Influenza vaccine, adjuvanted(Novartis Pharma AG)

Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type messenger RNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an adaptive regularized tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art semi-reference-based and reference-free methods as well as its robustness under challenges to its assumptions. In a real-world application to a dataset from a trivalent influenza vaccine study, our method accurately estimates cellular proportions, as evidenced by the nearly perfect Pearson’s correlation between ARTdeConv estimates and flow cytometry measurements. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners.

Influenza viruses cause 3-5 million severe cases and 300,000-600,000 deaths worldwide. Most of the disease burden is in Low- and Middle-Income Countries (LMICs) owing to factors such as high population density, infrastructure challenges, poor quality healthcare, lack of consistent recommendations, less prioritization of all high-risk groups, and prevalent use of trivalent influenza vaccines. Although influenza vaccines are effective in reducing the annual influenza disease burden, existing vaccines have several limitations. In this narrative review, we address the unmet needs of existing influenza vaccines in LMICs in Africa, Asia Pacific, Latin America and the Middle East and discuss the characteristics of novel vaccines in clinical development. We also describe features of a successful vaccination program that LMICs could emulate to improve their current vaccination coverage and reduce the public health burden of influenza.