The dominant viral etiologies responsible for acute respiratory infections (ARIs) are poorly understood, particularly among hospitalized patients. Improved etiological insight is needed to improve clinical management and prevention of ARIs. Clinical and demographic information and throat swabs were collected from 607 patients from 2011 to 2013 in Shandong Province, China. Multiplex RT-PCR (SeeplexTM RV detection, Seegene) was performed to detected 12 respiratory viral pathogens. A total of 607 hospitalized patients were enrolled from 2011 to 2013. Viruses were identified in 35.75 % (217/607) of cases, including 78 influenza virus A and B (IVA and IVB), 47 para-influenza viruses (PIVs), 41 respiratory syncytial virus (RSV) and 38 adenovirus (ADV). For the children under 15 year old, the common detected viruses were influenza viruses, RSV, PIVS and ADV, while the principal respiratory viruses were human coronaviruses (HCoV), PIVs, influenza viruses for the old adults. Co-infections with multiple viruses were detected in 15.67 % of patients. Children under 5 years were more likely to have one or more detectable virus associated with their ARI. The peak of ARI caused by the respiratory viruses occurred in winter. This study demonstrated respiratory viruses were the major cause of hospitalized ARI patients in Shandong Province, influenza virus was the most common detected, RSV was the highest incidence among the young children (≤5 years). These findings also gave a better understand of virus distribution among different age and seasons, which help to consider potential therapeutic approaches and develop effective prevention strategies for respiratory virus infection.

Background: Seasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate. Methods: The official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Results: In total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China. Conclusions: The results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.

No studies on the risk factors of 2009 pandemic influenza A (H1N1) in China have been reported. We aimed to investigate the risk factors for severe manifestations of 2009 pandemic H1N1 influenza in China A case–control study with 343 severe hospitalized patients and 343 randomly selected mild controls was conducted. The diagnosis was established by assessment of clinical symptoms and confirmed by the real-time reverse-transcriptase-polymerase chain reaction assay. Severe or mild patients were classified by uniform criteria issued by the Ministry of Health in China. The multivariable logistic regression analysis showed that the overweight or obese subjects admitted to hospital with H1N1 influenza were more likely to experience severe manifestations. The ORs were 3.70 (95% CI: 2.04-6.72) and 35.61 (95% CI: 7.96-159.21) respectively. Subjects at age less than 5 years or older than 60 years had an increased risk of severe manifestations (OR = 21.14, 95% CI: 7.79-57.33). We also observed increased risk among subjects with longer time interval from symptom onset to hospital admission (OR = 3.26, 95% CI: 2.08-5.11) or peasants (OR = 9.79, 95% CI: 5.11-18.78). Those with chronic disorders had increased risk of severe manifestations of H1N1 influenza. We provide evidence on the risk factors associated with severe manifestations of 2009 pandemic H1N1 influenza in a study of hospitalized subjects in China.

Background: Influenza A virus infections can occur in multiple species. Eurasian avian-like swine influenza A (H1N1) viruses (EAS-H1N1) are predominant in swine and occasionally infect humans. A Eurasian avian-like swine influenza A (H1N1) virus was isolated from a boy who was suffering from fever; this strain was designated A/Shandong-binzhou/01/2021 (H1N1). The aims of this study were to investigate the characteristics of this virus and to draw attention to the need for surveillance of influenza virus infection in swine and humans. Methods: Throat-swab specimens were collected and subjected to real-time fluorescent quantitative polymerase chain reaction (RT‒PCR). Positive clinical specimens were inoculated onto Madin-Darby canine kidney (MDCK) cells to isolate the virus, which was confirmed by a haemagglutination assay. Then, whole-genome sequencing was carried out using an Illumina MiSeq platform, and phylogenetic analysis was performed with MEGA X software. Results: RT‒PCR revealed that the throat-swab specimens were positive for EAS-H1N1, and the virus was subsequently successfully isolated from MDCK cells; this strain was named A/Shandong-binzhou/01/2021 (H1N1). Whole-genome sequencing and phylogenetic analysis revealed that A/Shandong-binzhou/01/2021 (H1N1) is a novel triple-reassortant EAS-H1N1 lineage that contains gene segments from EAS-H1N1 (HA and NA), triple-reassortant swine influenza H1N2 virus (NS) and A(H1N1) pdm09 viruses (PB2, PB1, PA, NP and MP). Conclusions: The isolation and analysis of the A/Shandong-binzhou/01/2021 (H1N1) virus provide further evidence that EAS-H1N1 poses a threat to human health, and greater attention should be given to the surveillance of influenza virus infections in swine and humans.

Rapid and accurate detection technology is significant for screening on population scale and control of infectious diseases. The dilution and contamination of samples can affect the accuracy of response signal, resulting in false positives, which makes diagnosis challenging, especially for early patients with mild symptoms. Here, an AuNPs@Ta2C-M/Au/TFBG sensor with saturation sites was reported for specific detection of pathogen taking SARS-CoV-2 as an example. Homogeneous periodic AuNPs@Ta2C-M structure was deposited on Au film-coated TFBG by a photo-induced method to further extend the SPR performance and surface plasmon polaritons (SPPs) response range. The ssDNA probes are immobilized on the sensing platform in a higher density using a double modification method to solve the targets concealment problem caused by the spatial secondary conformational structure of long sequence nucleic acids. Synergistic effect between the high-density probes and multi-plasmon structure enables a noticeable improvement in hybridization efficiency and signal transduction of trace target sequences. The experimental results demonstrate that the sensor can accurately detect 30 clinic samples within 5 min without amplification, which shows high agreement with RT-PCR (Kappa index = 1). In particular, dilution kinetic experiments with clinical single samples (LOD:2 pg/ml) and 10-in-1 pooled sample (Maximum dilution ratio:105) validates the excellent trace analysis capability of the sensor. Overall, this bio-sensing method provides a promising approach for trace detection of infectious pathogens.

Sapovirus (SaV) is a major pathogen responsible for acute gastroenteritis (AGE), and its incidence has been increasing in recent years. This study investigates the prevalence and the genetic characteristics of SaV in Shandong Province during 2022–2023, based on a surveillance network covering all age groups. Samples were obtained from a viral diarrhea surveillance network in Shandong Province during 2022–2023. SaVs were identified through quantitative reverse-transcription polymerase chain reaction (RT-qPCR). PCR amplification and Sanger sequencing were performed on positive samples, and whole-genome sequencing was conducted using metagenomic sequencing technology. Sequence analysis was conducted using BioEdit 7.0.9.0 and MEGA X, while statistical analysis was performed with SPSS 26.0. A total of 157 SaV-positive cases were identified, resulting in a positivity rate of 1.12%. The positivity rate for SaV was 0.75% in 2022 and it increased significantly to 1.42% in 2023. The highest positivity rates for both 2022 and 2023 were observed in November. The highest positivity rate was observed in the 3–5-year-old age group. In 2022, Dongying City had the highest positivity rate, while Zaozhuang City exhibited the highest rate in 2023. The incidence of vomiting in SaV-positive patients was significantly higher compared to SaV-negative patients (P = 0.002). Eight genotypes were identified in both the VP1 and RdRp regions. The complete genome sequence analysis of a GI.3 strain showed that NS1 (5.88%, 4/68) was the region most prone to amino acid variation, followed by VP2 (5.45%, 9/165) within the same genotype. SaV infections are more prevalent in cold weather, with young children being particularly susceptible. The SaV positivity rate in 2023 increased significantly accompanied by an increased diversity of genotypes, compared to that of 2022. The NS1 region exhibits the biggest variation within the same genotype, indicating that more attention should be paid to other regions besides VP1 in the future study. Ongoing surveillance of SaV is recommended for effective prevention and control.