The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of perceived learning outcomes whereas instructor–student dialogue was a determinant of student initiative. University support had no significant relationship with either perceived learning outcomes or student initiative. In terms of learning effectiveness, both perceived learning outcomes and student initiative determined student satisfaction. The results identified that student–student dialogue, course design, and instructor–student dialogue were the key predictive factors of CoOL learning effectiveness, which may determine the ultimate success of CoOL.

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Gastric B-cell lymphoma of mucosa-associated lymphoid tissue type is closely linked to chronic Helicobacter pylori infection. Most clinical and histopathological features of the tumor can be reproduced by prolonged Helicobacter infection of BALB/c mice. In this study, we have addressed the role of antigenic stimulation in the pathogenesis of the lymphoma by experimental infection with Helicobacter felis, followed by antibiotic eradication therapy and subsequent re-infection. Antimicrobial therapy was successful in 75% of mice and led to complete histological but not "molecular" tumor remission. Although lympho-epithelial lesions disappeared and most gastric lymphoid aggregates resolved, transcriptional profiling revealed the long-term mucosal persistence of residual B cells. Experimental re-introduction of Helicobacter led to very rapid recurrence of the lymphomas, which differed from the original lesions by higher proliferative indices and more aggressive behavior. Immunophenotyping of tumor cells revealed massive infiltration of lesions by CD4(+) T cells, which express CD 28, CD 69, and interleukin-4 but not interferon-gamma, suggesting that tumor B-cell proliferation was driven by Th 2-polarized, immunocompetent, and activated T cells. Tumors were also densely colonized by follicular dendritic cells, whose numbers were closely associated with and predictive of treatment outcome.

With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approachthe network analysisto provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.

The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instructors in an unprecedented way. The majority of educational establishments were forced to take their courses online within a very short period of time, and both instructors and students had to learn to navigate the digital array of courses without much training. Our study examined factors that affect students’ attitude toward online teaching and learning during the COVID-19 pandemic. It is different from other online learning studies where online courses are mostly a method of choice, with suitable support from institutions and expectation from instructors and students, rather than a contingency. Under this specific environment, we utilized an online survey to collect students’ feedback from eleven universities across Hong Kong. Using partial least squares for analysis on the 400 valid samples we received, we found that peer interactions and course design have the most salient impact on students’ attitude, whereas interactions with instructors has no effect at all on students’ attitude. Furthermore, we also provide suggestions on using the existing technologies purchased during COVID-19 for a more sustainable learning environment going forward.