Meat Sci. 2022 May 20;192:108849. doi: 10.1016/j.meatsci.2022.108849. Online ahead of print.

ABSTRACT

Nowadays, it is important to make the results of scientific research accessible in a simple and understandable way according to the Open Science policy. This movement uses tools to enhance findability and interoperability of data. This paper describes the transformation of the meat dictionary published by the French Meat Academy as a book into a machine actionable and freely accessible terminological resource based on the SKOS standard format. This thesaurus contains 1567 concepts describing the meat production chain. This work was carried out by experts in semantic web, meat biology and meat vocabulary. This thesaurus can be used to index articles, journals and datasets, thus facilitating consultation; it can also be used to facilitate interoperability of the indexed datasets and provide contextual definitions for building ontologies, i.e. formal descriptions of knowledge for reasoning on data. The thesaurus can be useful to enrich other vocabularies with new knowledge, such as French specificities in terms of meat cuts or definitions.

PMID:35728340 | DOI:10.1016/j.meatsci.2022.108849

Matrix Biol. 2022 Jun 14:S0945-053X(22)00083-X. doi: 10.1016/j.matbio.2022.06.003. Online ahead of print.

ABSTRACT

The extracellular matrix (ECM) is a highly dynamic, well-organized acellular network of tissue-specific biomolecules, that can be divided into structural or core ECM proteins and ECM-associated proteins. The ECM serves as a blueprint for organ development and function and, when structurally altered through mutation, altered expression, or degradation, can lead to debilitating syndromes that often affect one tissue more than another. Cross-referencing the FANTOM5 SSTAR (Semantic catalog of Samples, Transcription initiation And Regulators) and the defined catalog of core matrisome ECM (glyco)proteins, we conducted a comprehensive analysis of 511 different human samples to annotate the context-specific transcription of the individual components of the defined matrisome. Relative log expression normalized SSTAR cap analysis gene expression peak data files were downloaded from the FANTOM5 online database and filtered to exclude all cell lines and diseased tissues. Promoter-level expression values were categorized further into eight core tissue systems and three major ECM categories: proteoglycans, glycoproteins, and collagens. Hierarchical clustering and correlation analyses were conducted to identify complex relationships in promoter-driven gene expression activity. Integration of the core matrisome and curated FANTOM5 SSTAR data creates a unique tool that provides insight into the promoter-level expression of ECM-encoding genes in a tissue- and cell-specific manner. Unbiased clustering of cap analysis gene expression peak data reveals unique ECM signatures within defined tissue systems. Correlation analysis among tissue systems exposes both positive and negative correlation of ECM promoters with varying levels of significance. This tool can be used to provide new insight into the relationships between ECM components and tissues and can inform future research on the ECM in human disease and development. We invite the matrix biology community to continue to explore and discuss this dataset as part of a larger and continuing conversation about the human ECM. An interactive web tool can be found at matrixpromoterome.github.io along with additional resources that can be found at dx.doi.org/10.6084/m9.figshare.19794481 (figures) and https://figshare.com/s/e18ecbc3ae5aaf919b78 (python notebook).

PMID:35714875 | DOI:10.1016/j.matbio.2022.06.003

BMJ Open. 2022 Jun 10;12(6):e057284. doi: 10.1136/bmjopen-2021-057284.

ABSTRACT

OBJECTIVE: We described development, health and justice system outcomes for children in contact with child protection and public housing.

DESIGN: Descriptive analysis of outcomes for children known to child protection who also had contact with public housing drawn from the South Australian (SA) Better Evidence Better Outcomes Linked Data (BEBOLD) platform.

SETTING: The BEBOLD platform holds linked administrative records collected by government agencies for whole-population successive birth cohorts in SA beginning in 1999.

PARTICIPANTS: This study included data from birth registrations, perinatal, child protection, public housing, hospital, emergency department, early education and youth justice for all SA children born 1999-2013 and followed until 2016. The base population notified at least once to child protection was n=67 454.

PRIMARY OUTCOME MEASURE: Contact with the public housing system.

SECONDARY OUTCOME MEASURES: Hospitalisations and emergency department presentations before age 5, and early education at age 5, and youth justice contact before age 17.

RESULTS: More than 60% of children with at least one notification to child protection had contact with public housing, and 60.2% of those known to both systems were known to housing first. Children known to both systems experienced more emergency department and hospitalisation contacts, greater developmental vulnerability and were about six times more likely to have youth justice system contact.

CONCLUSIONS: There is substantial overlap between involvement with child protection and public housing in SA. Those children are more likely to face a life trajectory characterised by greater contact with the health system, greater early life developmental vulnerability and greater contact with the criminal justice system. Ensuring the highest quality of supportive early life infrastructure for families in public housing may contribute to prevention of contact with child protection and better life trajectories for children.

PMID:35688602 | DOI:10.1136/bmjopen-2021-057284

Int J Environ Res Public Health. 2022 Jun 1;19(11):6780. doi: 10.3390/ijerph19116780.

ABSTRACT

Neighbourhood-level interventions offer a promising opportunity to promote child mental health at a population level; however, neighbourhood effects are still regarded as a 'black box' and a better understanding of the specific design elements, such as public open space, is needed to inform actionable policy interventions.

METHODS: This study leveraged data from a population linked dataset (Australian Early Development Census-Built Environment) combining information from a national census of children's developmental outcomes with individualised geospatial data. Associations between access to (within 400 m and 800 m from home), and quality of, public open space and child mental health outcomes across eight capital cities were estimated using multilevel logistic regression models, adjusting for demographic and contextual factors. Access was defined based on proximity of public open space to children's home addresses, within distance thresholds (400 m, 800 m) measured along the road network. Effect modification was tested across maternal education groups.

RESULTS: Across the eight capital cities, inequities in access to child friendly public open spaces were observed across maternal education groups and neighbourhood disadvantage quintiles. Children with access to any type of public open space within 800 m of home had lower odds of demonstrating difficulties and higher odds of competence. Children with access to child friendly public open spaces within 800 m of home had the highest likelihood of demonstrating competence.

CONCLUSION: Improving access to neighbourhood public open space appears to be a promising strategy for preventing mental health difficulties and promoting competence in early childhood. Action is needed to redress socio-spatial inequities in access to child friendly public open space.

PMID:35682362 | PMC:PMC9180559 | DOI:10.3390/ijerph19116780

Sensors (Basel). 2022 May 29;22(11):4131. doi: 10.3390/s22114131.

ABSTRACT

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SAREF.

PMID:35684752 | DOI:10.3390/s22114131

JMIR Form Res. 2022 Jun 9;6(6):e31485. doi: 10.2196/31485.

ABSTRACT

BACKGROUND: Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual's quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers.

OBJECTIVE: Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD.

METHODS: We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model.

RESULTS: Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD.

CONCLUSIONS: The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research.

PMID:35679097 | DOI:10.2196/31485

Stud Health Technol Inform. 2022 Jun 6;290:76-80. doi: 10.3233/SHTI220035.

ABSTRACT

The heterogeneity of electronic health records model is a major problem: it is necessary to gather data from various models for clinical research, but also for clinical decision support. The Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) has emerged as a standard model for structuring health records populated from various other sources. This model is proposed as a relational database schema. However, in the field of decision support, formal ontologies are commonly used. In this paper, we propose a translation of OMOP-CDM into an ontology, and we explore the utility of the semantic web for structuring EHR in a clinical decision support perspective, and the use of the SPARQL language for querying health records. The resulting ontology is available online.

PMID:35672974 | DOI:10.3233/SHTI220035

Stud Health Technol Inform. 2022 Jun 6;290:61-65. doi: 10.3233/SHTI220032.

ABSTRACT

Research data management requires stable, trustworthy repositories to safeguard scientific research results. In this context, rich markup with metadata is crucial for the discoverability and interpretability of the relevant resources. SEEK is a web-based software to manage all important artifacts of a research project, including project structures, involved actors, documents and datasets. SEEK is organized along the ISA model (Investigation - Study - Assay). It offers several machine-readable serializations, including JSON and RDF. In this paper, we extend the power of RDF serialization by leveraging the W3C Data Catalog Vocabulary (DCAT). DCAT was specifically designed to improve interoperability between digital assets on the Web and enables cross-domain markup. By using community-consented gold standard vocabularies and a formal knowledge description language, findability and interoperability according to the FAIR principles are significantly improved.

PMID:35672971 | DOI:10.3233/SHTI220032

J Biomed Semantics. 2022 Jun 3;13(1):16. doi: 10.1186/s13326-022-00270-8.

ABSTRACT

BACKGROUND: Evidence-based medicine propagates that medical/clinical decisions are made by taking into account high-quality evidence, most notably in the form of randomized clinical trials. Evidence-based decision-making requires aggregating the evidence available in multiple trials to reach -by means of systematic reviews- a conclusive recommendation on which treatment is best suited for a given patient population. However, it is challenging to produce systematic reviews to keep up with the ever-growing number of published clinical trials. Therefore, new computational approaches are necessary to support the creation of systematic reviews that include the most up-to-date evidence.We propose a method to synthesize the evidence available in clinical trials in an ad-hoc and on-demand manner by automatically arranging such evidence in the form of a hierarchical argument that recommends a therapy as being superior to some other therapy along a number of key dimensions corresponding to the clinical endpoints of interest. The method has also been implemented as a web tool that allows users to explore the effects of excluding different points of evidence, and indicating relative preferences on the endpoints.

RESULTS: Through two use cases, our method was shown to be able to generate conclusions similar to the ones of published systematic reviews. To evaluate our method implemented as a web tool, we carried out a survey and usability analysis with medical professionals. The results show that the tool was perceived as being valuable, acknowledging its potential to inform clinical decision-making and to complement the information from existing medical guidelines.

CONCLUSIONS: The method presented is a simple but yet effective argumentation-based method that contributes to support the synthesis of clinical trial evidence. A current limitation of the method is that it relies on a manually populated knowledge base. This problem could be alleviated by deploying natural language processing methods to extract the relevant information from publications.

PMID:35659056 | DOI:10.1186/s13326-022-00270-8

Front Psychol. 2022 May 12;13:915443. doi: 10.3389/fpsyg.2022.915443. eCollection 2022.

ABSTRACT

This paper takes laptops as an example to carry out research on quantitative model of brand recognition based on sentiment analysis of big data. The basic idea is to use web crawler technology to obtain the most authentic and direct information of different laptop brands from first-line consumers from public spaces such as buyer reviews of major e-commerce platforms, including review time, text reviews, satisfaction ratings and relevant user information, etc., and then analyzes consumers' sentimental tendencies and recognition status of the product brands. This study extracted a total of 437,815 user reviews of laptops from e-commerce platforms from January 1, 2019 to December 31, 2021, and performed data preprocessing on the obtained review data, followed by sentiment dictionary construction, attribute expansion, text quantification and algorithm evaluation. This paper analyzed the information receiving and processing hierarchy of the quantitative model of brand recognition, discussed the interactive relationship between brand recognition and consumer sentiment, discussed the brand recognition bias, style and demand in the context of big data, and performed the sentiment statistics and dimension analysis in the quantitative model of brand recognition. The study results show that the quantitative model of brand recognition based on sentiment analysis of big data can transform and map the keywords in text to word vectors in the high-dimensional semantic space by performing unsupervised machine learning on the text based on artificial neural network computer bionic metaphors; the model can accumulate each brand-related buyer review in the corresponding brand recognition dimension, so as to obtain the value of each product in each dimension of brand recognition; finally, the model will add the values of each dimension of brand recognition, that is, obtain the relevant value of the sum of each brand recognition. The results of this paper may provide a reference for further research on the quantitative model of brand recognition based on sentiment analysis of big data.

PMID:35645872 | PMC:PMC9133927 | DOI:10.3389/fpsyg.2022.915443

J Pers Med. 2022 May 7;12(5):757. doi: 10.3390/jpm12050757.

ABSTRACT

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.

PMID:35629179 | DOI:10.3390/jpm12050757

Healthcare (Basel). 2022 May 10;10(5):881. doi: 10.3390/healthcare10050881.

ABSTRACT

The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.

PMID:35628018 | DOI:10.3390/healthcare10050881

Sci Data. 2022 May 27;9(1):239. doi: 10.1038/s41597-022-01352-z.

ABSTRACT

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

PMID:35624233 | DOI:10.1038/s41597-022-01352-z

Database (Oxford). 2022 May 25;2022:baac035. doi: 10.1093/database/baac035.

ABSTRACT

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

PMID:35616100 | DOI:10.1093/database/baac035

Stud Health Technol Inform. 2022 May 25;294:790-795. doi: 10.3233/SHTI220585.

ABSTRACT

SNIK is a knowledge base about the management of health information systems generated by extracting Linked Data from textbooks and other sources. SNIK describes functions, roles executing these functions, and entity types, the information used or updated by these functions. We present SNIK Quiz, a browser game in which students answer multiple-choice questions about information management in hospitals based on SNIK. The questions are semi-automatically generated using templates in order to train basic facts, more complex patterns, and connections between textbooks encoded in SNIK.

PMID:35612205 | DOI:10.3233/SHTI220585

Crit Rev Food Sci Nutr. 2022 May 25:1-19. doi: 10.1080/10408398.2022.2079112. Online ahead of print.

ABSTRACT

Fruits contain enormous source of vitamins that provides energy to the human body. These are also affluent in essential and vital vitamins, minerals, fiber, and health-promoting components, which has led to an increase in fruit consumption in recent years. Though fruit consumption has expanded considerably in recent years, the use of synthetic chemicals to ripen or store fruits has been steadily increasing, resulting in postharvest deterioration. Alternatives to synthetic chemicals should be considered to control this problem. Instead of utilizing synthetic chemicals, this study suggests using natural plant products to control postharvest decay. The aim of this study indicates how natural plant products can be useful and effective to eliminate postharvest diseases rather than using synthetic chemicals. Several electronic databases were investigated as information sources, including Google Scholar, PubMed, Web of Science, Scopus, ScienceDirect, SpringerLink, Semantic Scholar, MEDLINE, and CNKI Scholar. The current review focused on the postharvest of fruits has become more and more necessary because of these vast demands of fruits. Pathogen-induced diseases are the main component and so the vast portion of fruits get wasted after harvest. Besides, it may occur harmful during harvesting and subsequent handling, storage, and marketing and after consumer purchasing and also causes for numerous endogenous and exogenous diseases via activating ROS, oxidative stress, lipid peroxidation, etc. However, pathogenicity can be halted by using postharvest originating natural fruits containing bioactive elements that may be responsible for the management of nutritional deficiency, inflammation, cancer, and so on. However, issues arising during the postharvest diseases must be controlled and resolved before releasing the horticultural commodities for commercialization. Therefore, the control of postharvest pathogens still depends on the use of synthetic fungicides; however, due to the problem of the development of the fungicide-resistant strains there is a good demand of public to eradicate the use of pesticides with the arrival of numerous diseases that are expanded in their intensity by the specific chemical product. By using of the organic or natural products for controlling postharvest diseases of fruits has become a mandatory step to take. In addition, antimicrobial packaging may have a greater impact on long-term food security by lowering the risk of pathogenicity and increasing the longevity of fruit shelf life. Taken together, natural chemicals as acetaldehyde, hexanal, eugenol, linalool, jasmonates, glucosinolates, essential oils, and many plant bioactive are reported for combating of the postharvest illnesses and guide to way of storage of fruits in this review.

PMID:35612470 | DOI:10.1080/10408398.2022.2079112

Stud Health Technol Inform. 2022 May 25;294:419-420. doi: 10.3233/SHTI220491.

ABSTRACT

To enhance their practice, healthcare professionals need to cross-link various usage recommendations provided by heterogeneous vocabularies that must be retrieved and integrated conjointly. This is the aim of the Knowledge Warehouse / K-Ware platform. It enables establishing relevant bridges between different knowledge sources (structured vocabularies, thesaurus, ontologies) expressed in the semantic web standard languages (i.e. SKOS, OWL, RDF). This poster presents the strategy applied in K-Ware to hide the different aspects of linking literals with medical entities encoded in these knowledge sources to fetch some publications abstracts from Pubmed.

PMID:35612113 | DOI:10.3233/SHTI220491

Stud Health Technol Inform. 2022 May 25;294:372-376. doi: 10.3233/SHTI220477.

ABSTRACT

In a national effort aiming at cross-hospitals data interoperability, the Swiss Personalized Health Network elected RDF as preferred data and meta-data representation format. Yet, most clinical research software solutions are not designed to interact with RDF databases. We present a modular Python toolkit allowing easy conversion from RDF graphs to i2b2, adaptable to other common data models (CDM) with reasonable efforts. The tool was designed with feedback from clinicians in both oncology and laboratory research.

PMID:35612099 | DOI:10.3233/SHTI220477

Stud Health Technol Inform. 2022 May 25;294:302-306. doi: 10.3233/SHTI220463.

ABSTRACT

Suitable causal inference in biostatistics can be best achieved by knowledge representation thanks to causal diagrams or directed acyclic graphs. However, necessary and sufficient causes are not easily represented. Since existing ontologies do not fill this gap, we designed OntoBioStat in order to enable covariate selection support based on causal relation representations. OntoBioStat automatic ontological causal diagram construction and inferences are detailed in this study. OntoBioStat inferences are allowed by Semantic Web Rule Language rules and axioms. First, statements made by the users include outcome, exposure, covariate, and causal relation specification. Then, reasoning enable automatic construction using generic instances of Meta_Variable and Necessary_Variable classes. Finally, inferred classes highlighted potential bias such as confounder-like. Ontological causal diagram built with OntoBioStat was compared to a standard causal diagram (without OntoBioStat) in a theoretical study. It was found that confounding and bias were not completely identified by the standard causal diagram, and erroneous covariate sets were provided. Further research is needed in order to make OntoBioStat more usable.

PMID:35612081 | DOI:10.3233/SHTI220463

Bull Cancer. 2022 May 19:S0007-4551(22)00143-6. doi: 10.1016/j.bulcan.2022.03.006. Online ahead of print.

ABSTRACT

INTRODUCTION: Today, patients with lung cancer and their relatives can easily search information on the Internet and express themselves online.

METHODS: Within this web-ethnographic research, we found, based on 246 search terms related to lung cancer, and collected, a sample of 136 online conversations that were published between January 2004 and September 2018, including 1220 messages by 762 authors.

RESULTS: The authors of messages, many of them close relatives of patients (35%), share their experience (34%). Seven areas of worrying concern, each of them prominent in 10 to 24% of the corpus, can be grouped under three headings: accepting the disease in order for the patient or their caregiver to fight it ("decide on the prognosis", "managing the treatments", "stopping the progression"), conjuring fate ("naming the guilty ones", "conjuring powerlessness"), asserting resilience ("adopt the right attitude" and "telling one's story in order to survive"). The question of time - disrupted, lost, to be caught up with or controlled - runs through all the issues.

DISCUSSION: The patients' and caregivers' concerns go beyond the pace of medical treatment and beyond death. Their mental representations of the disease influence their adherence to the care pathway. Welcoming them in our care and dialogue goes hand in hand with personalized treatment.

PMID:35599172 | DOI:10.1016/j.bulcan.2022.03.006