Formal executable descriptions of biological systems

The similarities between systems of living entities and systems of concurrent processes may support biological experiments in silico. Process calculi offer a formal framework to describe biological systems, as well as to analyse their behaviour, both from a qualitative and a quantitative point of view. A couple of little examples help us in showing how this can be done. We mainly focus our attention on the qualitative and quantitative aspects of the considered biological systems, and briefly illustrate which kinds of analysis are possible. We use a known stochastic calculus for the first example. We then present some statistics collected by repeatedly running the specification, that turn out to agree with those obtained by experiments in vivo. Our second example motivates a richer calculus. Its stochastic extension requires a non trivial machinery to faithfully reflect the real dynamic behaviour of biological systems

The Yin and Yang of the Bone Marrow Microenvironment: Pros and Cons of Mesenchymal Stromal Cells in Acute Myeloid Leukemia

Mesenchymal stromal cells (MSCs) have, for a long time, been recognized as pivotal contributors in the set up and maintenance of the hematopoietic stem cell (HSC) niche, as well as in the development and differentiation of the lympho-hematopoietic system. MSCs also have a unique immunomodulatory capacity, which makes them able to affect, both in vitro and in vivo, the function of immune cells. These features, namely the facilitation of stem cell engraftment and the inhibition of lymphocyte responses, have both proven essential for successful allogeneic stem cell transplantation (allo-SCT), which remains the only curative option for several hematologic malignancies. For example, in steroid-refractory acute graft-vs. host disease developing after allo-SCT, MSCs have produced significant results and are now considered a treatment option. However, more recently, the other side of the MSC coin has been unveiled, because of their emerging role in creating a protective and immune-tolerant microenvironment able to support the survival of leukemic cells and affect the response to therapies. In this light, it has been proposed that the failure of current treatments to efficiently override the stroma-mediated protection of leukemic cells accounts for the high rate of relapse in acute myeloid leukemia, at least in part. In this review, we will focus on emerging microenvironment-driven mechanisms conferring a survival advantage to leukemic cells overt physiological HSCs. This body of evidence increasingly highlights the opportunity to consider tumor-microenvironment interactions when designing new therapeutic strategies

Chemotherapy-Induced Tumor Cell Death at the Crossroads Between Immunogenicity and Immunotolerance: Focus on Acute Myeloid Leukemia

In solid tumors and hematological malignancies, including acute myeloid leukemia, some chemotherapeutic agents, such as anthracyclines, have proven to activate an immune response via dendritic cell-based cross-priming of anti-tumor T lymphocytes. This process, known as immunogenic cell death, is characterized by a variety of tumor cell modifications, i.e., cell surface translocation of calreticulin, extracellular release of adenosine triphosphate and pro-inflammatory factors, such as high mobility group box 1 proteins. However, in addition to with immunogenic cell death, chemotherapy is known to induce inflammatory modifications within the tumor microenvironment, which may also elicit immunosuppressive pathways. In particular, DCs may be driven to acquire tolerogenic features, such as the overexpression of indoleamine 2,3-dioxygensase 1, which may ultimately hamper anti-tumor T-cells via the induction of T regulatory cells. The aim of this review is to summarize the current knowledge about the mechanisms and effects by which chemotherapy results in both activation and suppression of anti-tumor immune response. Indeed, a better understanding of the whole process underlying chemotherapy-induced alterations of the immunological tumor microenvironment has important clinical implications to fully exploit the immunogenic potential of anti-leukemia agents and tune their application

The tissue inhibitor of metalloproteinases-1 (TIMP-1) promotes survival and migration of acute myeloid leukemia cells through CD63/PI3K/Akt/p21 signaling

We and others have shown that the Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), a member of the inflammatory network exerting pleiotropic effects in the bone marrow (BM) microenvironment, regulates the survival and proliferation of different cell types, including normal hematopoietic progenitor cells. Moreover, TIMP-1 has been shown to be involved in cancer progression. However, its role in leukemic microenvironment has not been addressed. Here, we investigated the activity of TIMP-1 on Acute Myelogenous Leukemia (AML) cell functions. First, we found that TIMP-1 levels were increased in the BM plasma of AML patients at diagnosis. In vitro, recombinant human (rh)TIMP-1 promoted the survival and cell cycle S-phase entry of AML cells. These kinetic effects were related to the downregulation of cyclindependent kinase inhibitor p21. rhTIMP-1 increases CXCL12-driven migration of leukemic cells through PI3K signaling. Interestingly, activation of CD63 receptor was required for TIMP-1's cytokine/chemokine activity. Of note, rhTIMP-1 stimulation modulated mRNA expression of Hypoxia Inducible Factor (HIF)-1a, downstream of PI3K/Akt activation. We then co-cultured AML cells with normal or leukemic mesenchymal stromal cells (MSCs) to investigate the interaction of TIMP-1 with cellular component(s) of BM microenvironment. Our results showed that the proliferation and migration of leukemic cells were greatly enhanced by rhTIMP-1 in presence of AML-MSCs as compared to normal MSCs. Thus, we demonstrated that TIMP-1 modulates leukemic blasts survival, migration and function via CD63/PI3K/ Akt/p21 signaling. As a "bad actor" in a "bad soil", we propose TIMP-1 as a potential novel therapeutic target in leukemic BM microenvironment

Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis

Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading

World Press Photo 2012: the discursive construction of the Arab Spring

O presente artigo pretende analisar a forma como o fenómeno da Primavera Árabe foi retratado nas fotografias vencedoras do concurso World Press Photo em 2012. As fotografias jornalísticas, embora se apresentem como índices do real, condicionam frequentemente a percepção dos indivíduos e influenciam as suas práticas sociais. Também as fotografias vencedoras do World Press Photo, um dos concursos mais prestigiados de fotojornalismo, em 2012 não fogem ao construtivismo discursivo que molda a representação e a percepção dos acontecimentos. A partir da análise crítica do discurso, nomeadamente dos instrumentos teóricos da semiótica barthesiana e da semiótica social de Gunther Kress e Theo van Leeuwen, procurámos interpretar as 38 fotografias vencedoras do World Press Photo 2012, a fim de reflectirmos sobre o modo como contribuíram para a compreensão da Revolta Árabe e como despertaram o nosso interesse e a nossa imaginação para o desenrolar do conflito.ABSTRACT:This article aims to analyze how the Arab Spring phenomenon was represented in the award-winning photographs of the World Press Photo contest in 2012. Although news photographs are usually seen as indexes of the real, they often limit the perception of individuals and influence their social practices. Also the winning photographs in 2012 of the World Press Photo, one of the most prestigious photojournalism contests, do not escape the discursive constructivism that shapes the representation and perception of events. Drawing from critical discourse analysis, namely from the theoretical tools of barthesian semiotics and Gunther Kress’s and Theo van Leeuwen’s social semiotics, we sought to interpret the 38 award-winning photographs of the World Press Photo contest in 2012, in order to reflect on the way they have contributed to our understanding of the Arab Revolt and aroused our interest and imagination to the unfolding of the conflict.info:eu-repo/semantics/publishedVersio

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Featured Application The present study demonstrates that semi-automatic segmentation enables the identification of regions of interest affected by SARS-CoV-2 infection for the extraction of prognostic features from chest CT scans without suffering from the inter-operator variability typical of segmentation, hence offering a valuable and informative second opinion. Machine Learning methods allow identification of the prognostic features potentially reusable for the early detection and management of other similar diseases. (1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation

The KLEVER Survey: spatially resolved metallicity maps and gradients in a sample of 1.2 < z < 2.5 lensed galaxies

We present near-infrared observations of 42 gravitationally lensed galaxies obtained in the framework of the KMOS Lensed Emission Lines and VElocity Review (KLEVER) Survey, a programme aimed at investigating the spatially resolved properties of the ionized gas in 1.2 3σ) ‘inverted’ gradients are also found, showing an anticorrelation between metallicity and star formation rate density on local scales, possibly suggesting recent episodes of pristine gas accretion or strong radial flows in place. Nevertheless, the individual metallicity maps are characterized by a variety of different morphologies, with flat radial gradients sometimes hiding non-axisymmetric variations on kpc scales, which are washed out by azimuthal averages, especially in interacting systems or in those undergoing local episodes of recent star formation

Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study

Objective: The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19. Methods: Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72&nbsp;h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to β-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy. Results: Overall, 1733 patients were analyzed: 61.4% males, median age 69&nbsp;years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm3, PCT ≥ 0.2&nbsp;ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage. Conclusions: Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use