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Abstract Lynch syndrome is rarely associated with rectal cancer (RC) and thus, metachronous RC has been scarcely investigated. This study aimed to analyze the mucosal immune microenvironment in sporadic and metachronous RC. We analyzed the mucosal immune microenvironment in the 25 metachronous RCs present in the IMMUNOREACT 1 and 2 multicentre observational studies (624 patients). A panel of immune markers was retrospectively investigated at immunohistochemistry: CD3, CD4, CD8, CD8b, Tbet, FoxP3, PD-L1, MSH6, and PMS2 and CD80. Single-cell suspensions were subjected to flow-cytometry to determine the proportion of epithelial cells (pan-cytokeratin) acting as antigen-presenting cells (expressing CD80, CD86, HLA-ABC) and the proportion of activated CD8 + T cells (CD8 + positive for CD28, CD38), inhibitory T cells (CD3 + CTLA-4+) of activated CD4 + T helper cells (CD4 + CD25+) and activated T regulatory cells (CD4 + CD25 + FoxP3+). No mismatch repair gene deficiencies were observed in the patients. The previous history of colorectal adenoma was significantly more frequent in metachronous RC. In healthy epithelial cells, HLA-ABC expression was significantly higher in patients with metachronous RC. In therapy-naïve metachronous RC patients, a significantly lower level of circulating lymphocytes and CD3 + T-cell infiltration in the healthy mucosa surrounding the RC was observed compared to patients with non-metachronous cancer. Our study supports the hypothesis that metachronous RC can occur in a cancerization field in patients with weak systemic and local immune systems. The peculiar site of RC makes the mismatch-repair genes deficiency in metachronous cancer onset less relevant.

Abstract Background Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data. Methods Three classification problems were considered: predicting death (binary problem), predicting death or percutaneous endoscopic gastrostomy (PEG) (multiclass problem), and predicting death or non-invasive ventilation (NIV) (multiclass problem). Two supervised learning models, a logistic regression (LR) and a deep learning multilayer perceptron (MLP), were trained ensuring technical robustness and reproducibility. Moreover, to provide insights into model explainability and result interpretability, model coefficients for LR and Shapley values for both LR and MLP were considered to characterize the relationship between each variable and the outcome. Results On the one hand, predicting death was successful as both models yielded F1 scores and accuracy well above 0.7. The model explainability analysis performed for this outcome allowed for the understanding of how different methodological approaches consider the input variables when performing the prediction. On the other hand, predicting death alongside PEG or NIV proved to be much more challenging (F1 scores and accuracy in the 0.4-0.6 interval). Conclusions In conclusion, predicting death due to ALS proved to be feasible. However, predicting PEG or NIV in a multiclass fashion proved to be unfeasible with these data, regardless of the complexity of the methodological approach. The observed results suggest a potential ceiling on the amount of information extractable from the database, e.g., due to the intrinsic difficulty of the prediction tasks at hand, or to the absence of crucial predictors that are, however, not currently collected during routine practice.

Abstract Marine ecosystems are facing a dramatic loss of biodiversity worldwide, together with a widespread collapse of habitats and their functionality. In this context, Marine Citizen Science (MCS) can be a powerful tool to monitor these changes over time. The flowering of very well-structured international projects is strengthening the scientific credibility of MCS data, especially when data are collected after specifically designed training programs and shared in public user-friendly repositories. Here we present a new perspective on the use of MCS in the Mediterranean area, along with the main benefits for the stakeholders (i.e., diving centers, trainers, and policymakers) and the users (i.e., divers), resumed in three pillars: Pillar I – MCS as a tool for the site valorization; Pillar II – MCS as a new career opportunity for graduated students; Pillar III – MCS as a business opportunity for diving centers. In the frame of the Quintuple Helix Approach, for which there is a strong need of a socioecological transition of the society and economy, we show how MCS can be a win-win-win solution for all the actors involved, providing the vision for new and highly qualified job and business opportunities for the diving sector.

Abstract Comorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.

Abstract As known “ecosystem engineers”, beavers influence river hydrology, geomorphology, biochemistry, and biological assemblages. However, there is a lack of research regarding the effects of beaver activities on freshwater meiofauna. In this study, we investigated the taxonomic and functional composition of the benthic copepod assemblage of a segment of the Tiber River (Italy) where a beaver dam, created about 7 weeks before our survey, had formed a semi-lentic habitat upstream and a lotic habitat downstream of the dam. We also analyzed the copepod assemblage before and after a flood event that destroyed the beaver dam, providing a unique opportunity to observe changes in a naturally reversing scenario. Our analyses revealed that, while the taxonomic composition and functional traits of the copepod assemblage remained largely unchanged across the recently formed semi-lentic and lotic habitats, substantial differences were evident between the dammed and undammed states. The dammed state showed lower copepod abundances, biomass, and functionality than the undammed one. These results highlight the role of beaver dams in changing the composition and functionality of meiofaunal assemblages offering insights into the dynamic interactions within aquatic ecosystems.

Abstract Background Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods. Results Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes. Conclusions The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.

Abstract Background Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes. Results A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks. Conclusion The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes.

Abstract Writing notes is the most widespread method to report clinical events. Therefore, most of the information about the disease history of a patient remains locked behind free-form text. Natural language processing (NLP) provides a solution to automatically transform free-form text into structured data. In the present work, electronic healthcare records data of patients with diabetes were used to develop deep-learning based NLP models to automatically identify, within free-form text describing routine visits, the occurrence of hospitalisations related to cardiovascular disease (CVDs), an outcome of diabetes. Four possible time windows of increasing level of expected difficulty were considered: infinite, 24 months, 12 months, and 6 months. Model performance was evaluated by means of the area under the precision recall curve, as well as precision, recall, and F1-score after thresholding. Results showed that the proposed NLP approach was successful for both the infinite and 24-month windows, while, as expected, performance deteriorated with shorter time windows. Possible clinical applications of tools based on the proposed NLP approach include the retrospective filling of medical records with respect to a patient’s CVD history for epidemiological and research purposes as well as for clinical decision making.

Abstract Background The ageing global population presents significant public health challenges, especially in relation to the subjective wellbeing of the elderly. In this study, our aim was to investigate the potential for developing a model to forecast the two-year variation of the perceived wellbeing of individuals aged over 50. We also aimed to identify the variables that predict changes in subjective wellbeing, as measured by the CASP-12 scale, over a two-year period. Methods Data from the European SHARE project were used, specifically the demographic, health, social and financial variables of 9422 subjects. The subjective wellbeing was measured through the CASP-12 scale. The study outcome was defined as binary, i.e., worsening/not worsening of the variation of CASP-12 in 2 years. Logistic regression, logistic regression with LASSO regularisation, and random forest were considered candidate models. Performance was assessed in terms of accuracy in correctly predicting the outcome, Area Under the Curve (AUC), and F1 score. Results The best-performing model was the random forest, achieving an accuracy of 65%, AUC = 0.659, and F1 = 0.710. All models proved to be able to generalise both across subjects and over time. The most predictive variables were the CASP-12 score at baseline, the presence of depression and financial difficulties. Conclusions While we identify the random forest model as the more suitable, given the similarity of performance, the models based on logistic regression or on logistic regression with LASSO regularisation are also possible options.

Abstract The outbreak of Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, forced us to face a pandemic with unprecedented social, economic, and public health consequences. Several nations have launched campaigns to immunize millions of people using various vaccines to prevent infections. Meanwhile, therapeutic approaches and discoveries continuously arise; however, identifying infected patients that are going to experience the more severe outcomes of COVID-19 is still a major need, to focus therapeutic efforts, reducing hospitalization and mitigating drug adverse effects. Microbial communities colonizing the respiratory tract exert significant effects on host immune responses, influencing the susceptibility to infectious agents. Through 16S rDNAseq we characterized the upper airways’ microbiota of 192 subjects with nasopharyngeal swab positive for SARS-CoV-2. Patients were divided into groups based on the presence of symptoms, pneumonia severity, and need for oxygen therapy or intubation. Indeed, unlike most of the literature, our study focuses on identifying microbial signatures predictive of disease progression rather than on the probability of infection itself, for which a consensus is lacking. Diversity, differential abundance, and network analysis at different taxonomic levels were synergistically adopted, in a robust bioinformatic pipeline, highlighting novel possible taxa correlated with patients’ disease progression to intubation.