Cortex. 2024 Dec 27;184:172-193. doi: 10.1016/j.cortex.2024.11.019. Online ahead of print.

ABSTRACT

Retrieving words quickly and correctly is an important language competence. Semantic contexts, such as prior naming of categorically related objects, can induce conceptual priming but also lexical-semantic interference, the latter likely due to enhanced competition during lexical selection. In the continuous naming (CN) paradigm, such semantic interference is evident in a linear increase in naming latency with each additional member of a category out of a seemingly random sequence of pictures being named (cumulative semantic interference/CSI effect). Extensively studied in neurotypical participants, CSI studies in people with aphasia (PWA) are rare, although some lesions regularly and persistently impair word retrieval. In the present study, 20 PWA with lesions in the extended left hemispheric language network and 20 matched controls underwent a CN paradigm, naming photographs of closely related objects from 24 categories (e.g., birds) with 5 members each. The experiment was conducted web-based (Stark et al., 2022) on three days (day 1, 2, and 8). The main results are: (i) Mild-moderate aphasia does not preclude web-based testing. (ii) The CSI effect in naming latencies (∼21 ms per ordinal position) did not differ significantly between groups but was more variable in the PWA; the effect was stable across days. (iii) Overall response times decreased between day 1 and day 2, but remained stable on day 8. (iv) In PWA, increased error-rates paralleled the latency-based CSI effect, suggesting stronger interference in this group. (v) Exploratory analyses suggest that lesions in a large area, including frontal, inferior parietal, pre- and post-central opercular cortices, are linked to a larger CSI effect. At a more lenient statistical threshold, lesions in occipital and supramarginal cortices were associated with increased overall naming latencies. These results offer an initial step toward identifying the neuronal underpinnings of semantic context effects in PWA. We conclude that web-based assessment is feasible in PWA and yields a stable CSI effect over repetitive testing. While not directly clinically applicable, the findings could serve as a foundation for exploring training-interventions targeting lexical activation, interference resolution, or word selection.

PMID:39862560 | DOI:10.1016/j.cortex.2024.11.019

JMIR Res Protoc. 2025 Jan 23;14:e65137. doi: 10.2196/65137.

ABSTRACT

BACKGROUND: Injectable platelet-rich fibrin (i-PRF) has the capacity to release great amounts of several growth factors, as well as to stimulate increased fibroblast migration and the expression of collagen, transforming growth factor β, and platelet-derived growth factor. Consequently, i-PRF can be used as a bioactive agent to promote periodontal tissue regeneration.

OBJECTIVE: We aim to compare and evaluate the effectiveness of i-PRF in periodontal tissue regeneration.

METHODS: We will conduct an electronic search in the following databases: PubMed, Cochrane Library, Google Scholar, Semantic Scholar, Scopus, and Web of Science. Papers will be restricted to those in English and to those that are randomized controlled trials comparing PRF or any other biomaterial with i-PRF for periodontal regeneration during dental treatment. The included papers in this review and the reference lists of pertinent reviews will be manually searched. The selection of studies, data extraction, and assessment will be carried out separately by 2 reviewers using the Risk of Bias 2 tool for the included research.

RESULTS: The success of i-PRF will be evaluated by comparing the mean difference in periodontal regeneration of soft and hard tissues in terms of gingival recession, probing pocket depth, clinical attachment level, bone gain, and gingival width. The combined effect size measurements and the associated 95% CIs will be estimated using a random-effects model. The synthesis or work for this systematic review started in October 2023 and will last until December 2025.

CONCLUSIONS: i-PRF may play a role in dentistry and could enhance soft and hard tissue regeneration.

TRIAL REGISTRATION: PROSPERO CRD42023464250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=464250.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65137.

PMID:39847766 | DOI:10.2196/65137

Front Nutr. 2025 Jan 6;11:1503389. doi: 10.3389/fnut.2024.1503389. eCollection 2024.

ABSTRACT

INTRODUCTION: Contemporary data and knowledge management and exploration are challenging due to regular releases, updates, and different types and formats. In the food and nutrition domain, solutions for integrating such data and knowledge with respect to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are still lacking.

METHODS: To address this issue, we have developed a data and knowledge management system called NutriBase, which supports the compilation of a food composition database and its integration with evidence-based knowledge. This research is a novel contribution because it allows for the interconnection and complementation of food composition data with knowledge and takes what has been done in the past a step further by enabling the integration of knowledge. NutriBase focuses on two important challenges; data (semantic) harmonization by using the existing ontologies, and reducing missing data by semi-automatic data imputation made from conflating with existing databases.

RESULTS AND DISCUSSION: The developed web-based tool is highly modifiable and can be further customized to meet national or international requirements. It can help create and maintain the quality management system needed to assure data quality. Newly generated data and knowledge can continuously be added, as interoperability with other systems is enabled. The tool is intended for use by domain experts, food compilers, and researchers who can add and edit food-relevant data and knowledge. However, the tool is also accessible to food manufacturers, who can regularly update information about their products and thus give consumers access to current data. Moreover, the traceability of the data and knowledge provenance allows the compilation of a trustworthy management system. The system is designed to allow easy integration of data from different sources, which enables data borrowing and reduction of missing data. In this paper, the feasibility of NutriBase is demonstrated on Slovenian food-related data and knowledge, which is further linked with international resources. Outputs such as matched food components and food classifications have been integrated into semantic resources that are currently under development in various international projects.

PMID:39834464 | PMC:PMC11743969 | DOI:10.3389/fnut.2024.1503389

J Clin Nurs. 2025 Jan 20. doi: 10.1111/jocn.17664. Online ahead of print.

ABSTRACT

AIM: To cross-culturally adapt a framework for person-centred leadership in residential care for older people in Sweden.

DESIGN: This study has an exploratory and descriptive design.

METHODS: The translation procedure followed a cyclic process of translation into Swedish and back-translation into English by two independent bilingual linguists. An evaluation of conceptual and semantic equivalence and comprehensiveness between the original English version and the translated Swedish version was performed by an expert committee. The translated version of the framework was validated by leaders (n = 34) in residential care, who assessed its relevance through a web form. The adaptation of the framework followed recommended guidelines for cross-cultural adaptation.

RESULTS: The translation procedure resulted in two minor changes related to the wording in two descriptors. The results of the validation procedure showed that the framework is relevant for leaders in Swedish residential care for older people.

CONCLUSION: The cross-culturally adapted framework is useful and suitable for leaders in Swedish residential care for older people. The framework clarifies the leader's role and identifies leadership attributes and requirements for person-centred leadership in residential care, thereby providing support to leaders by framing person-centred leadership.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The framework can be used as a guide for leadership training and/or development initiatives in residential care. It can be further extended to nursing curriculums, leadership development programs, and organisational performance and development processes. It may also provide a foundation for policy and guidelines by establishing the activities required for leaders to promote person-centredness in the care of older people.

REPORTING METHOD: This study followed the STROBE checklist for cross-sectional studies.

PATIENT AND PUBLIC CONTRIBUTION: There was no patient or public contribution.

PMID:39831575 | DOI:10.1111/jocn.17664

MethodsX. 2024 Dec 16;14:103092. doi: 10.1016/j.mex.2024.103092. eCollection 2025 Jun.

ABSTRACT

This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. Motivated by some of the well-known universities and other Higher Education Institutions ontology, this study improvises the existing entities and introduces new entities in order to tackle a new topic identified from the preliminary interview conducted in the study to cover the study objective. The paper also proposes an innovative ontology, referred to as Student Performance and Course, to enhance resource management and evaluation mechanisms on course, students, and MOOC performance by the faculty. The model solves the issues of data accumulation and their heterogeneity, including the problem of having data in different formats and various semantic similarities, and is suitable for processing large amounts of data in terms of scalability. Thus, it also offers a way to confirm the process of data retrieval that is based on performance assessment with the help of an evaluation matrix.

PMID:39811619 | PMC:PMC11731703 | DOI:10.1016/j.mex.2024.103092

BMC Med Educ. 2025 Jan 12;25(1):52. doi: 10.1186/s12909-025-06644-7.

ABSTRACT

BACKGROUND: Despite the importance of effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning, unanswered questions remain. Understanding how these cognitive operations can be observed and assessed is crucial, particularly considering the rapid growth of artificial intelligence and its integration into health education. A scoping review was conducted to map the literature regarding educational strategies to support transformation and articulation of clinical data, the learning tasks expected of students when exposed to these strategies and methods used to assess individuals' proficiency METHODS: Based on the Joanna Briggs Institute methodology, the authors searched 5 databases (CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science), ProQuest Dissertations & Theses electronic database and Google Scholar. The data were synthesized narratively using descriptive statistics.

RESULTS: A total of 38 articles were included in the final synthesis. Most studies were conducted in North America and Europe (n = 30, 79%) focused primarily on medical students (n = 35, 92%) and mainly used observational (n = 17, 45%) or methodological (n = 8, 21%) designs. Various educational strategies were identified, the most common were resolution of written or computerized case-based scenarios (n = 13; 52%) and simulated or real patient encounters (n = 6; 24%). The learning tasks comprised, among others, identifying key findings, translating clinical information, synthesizing cases aloud, and writing a summary statement. Furthermore, the review included assessment methods and rubrics with assessment criteria for clinical data transformation and articulation. The narrative synthesis shows positive results when integrating various educational strategies within clinical reasoning curricula compared to a single strategy used episodically.

LIMITATIONS AND CONCLUSIONS: The varying objectives, diversity of educational strategies documented, and heterogeneity of the evaluation tools or rubrics limit our conclusions. However, insights gained will help educators develop effective approaches for teaching clinical reasoning. Additional research is needed to evaluate the impacts of educational strategies aimed at developing skills for the transformation and articulation of clinical data.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:39800713 | DOI:10.1186/s12909-025-06644-7

J Biomed Semantics. 2025 Jan 8;16(1):1. doi: 10.1186/s13326-024-00322-1.

ABSTRACT

BACKGROUND: TogoID ( https://togoid.dbcls.jp/ ) is an identifier (ID) conversion service designed to link IDs across diverse categories of life science databases. With its ability to obtain IDs related in different semantic relationships, a user-friendly web interface, and a regular automatic data update system, TogoID has been a valuable tool for bioinformatics.

RESULTS: We have recently expanded TogoID's ability to represent semantics between datasets, enabling it to handle multiple semantic relationships within dataset pairs. This enhancement enables TogoID to distinguish relationships such as "glycans bind to proteins" or "glycans are processed by proteins" between glycans and proteins. Additional new features include the ability to display labels corresponding to database IDs, making it easier to interpret the relationships between the various IDs available in TogoID, and the ability to convert labels to IDs, extending the entry point for ID conversion. The implementation of URL parameters, which reproduces the state of TogoID's web application, allows users to share complex search results through a simple URL.

CONCLUSIONS: These advancements improve TogoID's utility in bioinformatics, allowing researchers to explore complex ID relationships. By introducing the tool's multi-semantic and label features, TogoID expands the concept of ID conversion and supports more comprehensive and efficient data integration across life science databases.

PMID:39780290 | DOI:10.1186/s13326-024-00322-1

Sensors (Basel). 2024 Dec 20;24(24):8142. doi: 10.3390/s24248142.

ABSTRACT

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.

PMID:39771876 | DOI:10.3390/s24248142

Alzheimers Dement. 2024 Dec;20 Suppl 3:e086973. doi: 10.1002/alz.086973.

ABSTRACT

BACKGROUND: Automated speech and language analysis (ASLA) represents a powerful innovation for detecting and monitoring persons with or at risk for dementia. Given its cost-efficiency and automaticity, its impact can be vital for under-resourced communities, such Spanish-speaking Latinos. However, ASLA markers are understudied in this group and may differ from those established in widely studied populations (e.g., English speakers). Here I will describe a novel framework to boost ASLA research among Spanish-speaking Latinos.

METHOD: We have created hypothesis-driven metrics to capture semantic and episodic memory alterations via (semi-) spontaneous speech in Latinos. Our initial work includes measures of word selection patterns during verbal fluency, speech timing proxies of word retrieval effort, and algorithms quantifying egocentric/exocentric references in discourse. These metrics have been incorporated into the TELL app, our web-based speech testing platform. Initial analyses have been performed on 60 Latinos with Alzheimer's disease (AD), 65 with mild cognitive impairment (MCI), and 50 with behavioral variant frontotemporal dementia (bvFTD), including tests of cross-linguistic generalizability with English speakers.

RESULTS: Compared with healthy controls, AD and MCI (but not bvFTD) patients exhibit atypical vocabulary patterns during verbal fluency tasks, favoring high frequency and conceptually unspecific words, separated by longer pauses. These alterations discriminate between persons with and without these disorders (AD: AUC = .89; MCI: AUC = 81), and they predict atrophy of and hypo-connectivity among temporo-parietal regions implicated in semantic memory. Cross-linguistic generalizability between English and Spanish-speaking AD patients was maximal (AUC = .79) when based on speech timing features (e.g., pause duration, articulation rate). Finally, bvFTD (but not AD) patients showed abnormally exocentric discourse, with increased reliance on third-person references and reduced reliance on first-person references when describing daily activities.

CONCLUSION: Our initial findings attest to the usefulness of hypothesis-driven ASLA features to identify persons with AD, MCI, and bvFTD among the Latino population. The framework will now be leveraged in a large multicentric effort across the ReDLat consortium to establish the most robust markers in large, heterogeneous samples. This work will help us streamline ASLA research as an avenue for more equitable clinical testing of dementia in Latin America and beyond.

PMID:39750690 | DOI:10.1002/alz.086973

Front Psychol. 2024 Dec 18;15:1439397. doi: 10.3389/fpsyg.2024.1439397. eCollection 2024.

ABSTRACT

BACKGROUND: The present study investigated whether semantic processing of word and object primes can bias visual attention using top-down influences, even within an exogenous cueing framework. We hypothesized that real words and familiar objects would more effectively bias attentional engagement and target detection than pseudowords or pseudo-objects, as they can trigger prior knowledge to influence attention orienting and target detection.

METHODS: To examine this, we conducted two web-based eye-tracking experiments that ensured participants maintained central fixation on the screen during remote data collection. In Experiment 1, participants viewed a central prime-either a real word or pseudo-word-followed by a spatial cue directing them to a target on the left or right, which they located by pressing a key. Experiment 2 presented participants with real objects or pseudo-objects as primes, with primes and targets that either matched or did not match in identity. Importantly, primes in both experiments conveyed no information about target location.

RESULTS: Results from Experiment 1 indicated that real word primes were associated with faster target detection than pseudo-words. In Experiment 2, participants detected targets more quickly when primed with real objects and when prime-target identity matched. Comparisons across both experiments suggest an automatic influence of semantic knowledge on target detection and spatial attention.

DISCUSSION: These findings indicate that words can contribute to attentional capture, potentially through top-down processes, even within an exogenous cueing paradigm in which semantic processing is task-irrelevant.

PMID:39744025 | PMC:PMC11688633 | DOI:10.3389/fpsyg.2024.1439397

BBA Adv. 2024 Nov 30;6:100126. doi: 10.1016/j.bbadva.2024.100126. eCollection 2024.

ABSTRACT

Glycoconjugates are present on microbial surfaces and play critical roles in modulating interactions with the environment and the host. Extensive research on microbial glycans, including elucidating the structural diversity of the glycan moieties of glycoconjugates and polysaccharides, has been carried out to investigate the function of glycans in modulating the interactions between the host and microbes, to explore their potential applications in the therapeutic targeting of pathogenic species, and in the use as probiotics in gut microbiomes. However, glycan-related information is dispersed across numerous databases and a vast amount of literature, which makes it laborious and time-consuming to identify and gather the relevant information about microbial glycosylation. This challenge can be addressed by a comprehensive database, which could offer insight into the fundamental processes underlying glycosylation. We have developed a MicroGlycoDB database to provide integrated glycan information on important model microorganisms. The data is described using Semantic Web Technologies, which allow microbial glycan data to be represented in a structured format accessible by machines, thus facilitating data sharing and integration with other resources that catalog features such as pathways, diseases, or interactions. This semantic data based on ontologies will contribute to the discovery of new knowledge in the field of microbiology, along with the expansion of information on the glycosylation of other microorganisms.

PMID:39720162 | PMC:PMC11667048 | DOI:10.1016/j.bbadva.2024.100126

JMIR Aging. 2024 Dec 24;7:e67992. doi: 10.2196/67992.

ABSTRACT

BACKGROUND: As Alzheimer disease (AD) and AD-related dementias (ADRD) progress, individuals increasingly require assistance from unpaid, informal caregivers to support them in activities of daily living. These caregivers may experience high levels of financial, mental, and physical strain associated with providing care. CareVirtue is a web-based tool created to connect and support multiple individuals across a care network to coordinate care activities and share important information, thereby reducing care burden.

OBJECTIVE: This study aims to use a computational informatics approach to thematically analyze open text written by AD/ADRD caregivers in the CareVirtue platform. We then explore relationships between identified themes and use patterns.

METHODS: We analyzed journal posts (n=1555 posts; 170,212 words) generated by 51 unique users of the CareVirtue platform. Latent themes were identified using a neural network approach to topic modeling. We calculated a sentiment score for each post using the Valence Aware Dictionary and Sentiment Reasoner. We then examined relationships between identified topics; semantic sentiment; and use-related data, including post word count and self-reported mood.

RESULTS: We identified 5 primary topics in users' journal posts, including descriptions of specific events, professional and medical care, routine daily activities, nighttime symptoms, and bathroom/toileting issues. This 5-topic model demonstrated adequate fit to the data, having the highest coherence score (0.41) among those tested. We observed group differences across these topics in both word count and semantic sentiment. Further, posts made in the evening were both longer and more semantically positive than other times of the day.

CONCLUSIONS: Users of the CareVirtue platform journaled about a variety of different topics, including generalized experiences and specific behavioral symptomology of AD/ADRD, suggesting a desire to record and share broadly across the care network. Posts were the most positive in the early evening when the tool was used habitually, rather than when writing about acute events or symptomology. We discuss the value of embedding informatics-based tools into digital interventions to facilitate real-time content delivery.

PMID:39719081 | DOI:10.2196/67992

J Psychiatr Res. 2024 Dec 6;181:470-483. doi: 10.1016/j.jpsychires.2024.12.016. Online ahead of print.

ABSTRACT

OBJECTIVES: Previous studies have reported inconsistent findings regarding event-related potentials (ERPs) abnormalities in individuals with generalized anxiety disorder (GAD). This meta-analysis aimed to systematically review and synthesize the existing evidence on ERP alterations in individuals with GAD.

METHODS: A comprehensive literature search was conducted in PubMed, the Cochrane Library, Excerpta Medica Database, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), Wanfang database, and China Biology Medicine (CBM) databases from inception to November 11, 2024. Gray literature and reference lists were also manually searched. Studies investigating ERP component differences between individuals with GAD and healthy controls were included. Two independent reviewers conducted study selection, data extraction, and risk of bias assessment. Influence and sensitivity analyses were performed to assess the robustness of the pooled results. Effect sizes (SMD, Hedge's g) were calculated for latency and amplitude differences. Heterogeneity was assessed using the I2 statistic. Meta-regression and subgroup analyses were conducted to explore the source of heterogeneity. Trim-and-fill analyses were applied to assess potential publication bias. Data synthesis was performed using R (version 4.2.3) software.

RESULTS: A total of 37 studies involving 1086 individuals with GAD and 1315 healthy controls were included. The overall risk of bias was rated as low for 25 studies and moderate for 12 studies. Ten ERP components were included in the quantitative meta-analysis: P3, N2, N1, P2, Error Related Negativity (ERN), Correction Related Negativity (CRN), Mismatch Negativity (MMN), P1 (amplitude), Pe, and LPP. Pooled results indicated that individuals with GAD exhibited decreased P3 amplitude (g = -0.54, 95% CI: -0.70 to -0.38, I2 = 20%, P = 0.22) and increased ERN amplitude (g = -0.42, 95% CI: -0.72 to -0.12, I2 = 40%, P = 0.11) compared to healthy controls. In addition, delayed latency of P3 (g = 0.43, 95% CI: 0.09 to 0.78, I2 = 75%, P < 0.01), N2 (g = 0.36, 95% CI: 0.11 to 0.62, I2 = 30%, P = 0.20), and MMN (g = 0.63, 95% CI: 0.52 to 0.75, I2 = 0%, P < 0.0001) was observed in individuals with GAD. Due to the limited number of included studies, the results of N170, N1/P2, N270, N400, VPP, BAEP, P1 (latency), P50, EPN and Nf were summarized narratively. Individuals with GAD were reported to have increased N170, N400, and VPP amplitude and delayed P1 latency compared to healthy controls. Age, sex ratio, sample size, diagnostic criteria, task-related modality, and paradigm were identified as potential influencing factors of ERP characteristics.

CONCLUSIONS: Individuals with GAD exhibit increased ERN amplitude and decreased P3 amplitude in contrast with healthy controls. In addition, delayed latency of P3, N2, and MMN is detected in individuals with GAD. The identified ERP components in individuals with GAD are associated with attention, cognition, visual perception, error or conflict monitoring, semantic information integration, and auditory sensory memory processes. Due to the limited number of included studies and high heterogeneity, further studies with high quality are needed to confirm these findings.

PMID:39675130 | DOI:10.1016/j.jpsychires.2024.12.016

Heliyon. 2024 Apr 29;10(9):e30318. doi: 10.1016/j.heliyon.2024.e30318. eCollection 2024 May 15.

ABSTRACT

In the midst of a vast amount of scientific literature, the need for specific rules arise especially when it comes to deciding which impactful researchers should be nominated. These rules are based on measurable quantities that can easily be applied to a researcher's quantitative data. Various search engines, like Google Scholar, Semantic Scholar, Web of Science etc. Are used for recording metadata such as the researcher's total publications, their citations, h-index etc. However, the scientific community has not yet agreed upon a single set of criteria that a researcher has to meet in order to secure a spot on to the list of impactful researchers. In this study, we have provided a comprehensive set of rules for the scientific community within the field of mathematics, derived from the top five quantitative parameters belonging to each category. Within each categorical grouping, we meticulously selected the five most pivotal parameters. This selection process was guided by an importance score, that was derived after assessing its influence on the model's performance in the classification of data pertaining to both awardees and non awardees. To perform the experiment, we focused on the field of mathematics and used a dataset containing 525 individuals who received awards and 525 individuals who did not receive awards. The rules were developed for each parameter category using the Decision Tree Algorithm, which achieved an average accuracy of 70 to 75 percent for identifying awardees in mathematics domains. Moreover, the highest-ranked parameters belonging to each category were successful in elevating over 50 to 55 percent of the award recipients to positions within the top 100 ranked researchers' list. These findings have the potential to serve as a guidance for individual researchers, who aimed on to making it to the esteemed list of distinguished scientists. Additionally, the scientific community can utilize these rules to sift through the roster of researchers for a subjective evaluation, facilitating the recognition and rewarding of exceptional researchers in the field.

PMID:39669372 | PMC:PMC11636847 | DOI:10.1016/j.heliyon.2024.e30318

Comput Struct Biotechnol J. 2024 Nov 19;23:4232-4241. doi: 10.1016/j.csbj.2024.11.022. eCollection 2024 Dec.

ABSTRACT

The expansion of multi-omics datasets raises significant challenges for data integration and querying. To overcome these challenges, we developed a generic RDF-based integration schema that connects various types of differential -omics data, epigenomics, and regulatory information. This schema employs the FALDO ontology to enable querying based on genomic locations. It is designed to be fully or partially populated, providing both flexibility and extensibility while supporting complex queries. We validated the schema by reproducing two recently published studies, one in biomedicine and the other in environmental science, proving its genericity and its ability to integrate data efficiently. This schema serves as an effective tool for managing and querying a wide range of multi-omics datasets.

PMID:39660218 | PMC:PMC11629147 | DOI:10.1016/j.csbj.2024.11.022

Front Public Health. 2024 Nov 21;12:1488741. doi: 10.3389/fpubh.2024.1488741. eCollection 2024.

ABSTRACT

INTRODUCTION: Visceral Leishmaniasis, also known as kala-azar, is a potentially fatal, neglected tropical disease caused by the protozoan parasite Leishmania and transmitted through infected sandflies. It is one of the major global public health problems and contributors to economic crisis among people. Though different studies investigated human visceral leishmaniasis in Eastern Africa, the findings were inconsistent and inconclusive enough, and there is no representative data on this devastating public health concern. Therefore, this systematic review and meta-analysis aimed to determine the pooled prevalence and risk factors associated with human visceral leishmaniasis in Eastern Africa.

METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) guidelines were followed for this study. Databases such as PubMed/MEDLINE, CINAHL, LIVIVO, African Journals Online, African Index Medicus (AIM), HINARI, Science Direct, Web of Science, Cochrane Library, Google Scholar, Semantic Scholar, and Google were used to retrieve all the relevant articles. The search was carried out from 23 May 2024 to 17 July 2024. Data were analyzed using STATA 17 software to determine the pooled prevalence of human visceral leishmaniasis with a 95% confidence interval using a random-effects model.

RESULT: In this meta-analysis, thirty-nine articles with 40,367 study participants were included. The overall pooled prevalence of human visceral leishmaniasis in Eastern Africa was 26.16% [95%; CI: 19.96, 32.36%; I2 = 99.67%; p = 0.00]. Gender, age, family size, presence of termite hill/mound, presence of cattle/domestic animals, outdoor sleeping, presence of VL infected family member/s, and presence of water source/pathway near home were the risk factors significantly associated with human visceral leishmaniasis.

CONCLUSION: The recorded pooled prevalence of human visceral leishmaniasis in Eastern Africa underscores the urgent need for comprehensive intervention strategies. This includes rigorous health education for residents, covering the disease's cause, transmission, vector breeding sites, and prevention mechanisms.

PMID:39659723 | PMC:PMC11628699 | DOI:10.3389/fpubh.2024.1488741

PeerJ Comput Sci. 2024 Oct 10;10:e2383. doi: 10.7717/peerj-cs.2383. eCollection 2024.

ABSTRACT

BACKGROUND: The identification of foreign objects on transmission lines is crucial for their normal operation. There are risks and difficulties associated with identifying foreign objects on transmission lines due to their scattered distribution and elevated height.

METHODS: The dataset for this paper consists of search material from the web, including bird nests, kites, balloons, and rubbish, which are common foreign objects found on top of transmission lines, totaling 400 instances. To enhance the classical U-Net architecture, the coding component has been substituted with a ResNet50 network serving as the feature extraction module. In the decoding section, a batch normalization (BN) layer was added after each convolutional layer in the decoder to improve the model's efficiency and generalization capacity. Additionally, a combined loss function was implemented, merging Focal loss and Dice loss, to tackle class imbalance issues and improve accuracy.

RESULTS: In summary, RB-UNet, a novel semantic segmentation network, has been introduced. The experimental results show a mIoU of 88.43%, highlighting the significant superiority of the RB-UNet approach compared to other semantic segmentation techniques for detecting foreign objects on transmission lines. The findings indicate that the proposed RB-UNet algorithm is proficient in detecting and segmenting foreign objects on transmission lines.

PMID:39650379 | PMC:PMC11622974 | DOI:10.7717/peerj-cs.2383

PeerJ Comput Sci. 2024 Oct 22;10:e2242. doi: 10.7717/peerj-cs.2242. eCollection 2024.

ABSTRACT

Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.

PMID:39650368 | PMC:PMC11623210 | DOI:10.7717/peerj-cs.2242

Rofo. 2024 Dec 6. doi: 10.1055/a-2462-2351. Online ahead of print.

ABSTRACT

In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.The international standard Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is particularly suitable for the implementation of such a data warehouse. FHIR allows for easy and fast data access, supports modern web-based frontends, and offers high interoperability due to the integration of medical ontologies such as SNOMED-CT or RadLex. Furthermore, FHIR has a robust data security concept. Because of these properties, FHIR has been selected by the Medical Informatics Initiative (MII) as the data standard for the core data set and is intended to be promoted as an international standard in the European Health Data Space (EHDS).Implementing the FHIR standard in radiology data warehouses is therefore a logical and sensible step towards data-driven medicine. · A data warehouse is essential for data-driven medicine, clinical care, and research purposes.. · Data warehouses enable efficient integration of AI results and structured report templates.. · Fast Healthcare Interoperability Resources (FHIR) is a suitable standard for a data warehouse.. · FHIR provides an interoperable data standard, supported by proven web technologies.. · FHIR improves semantic consistency and facilitates secure data exchange.. · Arnold P, Pinto dos Santos D, Bamberg F et al. FHIR - Overdue Standard for Radiology Data Warehouses. Fortschr Röntgenstr 2024; DOI 10.1055/a-2462-2351.

PMID:39642924 | DOI:10.1055/a-2462-2351

BMC Bioinformatics. 2024 Dec 4;25(1):373. doi: 10.1186/s12859-024-05993-2.

ABSTRACT

BACKGROUND: Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.

RESULTS: Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient's profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker's performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.

CONCLUSIONS: Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool's source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

PMID:39633268 | DOI:10.1186/s12859-024-05993-2