J Biomed Semantics. 2024 Jun 6;15(1):9. doi: 10.1186/s13326-024-00303-4.

ABSTRACT

BACKGROUND: In healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner. In addition, there is a need for smart, dynamic, personalized and performant services provided on top of this data. Flexible workflows should be defined that realize their desired functionality, adhere to use case specific quality constraints and improve coordination across stakeholders. User interfaces should allow configuring all of this in an easy, user-friendly way.

METHODS: A distributed, generic, cascading reasoning reference architecture can solve the presented challenges. It can be instantiated with existing tools built upon Semantic Web technologies that provide data-driven semantic services and constructing cross-organizational workflows. These tools include RMLStreamer to generate Linked Data, DIVIDE to adaptively manage contextually relevant local queries, Streaming MASSIF to deploy reusable services, AMADEUS to compose semantic workflows, and RMLEditor and Matey to configure rules to generate Linked Data.

RESULTS: A use case demonstrator is built on a scenario that focuses on personalized smart monitoring and cross-organizational treatment planning. The performance and usability of the demonstrator's implementation is evaluated. The former shows that the monitoring pipeline efficiently processes a stream of 14 observations per second: RMLStreamer maps JSON observations to RDF in 13.5 ms, a C-SPARQL query to generate fever alarms is executed on a window of 5 s in 26.4 ms, and Streaming MASSIF generates a smart notification for fever alarms based on severity and urgency in 1539.5 ms. DIVIDE derives the C-SPARQL queries in 7249.5 ms, while AMADEUS constructs a colon cancer treatment plan and performs conflict detection with it in 190.8 ms and 1335.7 ms, respectively.

CONCLUSIONS: Existing tools built upon Semantic Web technologies can be leveraged to optimize continuous care provisioning. The evaluation of the building blocks on a realistic homecare monitoring use case demonstrates their applicability, usability and good performance. Further extending the available user interfaces for some tools is required to increase their adoption.

PMID:38845042 | DOI:10.1186/s13326-024-00303-4

Sci Rep. 2024 May 24;14(1):11879. doi: 10.1038/s41598-024-60832-x.

ABSTRACT

It is essential to delve into the strategy of multimodal model pre-training, which is an obvious impact on downstream tasks. Currently, clustering learning has achieved noteworthy benefits in multiple methods. However, due to the availability of open image-text pairs, it is challenging for multimodal with clustering learning. In this paper, we propose an approach that utilizes clustering swap prediction strategy to learn image-text clustering embedding space by interaction prediction between image and text features. Unlike existing models with clustering learning, our method (Clus) allows for an open number of clusters for web-scale alt-text data. Furthermore, in order to train the image and text encoders efficiently, we introduce distillation learning approach and evaluate the performance of the image-encoder in downstream visual tasks. In addition, Clus is pre-trained end-to-end by using large-scale image-text pairs. Specifically, both text and image serve as ground truth for swap prediction, enabling effective representation learning. Concurrently, extensive experiments demonstrate that Clus achieves state-of-the-art performance on multiple downstream fine-tuning and zero-shot tasks (i.e., Image-Text Retrieval, VQA, NLVR2, Image Captioning, Object Detection, and Semantic Segmentation).

PMID:38789489 | DOI:10.1038/s41598-024-60832-x

Saudi Dent J. 2024 May;36(5):688-697. doi: 10.1016/j.sdentj.2024.02.018. Epub 2024 Mar 3.

ABSTRACT

BACKGROUND: To our knowledge, no systematic review assessed and gathered information about the prevalence of impacted canines among the Saudi population. The purpose of this study was to critically assess the previously published studies about the prevalence of canine impaction according to impaction type (buccal/ palatal), gender (male/female), and location (maxillary/mandibular, right/left), are among the Saudi population.

METHODS: PubMed (MEDLINE), Scopus, the Web of Science, Dimensions, and Semantic Scholar databases were searched systemically for articles related to the topic of the study published between 1987 and 2022. The PRISMA statements were used to conduct a systematic review with the help of the Best Practice for Survey and the Public Opinion Research scales by the American Association for Public Opinion Research (AAPOR) to assess and evaluate the selected studies' quality.

RESULTS: The initial search of the databases yielded 221 articles. After discarding duplicates, 161 were selected for further evaluation. Eventually, 16 articles were selected for inclusion in this study. Regarding the quality of the selected articles, all articles, except one, were of high quality. Only one was of medium quality.

CONCLUSION: It was found that the incidence of palatal canine impactions was higher than buccal impactions. Females had a higher prevalence of canine impactions as compared to males. There were more canine impactions in the maxilla than the mandible and more on the left side than the right one.

PMID:38766287 | PMC:PMC11096605 | DOI:10.1016/j.sdentj.2024.02.018

Psychiatry Clin Psychopharmacol. 2022 Mar 1;32(1):80-88. doi: 10.5152/pcp.2022.21133. eCollection 2022 Mar.

ABSTRACT

BACKGROUND: Differentiating diagnosis between Alzheimer's disease and major depressive disorder in the elderly is a great clinical challenge. This study aimed to identify the establishment of differential diagnosis protocols between Alzheimer's disease and major depressive disorder.

METHODS: We searched studies in the Ovid MEDLINE, EMBASE, PsycINFO, and Web of Science databases between 2009 and 2019. A total of 155 references were found for searching relevant articles using Boolean search. After exclusion of redundancies and assessing of title, abstract, and full text for eligibility, 11 articles were selected. The total sample size was 1077 distributed in 8 different countries.

RESULTS: Significant results were found for differential diagnosis between Alzheimer's disease and major depressive disorder, such as overall mental status, episodic memory, visuospatial construction, delayed recognition task, semantic verbal fluency, visual task in short-term memory, atrophy of the hippocampus, cortical activation in specific tasks, and cerebrospinal fluid biomarkers.

CONCLUSION: These findings are good pathways for discriminating Alzheimer's disease from major depression in the elderly.

PMID:38764905 | PMC:PMC11099637 | DOI:10.5152/pcp.2022.21133

BMC Pulm Med. 2024 May 18;24(1):246. doi: 10.1186/s12890-024-03020-x.

ABSTRACT

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma.

METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted.

RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively.

CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics.

TRIAL REGISTRATION: CRD42022375712.

PMID:38762472 | DOI:10.1186/s12890-024-03020-x

PLoS One. 2024 May 17;19(5):e0299150. doi: 10.1371/journal.pone.0299150. eCollection 2024.

ABSTRACT

The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors.

PMID:38758949 | DOI:10.1371/journal.pone.0299150

Sensors (Basel). 2024 Apr 27;24(9):2804. doi: 10.3390/s24092804.

ABSTRACT

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

PMID:38732910 | DOI:10.3390/s24092804

PLoS One. 2024 May 10;19(5):e0302739. doi: 10.1371/journal.pone.0302739. eCollection 2024.

ABSTRACT

BACKGROUND: Deep brain stimulation (DBS) reliably ameliorates cardinal motor symptoms in Parkinson's disease (PD) and essential tremor (ET). However, the effects of DBS on speech, voice and language have been inconsistent and have not been examined comprehensively in a single study.

OBJECTIVE: We conducted a systematic analysis of literature by reviewing studies that examined the effects of DBS on speech, voice and language in PD and ET.

METHODS: A total of 675 publications were retrieved from PubMed, Embase, CINHAL, Web of Science, Cochrane Library and Scopus databases. Based on our selection criteria, 90 papers were included in our analysis. The selected publications were categorized into four subcategories: Fluency, Word production, Articulation and phonology and Voice quality.

RESULTS: The results suggested a long-term decline in verbal fluency, with more studies reporting deficits in phonemic fluency than semantic fluency following DBS. Additionally, high frequency stimulation, left-sided and bilateral DBS were associated with worse verbal fluency outcomes. Naming improved in the short-term following DBS-ON compared to DBS-OFF, with no long-term differences between the two conditions. Bilateral and low-frequency DBS demonstrated a relative improvement for phonation and articulation. Nonetheless, long-term DBS exacerbated phonation and articulation deficits. The effect of DBS on voice was highly variable, with both improvements and deterioration in different measures of voice.

CONCLUSION: This was the first study that aimed to combine the outcome of speech, voice, and language following DBS in a single systematic review. The findings revealed a heterogeneous pattern of results for speech, voice, and language across DBS studies, and provided directions for future studies.

PMID:38728329 | DOI:10.1371/journal.pone.0302739

Ann Clin Microbiol Antimicrob. 2024 May 3;23(1):39. doi: 10.1186/s12941-024-00698-z.

ABSTRACT

BACKGROUND: Non-surgical chronic wounds, including diabetes-related foot diseases (DRFD), pressure injuries (PIs) and venous leg ulcers (VLU), are common hard-to-heal wounds. Wound evolution partly depends on microbial colonisation or infection, which is often confused by clinicians, thereby hampering proper management. Current routine microbiology investigation of these wounds is based on in vitro culture, focusing only on a limited panel of the most frequently isolated bacteria, leaving a large part of the wound microbiome undocumented.

METHODS: A literature search was conducted on original studies published through October 2022 reporting metagenomic next generation sequencing (mNGS) of chronic wound samples. Studies were eligible for inclusion if they applied 16 S rRNA metagenomics or shotgun metagenomics for microbiome analysis or diagnosis. Case reports, prospective, or retrospective studies were included. However, review articles, animal studies, in vitro model optimisation, benchmarking, treatment optimisation studies, and non-clinical studies were excluded. Articles were identified in PubMed, Google Scholar, Web of Science, Microsoft Academic, Crossref and Semantic Scholar databases.

RESULTS: Of the 3,202 articles found in the initial search, 2,336 articles were removed after deduplication and 834 articles following title and abstract screening. A further 14 were removed after full text reading, with 18 articles finally included. Data were provided for 3,628 patients, including 1,535 DRFDs, 956 VLUs, and 791 PIs, with 164 microbial genera and 116 species identified using mNGS approaches. A high microbial diversity was observed depending on the geographical location and wound evolution. Clinically infected wounds were the most diverse, possibly due to a widespread colonisation by pathogenic bacteria from body and environmental microbiota. mNGS data identified the presence of virus (EBV) and fungi (Candida and Aspergillus species), as well as Staphylococcus and Pseudomonas bacteriophages.

CONCLUSION: This study highlighted the benefit of mNGS for time-effective pathogen genome detection. Despite the majority of the included studies investigating only 16 S rDNA, ignoring a part of viral, fungal and parasite colonisation, mNGS detected a large number of bacteria through the included studies. Such technology could be implemented in routine microbiology for hard-to-heal wound microbiota investigation and post-treatment wound colonisation surveillance.

PMID:38702796 | DOI:10.1186/s12941-024-00698-z

J Cheminform. 2024 May 1;16(1):49. doi: 10.1186/s13321-024-00833-0.

ABSTRACT

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. SCIENTIFIC CONTRIBUTION: This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

PMID:38693555 | DOI:10.1186/s13321-024-00833-0

Sci Data. 2024 Apr 30;11(1):434. doi: 10.1038/s41597-024-03169-4.

ABSTRACT

This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

PMID:38688949 | DOI:10.1038/s41597-024-03169-4

J Healthc Inform Res. 2024 Jan 23;8(2):313-352. doi: 10.1007/s41666-024-00159-4. eCollection 2024 Jun.

ABSTRACT

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs).

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

PMID:38681755 | PMC:PMC11052968 | DOI:10.1007/s41666-024-00159-4

J Med Syst. 2024 Apr 25;48(1):47. doi: 10.1007/s10916-024-02062-2.

ABSTRACT

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults.

PMID:38662184 | DOI:10.1007/s10916-024-02062-2

J Acoust Soc Am. 2024 Apr 1;155(4):2687-2697. doi: 10.1121/10.0025763.

ABSTRACT

One speech sound can be associated with multiple meanings through iconicity, indexicality, and/or systematicity. It was not until recently that this "pluripotentiality" of sound symbolism attracted serious attention, and it remains uninvestigated how pluripotentiality may arise. In the current study, Japanese, Korean, Mandarin, and English speakers rated unfamiliar jewel names on three semantic scales: size, brightness, and hardness. The results showed language-specific and cross-linguistically shared pluripotential sound symbolism. Japanese speakers associated voiced stops with large and dark jewels, whereas Mandarin speakers associated [i] with small and bright jewels. Japanese, Mandarin, and English speakers also associated lip rounding with darkness and softness. These sound-symbolic meanings are unlikely to be obtained through metaphorical or metonymical extension, nor are they reported to colexify. Notably, in a purely semantic network without the mediation of lip rounding, softness can instead be associated with brightness, as illustrated by synesthetic metaphors such as yawaraka-na hizashi /jawaɾakanaçizaɕi/ "a gentle (lit. soft) sunshine" in Japanese. These findings suggest that the semantic networks of sound symbolism may not coincide with those of metaphor or metonymy. The current study summarizes the findings in the form of (phono)semantic maps to facilitate cross-linguistic comparisons of pluripotential sound symbolism.

PMID:38639927 | DOI:10.1121/10.0025763

J Med Internet Res. 2024 Apr 18;26:e46777. doi: 10.2196/46777.

ABSTRACT

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PMID:38635981 | DOI:10.2196/46777

Heliyon. 2024 Apr 4;10(7):e29046. doi: 10.1016/j.heliyon.2024.e29046. eCollection 2024 Apr 15.

ABSTRACT

This article is dedicated to the development of a model for competencies within an educational program and its implementation through the use of semantic technologies. The model proposed by the authors is distinctive in that competencies are organized into a hierarchical data structure with arbitrary levels of nesting. Furthermore, the article presents an original solution for modelling the input requirements for studying a course, which is defined in the form of dependencies between the competencies generated by the course and the competencies of other courses. The outcome of this work is an ontological model of a competency-based curriculum, for which the authors have developed and implemented algorithms for data addition and retrieval, as well as for analyzing the consistency of the curriculum in terms of the input requirements for studying a discipline and the learning outcomes from previous periods. The findings presented in the article will prove to be valuable in the development of educational process management information systems and educational program constructors. They will also be instrumental in aligning diverse educational programs within the context of academic mobility.

PMID:38623249 | PMC:PMC11016605 | DOI:10.1016/j.heliyon.2024.e29046

Bioinform Adv. 2024 Mar 16;4(1):vbae045. doi: 10.1093/bioadv/vbae045. eCollection 2024.

ABSTRACT

MOTIVATION: With the proliferation of research means and computational methodologies, published biomedical literature is growing exponentially in numbers and volume. Cancer cell lines are frequently used models in biological and medical research that are currently applied for a wide range of purposes, from studies of cellular mechanisms to drug development, which has led to a wealth of related data and publications. Sifting through large quantities of text to gather relevant information on cell lines of interest is tedious and extremely slow when performed by humans. Hence, novel computational information extraction and correlation mechanisms are required to boost meaningful knowledge extraction.

RESULTS: In this work, we present the design, implementation, and application of a novel data extraction and exploration system. This system extracts deep semantic relations between textual entities from scientific literature to enrich existing structured clinical data concerning cancer cell lines. We introduce a new public data exploration portal, which enables automatic linking of genomic copy number variants plots with ranked, related entities such as affected genes. Each relation is accompanied by literature-derived evidences, allowing for deep, yet rapid, literature search, using existing structured data as a springboard.

AVAILABILITY AND IMPLEMENTATION: Our system is publicly available on the web at https://cancercelllines.org.

PMID:38560553 | PMC:PMC10978572 | DOI:10.1093/bioadv/vbae045

Database (Oxford). 2024 Feb 22;2024:baae008. doi: 10.1093/database/baae008.

ABSTRACT

In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.

PMID:38554132 | DOI:10.1093/database/baae008

Fortschr Neurol Psychiatr. 2024 Mar 28. doi: 10.1055/a-2276-3557. Online ahead of print.

ABSTRACT

Given the demographic change with an aging society in Germany, cognitive performance assessment of the elderly is of great importance. The Viacogscreen developed by us is a computer- and web-based brain performance screening for older adults that not only meets the criteria of a measurement instrument, but is also economical and repeatable. The test captures interlocking word list learning with delayed free recall and recognition, semantic word selection and fluidity, phonemic word fluidity and inverted number range, as well as incidental memory, resulting in a total of 17 performance parameters that provide a quick orientation (approximate test duration: 10-12 minutes) regarding the cognitive performance of a test subject. Three performance areas are depicted: executive functions, episodic and semantic memory. The test was standardized for 200 healthy test subjects in 6 different age groups (range: 50-85 years). For the first clinical validation, the test was used in the memory clinics in Bonn and Ulm, where 33 patients with MCI (mild cognitive impairment) and 42 patients with suspected Alzheimer's disease (VAD) were tested. A control group of 42 healthy people of approximately the same age served as the control group. With regard to the cognitive test procedure, all three groups showed significantly different results regarding the overall score (ANOVA F=73.9, p<0.001), executive functions (F=27.6 p<0.001) and semantic memory (F=54.4 p<0.001). Regarding episodic memory, both clinical groups differed significantly from the control group, but not from each other (F=48.7, p<0.001). The Viacogscreen thus produced very good results in its first validation in two memory clinics with regard to differentiation of VAD, and good results with regard to MCI. In addition to use in neurodegenerative diseases, the Viacogscreen is also suitable for other neurological and neuro-oncological diseases, as well as for use in large clinical studies since it enables electronic data collection.

PMID:38547902 | DOI:10.1055/a-2276-3557

Sensors (Basel). 2024 Mar 7;24(6):1739. doi: 10.3390/s24061739.

ABSTRACT

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

PMID:38544003 | DOI:10.3390/s24061739