2022-01-28

Ideal Test Time for Coronavirus Disease 2019 Contact Tracing

Background: Epidemiological contact tracing is a powerful tool to rapidly detect SARS-CoV-2 infection in persons with a close contact history with COVID-19-affected patients. However, it remains unclear whom and when should be PCR tested among the close contact subjects. Methods: We retrospectively analyzed 817 close contact subjects, including 144 potentially SARS-CoV-2-infected persons. The patient characteristics and contact type, duration between the date of the close contact and specimen sampling, and PCR test results in PCR positive and negative persons were compared. Results: We found that male gender {adjusted odds ratio 1.747 [95% confidence interval (CI) 1.180–2.608]}, age ≥ 60 [1.749 (95% CI 1.07–2.812)], and household contact [2.14 (95% CI 1.388–3.371)] are independent risk factors for close contact SARS-CoV-2 infection. Symptomatic subjects were predicted 6.179 (95% CI 3.985–9.61) times more likely to be infected compared to asymptomatic ones. We could observe PCR test positivity between days 1 and 17 after close contact. However, no subject could be found with a Ct-value <30, considered less infective, after day 14 of close contact. Conclusions: Based on our results, we suggest that contact tracing should be performed on the high-risk subjects between days 3 and 13 after close contacts.
2022-01-28

Galectin-9 restricts hepatitis B virus replication via p62/SQSTM1-mediated selective autophagy of viral core proteins

Autophagy has been linked to a wide range of functions, including a degradative process that defends host cells against pathogens.
2022-01-13

Improved clearing method contributes to deep imaging of plant organs

Tissue clearing methods are increasingly essential for the microscopic observation of internal tissues of thick biological organs. We previously developed TOMEI, a clearing method for plant tissues; however, it could not entirely remove chlorophylls nor reduce the fluorescent signal of fluorescent proteins.
2021-12-20

Machine learning to estimate the local quality of protein crystal structures

Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps.