Crystallization of calcite from differently concentrated calcium chloride solutions by the CO2 gas diffusion technique in the presence of polystyrene sulfonate yields crystal superstructures with unusual morphology. From the typical calcite rhombohedra as a starting situation, the morphology can be systematically varied via rounded edges and truncated triangles to finally concavely bended lens-like superstructures. Although these "crystals" are apparently well-faceted in light microscopy, electron microscopy analysis and BET reveal that the structures are highly porous and are composed of almost perfectly 3D-aligned calcite nanocrystals scaffolded to the final, partly nicely curved superstructures. At high supersaturations, superstructures with changed symmetry indicative of dipolar interaction potentials between the building blocks are found. The present model case also gives evidence for the importance of nonclassical, mesoscopic processes in crystallization in general.
Crystallization of calcite from different concentrated calcium chloride solutions by the CO(2) vapor diffusion technique in the presence of polystyrenesulfonate (PSS) yields defined assemblies of nanoparticles with unusual morphology. From the typical calcite rhombohedra, the morphology can be systematically varied via rounded edges and truncated triangles to concavely bent lens-like shapes. Although these "crystals" are apparently well facetted as observed in light microscopy, electron microscopy analysis and BET isotherms reveal that the structures are highly porous and are composed of almost perfectly three-dimensionally aligned calcite nanocrystals, scaffolded to the final, partly curved structures. The formation of all mesostructures is discussed within the framework of a polymer-mediated structure-formation process, in which the polymer is acting in four different ways. The present model case also provides evidence for the importance of nonclassical mesoscopic processes in polymer-controlled crystallization in general.
To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.
BackgroundPyroptosis is a new programmed cell death discovered in recent years. Pyroptosis plays an important role in various diseases. Nevertheless, there are few bibliometric analysis systematically studies this field. We aimed to visualize the research hotspots and trends of pyroptosis using a bibliometric analysis to help understand the future development of basic and clinical research.MethodsThe articles and reviews regarding pyroptosis were culled from Web of Science Core Collection. Countries, institutions, authors, references and keywords in this field were visually analyzed by using CtieSpace and VOSviewer software.ResultsA total of 2845 articles and reviews were included. The number of articles regarding pyroptosis significantly increased yearly. These publications mainly come from 70 countries led by China and the USA and 418 institutions. We identified 605 authors, among which Thirumaladevi Kanneganti had the most significant number of articles, and Shi JJ was co-cited most often. Frontiers in immunology was the journal with the most studies, and Nature was the most commonly cited journal. After analysis, the most common keywords are nod like receptor family pyrin domain containing 3 inflammasome, apoptosis, cell death, gasdermin D, mechanism, caspase-1, and others are current and developing areas of study.ConclusionResearch on the pyroptosis is flourishing. Cooperation and exchanges between countries and institutions must be strengthened in the future. The related pathway mechanism of pyroptosis, the relationship between pyroptosis and other types of programmed cell deaths as well as the role of pyroptosis in various diseases have been the focus of current research and developmental trends in the future research.
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI ( P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
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