Cells communicate with each other through secreting and releasing proteins and vesicles. Many cells can migrate. In this study, we report the discovery of migracytosis, a cell migration-dependent mechanism for releasing cellular contents, and migrasomes, the vesicular structures that mediate migracytosis. As migrating cells move, they leave long tubular strands, called retraction fibers, behind them. Large vesicles, which contain numerous smaller vesicles, grow on the tips and intersections of retraction fibers. These fibers, which connect the vesicles with the main cell body, eventually break, and the vesicles are released into the extracellular space or directly taken up by surrounding cells. Since the formation of these vesicles is migration-dependent, we named them “migrasomes”. We also found that cytosolic contents can be transported into migrasomes and released from the cell through migrasomes. We named this migration-dependent release mechanism “migracytosis”.

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Key Points Question What is the pooled evidence from high-quality randomized clinical trials regarding the safety and potential benefit of convalescent plasma to treat hospitalized patients with COVID-19? Findings In this meta-analysis of 8 randomized clinical trials enrolling 2341 participants, individual patient data were monitored in real time and analyzed using a robust bayesian framework and advanced statistical modeling. No association of convalescent plasma with clinical outcomes was found. Meaning These findings suggest that real-time individual patient data pooling and meta-analysis during a pandemic are feasible, offering a model for future research and providing a rich data resource.

Key Points Question What patient characteristics are associated with benefit from treatment with COVID-19 convalescent plasma (CCP)? Findings This prognostic study of 2287 patients hospitalized with COVID-19 identified a combination of baseline characteristics that predict a gradation of benefit from CCP compared with treatment without CCP. Preexisting health conditions (diabetes, cardiovascular and pulmonary diseases), blood type A or AB, and earlier stage of COVID-19 were associated with a larger treatment benefit. Meaning These findings suggest that simple patient information collected at hospitalization can be used to guide CCP treatment decisions for patients with COVID-19.

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

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