On listening to the dreams of children affected by serious physical illness

Authors would like to draw attention to the dreaming of children affected by serious physical illness, and the consequences that the listening skills of the caregivers (parents, nurses, doctors, teachers etc.) may have. How will an adult, too intensely distressed over the death of a child, be able to approach a child who has a fear of death and be able to offer a space in which to listen and share? Adults can connect with children, their dreams, as well as their drawings and plays, only if they have been able to perceive, tolerate and contain their own emotions, including their personal sufferings, but they must also be able to reconcile the realm of reality with that of their own creativity. This is because the identity of one (the suffering child) can only exist in a more integrated way if in the presence of another person: if I cannot speak to you of my pains and of my fears of dying, I will not exist for you as a living and suffering child

Semeiotica e diagnosi psico(pato)logica

With a subjective experience of pain, the patient is at the heart of the medical profession. Even in a psychiatric intervention, the meeting between clinician and patient, mutual observations and interaction reveal details about a certain problem, which - in this context - becomes configured and embodied. Herein, to the authors present arguments on how psychiatric semeiotics must be based on adequately articulated epistemological awarenes

The similarity of inherited diseases (I): clinical similarity within the phenotypic series.

peer reviewed[en] BACKGROUND: Mutations of different genes often result in clinically similar diseases. Among the datasets of similar diseases, we analyzed the 'phenotypic series' from Online Mendelian Inheritance in Man and examined the similarity of the diseases that belong to the same phenotypic series, because we hypothesize that clinical similarity may unveil shared pathogenic mechanisms. METHODS: Specifically, for each pair of diseases, we quantified their similarity, based on both number and information content of the shared clinical phenotypes. Then, we assembled the disease similarity network, in which nodes represent diseases and edges represent clinical similarities. RESULTS: On average, diseases have high similarity with other diseases of their own phenotypic series, even though about one third of diseases have their maximal similarity with a disease of another series. Consequently, the network is assortative (i.e., diseases belonging to the same series link preferentially to each other), but the series differ in the way they distribute within the network. Specifically, heterophobic series, which minimize links to other series, form islands at the periphery of the network, whereas heterophilic series, which are highly inter-connected with other series, occupy the center of the network. CONCLUSIONS: The finding that the phenotypic series display not only internal similarity (assortativity) but also varying degrees of external similarity (ranging from heterophobicity to heterophilicity) calls for investigation of biological mechanisms that might be shared among different series. The correlation between the clinical and biological similarities of the phenotypic series is analyzed in Part II of this study1

Quantitative analysis of proteins which are members of the same protein complex but cause locus heterogeneity in disease.

peer reviewedIt is still largely unknown how mutations in different genes cause similar diseases - a condition known as locus heterogeneity. A likely explanation is that the different proteins encoded by the locus heterogeneity genes participate in the same biological function and, specifically, that they belong to the same protein complex. Here we report that, in up to 30% of the instances of locus heterogeneity, the disease-causing proteins are indeed members of the same protein complex. Moreover, we observed that, in many instances, the diseases and protein complexes only partially intersect. Among the possible explanations, we surmised that some genes that encode proteins in the complex have not yet been reported as causing disease and are therefore candidate disease genes. Mutations of known human disease genes and murine orthologs of candidate disease genes that encode proteins in the same protein complex do in fact often cause similar phenotypes in humans and mice. Furthermore, we found that the disease-complex intersection is not only incomplete but also non-univocal, with many examples of one disease intersecting more than one protein complex or one protein complex intersecting more than one disease. These limits notwithstanding, this study shows that action on proteins in the same complex is a widespread pathogenic mechanism underlying numerous instances of locus heterogeneity

The similarity of inherited diseases (II): clinical and biological similarity between the phenotypic series.

peer reviewed[en] BACKGROUND: Despite being caused by mutations in different genes, diseases in the same phenotypic series are clinically similar, as reported in Part I of this study. Here, in Part II, we hypothesized that the phenotypic series too might be clinically similar. Furthermore, on the assumption that gene mutations indirectly cause clinical phenotypes by directly affecting biological functions, we hypothesized that clinically similar phenotypic series might be biologically similar as well. METHODS: To test these hypotheses, we generated a clinical similarity network and a set of biological similarity networks. In both types of network, the nodes represent the phenotypic series, and the edges linking the nodes indicate the similarity of the linked phenotypic series. The weight of each edge is proportional to a similarity coefficient, which depends on the clinical phenotypes and the biological features that are shared by the linked phenotypic series, in the clinical and biological similarity networks, respectively. RESULTS: After assembling and analyzing the networks, we raised the threshold for the similarity coefficient, to retain edges of progressively greater weight. This way all the networks were gradually split into fragments, composed of phenotypic series with increasingly greater degrees of similarity. Finally, by comparing the fragments from the two types of network, we defined subsets of phenotypic series with varying types and degrees of clinical and biological correlation. CONCLUSIONS: Like the individual diseases, the phenotypic series too are clinically and biologically similar to each other. Furthermore, our findings unveil different modalities of correlation between the clinical manifestations and the biological features of the inherited diseases

Can Antiviral Activity of Licorice Help Fight COVID-19 Infection?

peer reviewedThe phytotherapeutic properties of Glycyrrhiza glabra (licorice) extract are mainly attributed to glycyrrhizin (GR) and glycyrrhetinic acid (GA). Among their possible pharmacological actions, the ability to act against viruses belonging to different families, including SARS coronavirus, is particularly important. With the COVID-19 emergency and the urgent need for compounds to counteract the pandemic, the antiviral properties of GR and GA, as pure substances or as components of licorice extract, attracted attention in the last year and supported the launch of two clinical trials. In silico docking studies reported that GR and GA may directly interact with the key players in viral internalization and replication such as angiotensin-converting enzyme 2 (ACE2), spike protein, the host transmembrane serine protease 2, and 3-chymotrypsin-like cysteine protease. In vitro data indicated that GR can interfere with virus entry by directly interacting with ACE2 and spike, with a nonspecific effect on cell and viral membranes. Additional anti-inflammatory and antioxidant effects of GR cannot be excluded. These multiple activities of GR and licorice extract are critically re-assessed in this review, and their possible role against the spread of the SARS-CoV-2 and the features of COVID-19 disease is discussed

Physiological map to study kidney toxicity in the ONTOX project

editorial reviewedBackground and Objectives: Continuous improvements of computational approaches also increase the predictive performances of toxicological in silico models [1]. However, being mainly based on animal test data, these computational models lack a good correlation with human toxicity, and, being often based uniquely on chemical structures, they are unable to explain toxicological processes. To overcome these limitations, we have developed a new semi-automated strategy to build a Physiological Map (PM), a framework to study human toxicological mechanisms. Materials and Methods: Our method is useful to build a PM or to validate an existing PM. To retrieve information, a manual literature review was accompanied by computational interrogation of ontologies (e.g. Gene Ontology), thus creating a network of genes, proteins, molecules and phenotypes [2]. The network was converted manually into a PM using the CellDesigner software and visualized on the web using the MINERVA platform. The entire procedure was supported and revised by field experts. Results: We present here the human kidney PM, developed in the framework of ONTOX, a European project aimed at improving risk assessment avoiding the use of animal tests [3]. With the purpose to better understand tubular necrosis and nephrolithiasis, the PM represents the normal physiology in proximal tubule, the loop of Henle, distal tubule, and collecting duct cells, displaying the vitamin D metabolism and the urine production processes: filtration, reabsorption and secretion. Discussion and Conclusions: Our method assists the user to build a PM even starting from limited data. The PM is initially a static representation of physiological processes, also useful to study and develop new adverse outcome pathways. Subsequently, we could add kinetic parameters, transforming the PM into a dynamic model able to represent cellular perturbations. This approach presents an opportunity to investigate human toxicities, improving the toxicological predictions from a qualitative and quantitative perspective. References: [1] Manganelli S, Gamba A, Colombo E, Benfenati E (2022) 'Using VEGAHUB Within a Weight-of-Evidence Strategy'. In: Benfenati E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_18 [2] Gamba A, Salmona M, Bazzoni G (2020) 'Quantitative analysis of proteins which are members of the same protein complex but cause locus heterogeneity in disease', Sci Rep 10, 10423. https://doi.org/10.1038/s41598-020-66836-7 [3] Vinken M., et al. (2021) 'Safer chemicals using less animals: kick-off of the European ONTOX project', Toxicology 458, 152846. https://doi.org/10.1016/j.tox.2021.15284

Physiological maps and chemical-induced disease ontologies: tools to support NAMs development for next-generation risk assessment

editorial reviewedPhysiological maps (PM) can be defined as a graphical representation of cellular and molecular processes associated to specific organ functions (Vinken et al. 2021). Within the ONTOX project, we designed a total of 6 PMs describing physiological processes in the liver, the kidney and the brain. These PMs are then used as a tool to assess relevant mechanistic coverage and linkage between a specific organ function and a toxicological endpoint. Based on the Disease Maps project (Mazein et al. 2018) pipeline, we developed the first version of 6 PMs describing the following physiological processes: bile secretion & lipid metabolism (liver), vitamin D metabolism & urine composition (kidney), neural tube closure (update of the work of Heusinkveld et al. 2021) & brain development (brain). Our workflow included: (i) data collection from expert curated literature (ii) identification of the relevant biological mechanisms, (iii) screening of online databases (e.g. Wikipathways, Reactome, and KEGG) for previously described pathways, (iv) manual curation and integration of the data into a PM using CellDesigner, and (v) visualization on the MINERVA platform (Hoksza et al. 2019). These qualitative PMs represent an important tool for exploring curated literature, analyzing networks and benchmarking the development of new adverse outcome pathways (AOPs). These PMs provide the basis for developing quantitative disease ontologies, integrating different layers of pathological and toxicological information, chemical information (drug-induced pathways) and kinetic data. The resulting chemical-induced disease ontologies will provide a multi-layered platform for integration and visualization of such information. The ontologies will contribute to improving understanding of organ/disease related pathways in response to chemicals, visualize omics datasets, develop quantitative methods for computational disease modeling and for predicting toxicity, set up an in vitro & in silico test battery to detect a specific type of toxicity, and develop new animal-free approaches for next generation risk assessment

Growth options and credit risk

We calibrate a dynamic model of credit risk and analyze the relation between growth options and credit spreads. Our model features real and financing frictions, a technology with decreasing returns to scale, and endogenous investment options driven by both systematic and idiosyncratic shocks. We find a negative relation between credit spreads and growth options, after controlling for determinants of credit risk. This negative relation is due to the current decision to invest and the associated change in leverage which, in the presence of external financing needs and financing frictions, increase credit spreads while reducing the value of future investments. We do not find evidence that growth options accrue value in response to systematic risk, thus increasing credit risk premia