Optical microscopy predictions of focal recurrence in glioblastoma
Sanjeev J. Herr, Niels Olshausen, Melike Pekmezci, Jasleen Kaur, Youssef Sibih, Vardhaan Sai Ambati, Abraham Dada, Katie Scotford, Amit Persad, Thiébaud Picart, Akhil Kondepudi, Gabrielle Malte, Gabriella Vulakh, Johanna Pechmann, Jessica Makolli, Nancy Ann Oberheim-Bush, Albert H. Kim, Jacob S. Young, Mitchel Stuart Berger, Ammar Mallouhi, Barbara Kiesel, Georg Widhalm, Lisa Irina Wadiura, Madhumita Sushil, Todd Charles Hollon, Shawn L. Hervey‐Jumper
Peer-reviewed journalBold claims, read criticallyReal-world use
In the authors' words
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Main result
We present an artificial intelligence (AI)–based model to predict the recurrence risk of unprocessed surgical tissues at initial resection.
Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.
Mariya Mardamshina, Nicolai Dorka, Marvin Thielert and 11 more — Nature Communications
By optimizing against mass growth time series from 290 ice crystals grown in a levitation diffusion chamber, we identify a modified capacitance growth model that more accurately captures observed early-stage growth.
Kara Diane Lamb, Jerry Y. Harrington, Alfred M. Moyle and 5 more — Science Advances
Here we present StriMap, a unified framework for predicting TCR–peptide–HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces.
Kai Cao, Rui Li, Martin Stražar and 8 more — Nature Communications
Peer-reviewed journalBold claims, read criticallyReal-world use