Search Results (4,232)

The recent discovery of Orthonairovirus songlingense (SGLV) and Norwavirus beijiense (BJNV) in China has raised significant concern due to their potential to cause severe human disease. However, little is known about the structural features and function of their nucleoproteins, which play a key role in the viral life cycle. By combining small-angle X-ray scattering (SAXS) data and AlphaFold 3 simulations, we reconstructed the BJNV and SGLV nucleoprotein structures for the first time. The SGLV and BJNV nucleoproteins have structures that are broadly similar to those of Orthonairovirus haemorrhagiae (CCHFV) nucleoproteins despite low sequence similarity. Based on structural analysis, several residues located in the positively charged region of BJNV and SGLV nucleoproteins have been indicated to be important for viral RNA binding. A positively charged RNA-binding crevice runs along the interior of the SGLV and BJNV ribonucleoprotein complex (RNP), shielding the viral RNA. Despite the high structural similarity between SGLV and BJNV nucleoprotein monomers, their RNPs adopt distinct conformations. These findings provide important insights into the molecular mechanisms of viral genome packaging and replication in these emerging pathogens. Also, our work demonstrates that experimental SAXS data can validate and improve predicted AlphaFold 3 structures to reflect their solution structure and also provides the first low-resolution structures of the BJNV and SGLV nucleoproteins for the future development of POC tests, vaccines, and antiviral drugs. Full article

Political polarization in policy evaluations arises from identity-driven cognitive–affective dynamics, yet the neural mechanisms underlying the real-time processing of policy texts remain unexplored. This study bridges this gap by employing EEG to capture neurobehavioral responses during a COVID-19 vaccine mandate judgment task. The analysis of 70 politically stratified participants revealed significantly elevated gamma1 (30–50 Hz) activity in the right prefrontal cortex among policy supporters, reflecting enhanced attentional engagement and value integration. These topographically specific neural dissociations demonstrate how ideological alignment modulates cognitive–affective processing. Our findings establish EEG as a robust tool for quantifying implicit identity-driven evaluations, offering new pathways to decode polarization in contested policy contexts. Full article

Objectives: Most antiviral vaccines are created by inactivating the virus using chemical methods. The inactivation and production of viral vaccine preparations after the irradiation of viruses with accelerated electrons has a number of significant advantages. Determining the integrity of the genome of the resulting viral particles is necessary to assess the quality and degree of inactivation after irradiation. Methods: This work was performed on the Sabin 2 model polio virus. To determine the most sensitive and most radiation-resistant part, the polio virus genome was divided into 20 segments. After irradiation at temperatures of 25 °C, 2–8 °C, −20 °C, or −70 °C, the amplification intensity of these segments was measured in real time. Results: The best correlation between the amplification cycle and the irradiation dose at all temperatures was observed for segment 3D, left. Consequently, this section of the poliovirus genome is the least resistant to the action of accelerated electrons and is the most representative for determining genome integrity. The worst dependence was observed for the VP1 right section, which, therefore, cannot be used to determine genome integrity during inactivation. The electrochemical approach was also employed for a comparative assessment of viral RNA integrity before and after irradiation. An increase in the irradiation dose was accompanied by an increase in signals indicating the electrooxidation of RNA heterocyclic bases. The increase in peak current intensity of viral RNA electrochemical signals confirmed the breaking of viral RNA strands during irradiation. The shorter the RNA fragments, the greater the peak current intensities. In turn, this made the heterocyclic bases more accessible to electrooxidation on the electrode. Conclusions: These results are necessary for characterizing the integrity of the viral genome for the purpose of creating of antiviral vaccines. Full article

Recent outbreaks of highly pathogenic human RNA viruses from probable zoonotic origin have highlighted the relevance of epidemic preparedness as a society. However, research in vaccinology and virology, as well as epidemiologic surveillance, is often constrained by the biological risk that live virus experimentation entails. These also involve expensive costs, time-consuming procedures, and advanced personnel expertise, hampering market access for many drugs. Most of these drawbacks can be circumvented with the use of pseudotyped viruses, which are surrogate, non-pathogenic recombinant viral particles bearing the surface envelope protein of a virus of interest. Pseudotyped viruses significantly expand the research potential in virology, enabling the study of non-culturable or highly infectious pathogens in a safer environment. Most are derived from lentiviral vectors, which confer a series of advantages due to their superior efficiency. During the past decade, many studies employing pseudotyped viruses have evaluated the efficacy of vaccines or monoclonal antibodies for relevant pathogens such as HIV-1, Ebolavirus, Influenza virus, or SARS-CoV-2. In this review, we aim to provide an overview of the applications of pseudotyped viruses when evaluating the neutralization capacity of exposed individuals, or candidate vaccines and antivirals in both preclinical models and clinical trials, to further help develop effective countermeasures against emerging neutralization-escape phenotypes. Full article

COVID-19 vaccine hesitancy (VH), like attitudes towards other vaccines, is a critical global public health concern. Despite numerous studies covering psychological, sociodemographic, and other determinants of vaccine acceptance, resistance, and hesitance, few studies have reported these factors among students, particularly in politically unstable settings like Ukraine. This cross-sectional, descriptive, and quantitative study assesses hesitancy towards COVID-19 vaccines, utilizing the 5Cs Model. Among 936 respondents surveyed in 2023, 64% received at least one shot of the COVID-19 vaccine (acceptant), 11% were still considering getting vaccinated (hesitant), and 25% refused vaccination (resistant). Vaccination behavior is significantly associated with the 5Cs. Higher collective responsibility significantly increased acceptance and reduced resistance, while higher constraints lowered the chances of being either acceptant or resistant. Confidence protected against resistance. Complacency, counterintuitively, reduced odds of resistance, pointing to differences between passive hesitancy and active refusal. Male gender and sources of information and misinformation influenced confidence. Collective responsibility was positively associated with official sources and negatively with conspiracy beliefs. Complacency increased with official sources, while constraints and calculation were least explained by predictors. Practical barriers should be tackled through improved accessibility and fostering collective responsibility via targeted communication strategies. These findings provide actionable insights for policymakers, healthcare providers, and academic institutions to enhance vaccine uptake among university students, particularly in crisis settings. Full article

One concern in the yearly re-formulation of influenza vaccines is the time-consuming manufacturing of vaccine potency reagents, particularly for emergency responses. The continuous evaluation of modern techniques such as reversed-phase (RP) chromatography is an asset for streamlining this process. One challenge with RP methods, however, is the need to re-optimize methods for antigens that show poor separation, which can be highly dependent on analyst experience and available data. In this study, we leveraged a large RP dataset of influenza antigens to explore machine learning (ML) approaches of classifying challenging separations for computer-assisted method re-optimization across years, products, and analysts. Methods: To address recurring chromatographic issues—such as poor resolution, strain co-elution, and signal absence—we applied data augmentation techniques to correct class imbalance and trained multiple supervised ML classifiers to distinguish between these peak profiles. Results: With data augmentation, several ML models demonstrated promising accuracy in classifying chromatographic profiles according to the provided labels. These models effectively distinguished patterns indicative of separation issues in real-world data. Conclusions Our findings highlight the potential of ML as a computer assisted tool in the evaluation of vaccine quality, offering a scalable and objective approach to chromatogram classification. By reducing reliance on manual interpretation, ML can expedite the optimization of analytical methods, which is particularly needed for rapid responses. Future research involving larger, inter-laboratory datasets will further elucidate the utility of ML in vaccine analysis. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. 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 occurrence of PCV2 genotypes in domestic pig production is a dynamic process that undergoes continuous change. Beginning with PCV2a as the first recognized genotype, PCV2b, and subsequently PCV2d, has become the most prevalent one over time. The present study provides an update on the prevalence of the three major PCV2 genotypes in Germany in 2024. A total of 87 fattening farms were randomly selected, proportionally based on farm density within the respective federal states. On each farm, oral fluid samples (OFs) were collected from approximately 100 pigs aged 18 (±1) weeks. Oral fluids (OFs) were pooled and screened for PCV2 DNA by qPCR. Positive samples were subsequently examined by genotype specific qPCR. In total, 31.0% (27/87) of all farms were identified as PCV2-positive. PCV2a was detected in 8.0% (7/87) of farms, while 3.4% (3/87) tested positive for both PCV2a and PCV2d. Overall, 11.5% (10/87) of all farms were PCV2d-positive. No significant effect of vaccination status of the pigs on the viral load or frequency of detection of PCV2 DNA was detected. Full article

Background/Objectives: The aim of this study was to systematically assess Adverse Events Following Immunization (AEFI) among children following administration of the human papillomavirus (HPV) vaccine (Cervarix^®) included in the Dutch National Immunization Program (NIP) and to characterize the pattern and recurrence risk of AEFI after HPV revaccination. Methods: A longitudinal cohort event monitoring study, using patient-reported outcomes was used among recipients of the HPV vaccine at 10 years of age. Data were available for 3063 children following the first HPV vaccination and for 2209 children following the second HPV vaccination. Results: The most commonly reported AEFI following HPV vaccination were injection site reactions—reported by 46.5% of participants after the first dose and 31.9% after the second dose—followed by headache (8.2% and 3.9%, respectively) and joint pain (4.5% and 3.7%, respectively). Participants who received both HPV vaccine doses reported more AEFI after the first dose than after the second. Among girls, 61.2% reported at least one AEFI following the first dose, compared to 44.2% after the second dose. For boys, these percentages were 55.3% and 38.5%, respectively. This difference was statistically significant (p = 0.002). For some AEFI, such as injection site reactions, there appears to be a potential increased risk of recurrence following the second dose. Conclusions: This prospective longitudinal cohort event monitoring study showed that AEFI were more frequent after the first HPV dose and more frequent for girls compared to boys. An increased risk of recurrence was seen for AEFI, such as injection site reactions and headache. Furthermore, this study provides insight into the course of AEFI and the extent to which children were affected by these symptoms based on real-world data. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

This study aimed to estimate the end-of-season influenza vaccine effectiveness (VE) for the 2024/25 season in Beijing, China. Methods: We used a test-negative design (TND) to assess influenza VE among outpatients with influenza-like illness (ILI) enrolled through the influenza virological surveillance in sentinel hospitals in Beijing from week 44, 2024 to week 14, 2025. Cases were ILI patients who tested positive for influenza; controls were those who tested negative. Results: Among 18,405 ILI patients tested, 3690 (20.0%) were positive for influenza, with A(H1N1)pdm09 as the predominant strain (98.9%). The overall influenza vaccination coverage was 12.4%. Adjusted VE was 48.3% (95%CI: 40.4%–55.3%) against any influenza and 48.2% (95%CI: 40.3%–55.1%) against A(H1N1)pdm09, with the highest VE observed in adults aged 18–59 years (79.0%). The adjusted VE was similar for those vaccinated in 2023/24 only (53.1%) or both 2023/24 and 2024/25 seasons (50.8%), but lower for those vaccinated only in the 2024/25 season (48.5%). The adjusted VE was higher during the epidemic period (52.5%) than in the pre-epidemic (48.1%) and post-epidemic (35.3%) periods. Conclusions: Our findings indicate moderate VE against laboratory-confirmed influenza, especially A(H1N1)pdm09, during the end of the 2024/25 season in Beijing, China. Influenza vaccination provided protective effects across different epidemic periods. These timely estimates support ongoing public health communication and immunization strategies. Full article

Interferon (IFN) signaling plays vital roles in host defense against viral infection. However, a variety of observations have been reported in the literature regarding the roles of IFN signaling in COVID-19. Thus, it would be important to reach a clearer picture regarding the activation or suppression of IFN signaling in COVID-19. In this work, regulation of marker genes for IFN signaling was examined in natural infection, viral challenge, and vaccination based on 13 public transcriptome datasets. Three subsets of interferon-stimulated genes (ISGs) were selected for detailed examination, including one set of marker genes for type I IFN signaling (ISGa) and two sets of marker genes for type II IFN signaling (IFN-γ signaling, GBPs for the GBP gene cluster, and HLAd for the HLA-D gene cluster). In natural infection, activation of ISGa and GBPs was accompanied by the suppression of HLAd in hospitalized patients. Suppression of GBPs was also observed in certain critical conditions. The scale of regulation was much greater for ISGa than that of GBPs and HLAd. In addition, the suppression of HLAd was correlated with disease severity, and it took much longer for HLAd to return to the level of healthy controls than that for ISGa and GBPs. Upon viral challenge, the activation of ISGa and GBPs was similar to that of natural infection, while the suppression of HLAd was not observed. Moreover, GBPs’ return to the pre-infection level was at a faster pace than that of ISGa. Upon COVID-19 vaccination, activation was observed for all of these three gene sets, and the scale of activation was comparable for ISGa and GBPs. Notably, it took a much shorter time for GBPs and ISGa to return to the level of healthy controls than that in COVID-19 infection. In addition, the baseline values and transient activation of these gene sets were also associated with subsequent vaccination response. The intricate balance of IFN signaling was demonstrated in mild breakthrough infection, where attenuated response was observed in people with prior vaccination compared to that in vaccine-naïve subjects. Overall, distinctive temporal profiles of IFN signaling were observed in natural infection, viral challenge, and vaccination. The features observed in this work may provide novel insights into the disease management and vaccine development. Full article

Background/Objectives: The number needed to vaccinate (NNV) is a metric commonly used to evaluate the public health impact of a vaccine as it represents the number of individuals that must be vaccinated to prevent one case of disease. Traditional calculations may underestimate vaccine benefits by neglecting indirect effects and duration of protection (DOP), resulting in NNV overestimation. This study evaluated the NNV for the pediatric 20-valent pneumococcal conjugate (PCV20) US immunization program, as compared to PCV13, with a unique approach to NNV. Methods: A multi-cohort, population-based Markov model accounting for indirect effects was employed to calculate the NNV of PCV20 to avert a case of pneumococcal disease, invasive pneumococcal disease (IPD), hospitalized non-bacteremic pneumonia (NBP), ambulatory NBP, and otitis media (OM), as well as to prevent antibiotic-resistant cases and antibiotic prescriptions. Results: The mean NNV over a 25-year time horizon to prevent one case of pneumococcal disease was 6, with NNVs of 854 for IPD, 106 for hospitalized NBP, 25 for outpatient NBP, and 9 for OM, 11 for a course of antibiotic, and 4 for resistant disease. The mean NNV per year decreased over time, reflecting the DOP and increasing indirect effects over time. Conclusions: This study presents a novel approach to NNVs and shows that relatively few vaccinations are required to prevent disease. The decrease in NNV over time highlights the necessity of including DOP and indirect effects in NNV calculations, ensuring a more realistic assessment of a vaccine’s impact. Full article

Background: Understanding the duration and quality of immune memory following SARS-CoV-2 infection and vaccination is critical for informing public health strategies and vaccine development. While waning antibody levels have raised concerns about long-term protection, the persistence of memory B cells (MBCs) and T cells plays a vital role in sustaining immunity. Materials and Methods: We conducted a longitudinal prospective study over 12 months, enrolling 285 participants in total, either after natural infection or vaccination with BNT162b2 or mRNA-1273. Peripheral blood samples were collected at four defined time points (baseline, 1–2 months, 6–7 months, and 12–13 months after vaccination or disease onset). Immune responses were assessed through serological assays quantifying anti-RBD IgG and neutralizing antibodies, B-ELISPOT, and multiparameter flow cytometry for S1-specific memory B cells. Results: Both mRNA vaccines induced robust B cell and antibody responses, exceeding those observed after natural infection. Memory B cell frequencies peaked at 6 months and declined by 12 months, but remained above the baseline. The mRNA-1273 vaccine elicited stronger and more durable humoral and memory B-cell-mediated immunity compared to BNT162b2, likely influenced by its higher mRNA dose and longer prime-boost interval. Class-switched memory B cells and S1-specific B cells were significantly expanded in vaccine recipients. Natural infection induced more heterogeneous immune memory. Conclusions: Both mRNA vaccination and natural SARS-CoV-2 infection induce a comparable expansion of memory B cell subsets, reflecting a consistent pattern of humoral immune responses across all studied groups. These findings highlight the importance of vaccination in generating sustained immunological memory and suggest that the vaccine platform and dosage influence the magnitude and durability of immune responses against SARS-CoV-2. Full article