Detection of viable viruses in the air is critical in order to determine the level of risk associated with the airborne diffusion of viruses. Different methods have been developed for the isolation, purification, and detection of viable airborne viruses, but they require an extensive processing time and often present limitations including low physical efficiency (i.e., the amount of collected viruses), low biological efficiency (i.e., the number of viable viruses), or a combination of all. To mitigate such limitations, we have employed an efficient technique based on the magnetic levitation (Maglev) technique with a paramagnetic solution and successfully identified distinct variations in levitation and density characteristics among bacteria (Escherichia coli), phages (MS2), and human viruses (SARS-CoV-2 and influenza H1N1). Notably, the Maglev approach enabled a significant enrichment of viable airborne viruses in air samples. Furthermore, the enriched viruses obtained through Maglev exhibited high purity, rendering them suitable for direct utilization in subsequent analyses such as reverse transcription-polymerase chain reaction (RT-PCR) or colorimetric assays. The system is portable, easy to use, and cost-efficient and can potentially provide proactive surveillance data for monitoring future outbreaks of airborne infectious diseases and allow for the induction of various preventative and mitigative measures.

The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CLPRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CLPRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CLPRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CLPRO. These hits were then docked into the active site of 3CLPRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CLPRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CLPRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

Sex steroids are powerful modulators of the immune system and they may affect the immune response and inflammatory consequences of COVID-19. This systematic review aims to explore the impact of sex steroids on COVID-19 mortality and complications. We looked up the keywords of the study in Scopus, PubMed, and Web of Science. All related original articles published in English, as of October 16, 2021, were reviewed to be included in our research. Concerns regarding the effect of sex hormones on COVID-19, eight full texts have been identified for the conclusion. In these studies, the relationship between estradiol and COVID-19 mortality has been mentioned. The most significant findings were the higher COVID-19 mortality rate in men, compared to women; also, in menopausal women compared to younger women and who received estradiol. In two studies, oral contraceptive pills had a protective effect on the morbidity of SARS-CoV-2 infection. In a randomized controlled trial, subcutaneous injection of progesterone in hospitalized men significantly reduced their symptoms and need for oxygen therapy. Hormone replacement therapy was positively associated with reducing COVID-19 symptoms. Although the results were insufficient for a conclusion, this study represents estrogen as an appropriate pharmacological method for preventing and diminishing the inflammation related to COVID-19 disease. However, future prospective studies and clinical trials are needed to clarify and approve this protective effect.

Due to the COVID-19 pandemic, a series of sequelae, such as fatigue, tachypnea, and ageusia, appeared in long COVID patients, but the pathological basis was still uncertain. The targeted radiopharmaceuticals were of potential to systemically and dynamically trace the pathological changes. For the key ACE2 protein in the virus-host interaction, 68 Ga-cyc-DX600 was developed on the basis of DX600 as a PET tracer of ACE2 fluctuation and maintained the ability in differentiating ACE and ACE2. In the temporary infection model inhaled with the radio-traceable pseudovirus in the upper respiratory tract of male humanized ACE2 (hACE2) mice, organ-specific ACE2 dysfunction in acute period and the following ACE2 recovery in a relatively long period was visualized and quantified by ACE2 PET, revealing a complex pattern of virus concentration-dependent degree and time period-dependent tendency of ACE2 recovery, mainly a sudden decrease of apparent ACE2 in the heart, liver, kidneys, lungs, and so on, but the liver was of a quick functional compensation on ACE2 expression after a temporary decrease. ACE2 expression of most organs has recovered to a normal level at 15 days post inhalation, with brain and genitals still of a decreased SUVACE2 ;  meanwhile, kidneys were of an increased SUVACE2 . These findings on ACE2 PET were further verified by western blot. When compared with high-resolution computed tomography on structural changes and FDG PET on glycometabolism, ACE2 PET was superior in an earlier diagnostic window during infection and more comprehensive understanding of functional dysfunction post-infection. In the respective ACE2 PET/CT and ACE2 PET/MR scans of a volunteer, the repeatability of SUVACE2 and the ACE2 specificity were further confirmed. In conclusion, 68 Ga-cyc-DX600 was developed as an ACE2-specific tracer, and the corresponding ACE2 PET revealed the dynamic patterns of functional ACE2 recovery and provided a reference and approach to explore the ACE2-related pathological basis of sequelae in long COVID.

There are many pharmacologic therapies that are being used or considered for treatment of coronavirus disease 2019 (COVID-19), with rapidly changing efficacy and safety evidence from trials. The objective was to develop evidence-based, rapid, living guidelines intended to support patients, clinicians, and other healthcare professionals in their decisions about treatment and management of patients with COVID-19. In March 2020, the Infectious Diseases Society of America (IDSA) formed a multidisciplinary guideline panel of infectious disease clinicians, pharmacists, and methodologists with varied areas of expertise to regularly review the evidence and make recommendations about the treatment and management of persons with COVID-19. The process used a living guideline approach and followed a rapid recommendation development checklist. The panel prioritized questions and outcomes. A systematic review of the peer-reviewed and grey literature was conducted at regular intervals. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the certainty of evidence and make recommendations. Based on the most recent search conducted on 31 May 2022, the IDSA guideline panel has made 32 recommendations for the treatment and management of the following groups/populations: pre- and postexposure prophylaxis, ambulatory with mild-to-moderate disease, and hospitalized with mild-to-moderate, severe but not critical, and critical disease. As these are living guidelines, the most recent recommendations can be found online at: https://idsociety.org/COVID19guidelines. At the inception of its work, the panel has expressed the overarching goal that patients be recruited into ongoing trials. Since then, many trials were conducted that provided much-needed evidence for COVID-19 therapies. There still remain many unanswered questions as the pandemic evolved, which we hope future trials can answer.

Corona Virus Disease 2019 (COVID-19) is a highly infectious disease which has affected almost all countries globally. The disease is caused by the corona virus, transmitted from human to human through droplet infection. The virus was first identified in China in December 2019, and spread worldwide. Despite the virus being highly infectious, there is no cure for coronavirus disease COVID-19. The current global approach in the fight against COVID-19 focuses on travel bans including border closures, restrictions on mass gatherings, and mass vaccination of all adults. However, the travel bans and border closures have shown to negatively affect availability, accessibility, and affordability of basic needs such as food, especially for populations in the low- to middle-income countries. This is so since a good percentage of population in low- to middle-income countries live on hand to mouth, and cannot afford adequate food stock to sustain them for a long period of time. In addition, there is a challenge to afford purchasing storage facilities such as refrigerators for storage of fresh foods. Therefore, the purpose of this narrative literature review is to unveil the impact of government policies on individual choices of food and potential impact on outcomes and nutrition in children, elderly and chronically ill individuals in the COVID-19 pandemic era in low- and middle-income countries from 2020 to 2021.

At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

The present research aims to investigate the impact of air pollution on the number of mortalities caused by COVID-19 per Pakistani province. To do so, for each independent area of Pakistan, the observed mortality due to COVID-19 has been standardized over the entire population using standard age groups ranging from 0 to 4, 5 to 9, 10 to 14,…, 65, and above years, supported by the 2017 state people census. The impact of air pollution and COVID-19 transience among Pakistani areas, Islamabad Capital Territory (ICT), and the Federally Administered Tribal Region (FATA) was analyzed by a multiple-linear regression model, while the broad collection of attributes was observed by the resources of local spatial autocorrelation indicators, including the spatial portion of COVID-19 association. The result indicates that the observed mortality rate is much higher than predicted in certain provinces, namely, the Khyber Pakhtunkhwa and Punjab provinces, and the prevalence of PM10 was independently linked to mortality due to the corona virus. Additionally, the results of the local spatial autocorrelation indicators on the standardized mortality rate and PM10 define a collection of very higher ideologies in the broad range of KPK and the southern part of Punjab province, respectively, with a definite degree of connection between the two distributions in the Khyber Pakhtunkhwa region. In brief, this research seems to find a justification for confirming the existence of a correlation between the possibility of COVID-19 mortality and air pollution, more precisely considering air pollutants (i.e., particulate (PM10) and land take-over. To this end, the need to mediate in favor of measures aimed at eliminating emissions in the environment will be reiterated by speeding up current proposals and policies aimed at all causes of atmospheric pollution: urbanization, water and manufacturing, home heating, and transportation.

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

The novel coronavirus disease or COVID-19 is still posing an alarming situation around the globe. The whole world is facing the second wave of this novel pandemic. Recently, the researchers are focused to study the complex dynamics and possible control of this global infection. Mathematical modeling is a useful tool and gains much interest in this regard. In this paper, a fractional-order transmission model is considered to study its dynamical behavior using the real cases reported in Saudia Arabia. The classical Caputo type derivative of fractional order is used in order to formulate the model. The transmission of the infection through the environment is taken into consideration. The documented data since March 02, 2020 up to July 31, 2020 are considered for estimation of parameters of system. We have the estimated basic reproduction number ( R 0 ) for the data is 1.2937 . The Banach fixed point analysis has been used for the existence and uniqueness of the solution. The stability analysis at infection free equilibrium and at the endemic state are presented in details via a nonlinear Lyapunov function in conjunction with LaSalle Invariance Principle. An efficient numerical scheme of Adams-Molten type is implemented for the iterative solution of the model, which plays an important role in determining the impact of control measures and also sensitive parameters that can reduce the infection in the general public and thereby reduce the spread of pandemic as shown graphically. We present some graphical results for the model and the effect of the important sensitive parameters for possible infection minimization in the population.