Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections and hospitalization in infants and young children. Here, we analyzed the genetic diversity of RSV using partial G gene sequences in 84 RSV-A and 78 RSV- B positive samples collected in Seoul, South Korea, for 10 consecutive years, from 2010 to 2019. Our phylogenetic analysis revealed that RSV-A strains were classified into either the ON1 (80.9%) or NA1 (19.0%) genotypes. On the other hand, RSV-B strains demonstrated diversified clusters within the BA genotype. Notably, some sequences designated as BA-SE, BA-SE1, and BA-DIS did not cluster with previously identified BA genotypes in the phylogenetic trees. Despite this, they did not meet the criteria for the assignment of a new genotype based on recent classification methods. Selection pressure analysis identified three positive selection sites (amino acid positions 273, 274, and 298) in RSV-A, and one possible positive selection site (amino acid position 296) in RSV-B, respectively. The mean evolutionary rates of Korean RSV-A from 1999 to 2019 and RSV-B strains from 1991 and 2019 were estimated at 3.51 × 10⁻³ nucleotides (nt) substitutions/site/year and 3.32 × 10⁻³ nt substitutions/site/year, respectively. The population dynamics in the Bayesian skyline plot revealed fluctuations corresponding to the emergence of dominant strains, including a switch of the dominant genotype from NA1 to ON1. Our study on time-scaled cumulative evolutionary analysis contributes to a better understanding of RSV epidemiology at the local level in South Korea.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the “order ratio” and “minimum ratio” were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

There have been few studies that comprehensively evaluated factors affecting immune responses and the correlation between humoral and cellular immune responses after vector, mix-and-match, and mRNA vaccines against SARS-CoV-2. Therefore, we analyzed the effects of age, sex, and the different vaccine regimens on the immune responses to vaccination against SARS-CoV-2.