The outbreak of highly pathogenic influenza virus subtypes, such as H7 and H5, presents a significant global health challenge, necessitating the development of rapid and sensitive diagnostic methods. In this study, we have developed a novel dual-component biosensor assembly, each component of which incorporates an antibody fused with a nano-luciferase subunit. Our results demonstrate the effectiveness of this biosensor in enabling the rapid and sensitive detection of influenza H7 and other subtypes. Additionally, we successfully applied the biosensor in paper-based assay and lateral flow assay formats, expanding its versatility and potential for field-deployable applications. Notably, we achieved effective detection of the H7N9 virus using this biosensor. Furthermore, we designed and optimized a dedicated biosensor to the sensitive detection of the influenza H5 subtype. Collectively, our findings underscore the significant potential of this dual-component biosensor assembly as a valuable and versatile tool for accurate and timely diagnosis of influenza virus infections, promising to advance the field of influenza diagnostics and contribute to outbreak management and surveillance efforts.

OBJECTIVE: This study aimed to investigate the pathogen epidemiology of community-acquired pneumonia (CAP) among children in Southwest China before, during and after the COVID-19 non-pharmaceutical interventions (NPIs).
METHODS: Pathogen data of hospitalised children with CAP, including multiple direct immunofluorescence test for seven viruses, bacterial culture and polymerase chain reaction (PCR) for Mycoplasma pneumoniae, were analysed across three phases: Phase I (pre-NPIs: 1 January 2019 to 31 December 2019), Phase II (NPI period: 1 January 2020 to 31 December 2020) and Phase III (post-NPIs: 1 January 2023 to 31 December 2023).
RESULTS: A total of 7533 cases were enrolled, including 2444, 1642 and 3447 individuals in Phases I, II and III, respectively. M. pneumoniae predominated in Phases I and III (23.4% and 35.5%, respectively). In Phase II, respiratory syncytial virus (RSV) emerged as the primary pathogen (20.3%), whereas detection rates of influenza A virus (Flu A) and M. pneumoniae were at a low level (1.8% and 9.6%, respectively). In Phase III, both Flu A (15.8%) and M. pneumoniae epidemic rebounded, whereas RSV detection rate returned to Phase I level, and detection rates of Streptococcus pneumoniae and Haemophilus influenzae decreased significantly compared to those in Phase I. Detection rates of adenovirus and parainfluenza virus type 3 decreased phase by phase. Age-stratified analysis and monthly variations supported the above findings. Seasonal patterns of multiple pathogens were disrupted during Phases II and III.
CONCLUSIONS: COVID-19 NPIs exhibited a distinct impact on CAP pathogen epidemic among children, with post-NPIs increases observed in M. pneumoniae and Flu A prevalence. Continuous pathogen monitoring is crucial for effective prevention and control of paediatric CAP.

Congenital post-infectious hydrocephalus (PIH) is a condition characterized by enlargement of the ventricular system, consequently imposing a burden on the associated stem cell niche, the ventricular-subventricular zone (V-SVZ). To investigate how the V-SVZ adapts in PIH, we developed a mouse model of influenza virus-induced PIH based on direct intracerebroventricular injection of mouse-adapted influenza virus at two distinct time points: embryonic day 16 (E16), when stem cells line the ventricle, and postnatal day 4 (P4), when an ependymal monolayer covers the ventricle surface and stem cells retain only a thin ventricle-contacting process. Global hydrocephalus with associated regions of astrogliosis along the lateral ventricle was found in 82% of the mice infected at P4. Increased ependymogenesis was observed at gliotic borders and throughout areas exhibiting intact ependyma based on tracking of newly divided cells. Additionally, in areas of intact ependyma, stem cell numbers were reduced; however, we found no significant reduction in new neurons reaching the olfactory bulb following onset of ventriculomegaly. At P4, injection of only the non-infectious viral component neuraminidase resulted in limited, region-specific ventriculomegaly due to absence of cell-to-cell transmission. In contrast, at E16 intracerebroventricular injection of influenza virus resulted in death at birth due to hypoxia and multiorgan hemorrhage, suggesting an age-dependent advantage in neonates, while the viral component neuraminidase resulted in minimal, or no, ventriculomegaly. In summary, we tracked acute adaptations of the V-SVZ stem cell niche following onset of ventriculomegaly and describe developmental changes that help mitigate the severity of congenital PIH.

The detection of evolutionary transitions in influenza A (H3N2) viruses\' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman\'s index, indicative of the virus\'s cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD\'s efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

The influenza virus (IFV) imposes a considerable health and economic burden globally, requiring a comprehensive understanding of its pathogenic mechanisms. Ferroptosis, an iron-dependent lipid peroxidation cell death pathway, holds unique implications for the antioxidant defense system, with possible contributions to inflammation. This exploration focuses on the dynamic interplay between ferroptosis and the host defense against viruses, emphasizing the influence of IFV infections on the activation of the ferroptosis pathway. IFV causes different types of cell death, including apoptosis, necrosis, and ferroptosis. IFV-induced ferroptotic cell death is mediated by alterations in iron homeostasis, intensifying the accumulation of reactive oxygen species and promoting lipid peroxidation. A comprehensive investigation into the mechanism of ferroptosis in viral infections, specifically IFV, has great potential to identify therapeutic strategies. This understanding may pave the way for the development of drugs using ferroptosis inhibitors, presenting an effective approach to suppress viral infections.

UNASSIGNED: Adenovirus pneumonia progresses rapidly, with a high rate of progression to severe pneumonia, but the early clinical manifestations lack specificity and are not easy to be recognized.
UNASSIGNED: Reviewing the relevant literatures, we studied and summarized the early recognition, clinical features and treatment outlook of severe adenovirus pneumonia Case Presentation: An 11-year-old child with community-acquired pneumonia, with influenza A antigen positive by colloidal gold, which further developed into acute respiratory distress syndrome after hospitalization. Three days later, adenovirus was detected positively by PCR of throat swab and diagnosed as severe adenovirus pneumonia. After aggressive treatment, her condition improved and was discharged from the hospital.
UNASSIGNED: Clinically, adenovirus combined with influenza virus infection is uncommon, and adenovirus infection is even rarer in adolescent children.

Influenza viruses rapidly evolve to evade previously acquired human immunity. Maintaining vaccine efficacy necessitates continuous monitoring of antigenic differences among strains. Traditional serological methods for assessing these differences are labor-intensive and time-consuming, highlighting the need for efficient computational approaches. This paper proposes MetaFluAD, a meta-learning-based method designed to predict quantitative antigenic distances among strains. This method models antigenic relationships between strains, represented by their hemagglutinin (HA) sequences, as a weighted attributed network. Employing a graph neural network (GNN)-based encoder combined with a robust meta-learning framework, MetaFluAD learns comprehensive strain representations within a unified space encompassing both antigenic and genetic features. Furthermore, the meta-learning framework enables knowledge transfer across different influenza subtypes, allowing MetaFluAD to achieve remarkable performance with limited data. MetaFluAD demonstrates excellent performance and overall robustness across various influenza subtypes, including A/H3N2, A/H1N1, A/H5N1, B/Victoria, and B/Yamagata. MetaFluAD synthesizes the strengths of GNN-based encoding and meta-learning to offer a promising approach for accurate antigenic distance prediction. Additionally, MetaFluAD can effectively identify dominant antigenic clusters within seasonal influenza viruses, aiding in the development of effective vaccines and efficient monitoring of viral evolution.

Avian influenza virus has been long considered the main threat for a future pandemic. Among the possible avian influenza virus subtypes, A(H5N1) clade 2.3.4.4b is becoming enzootic in mammals, representing an alarming step towards a pandemic. In particular, genotype B3.13 has recently caused an outbreak in US dairy cattle. Since pandemic preparedness is largely based on the availability of prepandemic candidate vaccine viruses, in this review we will summarize the current status of the enzootics, and challenges for H5 vaccine manufacturing and delivery.

Since 2019, COVID-19 has been raging around the world. Respiratory viral infectious diseases such as influenza and respiratory syncytial virus (RSV) infection are also prevalent, with influenza having the ability to cause seasonal pandemics. While vaccines and antiviral drugs are available to prevent and treat disease, herbal extracts would be another option. This study investigated the inhibitory effects of extracts of Echinacea purpurea (EP) and Ganoderma lucidum (G. lucidum) and the advanced G. lucidum drink (AG) on influenza A/B viruses. To determine whether EP and G. lucidum extracts enhance cell immunity and thus prevent virus infection or act to directly suppress viruses, cell survival and hemagglutination (HA) assays were used in this study. Cells were treated with samples at different concentrations (each sample concentration was tested from the highest non-cytotoxic concentration) and incubated with influenza A/B for 24 h, with the results showing that both G. lucidum and EP extracts and mixtures exhibited the ability to enhance cell survival against viruses. In the HA assay, AG and EP extract showed good inhibitory effect on influenza A/B viruses. All of the samples demonstrated an improvement of the mitochondrial membrane potential and improved resistance to influenza A/B virus infection. EP and G. lucidum extracts at noncytotoxic concentrations increased cell viability, but only AG and EP extract directly decreased influenza virus titers. In conclusion, results indicate the ability of EP and G. lucidum extract to prevent viruses from entering cells by improving cell viability and mitochondrial dysfunction and EP extract showed direct inhibition on viruses and prevented viral infection at post-infection strategy.

BackgroundA new respiratory virus surveillance platform, based on nationwide hospital laboratory data, was established in Israel during the COVID-19 pandemic.AimWe aimed to evaluate the performance of this platform with respect to the detection of influenza and respiratory syncytial virus (RSV) from week 36 in 2020 to week 15 in 2023, and how it fits with the World Health Organization (WHO) mosaic surveillance framework.MethodsData of respiratory samples from hospitalised patients sent for laboratory confirmation of influenza virus or RSV from 25 general hospital laboratories nationwide were collected. We analysed the weekly number and percentage of samples positive for influenza virus or RSV vis-à-vis SARS-CoV-2 activity and compared data from the new surveillance platform with existing surveillance platforms. Using data in the new surveillance platform, we analysed early stages of a 2021 out-of-season RSV outbreak and evaluated the capabilities of the new surveillance system with respect to objectives and domains of the WHO mosaic framework.ResultsThe new hospital-laboratory surveillance platform captured the activity of influenza virus and RSV, provided crucial data when outpatient sentinel surveillance was not operational and supported an out-of-season RSV outbreak investigation. The new surveillance platform fulfilled important objectives in all three domains of the mosaic framework and could serve for gathering additional information to fulfil more domain objectives.ConclusionThe new hospital laboratory surveillance platform provided essential data during the COVID-19 pandemic and beyond, fulfilled important domain objectives of the mosaic framework and could be adapted for the surveillance of other viruses.