Data Brief. 2024 Sep 10;57:110910. doi: 10.1016/j.dib.2024.110910. eCollection 2024 Dec.

ABSTRACT

Papaya is a popular vegetable and fruit in both developing and developed countries. Nonetheless, Bangladesh's agricultural landscape is significantly influenced by papaya cultivation. However, disease is a common impediment to papaya productivity, adversely affecting papaya quality and yield and leading to substantial economic losses for farmers. Research suggests that computer-aided disease diagnosis and machine learning (ML) models can improve papaya production by detecting and classifying diseases. In this line, a dataset of papaya is required to diagnose the disease. Moreover, like many other fruits, papaya disease may vary from country to country. Therefore, the country-based papaya disease dataset is required. In this study, a papaya dataset is collected from Dhaka, Bangladesh. This dataset contains 2159 original images from five classes, including the healthy control class and four papaya leaf diseases: Anthracnose, Bacterial Spot, Curl, and Ring spot. Besides the original images, the dataset contains 210 annotated data for each of the five classes. The dataset contains two types of data: the whole image and the annotated image. The image will interest data scientists who apply disease detection through a convolutional neural network (CNN) and its variants. Furthermore, the annotated images, such as You Only Look Once (YOLO), U-Net, Mask R-CNN, and Single Shot Detection (SSD), will be helpful for semantic segmentation. Since firm-applicable AI devices and mobile and web applications are in demand, the dataset collected in this study will offer multiple options for integrating ML models into AI devices. In countries with weather and climate similar to Bangladesh, data scientists may use their dataset in that context.

PMID:39381009 | PMC:PMC11460515 | DOI:10.1016/j.dib.2024.110910

Brain Topogr. 2024 Oct 2;38(1):1. doi: 10.1007/s10548-024-01083-x.

ABSTRACT

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

PMID:39358648 | DOI:10.1007/s10548-024-01083-x

Acta Otolaryngol. 2024 Oct 2:1-8. doi: 10.1080/00016489.2024.2404614. Online ahead of print.

ABSTRACT

BACKGROUND: It has been shown that there is a link between thyroid-related diseases and hearing loss.

OBJECTIVES: The purpose of this study is to investigate the relationship between thyroid-related diseases and hearing loss by conducting a meta-analysis.

MATERIAL AND METHODS: A thorough search was carried out in the following electronic databases: PubMed, Cochrane Library, Embase, Web of Science, Google Scholar, Semantic Scholar, and ResearchRabbit. The chi-square test and the I2 index examined the research's heterogeneity. A funnel plot and the Eger test were used to examine publication-biased effects.

RESULTS: A total of 48,507 individuals (6482 hypothyroid patients, 4162 hearing loss patients, and 37863 controls) were included in this meta-analysis of 18 research. Individuals with hypothyroidism had a 1.69-fold increased risk of hearing loss compared to those without the condition (OR: 1.69; 95% CI: 1.11-2.57, p < 0.001). among hypothyroidism, the prevalence of hearing loss was 24% (EC: 0.24; 95% CI: 0.11-0.39, p = 0.00), while among hearing-impaired individuals, the prevalence of hypothyroidism was 7% (EC: 0.21; 95% CI: 0.07-0.40).

CONCLUSION: This study demonstrated how thyroid dysfunction can raise the chance of hearing loss. To completely comprehend the underlying mechanisms and create efficient treatments for this illness, more study is required.

PMID:39356749 | DOI:10.1080/00016489.2024.2404614

J Biomed Semantics. 2024 Oct 2;15(1):18. doi: 10.1186/s13326-024-00319-w.

ABSTRACT

Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these algorithms suffer in terms of generalization, precision, and reliability. The use of the distinctive characteristics of bibliographic metadata can prove effective in achieving better performance for this challenging task. In this research paper, we introduce an approach for biomedical relation classification using the qualifiers of co-occurring Medical Subject Headings (MeSH). First of all, we introduce MeSH2Matrix, our dataset consisting of 46,469 biomedical relations curated from PubMed publications using our approach. Our dataset includes a matrix that maps associations between the qualifiers of subject MeSH keywords and those of object MeSH keywords. It also specifies the corresponding Wikidata relation type and the superclass of semantic relations for each relation. Using MeSH2Matrix, we build and train three machine learning models (Support Vector Machine [SVM], a dense model [D-Model], and a convolutional neural network [C-Net]) to evaluate the efficiency of our approach for biomedical relation classification. Our best model achieves an accuracy of 70.78% for 195 classes and 83.09% for five superclasses. Finally, we provide confusion matrix and extensive feature analyses to better examine the relationship between the MeSH qualifiers and the biomedical relations being classified. Our results will hopefully shed light on developing better algorithms for biomedical ontology classification based on the MeSH keywords of PubMed publications. For reproducibility purposes, MeSH2Matrix, as well as all our source codes, are made publicly accessible at https://github.com/SisonkeBiotik-Africa/MeSH2Matrix .

PMID:39354632 | PMC:PMC11445994 | DOI:10.1186/s13326-024-00319-w

Dement Geriatr Cogn Disord. 2024 Sep 30:1-28. doi: 10.1159/000541581. Online ahead of print.

ABSTRACT

INTRODUCTION: The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0.

METHODS: First, we describe the app's usability features, including functions for collecting and processing data onsite, offline, and via videoconference. Second, we summarize its clinical survey, tapping on relevant habits (e.g., smoking, sleep) alongside linguistic predictors of performance (language history, use, proficiency, and difficulties). Third, we detail TELL's speech-based assessments, each combining strategic tasks and features capturing diagnostically relevant domains (motor function, semantic memory, episodic memory, and emotional processing). Fourth, we specify the app's new data analysis, visualization, and download options. Finally, we list core challenges and opportunities for development.

RESULTS: Overall, through its technical and scientific breakthroughs, TELL v.2.0 offers scalable, objective, and multidimensional insights for the field.

CONCLUSION: This tool can enhance disease detection, phenotyping, and monitoring.

PMID:39348797 | DOI:10.1159/000541581

Patient Prefer Adherence. 2024 Sep 24;18:1965-1977. doi: 10.2147/PPA.S478576. eCollection 2024.

ABSTRACT

BACKGROUND: Rapid recovery after lung cancer surgery is challenging. Exercise is a low-cost, effective method to expedite recovery. Despite numerous exercise interventions, many fail to consider patient perspectives, leading to low adherence and short-term effects. Understanding lung cancer patients' perspectives on postoperative exercise and exploring their exercise-related concerns and needs are crucial for enhancing the effectiveness of exercise-based rehabilitation programs.

OBJECTIVE: This study aims to analyze lung cancer patients' perspectives on postoperative exercise in their daily lives, exploring their concerns and needs related to postoperative exercise to help healthcare professionals develop personalized exercise plans.

METHODS: An internet crawling technique collected online inquiries from Baidu webpages about postoperative physical activity in lung cancer patients, using "lung cancer", "surgery", and "exercise" as keywords. The data was encoded, categorized, and analyzed using a large-scale semantic analysis platform in natural language processing and information retrieval to examine term frequency, sentiment tendencies, and attributes in the inquiry texts.

RESULTS: Initially, 2727 queries were retrieved; after screening, deduplication, and cleansing, 201 unique queries were identified. Queries related to "modes of exercise" constituted the largest proportion. The most frequently occurring words in the word frequency analysis were "lung", " cancer", "should", "can", "long", "early", and "surgery", "exercise", "respiratory". Postoperative lung cancer patients demonstrate significant interest in whether they should engage in exercise, as well as in the appropriate types and duration of such activities, indicating a strong need for detailed guidance and knowledge related to exercise. The sentiment analysis showed a positive score of 87.5% and a negative score of 12.5%, indicating that postoperative lung cancer patients view exercise positively and have an enthusiastic attitude towards it. Among the positive sentiment attributes, "good" was the most frequently mentioned term, whereas "bad" and "surprising" were the most prevalent terms within the negative sentiment attributes.

CONCLUSION: Postoperative physical activity receives limited attention from lung cancer patients, who emphasize their preferences for exercise modalities. Their inquiries often reflect psychological concerns, such as fear and helplessness caused by symptoms. Understanding patients' perspectives on postoperative physical activity within their real-life contexts can help integrate psychological support into exercise plans. This integration could guide healthcare professionals in developing more personalized postoperative exercise regimens for lung cancer patients.

PMID:39345759 | PMC:PMC11438453 | DOI:10.2147/PPA.S478576

Physiol Plant. 2024 Sep-Oct;176(5):e14537. doi: 10.1111/ppl.14537.

ABSTRACT

Long non-coding RNAs (lncRNAs) have emerged as important regulators of many biological processes, although their regulatory roles remain poorly characterized in woody plants, especially in gymnosperms. A major challenge of working with lncRNAs is to assign functional annotations, since they have a low coding potential and low cross-species conservation. We utilised an existing RNA-Sequencing resource and performed short RNA sequencing of somatic embryogenesis developmental stages in Norway spruce (Picea abies L. Karst). We implemented a pipeline to identify lncRNAs located within the intergenic space (lincRNAs) and generated a co-expression network including protein coding, lincRNA and miRNA genes. To assign putative functional annotation, we employed a guilt-by-association approach using the co-expression network and integrated these results with annotation assigned using semantic similarity and co-expression. Moreover, we evaluated the relationship between lincRNAs and miRNAs, and identified which lincRNAs are conserved in other species. We identified lincRNAs with clear evidence of differential expression during somatic embryogenesis and used network connectivity to identify those with the greatest regulatory potential. This work provides the most comprehensive view of lincRNAs in Norway spruce and is the first study to perform global identification of lincRNAs during somatic embryogenesis in conifers. The data have been integrated into the expression visualisation tools at the PlantGenIE.org web resource to enable easy access to the community. This will facilitate the use of the data to address novel questions about the role of lincRNAs in the regulation of embryogenesis and facilitate future comparative genomics studies.

PMID:39319989 | DOI:10.1111/ppl.14537

Front Bioinform. 2024 Sep 10;4:1395981. doi: 10.3389/fbinf.2024.1395981. eCollection 2024.

ABSTRACT

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool's interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different "visual channel" of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

PMID:39318761 | PMC:PMC11420163 | DOI:10.3389/fbinf.2024.1395981

JMIR Med Inform. 2024 Sep 24;12:e58445. doi: 10.2196/58445.

ABSTRACT

BACKGROUND: Data models are crucial for clinical research as they enable researchers to fully use the vast amount of clinical data stored in medical systems. Standardized data and well-defined relationships between data points are necessary to guarantee semantic interoperability. Using the Fast Healthcare Interoperability Resources (FHIR) standard for clinical data representation would be a practical methodology to enhance and accelerate interoperability and data availability for research.

OBJECTIVE: This research aims to provide a comprehensive overview of the state-of-the-art and current landscape in FHIR-based data models and structures. In addition, we intend to identify and discuss the tools, resources, limitations, and other critical aspects mentioned in the selected research papers.

METHODS: To ensure the extraction of reliable results, we followed the instructions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We analyzed the indexed articles in PubMed, Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, and Google Scholar. After identifying, extracting, and assessing the quality and relevance of the articles, we synthesized the extracted data to identify common patterns, themes, and variations in the use of FHIR-based data models and structures across different studies.

RESULTS: On the basis of the reviewed articles, we could identify 2 main themes: dynamic (pipeline-based) and static data models. The articles were also categorized into health care use cases, including chronic diseases, COVID-19 and infectious diseases, cancer research, acute or intensive care, random and general medical notes, and other conditions. Furthermore, we summarized the important or common tools and approaches of the selected papers. These items included FHIR-based tools and frameworks, machine learning approaches, and data storage and security. The most common resource was "Observation" followed by "Condition" and "Patient." The limitations and challenges of developing data models were categorized based on the issues of data integration, interoperability, standardization, performance, and scalability or generalizability.

CONCLUSIONS: FHIR serves as a highly promising interoperability standard for developing real-world health care apps. The implementation of FHIR modeling for electronic health record data facilitates the integration, transmission, and analysis of data while also advancing translational research and phenotyping. Generally, FHIR-based exports of local data repositories improve data interoperability for systems and data warehouses across different settings. However, ongoing efforts to address existing limitations and challenges are essential for the successful implementation and integration of FHIR data models.

PMID:39316433 | PMC:PMC11472501 | DOI:10.2196/58445

Perfusion. 2024 Sep 17:2676591241269729. doi: 10.1177/02676591241269729. Online ahead of print.

ABSTRACT

INTRODUCTION: Minimal Invasive Extracorporeal Circulation (MiECC) has recently emerged as a more 'physiologic' alternative to conventional extracorporeal circulation. However, its adoption is still limited due to lack of robust scientific evidence and ongoing debate about its potential benefits. This bibliometric analysis aims to analyze the scientific articles on MiECC and identify current research domains and existing gaps to be addressed in future studies.

METHODS: Pertinent articles were retrieved from the Web of Science (WOS) database. The search string included 'minimal invasive extracorporeal circulation' and its synonyms. The VOSviewer (version 1.6.17) software was used to conduct comprehensive analyses. Semantic and research networks, bibliographic coupling and journal analysis were performed.

RESULTS: Of the 1777 articles identified in WOS, 292 were retrieved. The trend in publications increased from 1991 to date. Most articles focused on transfusion requirements, acute kidney injury, inflammatory markers and cytokines, inflammation and delirium, though the impact of intraoperative optimal fluid and hemodynamic management as far as the occurrence of postoperative complications were poorly addressed. The semantic network analysis found inter-connections between the terms "cardiopulmonary bypass", "inflammatory response", and "cardiac surgery". Perfusion contributed the highest number of published documents. The most extensive research partnerships were between Germany, Greece, Italy, and England.

CONCLUSIONS: Notwithstanding the scientific community's growing interest in MiECC, crucial topics (i.e., the best anesthetic management and intraoperative need for inotropes, vasopressors and fluids) still require more comprehensive exploration. This investigation may prove to be a useful tool for clinicians, scientists, and students concerning global publication output and for the use of MiECC in cardiac surgery.

PMID:39288245 | DOI:10.1177/02676591241269729

JMIR Med Inform. 2024 Sep 17;12:e57853. doi: 10.2196/57853.

ABSTRACT

BACKGROUND: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs.

OBJECTIVE: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model.

METHODS: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the "Delta." Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources.

RESULTS: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts.

CONCLUSIONS: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users.

PMID:39287966 | PMC:PMC11445620 | DOI:10.2196/57853

Heliyon. 2024 Aug 26;10(17):e36936. doi: 10.1016/j.heliyon.2024.e36936. eCollection 2024 Sep 15.

ABSTRACT

Emergency response plans for tunnel vehicle accidents are crucial to ensure human safety, protect critical infrastructure, and maintain the smooth operation of transportation networks. However, many decision-support systems for emergency responses still rely significantly on predefined response strategies, which may not be sufficiently flexible to manage unexpected or complex incidents. Moreover, existing systems may lack the ability to effectively respond effectively to the impact different emergency scenarios and responses. In this study, semantic web technologies were used to construct a digital decision-support system for emergency responses to tunnel vehicle accidents. A basic digital framework was developed by analysing the knowledge system of the tunnel emergency response, examining its critical elements and intrinsic relationships, and mapping it to the ontology. In addition, the strategies of previous pre-plans are summarised and transformed into semantic rules. Finally, different accident scenarios were modelled to validate the effectiveness of the developed emergency response system.

PMID:39286211 | PMC:PMC11403512 | DOI:10.1016/j.heliyon.2024.e36936

Narra J. 2024 Aug;4(2):e917. doi: 10.52225/narra.v4i2.917. Epub 2024 Aug 5.

ABSTRACT

Since its public release on November 30, 2022, ChatGPT has shown promising potential in diverse healthcare applications despite ethical challenges, privacy issues, and possible biases. The aim of this study was to identify and assess the most influential publications in the field of ChatGPT utility in healthcare using bibliometric analysis. The study employed an advanced search on three databases, Scopus, Web of Science, and Google Scholar, to identify ChatGPT-related records in healthcare education, research, and practice between November 27 and 30, 2023. The ranking was based on the retrieved citation count in each database. The additional alternative metrics that were evaluated included (1) Semantic Scholar highly influential citations, (2) PlumX captures, (3) PlumX mentions, (4) PlumX social media and (5) Altmetric Attention Scores (AASs). A total of 22 unique records published in 17 different scientific journals from 14 different publishers were identified in the three databases. Only two publications were in the top 10 list across the three databases. Variable publication types were identified, with the most common being editorial/commentary publications (n=8/22, 36.4%). Nine of the 22 records had corresponding authors affiliated with institutions in the United States (40.9%). The range of citation count varied per database, with the highest range identified in Google Scholar (1019-121), followed by Scopus (242-88), and Web of Science (171-23). Google Scholar citations were correlated significantly with the following metrics: Semantic Scholar highly influential citations (Spearman's correlation coefficient ρ=0.840, p<0.001), PlumX captures (ρ=0.831, p<0.001), PlumX mentions (ρ=0.609, p=0.004), and AASs (ρ=0.542, p=0.009). In conclusion, despite several acknowledged limitations, this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics, highlighting its usefulness as a supplement to gauge the impact of publications, even in a rapidly growing research field.

PMID:39280327 | PMC:PMC11391998 | DOI:10.52225/narra.v4i2.917

J Thorac Cardiovasc Surg. 2024 Sep 14:S0022-5223(24)00809-2. doi: 10.1016/j.jtcvs.2024.09.014. Online ahead of print.

ABSTRACT

BACKGROUND: The task of writing structured content reviews and guidelines has grown stronger and more complex. We propose to go beyond search tools and toward curation tools by automating time-consuming and repetitive steps of extracting and organizing information.

METHODS: SciScribe is built as an extension of IBM's Deep Search platform, which provides document processing and search capabilities. This platform was used to ingest and search full-content publications from PubMed Central (PMC) and official, structured records from the ClinicalTrials and OpenPayments databases. Author names and NCT numbers, mentioned within the publications, were used to link publications to these official records as context. Search strategies involve traditional keyword-based search as well as natural language question and answering via large language models (LLMs).

RESULTS: SciScribe is a web-based tool that helps accelerate literature reviews through key features: (1) accumulating a personal collection from publication sources, such as PMC or other sources; (2) incorporating contextual information from external databases into the presented papers, promoting a more informed assessment by readers; (3) semantic questioning and answering of documents to quickly assess relevance and hierarchical organization; and (4) semantic question answering for each document within a collection, collated into tables.

CONCLUSIONS: Emergent language processing techniques are opening new avenues to accelerate and enhance the literature review process, for which we have demonstrated a use case implementation in cardiac surgery. SciScribe automates and accelerates this process, mitigates errors associated with repetition and fatigue, and contextualizes results by linking relevant external data sources instantaneously.

PMID:39278616 | DOI:10.1016/j.jtcvs.2024.09.014

Sci Data. 2024 Sep 7;11(1):978. doi: 10.1038/s41597-024-03760-9.

ABSTRACT

The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation.

PMID:39244629 | PMC:PMC11380662 | DOI:10.1038/s41597-024-03760-9

Health Inf Sci Syst. 2024 Sep 3;12(1):45. doi: 10.1007/s13755-024-00298-3. eCollection 2024 Dec.

ABSTRACT

Adolescent suicide has become an important social issue of general concern. Many young people express their suicidal feelings and intentions through online social media, e.g., Twitter, Microblog. The "tree hole" is the Chinese name for places on the Web where people post secrets. It provides the possibility of using Artificial Intelligence and big data technology to detect the posts where someone express the suicidal signal from those "tree hole" social media. We have developed the Web-based intelligent agents (i.e., AI-based programs) which can monitor the "tree hole" websites in Microblog every day by using knowledge graph technology. We have organized Tree-hole Rescue Team, which consists of more than 1000 volunteers, to carry out suicide rescue intervention according to the daily monitoring notifications. From 2018 to 2023, Tree-hole Rescue Team has prevented more than 6600 suicides. A few thousands of people have been saved within those 6 years. In this paper, we present the basic technology of Web-based Tree Hole intelligent agents and elaborate how the intelligent agents can discover suicide attempts and issue corresponding monitoring notifications and how the volunteers of Tree Hole Rescue Team can conduct online suicide intervention. This research also shows that the knowledge graph approach can be used for the semantic analysis on social media.

PMID:39238574 | PMC:PMC11371955 | DOI:10.1007/s13755-024-00298-3

NPJ Digit Med. 2024 Sep 3;7(1):231. doi: 10.1038/s41746-024-01225-2.

ABSTRACT

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous ("Go"/"No-Go") zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.

PMID:39227660 | DOI:10.1038/s41746-024-01225-2

Biomolecules. 2024 Aug 20;14(8):1033. doi: 10.3390/biom14081033.

ABSTRACT

The development of new treatments for neglected tropical diseases (NTDs) remains a major challenge in the 21st century. In most cases, the available drugs are obsolete and have limitations in terms of efficacy and safety. The situation becomes even more complex when considering the low number of new chemical entities (NCEs) currently in use in advanced clinical trials for most of these diseases. Natural products (NPs) are valuable sources of hits and lead compounds with privileged scaffolds for the discovery of new bioactive molecules. Considering the relevance of biodiversity for drug discovery, a chemoinformatics analysis was conducted on a compound dataset of NPs with anti-trypanosomatid activity reported in 497 research articles from 2019 to 2024. Structures corresponding to different metabolic classes were identified, including terpenoids, benzoic acids, benzenoids, steroids, alkaloids, phenylpropanoids, peptides, flavonoids, polyketides, lignans, cytochalasins, and naphthoquinones. This unique collection of NPs occupies regions of the chemical space with drug-like properties that are relevant to anti-trypanosomatid drug discovery. The gathered information greatly enhanced our understanding of biologically relevant chemical classes, structural features, and physicochemical properties. These results can be useful in guiding future medicinal chemistry efforts for the development of NP-inspired NCEs to treat NTDs caused by trypanosomatid parasites.

PMID:39199420 | DOI:10.3390/biom14081033

Stud Health Technol Inform. 2024 Aug 22;316:1406-1410. doi: 10.3233/SHTI240674.

ABSTRACT

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

PMID:39176643 | DOI:10.3233/SHTI240674

Stud Health Technol Inform. 2024 Aug 22;316:1785-1789. doi: 10.3233/SHTI240777.

ABSTRACT

Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification. To enhance interpretability, discriminant phenotypes characterizing each cluster are provided. Users can visually test hypotheses by marking patients exhibiting specific keywords in the EHR like genes, drugs and procedures. Implemented through a web interface, the pipeline enables clinicians to navigate through different modules, discover intricate patterns and generate interpretable insights that may advance rare diseases understanding, guide decision-making, and ultimately improve patient outcomes.

PMID:39176563 | DOI:10.3233/SHTI240777