Interaction Among Influenza Viruses A/H1N1, A/H3N2, and B in Japan

Global pattern and determinant for interaction of seasonal influenza viruses

BACKGROUND

The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored.

METHODS

Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM).

RESULTS

A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1.

CONCLUSION

The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

Intensity and drivers of subtypes interference between seasonal influenza viruses in mainland China: A modeling study

Subtype interference has a significant impact on the epidemiological patterns of seasonal influenza viruses (SIVs). We used attributable risk percent [the absolute value of the ratio of the effective reproduction number (Rₑ) of different subtypes minus one] to quantify interference intensity between A/H1N1 and A/H3N2, as well as B/Victoria and B/Yamagata. The interference intensity between A/H1N1 and A/H3N2 was higher in southern China 0.26 (IQR: 0.11-0.46) than in northern China 0.17 (IQR: 0.07-0.24). Similarly, interference intensity between B/Victoria and B/Yamagata was also higher in southern China 0.14 (IQR: 0.07-0.24) than in norther China 0.10 (IQR: 0.04-0.18). High relative humidity significantly increased subtype interference, with the highest relative risk reaching 20.59 (95% CI: 6.12-69.33) in southern China. Southern China exhibited higher levels of subtype interference, particularly between A/H1N1 and A/H3N2. Higher relative humidity has a more pronounced promoting effect on subtype interference.

Discrimination of the H1N1 and H5N2 Variants of Influenza A Virus Using an Isomeric Sialic Acid-Conjugated Graphene Field-Effect Transistor

There has been a continuous effort to fabricate a fast, sensitive, and inexpensive system for influenza virus detection to meet the demand for effective screening in point-of-care testing. Herein, we report a sialic acid (SA)-conjugated graphene field-effect transistor (SA-GFET) sensor designed using α2,3-linked sialic acid (3'-SA) and α2,6-linked sialic acid (6'-SA) for the detection and discrimination of the hemagglutinin (HA) protein of the H5N2 and H1N1 viruses. 3'-SA and 6'-SA specific for H5 and H1 influenza were used in the SA-GFET to capture the HA protein of the influenza virus. The net charge of the captured viral sample led to a change in the electrical current of the SA-GFET platform, which could be correlated to the concentration of the viral sample. This SA-GFET platform exhibited a highly sensitive response in the range of 10¹-10⁶ pfu mL^-1, with a limit of detection (LOD) of 10¹ pfu mL^-1 in buffer solution and a response time of approximately 10 s. The selectivity of the SA-GFET platform for the H1N1 and H5N2 influenza viruses was verified by testing analogous respiratory viruses, i.e., influenza B and the spike protein of SARS-CoV-2 and MERS-CoV, on the SA-GFET. Overall, the results demonstrate that the developed dual-channel SA-GFET platform can potentially serve as a highly efficient and sensitive sensing platform for the rapid detection of infectious diseases.

Self-Assembled Chromogenic Polymeric Nanoparticle-Laden Nanocarrier as a Signal Carrier for Derivative Binary Responsive Virus Detection

In the current biosensor, the signal generation is limited to single virus detection in the reaction chamber. An adaptive strategy is required to enable the recognition of multiple viruses for diagnostics and surveillance. In this work, a nanocarrier is deployed to bring specific signal amplification into the biosensor, depending on the target viruses. The nanocarrier is designed using pH-sensitive polymeric nanoparticle-laden nanocarriers (PNLNs) prepared by sequential nanoprecipitation. The nanoprecipitation of two chromogens, phenolphthalein (PP) and thymolphthalein (TP), is investigated in three different solvent systems in which PNLNs demonstrate a high loading of the chromogen up to 59.75% in dimethylformamide (DMF)/dimethyl sulfoxide (DMSO)/ethanol attributing to the coprecipitation degree of the chromogens and the polymer. The PP-encapsulated PNLNs (PP@PNLNs) and TP-encapsulated PNLNs (TP@PNLNs) are conjugated to antibodies specific to target viruses, influenza virus A subtype H1N1 (IV/A/H1N1) and H3N2 (IV/A/H3N2), respectively. After the addition of anti-IV/A antibody-conjugated magnetic nanoparticles (MNPs) and magnetic separation, the enriched PNLNs/virus/MNPs sandwich structure is treated in an alkaline solution. It demonstrates a synergy reaction in which the degradation of the polymeric boundary and the pH-induced colorimetric development of the chromogen occurred. The derivative binary biosensor shows feasible detection on IV/A with excellent specificities of PP@PNLNs on IV/A/H1N1 and TP@PNLNs on IV/A/H3N2 with LODs of 27.56 and 28.38 fg mL^-1, respectively. It intrigues the distinguished analytical signal in human serum with a variance coefficient of 25.8% and a recovery of 93.6-110.6% for one-step subtype influenza virus detection.

A Comparison of the Frequency of Prescription and Pharmacy Revisits between Baloxavir Marboxil and Neuraminidase Inhibitors in Influenza-Infected Pediatric Patients during the 2019-2020 Influenza Season

The novel anti-influenza virus agent baloxavir marboxil is a selective inhibitor of an influenza cap-dependent endonuclease. Although a single oral dose in tablet form of baloxavir marboxil is expected to improve drug compliance and rapidly reduce viral titers for pediatric patients with influenza, there is a concern that baloxavir marboxil-resistant influenza A variants could be generated. In this study, we investigated the frequency of prescription and pharmacy revisits for baloxavir marboxil at an outpatient clinic compared with that of neuraminidase inhibitors in pediatric patients with influenza. A total of 475 pediatric patients who were infected with the influenza virus visited the pharmacy between December 2019 and March 2020. Baloxavir marboxil (n = 149), oseltamivir (n = 161) and laninamivir (n = 162) were mainly prescribed and only a few patients were treated with peramivir (n = 2) or zanamivir (n = 1). Baloxavir marboxil-, oseltamivir- and laninamivir-treated pediatric patients were enrolled, and a log-rank test showed that the revisits of pediatric patients who were taking baloxavir marboxil was lower than those for oseltamivir (p < 0.001). Moreover, Cox proportional hazards models also revealed that baloxavir marboxil decreased the risk of revisits in comparison to oseltamivir (hazard ratio 0.28, 95% confidence interval 0.11-0.70, p = 0.006), while no difference was found between laninamivir and baloxavir marboxil. Although there is a need to acquire appropriate and relevant information concerning resistant viruses, our results suggest that baloxavir marboxil may be a useful drug for treating pediatric patients with influenza infections.

Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.