Very high sensitivity of a rapid influenza diagnostic test in adults and elderly individuals within 48 hours of the onset of illness

Plasmonic Fluorescence Sensors in Diagnosis of Infectious Diseases

The increasing demand for rapid, cost-effective, and reliable diagnostic tools in personalized and point-of-care medicine is driving scientists to enhance existing technology platforms and develop new methods for detecting and measuring clinically significant biomarkers. Humanity is confronted with growing risks from emerging and recurring infectious diseases, including the influenza virus, dengue virus (DENV), human immunodeficiency virus (HIV), Ebola virus, tuberculosis, cholera, and, most notably, SARS coronavirus-2 (SARS-CoV-2; COVID-19), among others. Timely diagnosis of infections and effective disease control have always been of paramount importance. Plasmonic-based biosensing holds the potential to address the threat posed by infectious diseases by enabling prompt disease monitoring. In recent years, numerous plasmonic platforms have risen to the challenge of offering on-site strategies to complement traditional diagnostic methods like polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA). Disease detection can be accomplished through the utilization of diverse plasmonic phenomena, such as propagating surface plasmon resonance (SPR), localized SPR (LSPR), surface-enhanced Raman scattering (SERS), surface-enhanced fluorescence (SEF), surface-enhanced infrared absorption spectroscopy, and plasmonic fluorescence sensors. This review focuses on diagnostic methods employing plasmonic fluorescence sensors, highlighting their pivotal role in swift disease detection with remarkable sensitivity. It underscores the necessity for continued research to expand the scope and capabilities of plasmonic fluorescence sensors in the field of diagnostics.

Association of frailty with influenza and hospitalization due to influenza among independent older adults: a longitudinal study of Japan Gerontological Evaluation Study (JAGES)

BACKGROUND

It is unknown that whether frailty is a risk factor of influenza and the hospitalization among older adults, although it has been shown that frailty was associated with poor recovery from the hospitalization among those. We examined the association of frailty with influenza and the hospitalization and the effect by sex among independent older adults.

METHODS

We used the longitudinal data from the Japan Gerontological Evaluation Study (JAGES), performed in 2016 and 2019 and conducted in 28 municipalities in Japan. The target population comprised 77,103 persons aged ≥ 65 years who did not need assistance from the public long-term care insurance. Primary outcome measures were influenza and hospitalization due to influenza. Frailty was evaluated with the Kihon check list. We estimated the risk of influenza, the hospitalization, those risks by sex, and the interaction for frailty and sex using Poisson regression adjusting for covariates.

RESULTS

Frailty was associated with both influenza and the hospitalization among the older adults compared with nonfrail individuals after adjusting for covariates (influenza, frail: risk ratio {RR}: 1.36, 95% confidence interval {95% CI}: 1.20 - 1.53, and prefrail: RR: 1.16, 95% CI: 1.09 - 1.23; the hospitalization, frail: RR: 3.18, 95% CI: 1.84 - 5.57, and prefrail: RR: 2.13, 95% CI: 1.44 - 3.16). Male was associated with the hospitalization, but not associated with influenza compared to female (the hospitalization: RR: 1.70, 95% CI: 1.15 - 2.52 and influenza: RR: 1.01, 95% CI: 0.95 - 1.08). The interaction for frailty and sex was significant neither in influenza nor in the hospitalization.

CONCLUSION

These results suggest that frailty is a risk of influenza and the hospitalization, that risks of the hospitalization are different by sex, but that the sex difference does not cause the effect heterogeneity of frailty on the susceptibility and severity among independent older adults.

Comparing the cobas Influenza A/B Nucleic acid test for use on the cobas Liat System (Liat) with rapid antigen tests for clinical management of Japanese patients at the point of care

Background

Rapid diagnosis of influenza is critical in preventing the spread of infection and ensuring patients quickly receive antiviral medication to reduce the severity and duration of influenza symptoms, whilst controlling the spread of the causative virus. In Japan patients are often administered anti-influenza medication following a positive rapid antigen detection test (RADT) result. However, the sensitivity of RADTs can lead to false negative results. The cobas^® Influenza A/B Nucleic acid test for use on the cobas Liat^® System (Liat) is a molecular point-of-care method that can provide a more sensitive alternative to RADTs for rapid influenza diagnosis and treatment.

Methods

In this prospective multicenter study, diagnostic performance of the Liat test was compared with RADTs in patients presenting with influenza-like-illness. Test performance was also assessed by time since symptom onset.

Results

Of 419 patients enrolled, 413 were evaluable for all designated tests. Most patients had type-A infection, and only one patient had influenza type B. In 413 patients, the sensitivity and specificity (95% CI) of the Liat test were 99.5% (97.2–99.9%) and 99.5% (97.4–99.9%), respectively, and were 79.7% (73.5–84.7%) and 95.4% (91.7–97.5%) for RADTs. For patients tested <12 hours from symptom onset, the Liat test had significantly higher sensitivity than RADTs (p<0.0001).

Conclusion

Overall, compared with standard of care RADTs, the Liat test was more sensitive and specific in children and adults, particularly in the early stages of infection. Greater sensitivity can enable earlier diagnosis and may better inform appropriate antiviral treatment decisions.

Association between visceral fat and influenza infection in Japanese adults: A population-based cross-sectional study

Background

Several studies have reported that obesity is associated with influenza infection; however, the role of visceral fat remains unclear. The aim of this study was to investigate the association between visceral fat and influenza infection in community-dwelling Japanese adults.

Methods

A cross-sectional study was performed using data from an annual community-based health check-up conducted from May to June in 2019. In total, 1,040 Japanese adults aged 20–89 years were enrolled in this study. Influenza infection status was determined by participants’ responses to a self-administered questionnaire. The visceral fat area (VFA) was measured using a bioimpedance-type visceral fat meter. Participants were classified into four groups using the following cut-off points: VFA < 100 cm² was set as the reference category according to the Japanese criteria, 100 ≤ VFA < 150 cm², 150 ≤ VFA < 200 cm², and 200 cm² ≤ VFA. Logistic regression models were used to assess the association between VFA and influenza infection.

Results

In total, 119 participants had influenza infections in the past year. In the multivariate adjusted model, a higher VFA was significantly associated with increased influenza infection; the adjusted odds ratio for 200 cm² ≤ VFA was 5.03 [95% confidence interval (CI): 1.07–23.6], that for 150 ≤ VFA < 200 cm² was 1.97 (95% CI: 0.71–5.45), and that for 100 ≤ VFA < 150 cm² was 1.62 (95% CI: 0.84–3.12), compared with that for VFA < 100 cm² (p for trend = 0.049). These findings were confirmed in the same cohort the following year.

Conclusions

Our results suggest that visceral fat accumulation is associated with influenza infection. Large-scale prospective studies using diagnostic information for influenza infection are required to confirm this association.

Trends in effectiveness of inactivated influenza vaccine in children by age groups in seven seasons immediately before the COVID-19 era

Background

Methods

Results

Conclusions

Risk factors in pediatric hospitalization for influenza A and B during the seven seasons immediately before the COVID-19 era in Japan

INTRODUCTION

The risk factors in pediatric influenza immediately before the COVID-19 era are not well understood. This study aims to evaluate the risk factors for hospitalization in pediatric influenza A and B for the recent seasons.

METHODS

Children with a fever of ≥38 °C and laboratory-confirmed influenza at 20 hospitals in outpatient settings in Japan in the 2013/14 to 2019/20 seasons were retrospectively reviewed. Possible risk factors, including gender, age, comorbidities, nursery school or kindergarten attendance, earlier diagnosis, no immunization, lower regional temperature, earlier season, and period of onset, were evaluated using binary logistic regression methods.

RESULTS

A total of 13,040 (type A, 8861; B, 4179) children were evaluated. Significant risk factors (p < 0.05) in multivariate analyses were young age, lower regional temperature, earlier season, respiratory illness (adjusted odds ratio [aOR]:2.76, 95% confidence interval [CI]:1.84-4.13), abnormal behavior and/or unusual speech (aOR:2.78, 95% CI:1.61-4.80), and seizures at onset (aOR:16.8, 95% CI:12.1-23.3) for influenza A; and young age, lower regional temperature, respiratory illness (aOR:1.99, 95% CI:1.00-3.95), history of febrile seizures (aOR:1.73, 95% CI:1.01-2.99), and seizures at onset (aOR:9.74, 95% CI:5.44-17.4) for influenza B.

CONCLUSIONS

In addition to previously known factors, including young age, seizures, and respiratory illness, abnormal behavior and/or unusual speech and lower regional temperature are new factors. Negative immunization status was not a risk factor for hospitalization. A better understanding of risk factors may help improve the determination of indications for hospitalization during the future co-circulation of influenza and COVID-19.

Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014-2015 to 2018-2019 seasons

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014-2015 to 2018-2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014-2015 and 2015-2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016-2017, 2017-2018, and 2018-2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.

Influenza vaccine effectiveness against influenza A in children based on the results of various rapid influenza tests in the 2018/19 season

During influenza epidemics, Japanese clinicians routinely conduct rapid influenza diagnostic tests (RIDTs) in patients with influenza-like illness, and patients with positive test results are treated with anti-influenza drugs within 48 h after the onset of illness. We assessed the vaccine effectiveness (VE) of inactivated influenza vaccine (IIV) in children (6 months-15 years old, N = 4243), using a test-negative case-control design based on the results of RIDTs in the 2018/19 season. The VE against influenza A(H1N1)pdm and A(H3N2) was analyzed separately using an RIDT kit specifically for detecting A(H1N1)pdm09. The adjusted VE against combined influenza A (H1N1pdm and H3N2) and against A(H1N1)pdm09 was 39% (95% confidence interval [CI], 30%-46%) and 74% (95% CI, 39%-89%), respectively. By contrast, the VE against non-A(H1N1)pdm09 influenza A (presumed to be H3N2) was very low at 7%. The adjusted VE for preventing hospitalization was 56% (95% CI, 16%-77%) against influenza A. The VE against A(H1N1)pdm09 was consistently high in our studies. By contrast, the VE against A(H3N2) was low not only in adults but also in children in the 2018/19 season.

Clinical usefulness of a rapid molecular assay, ID NOW™ influenza A & B 2, in adults

INTRODUCTION

ID NOW™ Influenza A & B 2 (ID NOW 2) is a rapid molecular assay that combines two characteristics, namely the rapidness of rapid antigen detection test (RADT) and the high sensitivity of molecular assay.

METHODS

The clinical performance of ID NOW 2 compared with real-time RT-PCR was evaluated in adults.

RESULTS

The sensitivity of ID NOW 2 over multiple seasons from 2016/2017 to 2019/2020 was 97.3% (95% CI: 90.7-99.7) for Type A, 100% (95% CI: 81.9-100) for Type B, and 97.8% (95% CI: 92.2-99.7) for influenza (Type A + Type B), and it was significantly higher than the sensitivity of RADT, which was 80.0% (95% CI: 69.2-88.4) for Type A, 73.3% (95% CI: 44.9-92.2) for Type B, and 78.9% (95% CI: 69.0-86.8) for influenza. The sensitivity of RADT tended to be lower in patients in the particularly early period, within 12 h from disease onset; however, the sensitivity of ID NOW 2 remained high, increasing the difference between the sensitivity of RADT and ID NOW 2. The viral loads were low within 12 h from onset, and it was considered this affected the sensitivity of RADT due to its low analytical sensitivity. The specificity of ID NOW 2 was 98% or greater in all groups.

CONCLUSIONS

Since ID NOW 2 has a high sensitivity and specificity in adults, it is anticipated to be used in clinical practice, particularly in patients who require early and accurate diagnosis.

Immunodominant regions prediction of nucleocapsid protein for SARS-CoV-2 early diagnosis: a bioinformatics and immunoinformatics study

COVID-19 caused by SARS-CoV-2 is sweeping the world and posing serious health problems. Rapid and accurate detection along with timely isolation is the key to control the epidemic. Nucleic acid test and antibody-detection have been applied in the diagnosis of COVID-19, while both have their limitations. Comparatively, direct detection of viral antigens in clinical specimens is highly valuable for the early diagnosis of SARS-CoV-2. The nucleocapsid (N) protein is one of the predominantly expressed proteins with high immunogenicity during the early stages of infection. Here, we applied multiple bioinformatics servers to forecast the potential immunodominant regions derived from the N protein of SARS-CoV-2. Since the high homology of N protein between SARS-CoV-2 and SARS-CoV, we attempted to leverage existing SARS-CoV immunological studies to develop SARS-CoV-2 diagnostic antibodies. Finally, N_229-269, N_349-399, and N_405-419 were predicted to be the potential immunodominant regions, which contain both predicted linear B-cell epitopes and murine MHC class II binding epitopes. These three regions exhibited good surface accessibility and hydrophilicity. All were forecasted to be non-allergen and non-toxic. The final construct was built based on the bioinformatics analysis, which could help to develop an antigen-capture system for the early diagnosis of SARS-CoV-2.