Machine learning applications for COVID-19 outbreak management.

Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

A Double Whammy Pneumonia: The First Reported Case of Concurrent Neisseria meningitidis and SARS-CoV-2 Pneumonia.

Meningococcal pneumonia (MP) is a rare manifestation of meningococcal disease. The MP was first described in 1907 when Neisseria meningitidis (NM) isolates were identified in sputum samples obtained from soldiers with pneumonia. Preceding and concurrent viral infections constitute a major risk for MP. During the 1918-1919 influenza pandemic, a significant increase in MP cases were reported in patients with preceding influenza infection. Despite the end of the last H1N1 influenza pandemic in 2010, seasonal influenza infections still pose a risk for simultaneous MP. History appears to be repeating itself with concomitant bacterial and viral coinfection amid the current SARS-CoV-2 pandemic. Herein presented is a unique case of an elderly woman who presented with, to the best of our knowledge, the first reported case of possible concurrent SARS-CoV-2 and MP infections.

Pulmonary Giant Cavitary Coccidioides With Fungal Ball and Hemoptysis.

Coccidioidomycosis (CM) is a fungal disease that results from inhalation of spores of Coccidioides immitis and C posadasii. If symptomatic, disease primarily manifests as community-acquired pneumonia; however, additional pulmonary manifestations such as pleural effusion, empyema, and cavitation may occur. Diabetic patients have an increased risk of severe and cavitary CM. Cavitary disease may erode vasculature and pulmonary parenchyma leading to further complications. Furthermore, chronic cavities can become colonized as well and develop superimposed infections. This is a case of cavitary CM in uncontrolled diabetic nonadherent to treatment presenting with hemoptysis and mycetoma.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain.

With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).

Two Cases of Miliary and Disseminated Coccidioidomycosis Following Glucocorticoid Therapy and Literature Review.

A 49-year-old man with no significant past medical history received dexamethasone as part of his treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Less than 3 weeks later, the patient developed acute respiratory distress syndrome. Radiological and serological testing led to a diagnosis of acute hypoxic miliary coccidioidomycosis. A 52-year-old man with a past medical history of chronic kidney disease (CKD) was treated with prednisone for focal segmental glomerulosclerosis (FSGS). Within 2 weeks, this patient developed bilateral lower extremity weakness. Radiology, serology, and lumbar puncture proved a diagnosis of reactivated coccidioidomycosis with miliary pattern and coccidioidomycosis meningoencephalitis with arachnoiditis. Whether treatment with glucocorticoids caused reactivation of coccidioidomycosis is discussed in this case series.

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

Serum magnesium levels in hospitalized patients with SARS-CoV-2.

Early studies have reported various electrolyte abnormalities at admission in patients with severe COVID-19. 104 out of 193 patients admitted to our institution presented with hypermagnesemia at presentation. It is believed this may be important in the evaluation of severe SARS-CoV-2 infections. This study evaluated the outcomes of hypermagnesemia in patients with COVID-19. A retrospective chart review of patients admitted to the hospital with confirmed SARS-CoV-2 infection was conducted. A review of the medical literature regarding hypermagnesemia, magnesium levels in critical care illness and electrolyte abnormalities in patients with COVID-19 was performed. Differences in demographic and clinical characteristics of patients with hypermagnesemia and normomagnesemia were evaluated using descriptive statistics. Other known variables of disease severity were analyzed. 104 patients (54%) were identified with hypermagnesemia (≥2.5 mg/dL). 48 of those patients were admitted to the intensive care unit (46%, p<0.001). 34 patients required ventilator support (32%, p<0.0001). With age-adjusted logistic regression analysis hypermagnesemia was associated with mortality (p=0.007). This study demonstrates that hypermagnesemia is a significant marker of disease severity and adverse outcome in SARS-CoV-2 infections. We recommend serum magnesium be added to the panel of tests routinely ordered in evaluation of severe SARS-CoV-2 infections.

Postinfectious COVID-19 Catatonia: A Report of Two Cases.

Neuropsychiatric symptoms are a common complication of COVID-19, with symptoms documented both during acute COVID-19 infection (parainfectious) and persisting or developing after the resolution of respiratory symptoms (postinfectious). Patients have presented with a variety of symptoms such as anosmia, thrombotic events, seizures, cognitive and attention deficits, new-onset anxiety, depression, psychosis, and rarely catatonia. Etiology appears to be related to disruption of regular neurotransmission and hypoxic injury secondary to systemic inflammation and cytokine storm. Although rare, catatonia and each of its subtypes have now been reported as complications of COVID-19 and therefore should be considered known to occur in both the parainfectious and postinfectious states. Diagnosis of catatonia in the context of COVID-19 should be considered when work-up for more common medical causes of encephalopathy are negative, there is no identifiable psychiatric etiology for catatonia, and there is a positive response to benzodiazepines.

Stem Cell Therapy Potency in Personalizing Severe COVID-19 Treatment.

Currently, there are no specific and efficient vaccines or drugs for COVID-19, particularly in severe cases. A wide range of variations in the clinical symptoms of different patients attributed to genomic differences. Therefore, personalized treatments seem to play a critical role in improving these symptoms and even similar conditions. Prompted by the uncertainties in the area of COVID-19 therapies, we reviewed the published papers and concepts to gather and provide useful information to clinicians and researchers interested in personalized medicine and cell-based therapy. One novel aspect of this study focuses on the potential application of personalized medicine in treating severe cases of COVID-19. However, it is theoretical, as any real-world examples of the use of genuinely personalized medicine have not existed yet. Nevertheless, we know that stem cells, especially MSCs, have immune-modulatory effects and can be stored for future personalized medicine applications. This theory has been conjugated with some evidence that we review in the present study. Besides, we discuss the importance of personalized medicine and its possible aspects in COVID-19 treatment, then review the cell-based therapy studies for COVID-19 with a particular focus on stem cell-based therapies as a primary personalized tool medicine. However, the idea of cell-based therapy has not been accepted by several scientific communities due to some concerns of lack of satisfactory clinical studies; still, the MSCs and their clinical outcomes have been revealed the safety and potency of this therapeutic approach in several diseases, especially in the immune-mediated inflammatory diseases and some incurable diseases. Promising outcomes have resulted in that clinical studies are going to continue.

Cytokine release syndrome: inhibition of pro-inflammatory cytokines as a solution for reducing COVID-19 mortality.

Coronavirus disease (COVID-19) reached pandemic proportions at the beginning of 2020 and continues to be a worldwide concern. End organ damage and acute respiratory distress syndrome are the leading causes of death in severely or critically ill patients. The elevated cytokine levels in severe patients in comparison with mildly affected patients suggest that cytokine release syndrome (CRS) occurs in the severe form of the disease. In this paper, the significant role of pro-inflammatory cytokines, including IL-1, IL-6, and TNF-alpha, and their mechanism of action in the CRS cascade is explained. Potential therapeutic approaches involving anti-IL-6 and anti-TNF-alpha antibodies to fight COVID-19 and reduce mortality rate in severe cases are also discussed.

Cardiovascular system is at higher risk of affecting by COVID-19.

SARS-CoV-2 has shown its potential to cause severe manifestations among individuals with underlying cardiovascular disease (CVD). The patients infected with SARS-CoV-2 with pre-existing CVD are more likely to relapse. There are several reasons, including the prolonged hospitalization time as a consequence of their more severe illness and aberrant expression of angiotensin-converting enzyme 2 (ACE2) - the cell surface receptor of SARS-COV2 that is present on cardiac cells - and using drugs such as ACE inhibitors and angiotensin receptor blockers (ARBs) that alter the expression of ACE2. Besides, SARS-CoV-2 shares structural similarities with SARS-CoV-1, and that patients recovered from SARS-CoV1 have shown an increased risk of developing inflammatory, metabolic, and cardiac diseases. It makes some concerns that people who recovered from SARS-CoV2 are also liable to develop these chronic conditions later. Further studies should investigate the probability of recurrence of COVID-19 in patients with CVD and the development of approaches for the prevention of chronic inflammatory conditions in patients with CVD who recovered from COVID-19.

Microfluidic devices for detection of RNA viruses.

There is a long way to go before the coronavirus disease 2019 (Covid-19) outbreak comes under control. qRT-PCR is currently used for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of Covid-19, but it is expensive, time-consuming, and not as sensitive as it should be. Finding a rapid, easy-to-use, and cheap diagnostic method is necessary to help control the current outbreak. Microfluidic systems provide a platform for many diagnostic tests, including RT-PCR, RT-LAMP, nested-PCR, nucleic acid hybridization, ELISA, fluorescence-Based Assays, rolling circle amplification, aptamers, sample preparation multiplexer (SPM), Porous Silicon Nanowire Forest, silica sol-gel coating/bonding, and CRISPR. They promise faster, cheaper, and easy-to-use methods with higher sensitivity, so microfluidic devices have a high potential to be an alternative method for the detection of viral RNA. These devices have previously been used to detect RNA viruses such as H1N1, Zika, HAV, HIV, and norovirus, with acceptable results. This paper provides an overview of microfluidic systems as diagnostic methods for RNA viruses with a focus on SARS-CoV-2.

The Coincidence of 2 Epidemics, Coccidioidomycosis and SARS-CoV-2: A Case Report.

In the middle of a pandemic, patients with cough and fever are thought to have SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2). It should be remembered that in the desert southwest of the United States, we have an ongoing epidemic of coccidioidomycosis (CM). There are additionally many other respiratory illnesses that could be confused with CoV-2 or overlooked. This is a case report of CoV-2 engrafted on chronic cavitary pulmonary CM. In a time where the coronavirus pandemic is becoming rampant, we demonstrate the case of a coinfection with cavitary pulmonary CM. In this case, the importance of detection of the coronavirus and treatment of the coinfection is explored.