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Many contemporaries considered the crusader conquest of Jerusalem in 1099 as a significant moment in Salvation History. This article investigates how the reception of the Roman conquest of the city (70 CE) contributed to such an understanding. The important Benedictine exegete Rupert of Deutz (c. 1070–1129) refers to the Roman conquest in 79 passages within his opus, notably in his various biblical commentaries. This case study shows how the past event provided a rationale, exegetical and providential in nature, to understand three dimensions: (a) the role of the Jews, especially that it had been necessary to deprive them of the Holy Land; (b) the current situation of and purpose of Christians in the Holy Land; and (c) the End of Time, which was expected in Jerusalem, and which Rupert anchored already significantly in his own present. His commentary on John’s Revelation even asserted that the Roman conquest had opened the sixth of seven seals (Rev. 6:12). Therefore, the Apocalypse had been ongoing since 70 CE—but only in the Holy Land, a fact that made it necessary for Christians to travel there. The article thus demonstrates that biblical commentaries are potent sources for both crusade studies and historical research in general.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.A deep residual learning framework identifies microsatellite instability in histology slides from patients with cancer and can be used to guide immunotherapy.

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a \"deep stroma score,\" which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the \"Darmkrebs: Chancen der Verhütung durch Screening\" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

The thymus generates T cells from immature thymocytes and prevents autoimmune diseases through negative selection and the generation of FOXP3 + regulatory T cells (Tregs). The thymic architecture is typically divided into two distinct microenvironments, the cortex and the medulla. These microenvironments are characterized by the presence of cortical thymic epithelial cells (cTECs) and medullary thymic epithelial cells (mTECs), respectively. Recent single-cell and spatial transcriptomic analyses have revealed the expanding diversity of TEC subpopulations in mice and humans. Myasthenia gravis (MG) is an autoimmune disorder characterized by fatigue resulting from muscle weakness, which is caused by antibodies toward structures within the neuromuscular junction. The most common target of pathogenic autoantibodies in MG is the acetylcholine receptor (AChR). MG patients are prone to thymic abnormalities, including thymic follicular hyperplasia and thymoma. Previous studies have suggested that mTECs expressing major histocompatibility complex (MHC)/AChR–peptide complexes are involved in the intrathymic pathogenesis of this MG type. However, the exact mechanisms are unknown. This review provides an update on the diversity of TEC subpopulations and other cellular alterations in the MG thymus. Additionally, we present hypotheses on the pathogenetic pathways leading to MG and suggest potential future directions in thymus research.

The thymus prevents autoimmune diseases through mechanisms that operate in the cortex and medulla, comprising positive and negative selection and the generation of regulatory T-cells (Tregs). Egress from the thymus through the perivascular space (PVS) to the blood is another possible checkpoint, as shown by some autoimmune/immunodeficiency syndromes. In polygenic autoimmune diseases, subtle thymic dysfunctions may compound genetic, hormonal and environmental cues. Here, we cover (a) tolerance-inducing cell types, whether thymic epithelial or tuft cells, or dendritic, B- or thymic myoid cells; (b) tolerance-inducing mechanisms and their failure in relation to thymic anatomic compartments, and with special emphasis on human monogenic and polygenic autoimmune diseases and the related thymic pathologies, if known; (c) polymorphisms and mutations of tolerance-related genes with an impact on positive selection (e.g. the gene encoding the thymoproteasome-specific subunit, PSMB11), promiscuous gene expression (e.g. AIRE, PRKDC, FEZF2, CHD4), Treg development (e.g. SATB1, FOXP3), T-cell migration (e.g. TAGAP) and egress from the thymus (e.g. MTS1, CORO1A); (d) myasthenia gravis as the prototypic outcome of an inflamed or disordered neoplastic ‘sick thymus’.

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.

Incomplete understanding of the metastatic process hinders personalized therapy. Here we report the most comprehensive whole-genome study of colorectal metastases vs. matched primary tumors. 65% of somatic mutations originate from a common progenitor, with 15% being tumor- and 19% metastasis-specific, implicating a higher mutation rate in metastases. Tumor- and metastasis-specific mutations harbor elevated levels of BRCAness. We confirm multistage progression with new components ARHGEF7/ARHGEF33 . Recurrently mutated non-coding elements include ncRNAs RP11-594N15.3, AC010091, SNHG14 , 3’ UTRs of FOXP2, DACH2, TRPM3, XKR4, ANO5, CBL, CBLB , the latter four potentially dual protagonists in metastasis and efferocytosis-/ PD-L1 mediated immunosuppression. Actionable metastasis-specific lesions include FAT1, FGF1, BRCA2, KDR , and AKT2 -, AKT3 -, and PDGFRA -3’ UTRs. Metastasis specific mutations are enriched in PI3K-Akt signaling, cell adhesion, ECM and hepatic stellate activation genes, suggesting genetic programs for site-specific colonization. Our results put forward hypotheses on tumor and metastasis evolution, and evidence for metastasis-specific events relevant for personalized therapy. The evolution and genetic nature of metastatic lesions is not completely characterized. Here the authors perform a comprehensive whole-genome study of colorectal metastases in comparison to matched primary tumors and define a multistage progression model and metastasis-specific changes that, in part, are therapeutically actionable.

The analysis of ultrafast electron diffraction (UED) data from low-symmetry single crystals of small molecules is often challenged by the difficulty of assigning unique Laue indices to the observed Bragg reflections. For a variety of technical and physical reasons, UED diffraction images are typically of lower quality when viewed from the perspective of structure determination by single-crystal x-ray or electron diffraction. Nevertheless, time series of UED images can provide valuable insight into structural dynamics, providing that an adequate interpretation of the diffraction patterns can be achieved. Garfield is a collection of tools with a graphical user interface designed to facilitate the interpretation of diffraction patterns and to index Bragg reflections in challenging cases where other indexing tools are ineffective. To this end, Garfield enables the user to interactively create, explore, and optimize sets of parameters that define the diffraction geometry and characteristic properties of the sample.

Myasthenia gravis (MG), Lambert-Eaton myasthenic syndrome (LEMS), and congenital myasthenic syndromes (CMS) represent an etiologically heterogeneous group of (very) rare chronic diseases. MG and LEMS have an autoimmune-mediated etiology, while CMS are genetic disorders. A (strain dependent) muscle weakness due to neuromuscular transmission disorder is a common feature. Generalized MG requires increasingly differentiated therapeutic strategies that consider the enormous therapeutic developments of recent years. To include the newest therapy recommendations, a comprehensive update of the available German-language guideline ‘Diagnostics and therapy of myasthenic syndromes’ has been published by the German Neurological society with the aid of an interdisciplinary expert panel. This paper is an adapted translation of the updated and partly newly developed treatment guideline. It defines the rapid achievement of complete disease control in myasthenic patients as a central treatment goal. The use of standard therapies, as well as modern immunotherapeutics, is subject to a staged regimen that takes into account autoantibody status and disease activity. With the advent of modern, fast-acting immunomodulators, disease activity assessment has become pivotal and requires evaluation of the clinical course, including severity and required therapies. Applying MG-specific scores and classifications such as Myasthenia Gravis Activities of Daily Living, Quantitative Myasthenia Gravis, and Myasthenia Gravis Foundation of America allows differentiation between mild/moderate and (highly) active (including refractory) disease. Therapy decisions must consider age, thymic pathology, antibody status, and disease activity. Glucocorticosteroids and the classical immunosuppressants (primarily azathioprine) are the basic immunotherapeutics to treat mild/moderate to (highly) active generalized MG/young MG and ocular MG. Thymectomy is indicated as a treatment for thymoma-associated MG and generalized MG with acetylcholine receptor antibody (AChR-Ab)-positive status. In (highly) active generalized MG, complement inhibitors (currently eculizumab and ravulizumab) or neonatal Fc receptor modulators (currently efgartigimod) are recommended for AChR-Ab-positive status and rituximab for muscle-specific receptor tyrosine kinase (MuSK)-Ab-positive status. Specific treatment for myasthenic crises requires plasmapheresis, immunoadsorption, or IVIG. Specific aspects of ocular, juvenile, and congenital myasthenia are highlighted. The guideline will be further developed based on new study results for other immunomodulators and biomarkers that aid the accurate measurement of disease activity.