The severity of COVID-19 ranges from mild to critical diseases. However, limited data have been published on the detailed kinetics of viral load and host immune response throughout the disease course depending on disease severity. In this study, we comprehensively analyzed viral load, antibody responses to SARS-CoV-2, and cytokines/chemokines during the disease course, and identified the factors related to severity. Nasopharyngeal (NP) and plasma specimens were obtained from 31 patients with COVID-19 during hospitalization. Viral RNA in NP specimens was quantified by reverse transcription–PCR. Anti–SARS-CoV-2 antibodies and cytokines/chemokines in plasma specimens were analyzed by ELISA and cytometric bead array. The viral load in patients with COVID-19 peaked at the early stage of the disease and continuously decreased. Severe and critical cases showed higher viral load and prolonged viral shedding than asymptomatic and mild cases. Whereas plasma IgG was gradually increased and maintained during hospitalization, plasma IgM peaked at 3 weeks after symptom onset and dissipated. The antibody response in severe and critical cases was slightly delayed but stronger than those in others. High levels of interferon (IFN)-α, IFN-γ–induced protein-10, monokine induced by IFN-γ, and interleukin-6 at 5–10 days from symptom onset were associated with the severity of COVID-19. Our data indicate that high viral load in the respiratory tract and excessive production of cytokines and chemokines between 1 and 2 weeks from the symptom onset were significantly associated with the severity of COVID-19.

Park SY, et al. Infect Chemother. 2024 Aug;56:e35. https://doi.org/10.3947/ic.2024.0038

Cheong HS, et al. J Korean Med Sci. 2022 Feb;37(6):e49. https://doi.org/10.3346/jkms.2022.37.e49

Significant progress has been made on the molecular biology of the severe fever with thrombopenia virus (SFTSV); however, many parts of the pathophysiological mechanisms of mortality in SFTS remain unclear. In this study, we investigated virologic and immunologic factors for fatal outcomes of patients with SFTS. We prospectively enrolled SFTS patients admitted from July 2015 to October 2020. Plasma samples were subjected to SFTSV RNA RT-PCR, multiplex microbead immunoassay for 17 cytokines, and IFA assay. A total of 44 SFTS patients were enrolled, including 37 (84.1%) survivors and 7 (15.9%) non-survivors. Non-survivors had a 2.5 times higher plasma SFTSV load than survivors at admission (p < 0.001), and the viral load in non-survivors increased progressively during hospitalization. In addition, non-survivors did not develop adequate anti-SFTSV IgG, whereas survivors exhibited anti-SFTSV IgG during hospitalization. IFN-α, IL-10, IP-10, IFN-γ, IL-6, IL-8, MCP-1, MIP-1α, and G-CSF were significantly elevated in non-survivors compared to survivors and did not revert to normal ranges during hospitalization (p < 0.05). Severe signs of inflammation such as a high plasma concentration of IFN-α, IL-10, IP-10, IFN-γ, IL-6, IL-8, MCP-1, MIP-1α, and G-CSF, poor viral control, and inadequate antibody response during the disease course were associated with mortality in SFTS patients.

Background: The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection. Objective: In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed. Methods: The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients’ admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model’s predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning–based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic. Results: The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance. Conclusions: The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Jeong YS, et al. Infect Chemother. 2021 Mar;53(1):53-62. https://doi.org/10.3947/ic.2020.0104