Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer.

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

Explainable online health information truthfulness in Consumer Health Search.

Introduction: People are today increasingly relying on health information they find online to make decisions that may impact both their physical and mental wellbeing. Therefore, there is a growing need for systems that can assess the truthfulness of such health information. Most of the current literature solutions use machine learning or knowledge-based approaches treating the problem as a binary classification task, discriminating between correct information and misinformation. Such solutions present several problems with regard to user decision making, among which: (i) the binary classification task provides users with just two predetermined possibilities with respect to the truthfulness of the information, which users should take for granted; indeed, (ii) the processes by which the results were obtained are often opaque and the results themselves have little or no interpretation. Methods: To address these issues, we approach the problem as an ad hoc retrieval task rather than a classification task, with reference, in particular, to the Consumer Health Search task. To do this, a previously proposed Information Retrieval model, which considers information truthfulness as a dimension of relevance, is used to obtain a ranked list of both topically-relevant and truthful documents. The novelty of this work concerns the extension of such a model with a solution for the explainability of the results obtained, by relying on a knowledge base consisting of scientific evidence in the form of medical journal articles. Results and discussion: We evaluate the proposed solution both quantitatively, as a standard classification task, and qualitatively, through a user study to examine the "explained" ranked list of documents. The results obtained illustrate the solution's effectiveness and usefulness in making the retrieved results more interpretable by Consumer Health Searchers, both with respect to topical relevance and truthfulness.

EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive.

Making raw data available to the research community is one of the pillars of Findability, Accessibility, Interoperability, and Reuse (FAIR) research. However, the submission of raw data to public databases still involves many manually operated procedures that are intrinsically time-consuming and error-prone, which raises potential reliability issues for both the data themselves and the ensuing metadata. For example, submitting sequencing data to the European Genome-phenome Archive (EGA) is estimated to take 1 month overall, and mainly relies on a web interface for metadata management that requires manual completion of forms and the upload of several comma separated values (CSV) files, which are not structured from a formal point of view. To tackle these limitations, here we present EGAsubmitter, a Snakemake-based pipeline that guides the user across all the submission steps, ranging from files encryption and upload, to metadata submission. EGASubmitter is expected to streamline the automated submission of sequencing data to EGA, minimizing user errors and ensuring higher end product fidelity.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

MOTIVATION: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging. RESULTS: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. AVAILABILITY AND IMPLEMENTATION: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

Vec4Cred: a model for health misinformation detection in web pages.

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

Health Misinformation Detection in the Social Web: An Overview and a Data Science Approach.

The increasing availability of online content these days raises several questions about effective access to information. In particular, the possibility for almost everyone to generate content with no traditional intermediary, if on the one hand led to a process of "information democratization", on the other hand, has negatively affected the genuineness of the information disseminated. This issue is particularly relevant when accessing health information, which impacts both the individual and societal level. Often, laypersons do not have sufficient health literacy when faced with the decision to rely or not rely on this information, and expert users cannot cope with such a large amount of content. For these reasons, there is a need to develop automated solutions that can assist both experts and non-experts in discerning between genuine and non-genuine health information. To make a contribution in this area, in this paper we proceed to the study and analysis of distinct groups of features and machine learning techniques that can be effective to assess misinformation in online health-related content, whether in the form of Web pages or social media content. To this aim, and for evaluation purposes, we consider several publicly available datasets that have only recently been generated for the assessment of health misinformation under different perspectives.

Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content.

In recent years we have witnessed a growing interest in the analysis of social media data under different perspectives, since these online platforms have become the preferred tool for generating and sharing content across different users organized into virtual communities, based on their common interests, needs, and perceptions. In the current study, by considering a collection of social textual contents related to COVID-19 gathered on the Twitter microblogging platform in the period between August and December 2020, we aimed at evaluating the possible effects of some critical factors related to the pandemic on the mental well-being of the population. In particular, we aimed at investigating potential lexicon identifiers of vulnerability to psychological distress in digital social interactions with respect to distinct COVID-related scenarios, which could be "at risk" from a psychological discomfort point of view. Such scenarios have been associated with peculiar topics discussed on Twitter. For this purpose, two approaches based on a "top-down" and a "bottom-up" strategy were adopted. In the top-down approach, three potential scenarios were initially selected by medical experts, and associated with topics extracted from the Twitter dataset in a hybrid unsupervised-supervised way. On the other hand, in the bottom-up approach, three topics were extracted in a totally unsupervised way capitalizing on a Twitter dataset filtered according to the presence of keywords related to vulnerability to psychological distress, and associated with at-risk scenarios. The identification of such scenarios with both approaches made it possible to capture and analyze the potential psychological vulnerability in critical situations.

Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario.

Social media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice," and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

Structural Transitions During Formation and Rehydration of Proton Conducting Polymeric Membranes.

Knowledge of the transitions occurring during the formation of ion-conducting polymer films and membranes is crucial to optimize material performances. The use of non-destructive scattering techniques that offer high spatio-temporal resolution is essential to investigating such structural transitions, especially when combined with complementary techniques probing at different time and spatial scales. Here, a simultaneous multi-technique study is performed on the membrane formation mechanism and the subsequent hydration of two ion-conducting polymers, the well-known commercial Nafion and a synthesized sulfonated poly(phenylene sulfide sulfone) (sPSS). The X-ray data distinguish the multi-stage processes occurring during drying. A sol-gel-membrane transition sequence is observed for both polymers. However, while Nafion membrane evolves from a micellar solution through the formation of a phase-separated gel, forming an oriented supported membrane, sPSS membrane evolves from a solution of dispersed polyelectrolyte chains via formation of an inhomogeneous gel, showing assembly and ionic phase separation only at the end of the drying process. Impedance spectroscopy data confirm the occurrence of the sol-gel transitions, while gel-membrane transitions are detected by optical reflectance data. The simultaneous multi-technique approach presented here can connect the nanoscale to the macroscopic behavior, unraveling information essential to optimize membrane formation of different ion-conducting polymers.

Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis.

BACKGROUND: The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. METHODS: Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. RESULTS: We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. CONCLUSIONS: Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions.

An Auto-Calibrating Semi-Adiabatic Calorimetric Methodology for Strength Prediction and Quality Control of Ordinary and Ultra-High-Performance Concretes.

A timely knowledge of concrete and ultra-high-performance concrete (UHPC) strength is possible through the so-called strength-equivalent time (Et) curves. A timely knowledge of concrete strength is useful, for instance, to precisely determine when the shores of a hardening structural element can be safely removed. At the present time, the preparation of the strength-Et curves requires time-consuming and labor-intensive testing prior to the beginning of construction operations. This paper proposes an innovative method to derive the strength-Et and total heat-Et curves for both normal strength and UHPC. Results confirmed that the proposed method is fast, inexpensive, self-calibrating, accurate and can detect any variation of the concrete mix proportions or components quality. In addition, the quality of predictions of strength-maturity curves can be constantly improved as the specimens' population increases. Finally, results obtained with the proposed method were compared with those obtained using standard methods, showing a good agreement.

The use of silicone wristbands to evaluate personal exposure to semi-volatile organic chemicals (SVOCs) in France and Italy.

In this exploratory study, we measured for the first-time human exposure to about 90 semi-volatile organic chemicals (SVOCs) in France and Italy using silicone wristbands. Participants in France (n = 40) and in Italy (n = 31) wore a silicone wristband for five days during 2018 and 2019. Samples were analyzed for 39 polybrominated diphenyl ethers (PBDEs), 10 novel brominated flame retardants (nBFRs), 25 organophosphate esters (OPEs), and 18 polycyclic aromatic hydrocarbons (PAHs). In both groups, the most commonly detected chemicals were BDE-209, BEHTBP, tris[(2R)-1-chloro-2-propyl] phosphate (TCIPP), and phenanthrene among PBDEs, nBFRs, OPEs, and PAHs, respectively. The concentrations of ∑39 PBDEs, ∑10 nBFRs, ∑25 OPEs, ∑18 PAHs, and of most individual chemicals were generally significantly higher in samples from France than in those from Italy, except for BDE-209 and TCIPP. On a broader scale, the chemical concentrations were generally significantly lower in this study than those measured in the United States in previous studies using the same type of wristbands. Efforts to standardize the protocols for the use of silicone wristbands are still needed but this study shows that wristbands are capable of capturing regional differences in human exposure to a large variety of SVOCs and, therefore, can be used as personal exposure monitor for studies with global coverage.

De novo rational design of a freestanding, supercharged polypeptide, proton-conducting membrane.

Proton translocation enables important processes in nature and man-made technologies. However, controlling proton conduction and fabrication of devices exploiting biomaterials remains a challenge. Even more difficult is the design of protein-based bulk materials without any functional starting scaffold for further optimization. Here, we show the rational design of proton-conducting, protein materials exceeding reported proteinaceous systems. The carboxylic acid-rich structures were evolved step by step by exploring various sequences from intrinsically disordered coils over supercharged nanobarrels to hierarchically spider ß sheet containing protein-supercharged polypeptide chimeras. The latter material is characterized by interconnected ß sheet nanodomains decorated on their surface by carboxylic acid groups, forming self-supportive membranes and allowing for proton conduction in the hydrated state. The membranes showed an extraordinary proton conductivity of 18.5 ± 5 mS/cm at RH = 90%, one magnitude higher than other protein devices. This design paradigm offers great potential for bioprotonic device fabrication interfacing artificial and biological systems.

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.

Binge Drinking (BD) is a common risky behaviour that people hardly report to healthcare professionals, although it is not uncommon to find, instead, personal communications related to alcohol-related behaviors on social media. By following a data-driven approach focusing on User-Generated Content, we aimed to detect potential binge drinkers through the investigation of their language and shared topics. First, we gathered Twitter threads quoting BD and alcohol-related behaviours, by considering unequivocal keywords, identified by experts, from previous evidence on BD. Subsequently, a random sample of the gathered tweets was manually labelled, and two supervised learning classifiers were trained on both linguistic and metadata features, to classify tweets of genuine unique users with respect to media, bot, and commercial accounts. Based on this classification, we observed that approximately 55% of the 1 million alcohol-related collected tweets was automatically identified as belonging to non-genuine users. A third classifier was then trained on a subset of manually labelled tweets among those previously identified as belonging to genuine accounts, to automatically identify potential binge drinkers based only on linguistic features. On average, users classified as binge drinkers were quite similar to the standard genuine Twitter users in our sample. Nonetheless, the analysis of social media contents of genuine users reporting risky behaviours remains a promising source for informed preventive programs.