Ryan Wallis

Jan 14, 2025 | genomemedicine.biomedcentral.com | Bethany Hughes |Andrew Davis |Deborah Milligan |Ryan Wallis |Federica Mossa |Michael Philpott | +3 more

ReferencesHernandez-Segura A, Nehme J, Demaria M. Hallmarks of cellular senescence. Trends Cell Biol. 2018;28:436–53. Article  PubMed  Google Scholar  González-Gualda E, Baker AG, Fruk L, Muñoz-Espín D. A guide to assessing cellular senescence in vitro and in vivo. FEBS J. 2021;288:56–80. Article  PubMed  Google Scholar  Huang W, Hickson LJ, Eirin A, Kirkland JL, Lerman LO. Cellular senescence: the good, the bad and the unknown. Nat Rev Nephrol. 2022;18:611–27.

Sep 24, 2024 | darlingmagazine.co.uk | Ryan Wallis

Just a few decades ago, women were often underrepresented in traditional business areas. Today, the tide has turned. Online business offers a flexible and scalable platform where women can successfully build a career without having to overcome the challenges of the traditional world of work, such as rigid working hours or work-life balance.

Sep 13, 2024 | nature.com | Ryan Wallis |Federica Mossa

AbstractSenescence is an anti-tumour mechanism and hallmark of cancer. Loss or mutation of key senescence effectors, such as p16INK4A, are frequently observed in cancer. Intriguingly, some human tumours are both proliferative and senescent-marker positive (Sen-Mark+). Here, we explore this paradox, focusing on the prognostic consequences and the current challenges in classifying these cells.

Apr 14, 2024 | newsbreak.com | Ryan Wallis

Welcome to NewsBreak, an open platform where diverse perspectives converge. Most of our content comes from established publications and journalists, as well as from our extensive network of tens of thousands of creators who contribute to our platform. We empower individuals to share insightful viewpoints through short posts and comments.

Oct 23, 2023 | biorxiv.org | Bethany Hughes |Andrew Davis |Ryan Wallis |Deborah Milligan

AbstractSenescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required for senescent cell identification. However, emerging scRNA-seq datasets have enabled increased understanding of the heterogeneity of senescence. Here we present SenPred, a machine-learning pipeline which can identify senescence based on single-cell transcriptomics.