Search Results (4,666)

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

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. 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

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

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

Background/Objectives: Syphilis, a re-emerging global public health issue, has shown increasing incidence over the past decade, particularly among key populations such as men who have sex with men (MSM), people living with HIV, and pregnant women. This narrative review aimed to synthesize global epidemiological trends of syphilis from 2015 to 2025, with a focus on surveillance gaps, regional disparities, and structural determinants. Methods: A broad narrative approach was used to collect and analyze epidemiological data from 2015 to 2025. The literature was retrieved from databases (PubMed, Scopus) and official reports from the WHO, CDC, and ECDC. Included materials span observational studies, surveillance reports, and modeling data relevant to global trends and public health responses. Results: Globally, syphilis incidence has increased, with notable surges in North America, Europe, and Asia. MSM remain disproportionately affected, while congenital syphilis is resurging even in high-income countries. Low- and middle-income countries report persistent burdens, especially among women of reproductive age, often exacerbated by limited screening and surveillance infrastructure. The COVID-19 pandemic disrupted syphilis-related services and further exacerbated underreporting, hindering timely detection and response efforts. Surveillance systems vary widely in their completeness and quality, which significantly hinders global data comparability and coordinated public health responses. Conclusions: Despite its curability, syphilis continues to spread due to fragmented prevention strategies, inequities in access to care, and insufficient surveillance. Strengthening diagnostic access, integrating prevention efforts into broader health systems, and addressing social determinants are essential. Improved surveillance, equitable access, and innovation—including diagnostics and potential vaccine research—are critical to controlling the global syphilis epidemic. Full article

COVID-19 vaccine hesitancy in Pakistan is a barrier to optimal vaccine uptake and has been situated within a context of hesitancy towards other vaccines. A mixed-methods study was conducted during the initial COVID-19 vaccine roll-out in 2021 in four union councils in Peshawar, consisting of a cross-sectional survey, eight focus group discussions (FGDs) with community members and eight in-depth interviews with healthcare workers (HCWs) to assess perceptions toward vaccines. Multivariable logistic regression was used to assess factors associated with COVID-19 vaccine hesitancy. Of 400 survey participants, 57.3% were vaccine acceptant and 42.8% vaccine hesitant. Just over half (56.8%) perceived COVID-19 vaccines to be safe. Most (88%) reported trust in HCWs to provide accurate vaccine information. FGDs revealed that women received less information about the vaccine compared to men and cultural restrictions were barriers even for those willing to be vaccinated. Correlates of vaccine acceptance included male sex (aOR 2.25; 95% CI 1.29–3.91), age 50 years or greater (aOR 1.74; 95% CI 1.19–6.31), social network support (e.g., vaccine acceptance among an individual’s social network) in receiving COVID-19 vaccines (aOR 2.38; 95% CI 1.45–3.89), community concern about COVID-19 spread (aOR 2.84; 95% CI 1.73–4.66), and trust in HCWs to provide vaccine information (aOR 3.47; 95% CI 1.62–7.42). Future vaccine promotion should prioritize engaging community leaders, sharing transparent information, combatting misinformation and rumors, and implementing household-based interventions especially targeting the importance of vaccination among women and young people to increase uptake. Full article

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

Background: The COVID-19 pandemic progressed unevenly across the 417 municipalities of Bahia, Brazil. Pinpointing where and when risk peaked is vital for preparing for future emergencies. Methods: We performed an ecological, spatiotemporal study using COVID-19-confirmed cases in Bahia, Brazil, from January 2020 to December 2022. A discrete Poisson space–time scan in SaTScan-identified clusters. For each cluster, we calculated relative risk (RR) and Log Likelihood Ratio, considering p < 0.05 as significant. Results: A total of 33 clusters were detected; 25 statistically significant. The largest cluster (164 municipalities; May 2020–June 2021) comprised 702,720 observed versus 338,822 expected cases (RR = 2.8). Two overlapping large clusters (185 and 136 municipalities) during January–February 2022—coinciding with Omicron circulation—showed RR > 2.0. Localized clusters reached RR > 3.0. Spatially, risk concentrated in the south, southwest, and east of the state, with isolated countryside outbreaks. Conclusions: The heterogeneous spatiotemporal dynamics of COVID-19 in Bahia underscore the value of cluster detection for targeted surveillance and resource allocation. We recommend employing statistical techniques for early detection and control, as well as conducting further studies on socioeconomic and behavioral factors. Full article

Post-viral conditions, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC), share > 95% of their symptoms, but the connection between disturbances in their underlying molecular biology is unclear. This study investigates DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from patients with ME/CFS, LC, and healthy controls (HC). Reduced Representation Bisulphite Sequencing (RRBS) was applied to the DNA of age- and sex-matched cohorts: ME/CFS (n = 5), LC (n = 5), and HC (n = 5). The global DNA methylomes of the three cohorts were similar and spread equally across all chromosomes, except the sex chromosomes, but there were distinct minor changes in the exons of the disease cohorts towards more hypermethylation. A principal component analysis (PCA) analysing significant methylation changes (p < 0.05) separated the ME/CFS, LC, and HC cohorts into three distinct clusters. Analysis with a limit of >10% methylation difference and at p < 0.05 identified 214 Differentially Methylated Fragments (DMF) in ME/CFS, and 429 in LC compared to HC. Of these, 118 DMFs were common to both cohorts. Those in promoters and exons were mainly hypermethylated, with a minority hypomethylated. There were rarer examples with either no change in methylation in ME/CFS but a change in LC, or a methylation change in ME/CFS but in the opposite direction in LC. The differential methylation in a number of fragments was significantly greater in the LC cohort than in the ME/CFS cohort. Our data reveal a generally shared epigenetic makeup between ME/CFS and LC but with specific, distinct changes. Differences between the two cohorts likely reflect the stage of the disease from onset (LC 1 year vs. ME/CFS 12 years), but specific changes imposed by the SARS-CoV-2 virus in the case of the LC patients cannot be discounted. These findings provide a foundation for further studies with larger cohorts at the same disease stage and for functional analyses to establish clinical relevance. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is known to affect multiple organ systems, including the respiratory tract and nervous and ocular systems. This retrospective study aimed to characterize the spatiotemporal distribution of viral antigen and associated pathological changes in the nose, lungs, brain, and eyes of K18-hACE2 mice intranasally infected with SARS-CoV-2. Using histology and immunohistochemistry, tissues were examined at 3, 6, and 7/8 days post-infection (dpi). In addition, lung and brain tissues were analyzed by means of RT-qPCR to determine viral RNA titers. Viral antigen was most pronounced in the nose, brain, and lung at 3, 6, and 7/8 dpi, respectively, whereas viral antigen was detected at 6 and 7/8 dpi in the retina. Quantitative PCR confirmed increasing viral RNA levels in both lung and brain, peaking at 7/8 dpi. Nasal and lung inflammation mirrored viral antigen distribution and localization. In the brain, the predominantly basal viral spread correlated with lymphohistiocytic meningoencephalitis, neuronal vacuolation, and altered neurofilament immunoreactivity. Retinal ganglion cells showed viral antigen expression without associated lesions. Microglial activation was evident in both the optic chiasm and the brain. These findings highlight the K18-hACE2 model’s utility for studying extrapulmonary SARS-CoV-2 pathogenesis. Understanding the temporal and spatial dynamics of viral spread enhances insights into SARS-CoV-2 neurotropism and its clinical manifestations. Full article

Background: Hospitalized COVID-19 patients are particularly vulnerable to secondary bacterial infections, which can significantly worsen clinical outcomes. The aim of the study was to identify the cause of bacteremia in a group of hospitalized COVID-19 patients and find out the source of the outbreak to prevent further spread. Methods: Pathogen identification in blood cultures and sensitivity testing were carried out using the automated VITEK2 system. A total of 110 samples were tested; these were collected from patients’ colonization sites and from surfaces, materials and fluids used in the setting. Furthermore, multilocus sequence typing (MLST) and next-generation sequencing (NGS) were employed to characterize the isolates. Results: Achromobacter xylosoxidans was detected in the blood of nine hospitalized patients and in cotton used for disinfection; all isolates presented an identical antibiotic resistance pattern, and all carried the bla_OXA-114 gene which is intrinsic to this species. Infection control measures were implemented promptly. With one exception, all patients recovered and were discharged in good health. Conclusions: This outbreak underscores the urgent need for investigation and control of hospital infections, as bacteremia is associated with increased morbidity, mortality, hospitalization time, and cost. It also highlights the importance of close collaboration among healthcare professionals. Full article

Background: This study aims to identify predictive factors for pulmonary fibrosis development in COVID-19 patients by analyzing thorax CT (computed tomography) findings, serum prolidase activity, MMP-1, MMP-7, TGF-β values, laboratory findings, and demographic characteristics. Materials and methods: The investigation involved 68 patients, both male and female, aged 18 years and older, who were volunteers and had been diagnosed with confirmed COVID-19. The pulmonologist and the radiologist evaluated the thorax CT by consensus. Patients were evaluated in two categories, group 1 and group 2, based on the status of fibrotic changes, and 3-month fibrosis scores were calculated. Findings in both lungs were calculated and noted for the lobes, considering lobar spread. Correlations between quantitative parameters were assessed with Spearman’s rho correlation coefficient. Comparisons between independent samples were evaluated using either the independent sample t-test or the Mann–Whitney U test. We evaluated the relationship between categorical variables using the Pearson chi-square test and Fisher’s exact test. Results: Serum prolidase activity, MMP-1, MMP-7, and TGF-β biomarkers were not statistically significant among groups. LDH was found to be significantly high in the group with fibrotic changes. Additionally, the group with fibrotic changes also had higher levels of fibrinogen. The percentage of neutrophils, the severity of the disease, muscle–joint pain and fatigue symptoms, and the length of hospitalization stay were correlated with the total scores of fibrosis at the third month. In the group with fibrotic changes, the duration of muscle–joint pain and fatigue symptoms and the length of hospitalization were longer than in the other group. Conclusions: The group with fibrotic changes showed an increase in biomarkers. However, this increase did not reach a statistically significant level, suggesting that the third month may be an early period for these changes. The group with fibrotic changes showed high levels of LDH, one of the most important laboratory parameters of pulmonary fibrosis risk factors, along with fibrinogen, suggesting that these parameters are valuable in predicting pulmonary fibrosis. Patients with fibrotic changes can experience specific symptoms, commonly seen in COVID-19. Full article

COVID-19 continues to spread six years after its discovery. Cancer patients are at an increased risk of severe outcomes, likely due to immunosuppression and tumor-related dysregulation. Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is a critical immune checkpoint involved in T-cell regulation. Since genetic polymorphisms can influence immune responses and individual susceptibility to SARS-CoV-2 infection, this case–control study aimed to investigate the association between the PDCD1 rs41386349 polymorphism and COVID-19 severity in individuals with and without cancer. This study included 279 COVID-19-positive and 160 negative individuals, genotyped by qPCR. COVID-19- positive cancer patients were significantly more likely to develop moderate (OR = 13.6) and severe (OR > 200) disease compared to cancer-negative individuals. No association was observed between the PDCD1 polymorphism and SARS-CoV-2 infection or disease severity, even after adjusting for cancer status, age and sex. However, age and sex were independently associated with severe outcomes: each additional year of age increased the odds of severe disease by 5.3%, and male patients had a three times higher risk of severe COVID-19. These findings confirm that cancer, male sex and older age are major predictors of worse prognosis in COVID-19, while the rs41386349 polymorphism alone does not appear to influence susceptibility or disease progression. Full article