI-Image and other data types such as signal or text-based utilized, respectively. We also highlight the significant challenges and future directions in this research domain © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.
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Considering the commonality of the pathogenetic links of the critical forms of COVID-19 and influenza AH1N1pdm09 (cytokine over-release syndrome), the question arises: will the predictors of an unfavorable outcome in patients on mechanical ventilation and, accordingly, the universal tactics of respiratory support in these diseases be identical? Objective. In a comparative aspect, to characterize patients with influenza AH1N1pdm09 and COVID-19 who were on mechanical ventilation, to identify additional clinical and laboratory risk factors for death, to determine the degree of influence of respiratory support (RP) tactics on an unfavorable outcome in the studied category of patients. Patients and methods. Patients treated on the basis of resuscitation and intensive care departments of the State Budgetary Healthcare Institution "SKIB" in Krasnodar and the State Budgetary Healthcare Institution "IB No 2" in Sochi were studied: group 1 - 31 people with influenza AH1N1pdm09 (21 people died - subgroup 1A;10 people survived - subgroup 1B) and group 2 - 50 people with COVID-19 (29 patients died - subgroup 2A;21 people survived - subgroup 2B). All patients developed hypoxemic ARF. All patients received step-by-step tactics of respiratory support, starting with oxygen therapy and ending with the use of "traditional" mechanical ventilation. Continuous variables were compared in subgroups of deceased and surviving patients for both nosologies at the stages: hospital admission;registration of hypoxemia and the use of various methods of respiratory therapy;development of multiple organ dysfunctions. With regard to the criteria for which a statistically significant difference was found (p < 0.05), we calculated a simple correlation, the relative risk of an event (RR [CI 25-75%]), the cut-off point, which corresponded to the best combination of sensitivity and specificity. Results. Risk factors for death of patients with influenza AH1N1pdm09 on mechanical ventilation: admission to the hospital later than the 8th day of illness;the fact of transfer from another hospital;leukocytosis >=10.0 x 109/l, granulocytosis >=5.5 x 109/l and LDH level >=700.0 U/l at admission;transfer of patients to mechanical ventilation on the 9th day of illness and later;SOFA score >=8;the need for pressor amines and replacement of kidney function. Predictors of poor outcome in ventilated COVID-19 patients: platelet count <=210 x 109/L on admission;the duration of oxygen therapy for more than 4.5 days;the use of HPNO and NIV as the 2nd step of RP for more than 2 days;transfer of patients to mechanical ventilation on the 14th day of illness and later;oxygenation index <=80;the need for pressors;SOFA score >=8. Conclusion. When comparing the identified predictors of death for patients with influenza and COVID-19 who needed mechanical ventilation, there are both some commonality and differences due to the peculiarities of the course of the disease. A step-by-step approach to the application of respiratory support methods is effective both in the case of patients with influenza AH1N1pdm09 and patients with COVID-19, provided that the respiratory support method used is consistent with the current state of the patient and his respiratory system, timely identification of markers of ineffectiveness of the respiratory support stage being carried out and determining the optimal moment escalation of respiratory therapy.Copyright © 2022, Dynasty Publishing House. All rights reserved.
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Background: Accurate determination of the immediate and contributory causes of death in patients with COVID-19 is important for optimal care and instituting mitigation strategies. Method(s): All deaths in Qatar between March 1, 2020 and August 31, 2022 flagged for likely relationship to COVID-19 by were evaluated by two independent reviewers trained to determine and assign the most likely immediate underlying cause of death. Each decedent's electronic medical records was comprehensively reviewed, and the cause of death was assigned based on the most plausible underlying event that triggered the event(s) that led to death based on clinical documentation and a review of laboratory, microbiology, pathology, and radiology data. After cause assignment, each case was categorized into major diagnostic groups by organ system, syndrome, or disease classification. Result(s): Among 749 deaths flagged for likely association with COVID-19, the most common admitting diagnoses were respiratory tract infection (91%) and major adverse cardiac event (MACE, 2.3%). The most common immediate cause of death was COVID pneumonia (66.2%), followed by MACE (7.1%), hospital associated pneumonia (HAP, 6.8%), bacteremia (6.3%), disseminated fungal infection (DFI, 5.2%), and thromboembolism (4.5%). The median length of hospital stay was 23 days (IQR 14,38). COVID pneumonia remained the predominant cause irrespective of the time from admission, though the proportion dropped with increasing length of stay in the hospital. Other than COVID pneumonia, MACE was the predominant cause of death in first two weeks but declined thereafter. No death occurred due to bacteremia, HAP, or DFI in the first week after hospitalization, but became increasing common with increased length of stay in the hospital accounting for 9%, 12%, and 10% of all deaths after 4 weeks in the hospital respectively. The majority of deaths (86%) occurred in the intensive care unit setting. COVID pneumonia accounted for approximately two-thirds of deaths in each setting. MACE and HAP were approximately equally represented in both settings while bacteremia and disseminated fungal infection were more common in the intensive care unit setting. Conclusion(s): Nearly one-third of patients with COVID infection die of non- COVID causes, some of which are preventable. Mitigation strategies should be instituted to reduce the risk of such deaths. (Figure Presented).
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Lung diseases rank among the world's top killers and disablers. Therefore, early identification is crucial for improving long-term survival rates and boosting the chances of recovery. Unlike the traditional method, machine learning (ML) showed great success in the medical field, mainly detecting and diagnosing different diseases. Most recently, the deep learning approach enhanced classification accuracy and eliminated the difficulty of manual feature extraction. As a literature conclusion, the model performance accuracy is inversely proportional to the number of lung diseases under consideration. In addition, no more than four classes (including normal) were considered previously. This work developed a lightweight CNN model, identified as DuaNet, with higher accuracy than the up-to-the-date models. The dataset has 930 X-ray images, categorized into five-class lung diseases: normal, tuberculosis, pneumonia COVID-19, pneumonia viral, and pneumonia bacterial. DuaNet comprises fifteen layers involving input, seven convolutional blocks, three max-pooling, three fully connected, and one output (Softmax) layer. Each convolutional block consists of a convolutional layer, Batch normalization, and ReLU activation function. The final model (DuaNet) obtained a performance accuracy of 99.87%, with 100% for other metrics. © 2022 IEEE.
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In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early. © 2023 IEEE.
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Mechanical ventilation (MV) is a life-support therapy that may predispose to morbid and lethal complications, with ventilator-associated pneumonia (VAP) being the most prevalent. In 2013, the Center for Disease Control (CDC) defined criteria for ventilator-associated events (VAE). Ten years later, a growing number of studies assessing or validating its clinical applicability and the potential benefits of its inclusion have been published. Surveillance with VAE criteria is retrospective and the focus is often on a subset of patients with higher than lower severity. To date, it is estimated that around 30% of ventilated patients in the intensive care unit (ICU) develop VAE. While surveillance enhances the detection of infectious and non-infectious MV-related complications that are severe enough to impact the patient's outcomes, there are still many gaps in its classification and management. In this review, we provide an update by discussing VAE etiologies, epidemiology, and classification. Preventive strategies on optimizing ventilation, sedative and neuromuscular blockade therapy, and restrictive fluid management are warranted. An ideal VAE bundle is likely to minimize the period of intubation. We believe that it is time to progress from just surveillance to clinical care. Therefore, with this review, we have aimed to provide a roadmap for future research on the subject.Copyright © 2022 The Author(s)
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Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.
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Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.
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Interferons (IFNs) were first discovered over 60 years ago in a classic experiment by Isaacs and Lindenman showing that type I IFNs have antiviral activity. IFNs are widely used in the treatment of multiple sclerosis, viral hepatitis B and C, and some forms of cancer. Preliminary clinical data support the efficacy of type I IFN against potential pandemic viruses such as Ebola and SARS. Nevertheless, more effective and specific drugs have found their place in the treatment of such diseases. As the COVID-19 (SARS-CoV-2) pandemic is evolving, type I IFN is being re-discussed as one of the main pathogenic drugs, and initial clinical trials have shown promising results in reducing the severity and duration of COVID-19. Although SARS-CoV-2 inhibits the production of IFN-beta and prevents a full innate immune response to this virus, it is sensitive to the antiviral activity of externally administered type I IFN. The review presents current data on the classification and mechanisms of action of IFN. Possible options for the optimal use of IFN in the fight against COVID-19 are discussed.Copyright © 2022 Ima-Press Publishing House. All rights reserved.