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

Front Artif Intell. 2024 Nov 11;7:1247712. doi: 10.3389/frai.2024.1247712. eCollection 2024.

ABSTRACT

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

PMID:39588293 | PMC:PMC11586345 | DOI:10.3389/frai.2024.1247712

J Funct Morphol Kinesiol. 2024 Nov 14;9(4):236. doi: 10.3390/jfmk9040236.

ABSTRACT

BACKGROUND: The impact of prolonged digital device exposure on physical and mental health in children has been widely investigated by the scientific community. Additionally, the lockdown periods due to the COVID-19 pandemic further exposed children to screen time for e-learning activities. The aim of this systematic review (PROSPERO Registration: CRD42022315596) was to evaluate the effect of digital device exposure on children's health. The impact of the COVID-19 pandemic was additionally explored to verify the further exposure of children due to the e-learning environment.

METHODS: Available online databases (PubMed, Google Scholar, Semantic Scholar, BASE, Scopus, Web of Science, and SPORTDiscus) were searched for study selection. The PICO model was followed by including a target population of children aged 2 to 12 years, exposed or not to any type of digital devices, while evaluating changes in both physical and mental health outcomes. The quality assessment was conducted by using the Joanna Briggs Institute (JBI) Critical Appraisal Tool. Synthesis without meta-analysis (SWiM) guidelines were followed to provide data synthesis.

RESULTS: Forty studies with a total sample of 75,540 children were included in this systematic review. The study design was mainly cross-sectional (n = 28) and of moderate quality (n = 33). Overall, the quality score was reduced due to recall, selection, and detection biases; blinding procedures influenced the quality score of controlled trials, and outcome validity reduced the quality score of cohort studies. Digital device exposure affected physical activity engagement and adiposity parameters; sleep and behavioral problems emerged in children overexposed to digital devices. Ocular conditions were also reported and associated with higher screen exposure. Home confinement during COVID-19 further increased digital device exposure with additional negative effects.

CONCLUSIONS: The prolonged use of digital devices has a significant negative impact on children aged 2 to 12, leading to decreased physical activity, sleep disturbances, behavioral issues, lower academic performance, socioemotional challenges, and eye strain, particularly following extended online learning during lockdowns.

PMID:39584889 | DOI:10.3390/jfmk9040236

Comput Struct Biotechnol J. 2024 Nov 4;23:3999-4010. doi: 10.1016/j.csbj.2024.10.045. eCollection 2024 Dec.

ABSTRACT

In systems biology, the study of biological pathways plays a central role in understanding the complexity of biological systems. The massification of pathway data made available by numerous online databases in recent years has given rise to an important need for standardization of this data. The BioPAX format (Biological Pathway Exchange) emerged in 2010 as a solution for standardizing and exchanging pathway data across databases. BioPAX is a Semantic Web format associated to an ontology. It is highly expressive, allowing to finely describe biological pathways at the molecular and cellular levels, but the associated intrinsic complexity may be an obstacle to its widespread adoption. Here, we report on the use of the BioPAX format in 2024. We compare how the different pathway databases use BioPAX to standardize their data and point out possible avenues for improvement to make full use of its potential. We also report on the various tools and software that have been developed to work with BioPAX data. Finally, we present a new concept of abstraction on BioPAX graphs that would allow to specifically target areas in a BioPAX graph needed for a specific analysis, thus differentiating the format suited for representation and the abstraction suited for contextual analysis.

PMID:39582893 | PMC:PMC11585474 | DOI:10.1016/j.csbj.2024.10.045

Stud Health Technol Inform. 2024 Nov 22;321:119-123. doi: 10.3233/SHTI241075.

ABSTRACT

Due to a lack of systematisation and unbiased information, finding the optimal combination of software products for health information systems is a challenging endeavour. We present a novel approach to visually explore the domain of application systems and software products for health care along the paths of the Health IT ontology (HITO). We present an algorithm and implementation in a web application that is freely available at the HITO website and licensed under the open source MIT licence. In comparison to other approaches of path-based exploration of knowledge graphs, the novelty of our approach is the use of path finding on the ontology level and combining this both with the instances of the classes along the chosen path as well as search filters to limit the search space. Our approach can be adapted to other domains where users with complex information needs interact with ontologies and knowledge graphs and can be supported by generative artificial intelligence in the future.

PMID:39575792 | DOI:10.3233/SHTI241075

Indian J Psychol Med. 2024 Jun 2:02537176241252411. doi: 10.1177/02537176241252411. Online ahead of print.

ABSTRACT

BACKGROUND: Graphophonological semantic flexibility (GSF) is a reading-specific cognitive flexibility that allows an individual to process a print's phonological and semantic elements simultaneously. The study aimed to explore the developmental profile of GSF in typical readers.

METHOD: Ninety typically developing children, ages 8 to 11 years, were recruited and divided into three age groups: 8, 9, and 10. They were given a web-based GSF task that required them to arrange 12-word cards in a 2 × 2 matrix according to their initial phoneme and meaning. Several GSF components were computed, such as sorting speed, accuracy, and index. Furthermore, word reading, non-word reading, and passage comprehension were used to assess their reading profile.

RESULTS: The Kruskal-Wallis analysis revealed significant differences in sorting accuracy (H (2) = 32.67, p < .001), speed (H (2) = 20.25, p < .001), and index (H (2) = 26.97, p < .001) across all ages. According to Dunn's post hoc analysis, accuracy improved across all age groups (p < .01) and in the index between 8 and 10 (p < .001). The Mann-Whitney U test showed gender differences in sorting speed (U = 717, p = .03). Additionally, Spearman's rank correlation showed a significant positive association between GSF and word reading (r = 0.47, p < .001) and text comprehension (r = 0.55, p < .001).

CONCLUSION: The findings demonstrated that GSF components are developmental and do not significantly impact gender other than sorting speed. Furthermore, a relationship between GSF and word reading and passage comprehension emerged.

PMID:39564303 | PMC:PMC11572501 | DOI:10.1177/02537176241252411

Nucleic Acids Res. 2024 Nov 18:gkae1059. doi: 10.1093/nar/gkae1059. Online ahead of print.

ABSTRACT

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

PMID:39558165 | DOI:10.1093/nar/gkae1059

Iran J Public Health. 2024 Oct;53(10):2239-2250. doi: 10.18502/ijph.v53i10.16701.

ABSTRACT

BACKGROUND: We aimed to review the effect of community-based health insurance on health-related outcomes in Ethiopia.

METHODS: A systematic review was undertaken utilizing a major relevant published literature review from September 2017 to June 15, 2023. PubMed, Scopus, Web of Science, Science Direct, Google Scholar, Semantic Scholar, EMBASE, ProQuest, Hinari, and the Cochrane Library were used to search for relevant literature. Moreover, the Prisma flow model was used to select eligible findings.

RESULTS: Overall, 72% of the articles employed cross-sectional comparative study designs and procedures, and 36% of them employed samples ranging in size from 501 to 1000 participants. Furthermore, 76% were studied using descriptive statistics and logistic regression, whereas fewer utilized a random model, a probity model, or a correlation model. Similarly, 32% of the research used two-stage stratified sampling methods, and around 40% of the data revealed that the scheme increased healthcare utilization services. About 72 % of the reviewed study results showed that the scheme reduced catastrophic health expenditure and increases utilization of healthcare services. And the 20% reviewed studies stated that the CBHI boosts household satisfaction level. Moreover 12% of the reviewed studies stated that, CBHI increased QoL (quality of life).

CONCLUSION: Most of the studies provide evidence of the positive effect of CBHI in Ethiopia. Mainly, its membership improved the utilization of health services and decreased the incidence of catastrophic health expenditures. Thus, all actors should cooperate to strengthen it to solve the effective attribute of the deprived value of health care and continuity of care delivery system related to the country's new policy.

PMID:39544864 | PMC:PMC11557765 | DOI:10.18502/ijph.v53i10.16701

Comput Biol Med. 2025 Jan;184:109355. doi: 10.1016/j.compbiomed.2024.109355. Epub 2024 Nov 14.

ABSTRACT

OBJECTIVE: This study aims at automatically quantifying and modelling the uncertainty of facts in biomedical knowledge graphs (BKGs) based on their textual supporting evidence using deep learning techniques.

MATERIALS AND METHODS: A sentence transformer is employed to extract deep features of sentences used to classify sentence factuality using a naive Bayes classifier. For each fact and its supporting evidence in a source KG, the deep feature extractor and the classifier are used to quantify the factuality of each sentence which are then transformed to numerical values in [0,1] before being averaged to get the confidence score of the fact.

RESULTS: The fact classification feature extractor enhances the separability of classes in the embedding space. This helped the fact classification model to achieve a better performance than existing factuality classification with hand-crafted features. Uncertainty quantification and modelling were demonstrated on SemMedDB by creating USemMedDB, showing KGB2U's ability to process large BKGs. A subset of USemMedDB facts is modelled to demonstrate the correlation between the structure of the uncertain BKG and the confidence scores. The best-trained model is used to predict confidence scores of existing and unseen facts. The top-ranked unseen facts were grounded using scientific evidence showing KGB2U's ability to discover new knowledge.

CONCLUSION: Supporting literature of BKG facts can be used to automatically quantify their uncertainty. Additionally, the resulting uncertain biomedical KGs can be used for knowledge discovery. BKG2U interface and source code are available at http://biofunk.datanets.org/ and https://github.com/BahajAdil/KBG2U respectively.

PMID:39541901 | DOI:10.1016/j.compbiomed.2024.109355