J Pers Med. 2024 Jun 24;14(7):676. doi: 10.3390/jpm14070676.

ABSTRACT

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

PMID:39063930 | DOI:10.3390/jpm14070676

Comput Methods Programs Biomed. 2024 Jul 18;255:108326. doi: 10.1016/j.cmpb.2024.108326. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Researchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods.

METHODS: In this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT.

RESULTS: InstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles.

CONCLUSIONS: The study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.

PMID:39029416 | DOI:10.1016/j.cmpb.2024.108326

Ann Nucl Med. 2024 Jul 19. doi: 10.1007/s12149-024-01958-w. Online ahead of print.

ABSTRACT

Primary progressive aphasia (PPA) is a disease known to affect the frontal and temporal regions of the left hemisphere. PPA is often an indication of future development of dementia, specifically semantic dementia (SD) for frontotemporal dementia (FTD) and logopenic progressive aphasia (LPA) as an atypical presentation of Alzheimer's disease (AD). The purpose of this review is to clarify the value of 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-positron emission tomography (PET) in the detection and diagnosis of PPA. A comprehensive review of literature was conducted using Web of Science, PubMed, and Google Scholar. The three PPA subtypes show distinct regions of hypometabolism in FDG-PET imaging with SD in the anterior temporal lobes, LPA in the left temporo-parietal junction, and nonfluent/agrammatic Variant PPA (nfvPPA) in the left inferior frontal gyrus and insula. Despite the distinct patterns, overlapping hypometabolic areas can complicate differential diagnosis, especially in patients with SD who are frequently diagnosed with AD. Integration with other diagnostic tools could refine the diagnostic process and lead to improved patient outcomes. Future research should focus on validating these findings in larger populations and exploring the therapeutic implications of early, accurate PPA diagnosis with more targeted therapeutic interventions.

PMID:39028529 | DOI:10.1007/s12149-024-01958-w

Gigascience. 2024 Jan 2;13:giae033. doi: 10.1093/gigascience/giae033.

ABSTRACT

BACKGROUND: As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves.

RESULTS: Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data.

AVAILABILITY: https://pephub.databio.org.

PMID:38991851 | DOI:10.1093/gigascience/giae033

JMIR Res Protoc. 2024 Jul 11;13:e56608. doi: 10.2196/56608.

ABSTRACT

BACKGROUND: Social communication is a key factor in maintaining cognitive function and contributes to well-being in later life.

OBJECTIVE: This study will examine the effects of "Photo-Integrated Conversation Moderated by Application version 2" (PICMOA-2), which is a web-based conversational intervention, on cognitive performance, frailty, and social and psychological indicators among community-dwelling older adults.

METHODS: This study is a randomized controlled trial with an open-label, 2-parallel group trial and 1:1 allocation design. Community dwellers aged 65 years and older were enrolled in the trial and divided into the intervention and control groups. The intervention group receives the PICMOA-2 program, a web-based group conversation, once every 2 weeks for 6 months. The primary outcome is verbal fluency, including phonemic and semantic fluency. The secondary outcomes are other neuropsychiatric batteries, including the Mini-Mental State Examination, Logical Memory (immediate and delay), verbal paired associates, and comprehensive functional status evaluated by questionnaires, including frailty, social status, and well-being. The effect of the intervention will be examined using a mixed linear model. As a secondary aim, we will test whether the intervention effects vary with the covariates at baseline to examine the effective target attributes.

RESULTS: Recruitment was completed in July 2023. A total of 66 participants were randomly allocated to intervention or control groups. As of January 1, 2024, the intervention is ongoing. Participants are expected to complete the intervention at the end of February 2024, and the postintervention evaluation will be conducted in March 2024.

CONCLUSIONS: This protocol outlines the randomized controlled trial study design evaluating the effect of a 6-month intervention with PICMOA-2. This study will provide evidence on the effectiveness of social interventions on cognitive function and identify effective target images for remote social intervention.

TRIAL REGISTRATION: UMIN Clinical Trials UMIN000050877; https://tinyurl.com/5eahsy66.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56608.

PMID:38990615 | DOI:10.2196/56608

Front Immunol. 2024 Jun 20;15:1393839. doi: 10.3389/fimmu.2024.1393839. eCollection 2024.

ABSTRACT

INTRODUCTION: Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases.

METHODS: To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources.

RESULTS AND DISCUSSION: In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

PMID:38975336 | PMC:PMC11225432 | DOI:10.3389/fimmu.2024.1393839

Sensors (Basel). 2024 Jun 9;24(12):3761. doi: 10.3390/s24123761.

ABSTRACT

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.

PMID:38931546 | DOI:10.3390/s24123761

J Clin Exp Neuropsychol. 2024 Jun 21:1-30. doi: 10.1080/13803395.2024.2331827. Online ahead of print.

ABSTRACT

INTRODUCTION: Semantic verbal fluency (SVF) is a widely used measure of frontal executive function and access to semantic memory. SVF scoring metrics include the number of unique words generated, perseverations, intrusions, semantic cluster size and switching between clusters, and scores vary depending on the language the test is administered in. In this paper, we review the existing normative data for Turkish, the main metrics used for scoring SVF data in Turkish, and the most frequently used categories.

METHOD: We conducted a systematic review of peer-reviewed papers using Medline, EMBASE, PsycInfo, Web of Science, and two Turkish databases, TR-Dizin and Yok-Tez. Included papers contained data on the SVF performance of healthy adult native speakers of Turkish, and reported the categories used. Versions of the SVF that required participants to alternate categories were excluded. We extracted and tabulated demographics, descriptions of groups, metrics used, categories used, and sources of normative data. Studies were assessed for level of detail in reporting findings.

RESULTS: 1400 studies were retrieved. After deduplication, abstract, full text screening, and merging of theses with their published versions, 121 studies were included. 114 studies used the semantic category "animal", followed by first names (N = 14, 12%). All studies reported word count. More complex measures were rare (perseverations: N = 12, 10%, clustering and switching: N = 5, 4%). Four of seven normative studies reported only word count, two also measured perseverations, and one reported category violations and perseverations. Two normative studies were published in English.

CONCLUSIONS: There is a lack of normative Turkish SVF data with more complex metrics, such as clustering and switching, and a lack of normative data published in English. Given the size of the Turkish diaspora, normative SVF data should include monolingual and bilingual speakers. Limitations include a restriction to key English and Turkish databases.

PMID:38904178 | DOI:10.1080/13803395.2024.2331827

Eur Rev Med Pharmacol Sci. 2024 Jun;28(11):3702-3710. doi: 10.26355/eurrev_202406_36375.

ABSTRACT

OBJECTIVE: Monitoring Jackson Pratt and Hemovac drains plays a crucial role in assessing a patient's recovery and identifying potential postoperative complications. Accurate and regular monitoring of the blood volume in the drain is essential for making decisions about patient care. However, transferring blood to a measuring cup and recording it is a challenging task for both patients and doctors, exposing them to bloodborne pathogens such as the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). To automate the recording process with a non-contact approach, we propose an innovative approach that utilizes deep learning techniques to detect a drain in a photograph, compute the blood level in the drain, estimate the blood volume, and display the results on both web and mobile interfaces.

MATERIALS AND METHODS: Our system employs semantic segmentation on images taken with mobile phones to effectively isolate the blood-filled portion of the drain from the rest of the image and compute the blood volume. These results are then sent to mobile and web applications for convenient access. To validate the accuracy and effectiveness of our system, we collected the Drain Dataset, which consists of 1,004 images taken under various background and lighting conditions.

RESULTS: With an average error rate of less than 5% in milliliters, our proposed approach achieves highly accurate blood level detection and estimation, as demonstrated by our trials on this dataset. The system also exhibits robustness to variations in lighting conditions and drain shapes, ensuring its applicability in different clinical scenarios.

CONCLUSIONS: The proposed automated blood volume estimation system can significantly reduce the time and effort required for manual measurements, enabling healthcare professionals to focus on other critical tasks. The dataset and annotations are available at: https://www.kaggle.com/datasets/ayenahin/liquid-volume-detection-from-drain-images and the code for the web application is available at https://github.com/itsjustaplant/AwesomeProject.git.

PMID:38884505 | DOI:10.26355/eurrev_202406_36375

Int J Telemed Appl. 2024 Jun 6;2024:5578056. doi: 10.1155/2024/5578056. eCollection 2024.

ABSTRACT

Introduction: Teleradiology allows distant facilities to electronically transmit images for interpretation, thereby bridging the radiology service gap between urban and rural areas. The technology improves healthcare quality, treatment options, and diagnostic accuracy. However, in low resource settings like Ethiopia, teleradiology services are limited, posing challenges for implementation. Therefore, this study is aimed at exploring the factors that facilitated or hindered the implementation of web-based teleradiology in the public hospitals of the South Gondar Zone, Northwest Ethiopia. Methods: In this study, a purposive sampling method was employed to select seventeen participants, including hospital managers, physicians, emergency surgeons, and radiologists, for an in-depth interview (IDI). The interviews were conducted from March to May 2023. A reflexive thematic analysis was conducted using an abductive coding technique at the semantic/explicit level. Data were collected through semistructured interviews conducted face-to-face and virtually, with audio recordings transcribed, translated, and analyzed using Open Code version 4.02 software. Trustworthiness was ensured through prolonged engagement, reflective journaling, and review by coauthors. Results: The study examined eight main themes, with barriers to sustainable teleradiology implementation falling into five categories: technological, organizational, environmental, individual, and workflow and communication. Conversely, identified facilitators included improved radiology service efficiency, system accessibility, collaboration opportunities, and user trust in the radiology ecosystem. Within each theme, factors with potential impacts on teleradiology system sustainability were identified, such as the lack of system handover mechanisms, absence of a central image consultation center, and inadequate staffing of full-time radiologists and technical personnel. Conclusions: The study highlights the positive user perception of a web-based teleradiology system's user-friendliness and efficiency. Overcoming challenges and leveraging facilitators are crucial for optimizing teleradiology and improving service delivery and patient outcomes. A centralized consultation center with dedicated radiologists and technical personnel is recommended for maximizing efficiency.

PMID:38883327 | PMC:PMC11178418 | DOI:10.1155/2024/5578056

Transl Cancer Res. 2024 May 31;13(5):2544-2560. doi: 10.21037/tcr-23-964. Epub 2024 May 22.

ABSTRACT

BACKGROUND AND OBJECTIVE: Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice.

METHODS: We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis.

KEY CONTENT AND FINDINGS: Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care.

CONCLUSIONS: Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.

PMID:38881914 | PMC:PMC11170525 | DOI:10.21037/tcr-23-964

Res Synth Methods. 2024 Jun 14. doi: 10.1002/jrsm.1729. Online ahead of print.

ABSTRACT

Citation indices providing information on backward citation (BWC) and forward citation (FWC) links are essential for literature discovery, bibliographic analysis, and knowledge synthesis, especially when language barriers impede document identification. However, the suitability of citation indices varies. While some have been analyzed, the majority, whether new or established, lack comprehensive evaluation. Therefore, this study evaluates the citation coverage of the citation indices of 59 databases, encompassing the widely used Google Scholar, Scopus, and Web of Science alongside many others never previously analyzed, such as the emerging Lens, Scite, Dimensions, and OpenAlex or the subject-specific PubMed and JSTOR. Through a comprehensive analysis using 259 journal articles from across disciplines, this research aims to guide scholars in selecting indices with broader document coverage and more accurate and comprehensive backward and forward citation links. Key findings highlight Google Scholar, ResearchGate, Semantic Scholar, and Lens as leading options for FWC searching, with Lens providing superior download capabilities. For BWC searching, the Web of Science Core Collection can be recommended over Scopus for accuracy. BWC information from publisher databases such as IEEE Xplore or ScienceDirect was generally found to be the most accurate, yet only available for a limited number of articles. The findings will help scholars conducting systematic reviews, meta-analyses, and bibliometric analyses to select the most suitable databases for citation searching.

PMID:38877607 | DOI:10.1002/jrsm.1729

Neuropsychol Rev. 2024 Jun 13. doi: 10.1007/s11065-024-09644-4. Online ahead of print.

ABSTRACT

Transcranial magnetic stimulation (TMS) has been found to be promising in the neurorehabilitation of post-stroke patients. Aphasia and cognitive impairment (CI) are prevalent post-stroke; however, there is still a lack of consensus about the characteristics of interventions based on TMS and its neuropsychological and anatomical-functional benefits. Therefore, studies that contribute to creating TMS protocols for these neurological conditions are necessary. To analyze the evidence of the neuropsychological and anatomical-functional TMS effects in post-stroke patients with CI and aphasia and determine the characteristics of the most used TMS in research practice. The present study followed the PRISMA guidelines and included articles from PubMed, Scopus, Web of Science, ScienceDirect, and EMBASE databases, published between January 2010 and March 2023. In the 15 articles reviewed, it was found that attention, memory, executive function, language comprehension, naming, and verbal fluency (semantic and phonological) are the neuropsychological domains that improved post-TMS. Moreover, TMS in aphasia and post-stroke CI contribute to greater frontal activation (in the inferior frontal gyrus, pars triangularis, and opercularis). Temporoparietal effects were also found. The observed effects occur when TMS is implemented in repetitive modality, at a frequency of 1 Hz, in sessions of 30 min, and that last more than 2 weeks in duration. The use of TMS contributes to the neurorehabilitation process in post-stroke patients with CI and aphasia. However, it is still necessary to standardize future intervention protocols based on accurate TMS characteristics.

PMID:38867020 | DOI:10.1007/s11065-024-09644-4

PeerJ Comput Sci. 2024 Apr 30;10:e1781. doi: 10.7717/peerj-cs.1781. eCollection 2024.

ABSTRACT

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.

PMID:38855229 | PMC:PMC11157569 | DOI:10.7717/peerj-cs.1781

PeerJ Comput Sci. 2024 May 13;10:e2004. doi: 10.7717/peerj-cs.2004. eCollection 2024.

ABSTRACT

This article presents a semantic web-based solution for extracting the relevant information automatically from the annual financial reports of the banks/financial institutions and presenting this information in a queryable form through a knowledge graph. The information in these reports is significantly desired by various stakeholders for making key investment decisions. However, this information is available in an unstructured format making it much more complex and challenging to understand and query manually or even through digital systems. Another challenge that makes the understanding of information more complex is the variation of terminologies among financial reports of different banks or financial institutions. The solution presented in this article signifies an ontological approach to solving the standardization problems of the terminologies in this domain. It further addresses the issue of semantic differences to extract relevant data sharing common semantics. Such semantics are then incorporated by implementing their representation as a Knowledge Graph to make the information understandable and queryable. Our results highlight the usage of Knowledge Graph in search engines, recommender systems and question-answering (Q-A) systems. This financial knowledge graph can also be used to serve the task of financial storytelling. The proposed solution is implemented and tested on the datasets of various banks and the results are presented through answers to competency questions evaluated on precision and recall measures.

PMID:38855202 | PMC:PMC11157543 | DOI:10.7717/peerj-cs.2004

BMJ Paediatr Open. 2024 Jun 5;8(Suppl 1):e002443. doi: 10.1136/bmjpo-2023-002443.

ABSTRACT

INTRODUCTION: As a topic of inquiry in its own right, data management for interdisciplinary research projects is in its infancy. Key issues include the inability of researchers to effectively query diverse data outputs and to identify potentially important synergies between discipline-specific data. Equally problematic, few semantic ontologies exist to better support data organisation and discovery. Finally, while interdisciplinary research is widely regarded as beneficial to unpacking complex problems, non-researchers such as policy-makers and planners often struggle to use and interrogate the related datasets. To address these issues, the following article details the design and development of the UKRI GCRF Action Against Stunting Hub (AASH)'s All-Hub Data Repository (AHDR).

METHODS AND ANALYSIS: The AHDR is a single application, single authentication web-based platform comprising a data warehouse to store data from across the AASH's three study countries and to support data querying. Four novel components of the AHDR are described in the following article: (1) a unique data discovery tool; (2) a metadata catalogue that provides researchers with an interface to explore the AASH's data outputs and engage with a new semantic ontology related to child stunting; (3) an interdisciplinary aid to support a directed approach to identifying synergies and interactions between AASH data and (4) a decision support tool that will support non-researchers in engaging with the wider evidence-based outputs of the AASH.

ETHICS AND DISSEMINATION: Ethical approval for this study was granted by institutional ethics committees in the UK, India, Indonesia and Senegal. Results will be disseminated via publications in peer-reviewed journals; presentations at international conferences and community-level public engagement events; key stakeholder meetings; and in public repositories with appropriate Creative Commons licences allowing for the widest possible use.

PMID:38843904 | DOI:10.1136/bmjpo-2023-002443

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