Search Results (6,638)

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Background/Objectives: Due to the significant overlap in symptoms between COVID-19 and other respiratory infections, a multiplex PCR-based platform was developed to simultaneously detect 22 respiratory pathogens. Target sequences were retrieved from the GenBank database and aligned using Clustal Omega 2.1 to identify conserved regions prioritized for primer design. Primers were designed using Primer Express^® 3.0.1 and evaluated in Primer Explorer to ensure specificity and minimize secondary structures. A multiplex strategy organized primers into three groups, each labeled with distinct fluorophores (FAM, VIC, or NED), allowing for detection by conventional PCR or capillary electrophoresis (CE). Methods: After reverse transcription for RNA targets, amplification was performed in a single-tube reaction. A total of 340 clinical samples—nasopharyngeal and saliva swabs—were collected from patients, during the COVID-19 pandemic period. The automated analysis of electropherograms enabled precise pathogen identification. Results: Of the samples analyzed, 57.1% tested negative for all pathogens. SARS-CoV-2 was the most frequently detected pathogen (29%), followed by enterovirus (6.5%). Positive results were detected in both nasopharyngeal and saliva swabs, with SARS-CoV-2 predominating in saliva samples. Conclusion: This single-tube multiplex PCR-CE assay represents a cost-effective and robust approach for comprehensive respiratory pathogen detection. It enables rapid and simultaneous diagnosis, facilitating targeted treatment strategies and improved patient outcomes. Full article

Background: The surge in COVID-19 cases during the pandemic created a disease burden. An epidemiological study on COVID-19 is required as not much is known about the impact of containment and mitigation on health. We aimed to compare the epidemiological features during the years of the COVID-19 pandemic in the Vidarbha region in Maharashtra, India, to understand the epidemiology changes throughout the pandemic’s progression. Method: All of the cases reported at our testing centers in Nagpur and its periphery during the three years of the pandemic (i.e., from February 2020 to December 2022) were included. Descriptive analyses of variables of interest and statistical measures were compared. Results: There were 537,320 tests recorded during the study period. Of these, 13,035 (13.29%), 42,909 (13.70%), and 19,936 (15.91%) tested positive in 2020, 2021, and 2022, respectively. Hospitalization decreased from 2020 to 2022. An age group shift from 45 to 16–30 years over the pandemic was noticed. Seasonally, positivity peaked in September (27.04%) in 2020, in April (43.4%) in 2021, and in January in 2022 (35.30%). The estimated case fatality ratio was highest in 2021 (36.68%) over the three years in the hospital setting. Conclusion: Understanding the changing epidemiology of SARS-CoV-2 strengthens our perceptive of this disease, which will aid in improving the healthcare system in terms of both controlling and preventing the spread of COVID-19. Full article

Introduction: Long COVID syndrome is defined as persistent or new symptoms that appear after an acute SARS-CoV-2 infection and last at least three months without explanation. It is estimated that between 10% and 20% of those infected develop long COVID; however, data is not precise in Latin America. Although high immunization rates have reduced acute symptoms and the pandemic’s impact, there is a lack of evidence of its efficacy in preventing long COVID in the region. Methods: This scoping review followed PRISMA-ScR guidelines. Studies on vaccinated adults with long COVID from Central and South America and the Caribbean were included (Mexico was also considered). A comprehensive search across multiple databases was conducted. Data included study design, participant characteristics, vaccine type, and efficacy outcomes. Results are presented narratively and in tables. Results: Out of 3466 initial records, 8 studies met the inclusion criteria after rigorous selection processes. These studies encompassed populations from Brazil, Mexico, Latin America, and Bonaire, with 11,333 participants, 69.3% of whom were female. Vaccination, particularly with three or more doses, substantially reduces the risk and duration of long COVID. Variability was noted in the definitions and outcomes assessed across studies. Conclusions: This scoping review highlights that SARS-CoV-2 vaccination exhibits potential in reducing the burden of long COVID in the Americas. However, discrepancies in vaccine efficacy were observed depending on the study design, the population studied, and the vaccine regimen employed. Further robust, region-specific investigations are warranted to delineate the effects of vaccination on long COVID outcomes. Full article

Background: Since the COVID-19 pandemic, managing acute infections in symptomatic individuals, regardless of vaccination status, has been widely debated and extensively studied. Even more concerning, however, is the impact of COVID-19 on pregnant women—especially its effects on fetuses and newborns. Several studies have documented complications in both expectant mothers and their infants following infection. Methods: In our previous works, we provided scientific evidence of the bacteriophage behavior of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). This demonstrated that a well-defined combination of two antibiotics, amoxicillin and rifaximin, is associated with the same statistics for subjects affected by severe cases of SARS-CoV-2, regardless of vaccination status. We considered the few cases in the literature regarding the management of pregnancies infected with SARS-CoV-2, as well as previous data published in our works. In this brief case series, we present two pregnancies from the same unvaccinated mother—one prior to the COVID-19 pandemic and the other during the spread of the Omicron variant—as well as one pregnancy from a mother vaccinated against COVID-19. We describe the management of acute maternal infection using a previously published protocol that addresses the bacteriophage and toxicological mechanisms associated with SARS-CoV-2. Results: The three pregnancies are compared based on fetal growth and ultrasound findings. This report highlights that, even in unvaccinated mothers, timely and well-guided management of symptomatic COVID-19 can result in positive outcomes. In all cases, intrauterine growth remained within excellent percentiles, and the births resulted in optimal APGAR scores. Conclusions: This demonstrates that a careful and strategic approach, guided by ultrasound controls, can support healthy pregnancies during SARS-CoV-2 infection, regardless of vaccination status. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Since the onset of the COVID-19 pandemic, remarkable progress has been made in the development of antiviral therapies for SARS-CoV-2. Several direct-acting antivirals, such as remdesivir, molnupiravir, and nirmatrelvir/ritonavir, offer clinical benefits. These agents have significantly contributed to reducing the viral loads and duration of the illness, as well as the disease’s severity and mortality. However, despite these advances, important limitations remain. The continued emergence of resistant SARS-CoV-2 variants highlights the urgent need for adaptable and durable therapeutic strategies. Therefore, this review aims to provide an updated overview of the main antiviral strategies that are used and the discovery of new drugs against SARS-CoV-2, as well as the therapeutic limitations that have shaped clinical management in recent years. The major challenges include resistance associated with viral mutations, limited treatment windows, and unequal access to treatment. Moreover, there is an ongoing need to identify novel compounds with broad-spectrum activity, improved pharmacokinetics, and suitable safety profiles. Combination treatment regimens represent a promising strategy to increase the efficacy of treating COVID-19 while minimizing the potential for resistance. Ideally, these interventions should be safe, affordable, and easy to administer, which would ensure broad global access and equitable treatment and enable control of COVID-19 cases and preparedness for future threats. Full article

Recombinant vesicular stomatitis virus (rVSV) is a promising viral vaccine vector for addressing the COVID-19 pandemic. Inducing mucosal immunity via the intranasal route is an ideal strategy for rVSV-based vaccines, but it requires extremely stringent safety standards. In this study, we constructed two rVSV variants with amino acid mutations in their M protein: rVSV-M2 with M33A/M51R mutations and rVSV-M4 with M33A/M51R/V221F/S226R mutations, and developed COVID-19 vaccines based on these attenuated vectors. By comparing viral replication capacity, intranasal immunization, intracranial injection, and blood cell counts, we demonstrated that the M protein mutation variants exhibit significant attenuation effects both in vitro and in vivo. Moreover, preliminary investigations into the mechanisms of virus attenuation revealed that these attenuated viruses can induce a stronger type I interferon response while reducing inflammation compared to the wild-type rVSV. We developed three candidate vaccines against SARS-CoV-2 using the wildtype VSV backbone with either wild-type M (rVSV-JN.1) and two M mutant variants (rVSV-M2-JN.1 and rVSV-M4-JN.1). Our results confirmed that rVSV-M2-JN.1 and rVSV-M4-JN.1 retain strong immunogenicity while enhancing safety in hamsters. In summary, the rVSV variants with M protein mutations represent promising candidate vectors for mucosal vaccines and warrant further investigation. Full article

The COVID-19 pandemic, caused by SARS-CoV-2, has led to a wide range of acute and chronic disease manifestations. While most infections are mild, a significant number of patients develop severe illness marked by respiratory failure, thromboinflammation, and multi-organ dysfunction. In addition, post-acute sequelae—commonly known as long-COVID—can persist for months. Recent studies have identified the emergence of diverse autoantibodies in COVID-19, including those targeting nuclear antigens, phospholipids, type I interferons, cytokines, endothelial components, and G-protein-coupled receptors. These autoantibodies are more frequently detected in patients with moderate to severe disease and have been implicated in immune dysregulation, vascular injury, and persistent symptoms. This review examines the underlying immunological mechanisms driving autoantibody production during SARS-CoV-2 infection—including molecular mimicry, epitope spreading, and bystander activation—and discusses their functional roles in acute and post-acute disease. We further explore the relevance of autoantibodies in maternal–fetal immunity and comorbid conditions such as autoimmunity and cancer, and we summarize current and emerging therapeutic strategies. A comprehensive understanding of SARS-CoV-2-induced autoantibodies may improve risk stratification, inform clinical management, and guide the development of targeted immunomodulatory therapies. Full article

During the COVID-19 pandemic, some studies suggested that transmission events could originate from schools. This study aimed to evaluate early-warning methods for identifying asymptomatic COVID-19 cases by implementing screening programs in schools. This study was conducted between September 2021 and May 2023, employing a rotation-screening plan for COVID-19 detection on a sample of students aged 14 to 19 years attending secondary schools in the regions of Tuscany, Veneto, Apulia and Friuli-Venezia Giulia. The schools were divided into two groups: experimental and control, with a ratio of 1:2. Two types of molecular salivary tests for SARS-CoV-2 were used to conduct the screening. This study included 16 experimental schools and 32 control schools. Out of 2527 subjects, 11,475 swabs were administrated, with 9177 tests deemed valid for analysis (a 20% loss of tests). Among these, 89 subjects (3.5%) tested positive. In control schools, 1895 subjects (6.5%) tested positive for SARS-CoV-2. This study recorded peaks in infections during the winter and autumn months, consistent with patterns observed in the general population. Beginning in September 2022, a shift occurred, with 2.6% of positive cases reported in the case schools compared to 0.3% in the control schools. Initially, most cases of COVID-19 were detected in the control schools; however, as the pandemic emergency phase concluded, cases were primarily identified through active screening in experimental schools. Although student participation in the active screening campaign was low during the project’s extension phase, this approach was efficacious in the early identification of positive cases. Full article

In late 2019, a new virus, SARS-CoV-2, emerged in Wuhan, China, causing COVID-19 and the subsequent global pandemic. As of 30 April 2023, more than 774 million cases of COVID-19 had been reported worldwide, including over 7.5 million in Mexico. Despite advances in vaccination, epidemic surges of COVID-19 continued to occur globally, highlighting the importance of sharing and disseminating the experiences gained during these first years to better understand the virus’s evolution and respond accordingly. For this reason, the National Council for Science and Technology (CONACYT) organized the meeting “Challenges and Opportunities for Genomic Surveillance of SARS-CoV-2 in Mexico” from 15 to 17 August 2022, to present the efforts and results accumulated over more than two years of the pandemic. In this meeting report, we summarize the key findings of each participant and provide their contact information. Full article

Background: Influenza A viruses (IAV) cause seasonal flu and occasional pandemics. In addition, the potential for the emergence of new strains presents unknown challenges for public health. Face masks and other personal protective equipment (PPE) can act as barriers that prevent the spread of these viruses. Metal ions embedded into PPE have been demonstrated to inactivate respiratory viruses, but the underlying mechanism of inactivation and potential for resistance is presently not well understood. Methods: In this study, we used hemagglutination assays to quantify the effect of zinc ions on IAV sialic acid receptor binding. We varied the zinc concentration, incubation time, incubation temperature, and passaged IAV in the presence of zinc ions to investigate if resistance to zinc ions could evolve. Results: We found that zinc ions impact the ability of IAV particles to hemagglutinate and observed inhibition within 1 min of exposure. Maximum inhibition was achieved within 1 h and sustained for at least 24 h in a concentration-dependent manner. Inhibition was also temperature-dependent, and optimal above room temperature. Serial passaging of IAV in the presence of zinc ions did not result in resistance. Conclusions: e conclude that zinc ions prevent IAV hemagglutination in a concentration and temperature-dependent manner for at least 24 h. Overall, these findings are in line with previous observations indicating that zinc-embedded materials can inactivate the IAV hemagglutinin and SARS-CoV-2 spike proteins, and they support work toward developing robust, passive, self-cleaning antiviral barriers in PPE. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Background/Objectives: In late 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a pandemic called the ‘coronavirus disease 2019’ (COVID-19). After the acute SARS-CoV-2 infection, many individuals (up to 33%) complained of unexplained symptoms involving multiple organ systems and were diagnosed as having Long COVID-19 (LC-19). Currently, LC-19 is inadequately defined, requiring the formation of consistent diagnostic parameters to provide a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy. LC-19 represents a significant burden on multiple levels. The reduced ability of workers to return to work or compromised work efficiency has led to consequences at national, economic, and societal levels by increasing dependence on community services. On a personal scale, the isolation and helplessness caused by the disease and its subsequent impact on the patient’s mental health and quality of life are incalculable. Methods: In this paper, we used Walker and Avants’ eight-step approach to perform a concept analysis of the term “Long COVID-19” and define its impact across these parameters. Results: Using this methodology, we provide an improved definition of LC-19 by connecting the clinical symptomology with previously under-addressed factors, such as mental, psychological, economic, and social effects. This definition of LC-19 features can help improve diagnostic procedures and help plan relevant healthcare services. Conclusions: LC-19 represents a complex and pressing public health challenge with diverse symptomology, an unpredictable timeline, and complex pathophysiology. This concept analysis serves as a tool for improving LC-19 definition, but it remains a dynamic disease with evolving diagnostic and therapeutic approaches, requiring deeper investigation and understanding of its long-term effects. Full article

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, whose 2020 outbreak was characterized as a pandemic by the World Health Organization. Restriction measures changed healthcare delivery, with telehealth providing a viable alternative throughout the pandemic. This study analyzed a telemedicine platform database with the goal of developing a diagnostic prediction model for COVID-19 patients. This is a longitudinal study of patients seen on the Conexa Saúde telemedicine platform in 2022. A multiple binary logistic regression model of controls (negative confirmation for COVID-19 or confirmation of other influenza-like illness) versus COVID-19 was developed to obtain an odds ratio (OR) and a 95% confidence interval (CI). In the final binary logistic regression model, six factors were considered significant: presence of rhinorrhea, ocular symptoms, abdominal pain, rhinosinusopathy, and wheezing/asthma and bronchospasm were more frequent in controls, thus indicating a greater chance of flu-like illnesses than COVID-19. The presence of tiredness and fatigue was three times more prevalent in COVID-19 cases (OR = 3.631; CI = 1.138–11.581; p-value = 0.029). Our findings suggest potential predictors associated with influenza-like illness and COVID-19 that may distinguish between these infections. Full article