Epidemiological trends and trajectories of MAFLD-associated hepatocellular carcinoma 2002-2033: the ITA.LI.CA database.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a new inclusive definition of the whole spectrum of liver diseases associated to metabolic disorders. The main objective of this study was to compare patients with MAFLD and non-MAFLD with hepatocellular carcinoma (HCC) included in a nationally representative cohort. METHODS: We analysed 6882 consecutive patients with HCC enrolled from 2002 to 2019 by 23 Italian Liver Cancer centres to compare epidemiological and future trends in three subgroups: pure, single aetiology MAFLD (S-MAFLD); mixed aetiology MAFLD (metabolic and others, M-MAFLD); and non-MAFLD HCC. RESULTS: MAFLD was diagnosed in the majority of patients with HCC (68.4%). The proportion of both total MAFLD and S-MAFLD HCC significantly increased over time (from 50.4% and 3.6% in 2002-2003, to 77.3% and 28.9% in 2018-2019, respectively, p<0.001). In Italy S-MAFLD HCC is expected to overcome M-MAFLD HCC in about 6 years. Patients with S-MAFLD HCC were older, more frequently men and less frequently cirrhotic with clinically relevant portal hypertension and a surveillance-related diagnosis. They had more frequently large tumours and extrahepatic metastases. After weighting, and compared with patients with non-MAFLD, S-MAFLD and M-MAFLD HCC showed a significantly lower overall (p=0.026, p=0.004) and HCC-related (p<0.001, for both) risk of death. Patients with S-MAFLD HCC showed a significantly higher risk of non-HCC-related death (p=0.006). CONCLUSIONS: The prevalence of MAFLD HCC in Italy is rapidly increasing to cover the majority of patients with HCC. Despite a less favourable cancer stage at diagnosis, patients with MAFLD HCC have a lower risk of HCC-related death, suggesting reduced cancer aggressiveness.

Related plants tend to share pollinators and herbivores, but strength of phylogenetic signal varies among plant families.

Related plants are often hypothesized to interact with similar sets of pollinators and herbivores, but this idea has only mixed empirical support. This may be because plant families vary in their tendency to share interaction partners. We quantify overlap of interaction partners for all pairs of plants in 59 pollination and 11 herbivory networks based on the numbers of shared and unshared interaction partners (thereby capturing both proportional and absolute overlap). We test for relationships between phylogenetic distance and partner overlap within each network; whether these relationships varied with the composition of the plant community; and whether well-represented plant families showed different relationships. Across all networks, more closely related plants tended to have greater overlap. The strength of this relationship within a network was unrelated to the composition of the network's plant component, but, when considered separately, different plant families showed different relationships between phylogenetic distance and overlap of interaction partners. The variety of relationships between phylogenetic distance and partner overlap in different plant families probably reflects a comparable variety of ecological and evolutionary processes. Considering factors affecting particular species-rich groups within a community could be the key to understanding the distribution of interactions at the network level.

Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks.

Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction.

Solid Organ Transplantation During COVID-19 Pandemic: An International Web-based Survey on Resources' Allocation.

Solid organ transplants (SOTs) are life-saving interventions, recently challenged by coronavirus disease 2019 (COVID-19). SOTs require a multistep process, which can be affected by COVID-19 at several phases. METHODS: SOT-specialists, COVID-19-specialists, and medical ethicists designed an international survey according to CHERRIES guidelines. Personal opinions about continuing SOTs, safe managing of donors and recipients, as well as equity of resources' allocation were investigated. The survey was sent by e-mail. Multiple approaches were used (corresponding authors from Scopus, websites of scientific societies, COVID-19 webinars). After the descriptive analysis, univariate and multivariate ordinal regression analysis was performed. RESULTS: There were 1819 complete answers from 71 countries. The response rate was 49%. Data were stratified according to region, macrospecialty, and organ of interest. Answers were analyzed using univariate-multivariate ordinal regression analysis and thematic analysis. Overall, 20% of the responders thought SOTs should not stop (continue transplant without restriction); over 70% suggested SOTs should selectively stop, and almost 10% indicated they should completely stop. Furthermore, 82% agreed to shift resources from transplant to COVID-19 temporarily. Briefly, main reason for not stopping was that if the transplant will not proceed, the organ will be wasted. Focusing on SOT from living donors, 61% stated that activity should be restricted only to "urgent" cases. At the multivariate analysis, factors identified in favor of continuing transplant were Italy, ethicist, partially disagreeing on the equity question, a high number of COVID-19-related deaths on the day of the answer, a high IHDI country. Factors predicting to stop SOTs were Europe except-Italy, public university hospital, and strongly agreeing on the equity question. CONCLUSIONS: In conclusion, the majority of responders suggested that transplant activity should be continued through the implementation of isolation measures and the adoption of the COVID-19-free pathways. Differences between professional categories are less strong than supposed.

Unmasking structural patterns in incidence matrices: an application to ecological data.

Null models have become a crucial tool for understanding structure within incidence matrices across multiple biological contexts. For example, they have been widely used for the study of ecological and biogeographic questions, testing hypotheses regarding patterns of community assembly, species co-occurrence and biodiversity. However, to our knowledge we remain without a general and flexible approach to study the mechanisms explaining such structures. Here, we provide a method for generating 'correlation-informed' null models, which combine the classic concept of null models and tools from community ecology, like joint statistical modelling. Generally, this model allows us to assess whether the information encoded within any given correlation matrix is predictive for explaining structural patterns observed within an incidence matrix. To demonstrate its utility, we apply our approach to two different case studies that represent examples of common scenarios encountered in community ecology. First, we use a phylogenetically informed null model to detect a strong evolutionary fingerprint within empirically observed food webs, reflecting key differences in the impact of shared evolutionary history when shaping the interactions of predators or prey. Second, we use multiple informed null models to identify which factors determine structural patterns of species assemblages, focusing in on the study of nestedness and the influence of site size, isolation, species range and species richness. In addition to offering a versatile way to study the mechanisms shaping the structure of any incidence matrix, including those describing ecological communities, our approach can also be adapted further to test even more sophisticated hypotheses.

Analysing ecological networks of species interactions.

Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems - such as communities - through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with - what we believe to be - their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.