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Background: The coronavirus disease 2019 (COVID-19) pandemic and its associated morbidities are a great global concern. Diabetes mellitus (DM) is associated with adverse clinical outcomes and high mortality in patients with COVID-19. Objective(s): This study examined the frequency of BM, newly diagnosed hyperglycemia, and their impacts on hospitalized patients with COVID-19. Method(s): This retrospective study examined 810 medical records of PCR-confirmed COVID-19 patients admitted to Razi Hospital, Ahvaz, Iran. The clinical presentations, severity, and impacts of COVID-19 were compared between patients with and without DM. Disease severity was determined based on the NEWS2 scoring system. Result(s): This study included 810 medical records of COVID-19 patients, of whom 326 had pre-existing DM, and 484 were non-DM. The rates of diabetes and newly diagnosed hyperglycemia were 40.2% and 11.2%, respectively. The most common underlying diseases were hypertension (35.3%), ischemic heart disease (17.9%), and chronic kidney disease (11.9%), which were higher in people with diabetes than non-diabetics. The rate of acute kidney injury was higher in patients with diabetes than in non-diabetics (30.7% vs. 19.2%;P < 0.001) and in patients with severe COVID-19 than in those whose disease was not severe (27.8% vs. 21.5%;P = 0.04). The rates of severe COVID-19 (46.3% vs. 34.7%;P = 0.093), ICU admission (40.7% vs. 27.4%;P = 0.012), and mortality (18.5% vs. 10.5%;P = 0.079) were higher in patients with newly diagnosed hyperglycemia than in euglycemic patients. Conclusion(s): This study showed that COVID-19 infection is linked with newly diagnosed hyperglycemia and pre-existing DM, both associated with severe COVID-19, more need for ICU admission, and mortality.Copyright © 2023, Author(s).
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This manuscript aims to provide an overview of the etiology and diagnosis of olfactory and gustatory disorders. Not only are they common with about 5% of the population affected, but olfactory and gustatory disorders have recently gained attention in light of the rising SARS-CoV2 pandemic: sudden loss of smell and/or taste is regarded as one of the cardinal symptoms. Furthermore, in the early diagnostics of neurodegenerative diseases, olfactory disorders are of great importance. Patients with olfactory dysfunction often show signs of depression. The impact of olfactory/gustatory disorders is thus considerable, but therapeutic options are unfortunately still limited. Following a description of the etiology, the diagnostic and therapeutic options are discussed on the basis of current literature. Potential future treatments are also addressed, e.g. autologous mucosal grafts or olfactory implants.
Subject(s)
COVID-19 , Olfaction Disorders , Humans , Olfaction Disorders/diagnosis , Olfaction Disorders/etiology , Olfaction Disorders/therapy , SARS-CoV-2 , Smell , Taste Disorders/diagnosis , Taste Disorders/etiology , Taste Disorders/therapyABSTRACT
We look back at the end of what soon will be seen as an historic year, from COVID-19 to real-world introduction of biologicals influencing the life of our patients. This review describes the important findings in Rhinology over the past year. A large body of evidence now demonstrates loss of sense of smell to be one of the most common symptoms of COVID-19 infection; a meta-analysis of 3563 patients found the mean prevalence of self-reported loss to be 47%. A number of studies have now shown long-term reduced loss of smell and parosmia. Given the high numbers of people affected by COVID-19, even with the best reported recovery rates, a significant number worldwide will be left with severe olfactory dysfunction. The most prevalent causes for olfactory dysfunction, besides COVID-19 and upper respiratory tract infections in general, are trauma and CRSwNP. For these CRSwNP patients a bright future seems to be starting with the development of treatment with biologics. This year the Nobel prize in Medicine 2021 was awarded jointly to David Julius and Ardem Patapoutian for their discoveries of receptors for temperature and touch which has greatly enhanced our understanding of nasal hyperreactivity and understanding of intranasal trigeminal function. Finally, a new definition of chronic rhinitis has been proposed in the last year and we have seen many papers emphasizing the importance of endotyping patients in chronic rhinitis and rhinosinusitis in order to optimise treatment effect.
Subject(s)
Biological Products , COVID-19 , Nasal Polyps , Olfaction Disorders , Rhinitis , Sinusitis , Biological Products/therapeutic use , Chronic Disease , Humans , Rhinitis/drug therapy , SARS-CoV-2 , SmellABSTRACT
Introduction: Current epidemiologic predictions of COVID- 19 suggest SARS-CoV-2 mitigation strategies must be implemented long term. In-office aerosol-generating procedures (AGPs) pose a risk to staff and patients while also necessitating exam room shutdown to allow aerosol decontamination by indwelling ventilation. This review summarizes the current state of knowledge on portable high-efficiency particulate air (HEPA) purifiers' effectiveness in eliminating airborne SARSCoV- 2 from indoor environments. Method: Medline, Embase, Cochrane databases, and the World Health Organization COVID-19 Global literature on coronavirus disease were systematically searched for original English-language published studies on (“HEPA” OR “Purifier” OR “Filter” OR “Cleaner” OR 'Filtration) AND (“COVID” OR “COVID-19” OR “SARS-CoV-2” OR “Coronavirus”) indexed up to January 14, 2021. Additional relevant studies were identified by searching the reference lists of included articles. Two authors (D.T.L. and A.R.S.) independently reviewed abstracts and full-text studies. Results: Ten published studies have evaluated the effectiveness of portable HEPA purifiers in eliminating airborne SARS-CoV-2 using relevantly sized surrogate particles. Nine studies evaluated aerosols and submicron particles similar in size to SARS-CoV-2 virions, while 1 study evaluated particles with diameters up to the submillimeter range. In all studies, portable HEPA purifiers were able to significantly reduce airborne SARS-CoV-2-surrogate particles. The addition of portable HEPA purifiers augmented other decontamination strategies such as ventilation. Conclusion: Experimental studies provide evidence for portable HEPA air purifiers' potential to eliminate airborne SARS-CoV-2 and augment primary decontamination strategies such as ventilation. Based on filtration rates, additional air exchanges provided by portable HEPA purifiers may be calculated and room shutdown times after AGPs potentially reduced.
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As a novel risk factor, COVID-19 has led to an increase in the incidence of candidemia and an elevated mortality rate. Despite being of clinical importance, there is a lack of data regarding COVID-19-associated candidemia (CAC) among Iranian patients. Therefore, in this retrospective study, we assessed CAC epidemiology in the intensive care units (ICUs) of two COVID-19 centers in Mashhad, Iran, from early November 2020 to late January 2021. Yeast isolates from patients' blood were identified by 21-plex polymerase chain reaction (PCR) and sequencing, then subjected to antifungal susceptibility testing according to the CLSI M27-A3 protocol. Among 1988 patients with COVID-19 admitted to ICUs, seven had fungemia (7/1988;0.03%), among whom six had CAC. The mortality of the limited CAC cases was high and greatly exceeded that of patients with COVID-19 but without candidemia (100% (6/6) vs. 22.7% (452/1988)). In total, nine yeast isolates were collected from patients with fungemia: five Candida albicans, three C. glabrata, and one Rhodotorula mucilaginosa. Half of the patients infected with C. albicans (2/4) were refractory to both azoles and echinocandins. The high mortality of patients with CAC, despite antifungal therapy, reflects the severity of the disease in these patients and underscores the importance of rapid diagnosis and timely initiation of antifungal treatment.
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On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21,2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II. © 2020 IEEE.
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The SIR type models are built by a set of ordinary differential equations (ODE), which are strongly initial value dependant. To fit multiple biological data with SIR type equations requires fitting coefficients of these equations by an initial guess and applying optimization methods. These coefficients are also extremely initial value-dependent. In the vast publication of these types, we hardly see, among simple to highly complicated SIR type methods, that these methods presented more than a maximum of two biological data sets. We propose a novel method that integrates an analytical solution of the infectious population using Weibull distribution function into any SIR type models. The Weibull-SIRD method has easily fitted 4 set of COVID-19 biological data simultaneously. It is demonstrated that the Weibull-SIRD method predictions for susceptible, infected, recovered, and deceased populations from COVID-19 in Kuwait and UAE are superior compared with SIRD original ODE model. The proposed method here opens doors for new deeper studying of biological dynamic systems with realistic biological data trends than providing some complicated, cumbersome mathematical methods with little insight into biological data's real physics. © 2020 IEEE.
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Eruption of COVID-19 patients in 215 countries worldwide have urged for robust predictive methods that can detect as early as possible size and duration of the contagious disease and also providing precision predictions. In many recent literatures reported on COVID-19, one or more essential parts of such investigation were missed. One of crucial elements for any predictive method is that such methods should fit simultaneously as many data as possible;these data could be total infected cases, daily hospitalized cases, cumulative recovered cases and deceased cases and so on. Other crucial elements include sensitivity and precision of such predictive methods on amount of data as the contagious disease evolved day by day. To show importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on development of COVID-19 in Kuwait. It is observed that SIRD model quickly pick up main trends of COVID-19 development;but Gaussian-SIRD model provides precise prediction at longer period of time. © 2020 IEEE.
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Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIR-PAD model provide insight into better management of COVID-19 pandemic in GCC countries. © 2020 IEEE.
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Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic. © 2020 IEEE.
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Introduction: The outbreak of coronavirus disease 2019 (COVID-19) should be considered a serious threat to global public health. Due to a large number of infected and dead people, the development of approaches to control the epidemic condition, as well as effective and available drugs, is very important. Case Presentation: In this study, we presented three cases with COVID-19 admitted to the Imam Reza Hospital. A high dose of intravenous immunoglobulin (IVIG) was used for patients as potent and safe treatment. Moreover, case reports and case series focusing on the patients with COVID-19 were reviewed in the present study. During the literature search, 27 patients with COVID-19 were identified in 14 studies. Fever, sore throat, dry cough, fatigue, chills, and muscle pain were the common primary complications of the patients. Kaletra, oseltamivir, ceftriaxone, hydroxychloroquine, azithromycin, and IVIG were the most prevalent drugs for the treatment of COVID-19. Conclusions: Except for the current study, IVIG was utilized in two other studies to treat patients with COVID-19, who did not respond to other therapies.