J Psychiatr Res. 2024 Apr;172:34. doi: 10.1016/j.jpsychires.2024.01.040. Epub 2024 Feb 7.

NO ABSTRACT

PMID:38354545 | DOI:10.1016/j.jpsychires.2024.01.040

IEEE Trans Pattern Anal Mach Intell. 2024 Feb 14;PP. doi: 10.1109/TPAMI.2024.3365739. Online ahead of print.

ABSTRACT

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese. CREATE comprises a high-quality labeled 210K dataset and two web-scale 3M and 10M pre-training datasets, covering 51 categories, 50K+ tags, 537K+ manually annotated titles and captions, and 10M+ short videos with original video information. This work presents ACTEr, a unique Attractiveness-Consensus-based Title Evaluation, to objectively evaluate the quality of video title generation. This metric measures the semantic correlation between the candidate (model-generated title) and references (manual-labeled titles) and introduces attractive consensus weights to assess the attractiveness and relevance of the video title. Accordingly, this work proposes a novel multi-modal ALignment WIth Generation model, ALWIG, as one strong baseline to aid future model development. With the help of a tag-driven video-text alignment module and a GPT-based generation module, this model achieves video titling, captioning, and retrieval simultaneously. We believe that the release of the CREATE dataset, ACTEr metric, and ALWIG model will encourage in-depth research on the analysis and creation of Chinese short videos. Project webpage: https://createbenchmark.github.io/.

PMID:38354071 | DOI:10.1109/TPAMI.2024.3365739

Age Ageing. 2024 Feb 1;53(2):afae015. doi: 10.1093/ageing/afae015.

ABSTRACT

BACKGROUND: The impact of the COVID-19 pandemic on long-term care residents remains of wide interest, but most analyses focus on the initial wave of infections.

OBJECTIVE: To examine change over time in: (i) The size, duration, classification and pattern of care-home outbreaks of COVID-19 and associated mortality and (ii) characteristics associated with an outbreak.

DESIGN: Retrospective observational cohort study using routinely-collected data.

SETTING: All adult care-homes in Scotland (1,092 homes, 41,299 places).

METHODS: Analysis was undertaken at care-home level, over three periods. Period (P)1 01/03/2020-31/08/2020; P2 01/09/2020-31/05/2021 and P3 01/06/2021-31/10/2021. Outcomes were the presence and characteristics of outbreaks and mortality within the care-home. Cluster analysis was used to compare the pattern of outbreaks. Logistic regression examined care-home characteristics associated with outbreaks.

RESULTS: In total 296 (27.1%) care-homes had one outbreak, 220 (20.1%) had two, 91 (8.3%) had three, and 68 (6.2%) had four or more. There were 1,313 outbreaks involving residents: 431 outbreaks in P1, 559 in P2 and 323 in P3. The COVID-19 mortality rate per 1,000 beds fell from 45.8 in P1, to 29.3 in P2, and 3.5 in P3. Larger care-homes were much more likely to have an outbreak, but associations between size and outbreaks were weaker in later periods.

CONCLUSIONS: COVID-19 mitigation measures appear to have been beneficial, although the impact on residents remained severe until early 2021. Care-home residents, staff, relatives and providers are critical groups for consideration and involvement in future pandemic planning.

PMID:38342752 | PMC:PMC10859243 | DOI:10.1093/ageing/afae015

Hum Brain Mapp. 2024 Feb 1;45(2):e26564. doi: 10.1002/hbm.26564.

ABSTRACT

Wine tasting is a very complex process that integrates a combination of sensation, language, and memory. Taste and smell provide perceptual information that, together with the semantic narrative that converts flavor into words, seem to be processed differently between sommeliers and naïve wine consumers. We investigate whether sommeliers' wine experience shapes only chemosensory processing, as has been previously demonstrated, or if it also modulates the way in which the taste and olfactory circuits interact with the semantic network. Combining diffusion-weighted images and fMRI (activation and connectivity) we investigated whether brain response to tasting wine differs between sommeliers and nonexperts (1) in the sensory neural circuits representing flavor and/or (2) in the neural circuits for language and memory. We demonstrate that training in wine tasting shapes the microstructure of the left and right superior longitudinal fasciculus. Using mediation analysis, we showed that the experience modulates the relationship between fractional anisotropy and behavior: the higher the fractional anisotropy the higher the capacity to recognize wine complexity. In addition, we found functional differences between sommeliers and naïve consumers affecting the flavor sensory circuit, but also regions involved in semantic operations. The former reflects a capacity for differential sensory processing, while the latter reflects sommeliers' ability to attend to relevant sensory inputs and translate them into complex verbal descriptions. The enhanced synchronization between these apparently independent circuits suggests that sommeliers integrated these descriptions with previous semantic knowledge to optimize their capacity to distinguish between subtle differences in the qualitative character of the wine.

PMID:38339911 | PMC:PMC10823763 | DOI:10.1002/hbm.26564

Front Pharmacol. 2024 Jan 24;14:1287793. doi: 10.3389/fphar.2023.1287793. eCollection 2023.

ABSTRACT

Despite its millennial existence and empirical documentation, the ethnological knowledge of herbs is a more recent phenomenon. The knowledge of their historical uses as food, medicine, source of income and small-scale businesses, and the sociological impacts are threatened due to the slow ethnobotanical research drive. Species of the genus Solanum have long been extensively used in folk medicine to treat various illnesses of humans since the dawn of civilization. All data were systematically obtained from papers, monographs, and books written in Uzbek, Russian, and English through various scientific online databases, including Google, Google Scholar, PubMed, Scopus, Semantic Scholar, Science Direct, and Web of Science using specific keywords focused on eight Solanum species. Eight native and non-native Solanum species as S. dulcamara L., S. lycopersicum L., S. melongena L., S. nigrum L., S. rostratum Dunal., S. sisymbriifolium Lam., S. tuberosum L., and S. villosum Mill. have been recorded in Uzbekistan of Central Asia. In this article we presented recently obtained data on the diversity, morphological characteristics, global distribution, habitat, population status, phenology, reproduction, pharmacology and phytochemistry of these Solanum species in Uzbekistan. Furthermore, relying on a combination of literature reviews and analyses from various scientific papers, we focus on food consumption coupled with global ethnobotanical and ethnopharmacological uses in human diseases of the Solanum species growing in Uzbekistan. Since the dawn of civilization, these eight cultivated and non-cultivated species of Solanum have provided sustainable resources of medicinal plants in Uzbekistan to prevent and treat various human diseases. Based on the collected data, it was shown that Solanum species have not been studied ethnobotanically and ethnomedicinally in Uzbekistan and it is necessary to conduct phytochemical and biotechnological research on them in the future. Traditional uses and scientific evaluation of Solanum indicate that S. nigrum, S. sisymbriifolium and S. tuberosum are one of the most widely used species in some parts of the world. Although considerable progress has been made to comprehend the chemical and biological properties of S. nigrum and S. tuberosum species, more research on the pharmacology and toxicology of these species is needed to ensure the safety, efficacy, and quality of their biologically active extracts and isolated bioactive compounds. Additionally, conducting additional research on the structure-activity relationship of certain isolated phytochemicals has the potential to enhance their biological efficacy and advance the scientific utilization of traditional applications of Solanum taxa.

PMID:38333226 | PMC:PMC10851437 | DOI:10.3389/fphar.2023.1287793

Proc Int World Wide Web Conf. 2023 Apr;2023(Companion):820-825. doi: 10.1145/3543873.3587601. Epub 2023 Apr 30.

ABSTRACT

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.

PMID:38327770 | PMC:PMC10848146 | DOI:10.1145/3543873.3587601

J Cheminform. 2024 Feb 7;16(1):16. doi: 10.1186/s13321-024-00807-2.

ABSTRACT

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .

PMID:38326906 | PMC:PMC10851519 | DOI:10.1186/s13321-024-00807-2

Brain Lang. 2024 Mar;250:105388. doi: 10.1016/j.bandl.2024.105388. Epub 2024 Jan 31.

ABSTRACT

Multiple sensory-motor and non-sensory-motor dimensions have been proposed for semantic representation, but it remains unclear how the semantic system is organized along them in the human brain. Using naturalistic fMRI data and large-scale semantic ratings, we investigated the overlaps and dissociations between the neural correlates of six semantic dimensions: vision, motor, socialness, emotion, space, and time. Our findings revealed a more complex semantic atlas than what is predicted by current neurobiological models of semantic representation. Brain regions that are selectively sensitive to specific semantic dimensions were found both within and outside the brain networks assumed to represent multimodal general and/or abstract semantics. Overlaps between the neural correlates of different semantic dimensions were mainly found inside the default mode network, concentrated in the left anterior superior temporal gyrus and angular gyrus, which have been proposed as two connector hubs that bridge the multimodal experiential semantic system and the language-supported semantic system.

PMID:38295716 | DOI:10.1016/j.bandl.2024.105388

J Med Internet Res. 2024 Jan 30;26:e45209. doi: 10.2196/45209.

ABSTRACT

BACKGROUND: The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard.

OBJECTIVE: The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies.

METHODS: A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard.

RESULTS: A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability.

CONCLUSIONS: This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

PMID:38289660 | PMC:PMC10865191 | DOI:10.2196/45209

Stud Health Technol Inform. 2024 Jan 25;310:184-188. doi: 10.3233/SHTI230952.

ABSTRACT

In multicenter clinical research, case-reported clinical data are managed for each research project. Participating institutions manage the mapping between standardized codes and in-house codes. To use the data extracted from electronic medical records in case report forms, it is necessary to pay attention to the gap in the semantic hierarchy. Managing mapping information between in-house and standardized codes is centralized in Resource Description Framework (RDF) stores. The relationship between standardized and in-house codes is described in RDF and stored in RDF stores. RESTful APIs for accessing RDF stores in SPARQL was developed and verified. The relationship between standardized codes and in-house codes of pharmaceuticals was expressed in RDF triples. As a +result of the operational verification of the implemented APIs, it was confirmed that data management with knowledge bases expressed in RDF graphs is possible. The ability to dynamically modify mapping definitions enables flexible data management and ease of operational restrictions.

PMID:38269790 | DOI:10.3233/SHTI230952

Turk Patoloji Derg. 2024 Jan 24. doi: 10.5146/tjpath.2023.13048. Online ahead of print.

ABSTRACT

OBJECTIVE: This review which aims to examine the recent and current status of pathology education in medical schools, and covers the publications related to undergraduate pathology education published between 2010 January and June 2023.

MATERIAL AND METHOD: A search was performed through PubMed, Google Scholar, Semantic Scholar, and Ulakbim search engines for the Science Citation Index, Science Citation Index Expanded, Emerging Sources Citation Index, Directory of Open Access Journals, Scopus, PubMed as well as TR Dizin indexed articles. The findings are categorized into two periods as 2010 January - 2020 April (pre-COVID-19 pandemic) and May 2020 - 2023 June. A total of 24 reviews/editorials/letters to the editor and 63 research articles in the pre-pandemic period and 11 reviews/ editorials/ letters to the editor and 35 research articles between 2020 May and 2023 June are included in the analysis.

RESULTS: Currently, medical education generally depends on core education programs with defined learning objectives and outcomes. Moreover, problem-based, case-based, and team-based interactive learning are being used along with traditional didactic courses. Additionally, digital/ web-based/remote education methods have gained prominence after the COVID-19 pandemic. The virtual or augmented reality and 3D drawing applications are offered as a solution for the autopsy and macroscopy courses. A scarce number of publications are found on measuring and evaluating the effectiveness of learning.

CONCLUSION: Artificial intelligence in pathology education is a topic that looks likely to become important in the near future. National and international comprehensive standardization is a necessity. A joint effort and collective intelligence are needed to achieve the desired goals in undergraduate pathology education.

PMID:38265100 | DOI:10.5146/tjpath.2023.13048

Br J Radiol. 2024 Jan 23;97(1153):98-112. doi: 10.1093/bjr/tqad023.

ABSTRACT

OBJECTIVES: To build a data set capturing the whole breast cancer screening journey from individual breast cancer screening records to outcomes and assess data quality.

METHODS: Routine screening records (invitation, attendance, test results) from all 79 English NHS breast screening centres between January 1, 1988 and March 31, 2018 were linked to cancer registry (cancer characteristics and treatment) and national mortality data. Data quality was assessed using comparability, validity, timeliness, and completeness.

RESULTS: Screening records were extracted from 76/79 English breast screening centres, 3/79 were not possible due to software issues. Data linkage was successful from 1997 after introduction of a universal identifier for women (NHS number). Prior to 1997 outcome data are incomplete due to linkage issues, reducing validity. Between January 1, 1997 and March 31, 2018, a total of 11 262 730 women were offered screening of whom 9 371 973 attended at least one appointment, with 139 million person-years of follow-up (a median of 12.4 person years for each woman included) with 73 810 breast cancer deaths and 1 111 139 any-cause deaths. Comparability to reference data sets and internal validity were demonstrated. Data completeness was high for core screening variables (>99%) and main cancer outcomes (>95%).

CONCLUSIONS: The ATHENA-M project has created a large high-quality and representative data set of individual women's screening trajectories and outcomes in England from 1997 to 2018, data before 1997 are lower quality.

ADVANCES IN KNOWLEDGE: This is the most complete data set of English breast screening records and outcomes constructed to date, which can be used to evaluate and optimize screening.

PMID:38263823 | DOI:10.1093/bjr/tqad023

BMC Med Inform Decis Mak. 2024 Jan 19;24(1):18. doi: 10.1186/s12911-023-02405-y.

ABSTRACT

OBJECTIVE: To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.

MATERIALS AND METHODS: We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.

RESULTS: The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.

CONCLUSION: CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

PMID:38243204 | PMC:PMC10799385 | DOI:10.1186/s12911-023-02405-y

PLoS One. 2024 Jan 19;19(1):e0293498. doi: 10.1371/journal.pone.0293498. eCollection 2024.

ABSTRACT

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval issue aiming to match the same pedestrian between visible and infrared cameras. Thus, the modality discrepancy presents a significant challenge for this task. Most methods employ different networks to extract features that are invariant between modalities. While we propose a novel channel semantic mutual learning network (CSMN), which attributes the difference in semantics between modalities to the difference at the channel level, it optimises the semantic consistency between channels from two perspectives: the local inter-channel semantics and the global inter-modal semantics. Meanwhile, we design a channel-level auto-guided double metric loss (CADM) to learn modality-invariant features and the sample distribution in a fine-grained manner. We conducted experiments on RegDB and SYSU-MM01, and the experimental results validate the superiority of CSMN. Especially on RegDB datasets, CSMN improves the current best performance by 3.43% and 0.5% on the Rank-1 score and mINP value, respectively. The code is available at https://github.com/013zyj/CSMN.

PMID:38241236 | PMC:PMC10798514 | DOI:10.1371/journal.pone.0293498

Comput Biol Med. 2024 Mar;170:107767. doi: 10.1016/j.compbiomed.2023.107767. Epub 2023 Nov 28.

ABSTRACT

Medical image inpainting holds significant importance in enhancing the quality of medical images by restoring missing areas, thereby rendering them suitable for diagnostic purposes. While several techniques have been previously proposed for medical image inpainting, they are not suitable for distorted images containing metallic implants due to their limited consideration of known shaped masking. To overcome this limitation, a novel Vectorized Box Interpolation with Arbitrary Auto-Rand Augment Masking technique has been proposed which involves scaling and vectorizing images to expand their details and generating asymmetrically shaped masking in an automatic random format. One of the challenging tasks in this regard is the precise detection of lost regions, which is addressed through the introduction of the Regional Pixel Semantic Network. This technique employs the locally shared features (LSF) based region sensing with FCN (fully convolutional network) segmentation, which performs automatic segmentation based on neighboring pixel local dependency and regional features to determine the location of masked regions. During the reconstruction of missing parts, a significant challenge posed is the inability to recognize proximity in encoding owing to the generation of shadow-like regions on the feature map. To address this issue, a novel Multilayered DRC Regularized Pyramidal Attention AE Model has been proposed which employs dilated convolution with coherent pyramidal attention for feature extraction and improves image resolution using a Laplacian convolutional layer. Moreover, the realness of the generated image is determined using the Quantile Differential Mechanism model, where in the Quantile Differential Partial Convolutional Discriminator utilizes the hyperbolic tangent activation function in the partial convolutional layer to calculate recognition accuracy. As a result, the proposed method achieves high percentages for accuracy (98 %), precision (97 %), sensitivity (96 %), recall (95 %), and F-measure (96 %) thereby outperforming existing methods. Overall, this proposed method effectively handles distorted images with metallic implants, accurately detects lost regions, and improves the reconstructed image quality.

PMID:38215616 | DOI:10.1016/j.compbiomed.2023.107767

Comput Biol Med. 2024 Jan 4;169:107929. doi: 10.1016/j.compbiomed.2024.107929. Online ahead of print.

ABSTRACT

In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.

PMID:38184862 | DOI:10.1016/j.compbiomed.2024.107929

BMJ Open. 2024 Jan 6;14(1):e074624. doi: 10.1136/bmjopen-2023-074624.

ABSTRACT

OBJECTIVE: Timely access to primary care and supporting specialist care relative to need is essential for health equity. However, use of services can vary according to an individual's socioeconomic circumstances or where they live. This study aimed to quantify individual socioeconomic variation in general practitioner (GP) and specialist use in New South Wales (NSW), accounting for area-level variation in use.

DESIGN: Outcomes were GP use and quality-of-care and specialist use. Multilevel logistic regression was used to estimate: (1) median ORs (MORs) to quantify small area variation in outcomes, which gives the median increased risk of moving to an area of higher risk of an outcome, and (2) ORs to quantify associations between outcomes and individual education level, our main exposure variable. Analyses were adjusted for individual sociodemographic and health characteristics and performed separately by remoteness categories.

SETTING: Baseline data (2006-2009) from the 45 and Up Study, NSW, Australia, linked to Medicare Benefits Schedule and death data (to December 2012).

PARTICIPANTS: 267 153 adults aged 45 years and older.

RESULTS: GP (MOR=1.32-1.35) and specialist use (1.16-1.18) varied between areas, accounting for individual characteristics. For a given level of need and accounting for area variation, low education-level individuals were more likely to be frequent users of GP services (no school certificate vs university, OR=1.63-1.91, depending on remoteness category) and have continuity of care (OR=1.14-1.24), but were less likely to see a specialist (OR=0.85-0.95).

CONCLUSION: GP and specialist use varied across small areas in NSW, independent of individual characteristics. Use of GP care was equitable, but specialist care was not. Failure to address inequitable specialist use may undermine equity gains within the primary care system. Policies should also focus on local variation.

PMID:38184309 | DOI:10.1136/bmjopen-2023-074624

BMC Med Inform Decis Mak. 2024 Jan 4;24(1):10. doi: 10.1186/s12911-023-02409-8.

ABSTRACT

BACKGROUND: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources.

METHODS: We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs).

RESULTS: We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster.

CONCLUSIONS: Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").

PMID:38178113 | DOI:10.1186/s12911-023-02409-8

BMC Biol. 2024 Jan 2;22(1):3. doi: 10.1186/s12915-023-01803-y.

ABSTRACT

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

PMID:38166858 | DOI:10.1186/s12915-023-01803-y

Behav Res Methods. 2023 Dec 21. doi: 10.3758/s13428-023-02257-4. Online ahead of print.

ABSTRACT

We present a collection of concreteness ratings for 35,979 words in Estonian. The data were collected via a web application from 2278 native Estonian speakers. Human ratings of concreteness have not been collected for Estonian beforehand. We compare our results to Aedmaa et al. (2018), who assigned concreteness ratings to 240,000 Estonian words by means of machine learning. We show that while these two datasets show reasonable correlation (R = 0.71), there are considerable differences in the distribution of the ratings, which we discuss in this paper. Furthermore, the results also raise questions about the importance of the type of scale used for collecting ratings. While most other datasets have been compiled based on questionnaires entailing five- or seven-point Likert scales, we used a continuous 0-10 scale. Comparing our rating distribution to those of other studies, we found that it is most similar to the distribution in Lahl et al. (Behavior Research Methods, 41(1), 13-19, 2009), who also used a 0-10 scale. Concreteness ratings for Estonian words are available at OSF .

PMID:38129738 | DOI:10.3758/s13428-023-02257-4