Publications by authors named "Manfred Claassen"

Background: Registered systemic treatment options for glioblastoma patients are limited. The phase II REGOMA trial suggested an improvement of median overall survival in progressive glioblastoma by the multi-tyrosine kinase inhibitor regorafenib. This has not been confirmed by GBM AGILE.

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Background: Atypical teratoid rhabdoid tumors (ATRT) are incurable high-grade pediatric brain tumors. Despite intensive research efforts, the prognosis for ATRT patients under currently established treatment protocols is poor. While novel therapeutic strategies are urgently needed, the generation of molecular-driven treatment concepts is a challenge mainly due to the absence of actionable genetic alterations.

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  • * This study investigates how different stakeholders view and experience AI in medicine, aiming to identify key AI-related topics and necessary competencies for medical students.
  • * Interviews with 38 participants revealed six main categories of insights, emphasizing the importance of curriculum contents, programming skills, and a standardized approach to teaching AI in medical education.
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  • - This study introduces a new high-dimensional technique to analyze human γδ T cell subsets in their natural tissue settings, overcoming previous challenges related to antibody availability and technology limitations.
  • - Researchers utilized a combination of multiplexed imaging and bioinformatics to identify and characterize γδ T cells in colon and colorectal cancer samples, uncovering various microenvironments and their interactions with both immune and cancer cells.
  • - Although this initial study shows the promise of this advanced technology for exploring T cell diversity and microenvironments, further research is needed to connect specific T cell characteristics and microenvironment features with important clinical factors.
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Introduction: Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%.

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  • Liver metastases often resist immune checkpoint inhibitor (ICI) therapy, and regulatory T cells (Tregs) in the liver may contribute to this resistance, but their specific role is not well understood.* -
  • The study utilized flow cytometry and RNA sequencing to analyze the behavior of hepatic Tregs, revealing a unique population that grows significantly in response to ICI treatment, regardless of tumor presence.* -
  • The findings suggest that a specific subpopulation of CD29 Tregs in the liver is crucial for the enhanced suppression and resistance to ICI therapy, indicating their potential as targets for improving treatment outcomes.*
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The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer.

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Mucosal-associated invariant T (MAIT) cells represent an abundant innate-like T cell subtype in the human liver. MAIT cells are assigned crucial roles in regulating immunity and inflammation, yet their role in liver cancer remains elusive. Here, we present a MAIT cell-centered profiling of hepatocellular carcinoma (HCC) using scRNA-seq, flow cytometry, and co-detection by indexing (CODEX) imaging of paired patient samples.

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  • Deep learning (DL) has significantly advanced oncology by enabling new discoveries and predictions using diverse biological data types like genomics, proteomics, and imaging.
  • DL algorithms can analyze complex data to predict the effects of genetic variants and understand protein structures, alongside offering insights through spatial omics technologies.
  • The review emphasizes the superiority of highly multiplexed images over traditional histopathological methods, highlighting how DL can support clinical practices in cancer diagnosis, prognosis, and treatment decisions.
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Background: The need for reliable clinical biomarkers to predict which patients with melanoma will benefit from immune checkpoint blockade (ICB) remains unmet. Several different parameters have been considered in the past, including routine differential blood counts, T cell subset distribution patterns and quantification of peripheral myeloid-derived suppressor cells (MDSC), but none has yet achieved sufficient accuracy for clinical utility.

Methods: Here, we investigated potential cellular biomarkers from clinical routine blood counts as well as several myeloid and T cell subsets, using flow cytometry, in two independent cohorts of a total of 141 patients with stage IV M1c melanoma before and during ICB.

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Background & Aims: The progression of non-alcoholic steatohepatitis (NASH) to fibrosis and hepatocellular carcinoma (HCC) is aggravated by auto-aggressive T cells. The gut-liver axis contributes to NASH, but the mechanisms involved and the consequences for NASH-induced fibrosis and liver cancer remain unknown. We investigated the role of gastrointestinal B cells in the development of NASH, fibrosis and NASH-induced HCC.

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Cellular decision making often builds on ultrasensitive MAPK pathways. The phosphorylation mechanism of MAP kinase has so far been described as either distributive or processive, with distributive mechanisms generating ultrasensitivity in theoretical analyses. However, the in vivo mechanism of MAP kinase phosphorylation and its activation dynamics remain unclear.

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Adult-born cells, arriving daily into the rodent olfactory bulb, either integrate into the neural circuitry or get eliminated. However, whether these two populations differ in their morphological or functional properties remains unclear. Using longitudinal in vivo two-photon imaging, we monitored dendritic morphogenesis, odor-evoked responsiveness, ongoing Ca signaling, and survival/death of adult-born juxtaglomerular neurons (abJGNs).

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Healing wounds and cancers present remarkable cellular and molecular parallels, but the specific roles of the healing phases are largely unknown. We developed a bioinformatics pipeline to identify genes and pathways that define distinct phases across the time-course of healing. Their comparison to cancer transcriptomes revealed that a resolution phase wound signature is associated with increased severity in skin cancer and enriches for extracellular matrix-related pathways.

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We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study.

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Dimethyl fumarate (DMF) is an immunomodulatory treatment for multiple sclerosis (MS). Despite its wide clinical use, the mechanisms underlying clinical response are not understood. This study aimed to reveal immune markers of therapeutic response to DMF treatment in MS.

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Immune checkpoint blockade (ICB) is standard-of-care for patients with metastatic melanoma. It may re-invigorate T cells recognizing tumors, and several tumor antigens have been identified as potential targets. However, little is known about the dynamics of tumor antigen-specific T cells in the circulation, which might provide valuable information on ICB responses in a minimally invasive manner.

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Motivation: Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.

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Motivation: Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series.

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Background: Comorbidities are risk factors for development of severe coronavirus disease 2019 (COVID-19). However, the extent to which an underlying comorbidity influences the immune response to severe acute respiratory syndrome coronavirus 2 remains unknown.

Objective: Our aim was to investigate the complex interrelations of comorbidities, the immune response, and patient outcome in COVID-19.

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Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, , for complex models such as convolutional neural networks.

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The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics.

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Ample evidence pinpoints the phenotypic diversity of blood vessels (BVs) and site-specific functions of their lining endothelial cells (ECs). We harnessed single-cell RNA sequencing (scRNA-seq) to dissect the molecular heterogeneity of blood vascular endothelial cells (BECs) in healthy adult human skin and identified six different subpopulations, signifying arterioles, post-arterial capillaries, pre-venular capillaries, post-capillary venules, venules and collecting venules. Individual BEC subtypes exhibited distinctive transcriptomic landscapes associated with diverse biological pathways.

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Treatment with dimethyl fumarate (DMF) leads to lymphopenia and infectious complications in a subset of patients with multiple sclerosis (MS). Here, we aimed to reveal immune markers of DMF-associated lymphopenia. This prospective observational study longitudinally assessed 31 individuals with MS by single-cell mass cytometry before and after 12 and 48 weeks of DMF therapy.

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