Advancements in organs-on-chips technology for viral disease and anti-viral research

Disease models that can accurately recapitulate human pathophysiology during infection and clinical response to antiviral therapeutics are still lacking, which represents a major barrier in drug development. The emergence of human Organs-on-a-Chip that integrated microfluidics with three-dimensional (3D) cell culture, may become the potential solution for this urgent need. Human Organs-on-a-Chip aims to recapitulate human pathophysiology by incorporating tissue-relevant cell types and their microenvironment, such as dynamic fluid flow, mechanical cues, tissue–tissue interfaces, and immune cells to increase the predictive validity of in vitro experimental models. Human Organs-on-a-Chip has a broad range of potential applications in basic biomedical research, preclinical drug development, and personalized medicine. This review focuses on its use in the fields of virology and infectious diseases. We reviewed various types of human Organs-on-a-Chip-based viral infection models and their application in studying viral life cycle, pathogenesis, virus-host interaction, and drug responses to virus- and host-targeted therapies. We conclude by proposing challenges and future research avenues for leveraging this promising technology to prepare for future pandemics.

Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning.

Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention.

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

BACKGROUND: Most evidence regarding the risk factors for early in-hospital mortality in patients with severe COVID-19 focused on laboratory data at the time of hospital admission without adequate adjustment for confounding variables. A multicenter, age-matched, case-control study was therefore designed to explore the dynamic changes in laboratory parameters during the first 10 days after admission and identify early risk indicators for in-hospital mortality in this patient cohort. METHODS: Demographics and clinical data were extracted from the medical records of 93 pairs of patients who had been admitted to hospital with severe COVID-19. These patients had either been discharged or were deceased by March 3, 2020. Data from days 1, 4, 7, and 10 of hospital admission were compared between survivors and non-survivors. Univariate and multivariate conditional logistic regression analyses were employed to identify early risk indicators of in-hospital death in this cohort. RESULTS: On admission, in-hospital mortality was associated with five risk indicators (ORs in descending order): aspartate aminotransferase (AST, >32 U/L) 43.20 (95% CI: 2.63, 710.04); C-reactive protein (CRP) greater than 100 mg/L 13.61 (1.78, 103.941); lymphocyte count lower than 0.6×109/L 9.95 (1.30, 76.42); oxygen index (OI) less than 200 8.23 (1.04, 65.15); and D-dimer over 1 mg/L 8.16 (1.23, 54.34). Sharp increases in D-dimer at day 4, accompanied by decreasing lymphocyte counts, deteriorating OI, and persistent remarkably high CRP concentration were observed among non-survivors during the early stages of hospital admission. CONCLUSIONS: The potential risk factors of high D-dimer, CRP, AST, low lymphocyte count and OI could help clinicians identify patients at high risk of death early in the hospital admission. This might assist with rationalization of health care resources.