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"Porta, M."
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Cell–cell adhesion and 3D matrix confinement determine jamming transitions in breast cancer invasion
Plasticity of cancer invasion and metastasis depends on the ability of cancer cells to switch between collective and single-cell dissemination, controlled by cadherin-mediated cell–cell junctions. In clinical samples, E-cadherin-expressing and -deficient tumours both invade collectively and metastasize equally, implicating additional mechanisms controlling cell–cell cooperation and individualization. Here, using spatially defined organotypic culture, intravital microscopy of mammary tumours in mice and in silico modelling, we identify cell density regulation by three-dimensional tissue boundaries to physically control collective movement irrespective of the composition and stability of cell–cell junctions. Deregulation of adherens junctions by downregulation of E-cadherin and p120-catenin resulted in a transition from coordinated to uncoordinated collective movement along extracellular boundaries, whereas single-cell escape depended on locally free tissue space. These results indicate that cadherins and extracellular matrix confinement cooperate to determine unjamming transitions and stepwise epithelial fluidization towards, ultimately, cell individualization.Ilina et al. investigate the balance between cell adhesion and matrix density on patterns of collective breast cancer cell invasion using three-dimensional models of the extracellular matrix, in vivo imaging and in silico modelling
Journal Article
Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells
To gain insight into the molecular pathogenesis of the myelodysplastic syndromes (MDS), we performed global gene expression profiling and pathway analysis on the hematopoietic stem cells (HSC) of 183 MDS patients as compared with the HSC of 17 healthy controls. The most significantly deregulated pathways in MDS include interferon signaling, thrombopoietin signaling and the Wnt pathways. Among the most significantly deregulated gene pathways in early MDS are immunodeficiency, apoptosis and chemokine signaling, whereas advanced MDS is characterized by deregulation of DNA damage response and checkpoint pathways. We have identified distinct gene expression profiles and deregulated gene pathways in patients with del(5q), trisomy 8 or −7/del(7q). Patients with trisomy 8 are characterized by deregulation of pathways involved in the immune response, patients with −7/del(7q) by pathways involved in cell survival, whereas patients with del(5q) show deregulation of integrin signaling and cell cycle regulation pathways. This is the first study to determine deregulated gene pathways and ontology groups in the HSC of a large group of MDS patients. The deregulated pathways identified are likely to be critical to the MDS HSC phenotype and give new insights into the molecular pathogenesis of this disorder, thereby providing new targets for therapeutic intervention.
Journal Article
Revisiting Minamata disease through computational phenotypic similarity analysis
Minamata disease, a severe neurological disorder caused by methylmercury exposure in 1950s Japan, is historically recognized for its profound impact on environmental health awareness. However, its phenotypic complexity and potential overlap with other neurological disorders have not been systematically assessed in a modern computational framework. In this study, we adopt a network approach to reinterpret Minamata disease within a broader disease similarity landscape. We mapped clinical symptoms from an extensive epidemiological survey of 269 Minamata patients to standardized Human Phenotype Ontology (HPO) terms, constructing a comprehensive phenotypic profile. Using network-based and computational similarity measures—Jaccard Index, ontology-informed metrics (Resnik and GraphIC), and information retrieval techniques (TF-IDF with query expansion), we compared this profile to over 12,000 diseases. Our results consistently identified strong phenotypic ties between Minamata disease and several movement and neurodegenerative disorders, including cyanide-induced parkinsonism and progressive supranuclear palsy. A weighted rank aggregation across methods revealed a robust consensus network of diseases with overlapping symptomatology, underscoring the systemic nature of these complex neurological disorders. Our study highlights the utility of integrating historical epidemiological data with contemporary network tools to reveal novel associations between environmental exposures and systemic pathophysiological responses. Our findings provide a blueprint for exploring environmentally triggered disease mechanisms and their broader implications for network-based understanding of human disease.
Journal Article
Minimal morphological criteria for defining bone marrow dysplasia: a basis for clinical implementation of WHO classification of myelodysplastic syndromes
The World Health Organization classification of myelodysplastic syndromes (MDS) is based on morphological evaluation of marrow dysplasia. We performed a systematic review of cytological and histological data from 1150 patients with peripheral blood cytopenia. We analyzed the frequency and discriminant power of single morphological abnormalities. A score to define minimal morphological criteria associated to the presence of marrow dysplasia was developed. This score showed high sensitivity/specificity (>90%), acceptable reproducibility and was independently validated. The severity of granulocytic and megakaryocytic dysplasia significantly affected survival. A close association was found between ring sideroblasts and SF3B1 mutations, and between severe granulocytic dysplasia and mutation of
ASXL1
,
RUNX1
,
TP53
and
SRSF2
genes. In myeloid neoplasms with fibrosis, multilineage dysplasia, hypolobulated/multinucleated megakaryocytes and increased CD34+ progenitors in the absence of
JAK2
,
MPL
and
CALR
gene mutations were significantly associated with a myelodysplastic phenotype. In myeloid disorders with marrow hypoplasia, granulocytic and/or megakaryocytic dysplasia, increased CD34+ progenitors and chromosomal abnormalities are consistent with a diagnosis of MDS. The proposed morphological score may be useful to evaluate the presence of dysplasia in cases without a clearly objective myelodysplastic phenotype. The integration of cytological and histological parameters improves the identification of MDS cases among myeloid disorders with fibrosis and hypocellularity.
Journal Article
Therapy-related myelodysplastic syndromes deserve specific diagnostic sub-classification and risk-stratification—an approach to classification of patients with t-MDS
In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both
p
< 0.001). The results regarding carefully reviewed cytogenetic data, classifications, and prognostic scores confirmed that t-MDS are similarly heterogeneous as p-MDS and therefore deserve the same careful differentiation regarding risk. As reference, these results were compared with 4593 primary MDS (p-MDS) patients represented in the International Working Group for Prognosis in MDS database (IWG-PM). Although a less favorable clinical outcome occurred in each t-MDS subset compared with p-MDS subgroups, FAB and WHO-classification, IPSS-R, and WPSS-R separated t-MDS patients into differing risk groups effectively, indicating that all established risk factors for p-MDS maintained relevance in t-MDS, with cytogenetic features having enhanced predictive power. These data strongly argue to classify t-MDS as a separate entity distinct from other WHO-classified t-myeloid neoplasms, which would enhance treatment decisions and facilitate the inclusion of t-MDS patients into clinical studies.
Journal Article
Classification of triple negative breast cancer by epithelial mesenchymal transition and the tumor immune microenvironment
Triple-negative breast cancer (TNBC) accounts for about 15–20% of all breast cancers and differs from other invasive breast cancer types because it grows and spreads rapidly, it has limited treatment options and typically worse prognosis. Since TNBC does not express estrogen or progesterone receptors and little or no human epidermal growth factor receptor (HER2) proteins are present, hormone therapy and drugs targeting HER2 are not helpful, leaving chemotherapy only as the main systemic treatment option. In this context, it would be important to find molecular signatures able to stratify patients into high and low risk groups. This would allow oncologists to suggest the best therapeutic strategy in a personalized way, avoiding unnecessary toxicity and reducing the high costs of treatment. Here we compare two independent patient stratification strategies for TNBC based on gene expression data: The first is focusing on the epithelial mesenchymal transition (EMT) and the second on the tumor immune microenvironment. Our results show that the two stratification strategies are not directly related, suggesting that the aggressiveness of the tumor can be due to a multitude of unrelated factors. In particular, the EMT stratification is able to identify a high-risk population with high immune markers that is, however, not properly classified by the tumor immune microenvironment based strategy.
Journal Article
Harnessing deep learning to forecast local microclimate using global climate data
Microclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located in the Lombardian foothills. The model is exploited to perform a comparative study investigating whether employing the global climate dataset ERA5 as input reduces model’s accuracy in reproducing the microclimate variations compared to using data collected by the Lombardy Regional Environment Protection Agency (ARPA) from a nearby meteorological station. The comparative analysis shows that using local meteorological data as inputs provides more accurate results for microclimate modeling. However, in situations where local data is not available, the use of global climate data remains a viable and reliable approach.
Journal Article