Coronavirus disease-19 (COVID-19) is a pandemic infectious disease caused by a novel coronavirus. Infection can result in a wide range of clinical outcomes, from an asymptomatic condition to severe bilateral pneumonia and life-threatening conditions. Diagnosis is based on the combination of a history of exposure, clinical presentation, and real-time polymerase chain reaction (RT-PCR) assays. In endemic areas, imaging tests including computed tomography (CT), chest X-ray (CXR), and ultrasound (US) have been included in the diagnostic workup. Multiple and peripheral areas of parenchymal injury is the hallmark of COVID-19 lung infection, seen as ground-glass opacification and consolidation on CT, as hazy opacities on CXR, and as multiple B-lines and subpleural consolidations on US. Of these modalities, CT has the best sensitivity and specificity, while CXR has moderate sensitivity and unknown specificity. Both CT and CXR involve ionizing radiation, increase the risk of cross-infection, and require a long sterilization time. Ultrasound is the only modality used by clinicians. Early reports have shown promising results, comparable to CT. With high availability, the lowest risk of cross-infection, and a rapid sterilization process, US may potentially become the primary imaging tool for COVID-19 pulmonary injury. Lung US training programs are needed to provide clinicians with the ability to better implement this technique.
The coronavirus disease-2019 (COVID-19) epidemic started in late 2019, and was upgraded to a pandemic on March 11, 2020 by the World Health Organization (WHO). Well established epidemiological models have been used over the last few months in an attempt to predict how the virus would spread. The predictions were frightening, and the resulting panic caused many governments to impose lockdowns or other severe restrictions, with lasting effects. This short paper discusses another way of looking at the spread of COVID-19, by focusing on the daily rate of infection, defined as the daily rate of increase in the number of infected persons. It is shown that the daily rate is monotonically decreasing, after a short initial period, in all countries, and that the pattern is similar in all countries. This appears to be a universal phenomenon. Based on these calculations, the April 1, 2020 data for Western Europe were sufficient to predict the beginning of the end of COVID-19 in that region before the end of that month.
Diarrhea, an illness of both the developed and developing world, involves the burdensome characteristics of frequent bowel movements, loose stools, and abdominal discomfort. Diarrhea is a long-standing challenge in palliative care and can have a myriad of causes, making symptomatic treatment pertinent when illness evaluation is ongoing, when there is no definitive treatment approach, or when effective treatment cannot be attained. Symptomatic therapy is a common approach in palliative care settings. Bismuth is a suitable agent for symptomatic therapy and can be effectively employed for management of chronic diarrhea. The objective of this narrative review is to examine the role of bismuth in management of diarrheal symptoms. To explore this, PubMed (including Medline) and Embase were used to search the existing literature on bismuth and diarrhea published from 1980 to 2019. It was found that bismuth has potential utility for diarrheal relief in multiple settings, including microscopic colitis, traveler’s diarrhea, gastrointestinal infection, cancer, and chemotherapy. It also has great potential for use in palliative care patients, due to its minimal side effects. Overall, the antisecretory, anti-inflammatory, and antibacterial properties of bismuth make it a suitable therapy for symptomatic treatment of diarrhea. The limited range of adverse side effects makes it an appealing option for patients with numerous comorbidities. Healthcare providers can explore bismuth as an adjunct therapy for diarrhea management in an array of conditions, especially in the palliative care setting.
In December 2019, the first cases of a new contagious disease were diagnosed in the city of Wuhan, the capital of Hubei province in China. Within a short period of time the outbreak developed exponentially into a pandemic that infected millions of people, with a global death toll of more than 500,000 during its first 6 months. Eventually, the novel disease was named coronavirus disease 2019 (COVID-19), and the new virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Similar to all known pandemics throughout history, COVID-19 has been accompanied by a large degree of fear, anxiety, uncertainty, and economic disaster worldwide. Despite multiple publications and increasing knowledge regarding the biological secrets of SARS-CoV-2, as of the writing of this paper, there is neither an approved vaccine nor medication to prevent infection or cure for this highly infectious disease. Past pandemics were caused by a wide range of microbes, primarily viruses, but also bacteria. Characteristically, a significant proportion of them originated in different animal species (zoonoses). Since an understanding of the microbial cause of these diseases was unveiled relatively late in human history, past pandemics were often attributed to strange causes including punishment from God, demonic activity, or volatile unspecified substances. Although a high case fatality ratio was common to all pandemic diseases, some striking clinical character¬istics of each disease allowed contemporaneous people to clinically diagnose the infection despite null microbiological information. In comparison to past pandemics, SARS-CoV-2 has tricky and complex mech¬anisms that have facilitated its rapid and catastrophic spread worldwide.
At the time of writing, in July 2020, the COVID-19 pandemic has already inflicted dramatic international restrictions, including airports closing and limiting international travel. It has been suggested that re-opening of airports should involve and even rely on testing travelers for COVID-19. This paper discusses the methodology of estimating the detection and diagnostic accuracy of COVID-19 tests. It explains the clear distinction between the technical characteristics of the tests, the detection measures, and the diagnostic measures that have clinical and public health implications. It demonstrates the importance of the prevalence of COVID-19 in terms of determining the ability of a test to yield a diagnosis. We explain the methodology of evaluating diagnostic tests, using the predictive summary index (PSI), and the minimum number of tests that need to be performed in order to correctly diagnose one person, which is estimated by 1/PSI. In a population with low prevalence, even a high-sensitivity test may lead to a high percentage of false positive diagnoses, resulting in the need for multiple high-cost tests to achieve a correct diagnosis. Thus, basing a policy for opening airports on diagnostic testing, even with the best test for COVID-19, has some limits.
The outbreak of coronavirus disease 2019 (COVID-19) in Italy, the first Western country hit by the pandemic, seriously impacted the Italian healthcare system and social and economic environment. This perspective piece focuses on the main challenges faced by Italian hospital managements: hospital overcrowding; the need for urgent reorganization of the country’s healthcare systems; the lack of data regarding COVID-19 diagnostics, clinical course, and effective treatment; individual and collective consequences of the crisis; and the importance of disease containment measures and early treatment strategies.
At the time of writing, in July 2020, the recently emerging SARS-CoV-2 pandemic has attracted major attention to viral diseases, in particular coronaviruses. In spite of alarming molecular evidence, documentation of interspecies transmission in livestock, and the emergence of two new and relatively virulent human coronaviruses within a 10-year period, many gaps remain in the study and understanding of this family of viruses. This paper provides an overview of our knowledge regarding the coronavirus family, while highlighting their key biological properties in the context of our overall understanding of viral diseases.
Biofilms are formed by the congregation of one or more types of microorganisms that can grow on a firm surface. Dental plaque is one of the most commonly forming biofilms in the oral cavity and appears as a slimy layer on the surface of the teeth. In general, the formation is slow, but biofilms are very adaptive to the changing environment, and a mature biofilm can cause many health-related problems in humans. These biofilms remain unaffected by antibiotics as they do not allow the penetration of antibiotics. Moreover, the increased level of virulence and antibiotic resistance of microorganisms in the oral biofilm or dental plaque has made its clinical management a serious clinical challenge worldwide. Chlorhexidine-like antimicrobial drugs have been partially effective in removing such organisms; however, the precise and continuous elimination of these microorganisms without disturbing the normal microbial flora of the oral cavity is still a challenge. This paper focuses on the process of oral biofilm formation, related complications, development of drug-resistant bacteria in these biofilms, and their effective management by the use of different novel techniques.
The coronavirus disease 2019 (COVID-19) pandemic has remarkably challenged health care organizations and societies. A key strategy for confronting the disease implications on individuals and communities was based on harnessing multidisciplinary efforts to develop technologies for mitigating the disease spread and its deleterious clinical implications. One of the main challenging characteristics of COVID-19 is the provision of medical care to patients with a highly infective disease mandating the use of isolation measures. Such care is complicated by the need for complex critical care, dynamic treatment guidelines, and a vague knowledge regarding the disease’s pathophysiology. A second key component of this challenge was the over¬whelming surge in patient burden and the relative lack of trained staff and medical equipment which required rapid re-organization of large systems and augmenting health care efficiencies to unprecedented levels. In contrast to the risk management strategies employed to mitigate other serious threats and the billions of dollars that are invested in reducing these risks annually by governments around the world, no such preparation has been shown to be of effect during the current COVID-19 pandemic. Unmet needs were identified within the newly opened COVID-19 departments together with the urgent need for reliable information for effective decision-making at the state level.
This review article describes the early research and development response in Israel under the scope of in-hospital patient care, such as non-contact sensing of patients’ vital signs, and how it could potentially be weaved into a practical big picture at the hospital or national level using a strategic management system. At this stage, some of the described technologies are still in developmental or clinical evidence generation phases with respect to COVID-19 settings. While waiting for future publications describing the results of the ongoing evidence generation efforts, one should be aware of this trend as these emerging tools have the potential to further benefit patients as well as caregivers and health care systems beyond the scope of the current pandemic as well as confronting future surges in the number of cases.
On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21–May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R2>0.99), with the ARIMA model being the best for infec¬tions and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politi¬cians so that appropriate measures are taken and countries’ health care systems do not collapse.
