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Life Science

Drug Development for NASH with Fibrosis: Expedited Programs

George Makar spoke at the Global NASH Congress. He gave a regulatory perspective on Drug Development for NASH with Fibrosis. The views were his own. In part one, we explore expedited programs.

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Gut dysbiosis in clinical Huntington’s Disease

Speaking at the recent Microbiome R&D and Business Collaboration Forum, Anthony Hannan explored research using mouse models for brain disorders, particularly in Huntington’s disease (HD), schizophrenia, depression, and anxiety disorders. He also examined the role of gene-environment interactions involving mental activity, physical activity, stressors, and diet.

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Circulating biomarkers in melanoma immunotherapy

Genevieve Boland, speaking at the Research & Technology Series, described the translational research conducted at her laboratory. Using tumour and blood samples from patients before treatment, at meaningful clinical changes, progression, and post-mortem she, and her team are trying to understand the biology of Melanoma to treat patients better.

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Guidelines for Validating Whole Slide Imaging for Diagnostic Purposes

Andrew Evans, speaking at the Digital Pathology & AI Congress USA, described new guidelines he helped to draft for validating whole-slide-imaging for diagnostic purposes. First published in 2013 the guidelines were designed to address the fundamental question, “what needs to be done to validate a whole slide imaging for diagnostic use?”. The review producing the new guidelines was published in May 2021.

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Using the abYsis database for drug discovery

Professor Andrew Martin, UCL, speaking at the 4th Global Pharma R&D AI, Data Science and Informatics Summit, described using the abYsis database and workbench for drug discovery. He showed how it is possible to explore an annotate antibody sequence and structure, including comparisons with observed residue distributions. The database can also aid with humanization by making sequences more human and library design.

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Training AI to predict outcomes for cancer patients

Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of the final keynote speech to the online 7th Digital Pathology and AI Congress: Europe, by Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institutet.

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Multiplex Super-Selective PCR Assays for the Detection and Quantitation of Rare Somatic Mutations

Professor Fred Kramer spoke at the recent Research & Technology Series exploring Flow Cytometry / qPCR & Digital PCR / Liquid Biopsies. During his presentation, he explained how Super Selective primers enable the simultaneous identification and quantitation of rare somatic mutations in routine multiplex PCR assays, while virtually eliminating signals from abundant closely related wild-type sequences.

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AI use in clinical diagnosis

Deep learning tool predicts tumour expression from whole slide images

A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by French-American start-up Owkin, the detail of how the company’s HE2RNA model provides virtual spatialization of gene expression was detailed to online delegates by senior translational scientist Alberto Romagnoni who highlighted its use in clinical diagnosis. During his presentation, delegates heard how Owkin has collaborated with doctors, hospitals and academic institutions to develop the tool.

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