Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

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The relationships between viral load, severity of illness, and transmissibility of virus, have been the subject of intense interest since the start of the COVID-19 pandemic. They are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies. In this report we present within-host modelling to examine the viral load dynamics observed in the upper respiratory tract, drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic within-host model to describe viral load dynamics and host response, and also contrasted simpler mixed-effects regression analysis of peak viral load and its subsequent decline. The inclusion of age, sex, or disease severity of the subjects did not appreciably improve the fit of either the mechanistic model or the regression. In future work, this model will be used to connect viral load dynamics to underlying host traits, to better understand these complex interactions.

Despite decades of research and the growing emergence of new treatment modalities, Glioblastoma (GBM) frustratingly remains an incurable brain cancer with largely stagnant 5-year survival outcomes of around 5%. Historically, a significant challenge has been the effective delivery of anti-cancer treatment. This review aims to summarize key innovations in the field of medical devices, developed either to improve the delivery of existing treatments, for example that of chemo-radiotherapy, or provide novel treatments using devices, such as sonodynamic therapy, thermotherapy and electric field therapy. It will highlight current as well as emerging device technologies, non-invasive versus invasive approaches, and by doing so provide a detailed summary of evidence from clinical studies and trials undertaken to date. Potential limitations and current challenges are discussed whilst also highlighting the exciting potential of this developing field. It is hoped that this review will serve as a useful primer for clinicians, scientists, and engineers in the field, united by a shared goal to translate medical device innovations to help improve treatment outcomes for patients with this devastating disease.

Background: The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load. Methods: COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString nCounter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time. Results: Eighty-two subjects (50% female, median age 54 years (range 3-73)) with COVID-19 were recruited. Paired URT viral load samples were available for 16 blood transcriptome samples, and 17 respiratory epithelial transcriptome samples. Natural Killer (NK) cells were the only blood cell type significantly correlated with URT viral load z-scores (r = -0.62, P = 0.010). Twenty-four blood gene expression modules were significantly correlated with URT viral load z-score, the most significant being a module of genes connected around IFNA14 (Interferon Alpha-14) expression (r = -0.60, P = 1e-10). In fixed repertoire analysis, prostanoid-related gene expression was significantly associated with higher viral load. In nasal epithelium, only GNLY (granulysin) gene expression showed significant negative correlation with viral load. Conclusions: Correlations between the transcriptional host response and inter-individual variations in SARS-CoV-2 URT viral load, revealed many molecular mechanisms plausibly favouring or constraining viral load. Existing evidence corroborates many of these mechanisms, including likely roles for NK cells, granulysin, prostanoids and interferon alpha-14. Inhibition of prostanoid production, and administration of interferon alpha-14 may be attractive transmission-blocking interventions.