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

Open Res Eur. 2024 Oct 18;4:133. doi: 10.12688/openreseurope.17992.2. eCollection 2024.

ABSTRACT

BACKGROUND: Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data.

METHODS: To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates:1.An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications.2.The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets.3.The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration.

RESULTS: EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets.

CONCLUSIONS: The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers.

PMID:39534879 | PMC:PMC11555329 | DOI:10.12688/openreseurope.17992.2

Int J Med Inform. 2024 Nov 7;193:105663. doi: 10.1016/j.ijmedinf.2024.105663. Online ahead of print.

ABSTRACT

BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.

OBJECTIVE: This study was conducted to evaluate both the quality and semantic similarity of responses created by AI models to the most frequently asked questions about healthy nutrition and weight management during the antenatal period, based on existing clinical knowledge.

METHODS: In this study, a cross-sectional assessment design was used to explore data from 3 AI models (GPT-4, MedicalGPT, Med-PaLM). We directed the most frequently asked questions about nutrition during pregnancy, obtained from the American College of Obstetricians and Gynecologists (ACOG) to each model in a new and single session on October 21, 2023, without any prior conversation. Immediately after, instructions were given to the AI models to generate responses to these questions. The responses created by AI models were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. Additionally, to assess the semantic similarity between answers to 31 pregnancy nutrition-related frequently asked questions sourced from the ACOG and responses from AI models we evaluated cosine similarity using both WORD2VEC and BioLORD-2023.

RESULTS: Med-PaLM outperformed GPT-4 and MedicalGPT in response quality (mean = 3.93), demonstrating superior clinical accuracy over both GPT-4 (p = 0.016) and MedicalGPT (p = 0.001). GPT-4 had higher quality than MedicalGPT (p = 0.027). The semantic similarity between ACOG and Med-PaLM is higher with WORD2VEC (0.92) compared to BioLORD-2023 (0.81), showing a difference of +0.11. The similarity scores for ACOG-MedicalGPT and ACOG-GPT-4 are similar across both models, with minimal differences of -0.01. Overall, WORD2VEC has a slightly higher average similarity (0.82) than BioLORD-2023 (0.79), with a difference of +0.03.

CONCLUSIONS: Despite the superior performance of Med-PaLM, there is a need for further evidence-based research and improvement in the integration of AI in healthcare due to varying AI model performances.

PMID:39531902 | DOI:10.1016/j.ijmedinf.2024.105663

Front Neuroinform. 2024 Oct 16;18:1378281. doi: 10.3389/fninf.2024.1378281. eCollection 2024.

ABSTRACT

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system's ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2 = 0.830, only below bagging, F2BAG = 0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system's versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.

PMID:39478874 | PMC:PMC11522961 | DOI:10.3389/fninf.2024.1378281

Proc (IEEE Int Conf Healthc Inform). 2024 Jun;2024:31-39. doi: 10.1109/ichi61247.2024.00012. Epub 2024 Aug 22.

ABSTRACT

Many studies have examined the impact of exercise and other physical activities in influencing the health outcomes of individuals. These physical activities entail an intricate sequence and series of physical anatomy, physiological movement, movement of the anatomy, etc. To better understand how these components interact with one another and their downstream impact on health outcomes, there needs to be an information model that conceptualizes all entities involved. In this study, we introduced our early development of an ontology model to computationally describe human physical activities and the various entities that compose each activity. We developed an open-sourced biomedical ontology called the Kinetic Human Movement Ontology that reused OBO Foundry terminologies and encoded in OWL2. We applied this ontology in modeling and linking a specific Tai Chi movement. The contribution of this work could enable modeling of information relating to human physical activity, like exercise, and lead towards information standardization of human movement for analysis. Future work will include expanding our ontology to include more expressive information and completely modeling entire sets of movement from human physical activity.

PMID:39464170 | PMC:PMC11503552 | DOI:10.1109/ichi61247.2024.00012

Cureus. 2024 Sep 23;16(9):e70029. doi: 10.7759/cureus.70029. eCollection 2024 Sep.

ABSTRACT

Dementia encompasses symptoms resulting from brain damage that impairs cognitive functions, surpassing natural aging effects. This condition affects emotional regulation, behavior, and motivation while preserving consciousness. Dr. Manfred Spitzer coined the term 'digital dementia,' highlighting the cognitive decline associated with excessive reliance on digital devices such as smartphones and Google, potentially exacerbating attention deficit hyperactivity disorder (ADHD) and memory loss. This condition mirrors terms like 'digital amnesia' and 'the Google Effect,' highlighting the brain's tendency to offload peripheral information, leading to panic and forgetfulness. Spitzer's book, Digital Dementia, focuses on gaming effects on children and has thus popularized the term. Teenagers are known to use electronic devices regularly, correlating with rising cognitive impairments. The advent of the internet's fifth generation (5G) has transformed technology use, impacting mental health treatments and clinical practices globally. Digital media's influence on the developing brain encompasses motor skills, language, and cognition. Excessive digital media use in young adults correlates with lower cognitive empathy, affecting interpersonal understanding and facial recognition. Studies link heavy reliance on web-based media to decreased white matter integrity, crucial for language skills. Adolescents may be more vulnerable to anxiety and unrealistic expectations due to digital media overuse. Digital media overuse impacts brain development, especially cognitive and inhibitory control, attention, memory, and reasoning, essential for adapting to dynamic environments. Early exposure to fast-paced media can impair motor skills, spatial awareness, problem-solving, and language learning. Neuroimaging studies reveal that environmental factors like screen usage affect brain networks controlling social-emotional behavior and executive functions. Overreliance on smartphones diminishes gray matter in key brain regions, affecting cognitive and emotional regulation. The internet generation, characterized by advancements such as Web 3.0, introduces artificial intelligence and semantic web technologies, reshaping digital content processing. The neurobiological basis of digital dementia involves changes in the brain structure and function, with excessive screen exposure linked to cognitive impairments. Neuroplasticity, or the brain's adaptability, plays a role in cognitive decline from digital media overuse. Early childhood and adolescent brain development stages exhibit significant plasticity, influencing cognitive trajectories. Addressing digital dementia requires strategies to reduce screen time, promote cognitive exercises, and enhance awareness. Parents should regulate children's screen usage, encourage digital detox periods, and substitute screen time with other activities. Cognitive training programs such as Cogmed (Neural Assembly Int AB, Stockholm, SWE) and CogniFit (San Francisco, CA, USA) can improve memory and attention in older adults. Promoting balanced technology use and educating on the risks of excessive digital media consumption is crucial for maintaining cognitive health in the digital age.

PMID:39449887 | PMC:PMC11499077 | DOI:10.7759/cureus.70029

IEEE/ACM Trans Comput Biol Bioinform. 2024 Oct 24;PP. doi: 10.1109/TCBB.2024.3486216. Online ahead of print.

ABSTRACT

Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances have witnessed the achievements of artificial intelligence (AI) methods aimed at predicting PPIs. However, these approaches often handle the intricate web of relationships and mechanisms among proteins, drugs, diseases, ribonucleic acid (RNA), and protein structures in a fragmented or superficial manner. This is typically due to the limitations of non-end-to-end learning frameworks, which can lead to sub-optimal feature extraction and fusion, thereby compromising the prediction accuracy. To address these deficiencies, this paper introduces a novel end-to-end learning model, the Knowledge Graph Fused Graph Neural Network (KGF-GNN). This model comprises three integral components: (1) Protein Associated Network (PAN) Construction: We begin by constructing a PAN that extensively captures the diverse relationships and mechanisms linking proteins with drugs, diseases, RNA, and protein structures. (2) Graph Neural Network for Feature Extraction: A Graph Neural Network (GNN) is then employed to distill both topological and semantic features from the PAN, alongside another GNN designed to extract topological features directly from observed PPI networks. (3) Multi-layer Perceptron for Feature Fusion: Finally, a multi-layer perceptron integrates these varied features through end-to-end learning, ensuring that the feature extraction and fusion processes are both comprehensive and optimized for PPI prediction. Extensive experiments conducted on real-world PPI datasets validate the effectiveness of our proposed KGF-GNN approach, which not only achieves high accuracy in predicting PPIs but also significantly surpasses existing state-of-the-art models. This work not only enhances our ability to predict PPIs with a higher precision but also contributes to the broader application of AI in Bioinformatics, offering profound implications for biological research and therapeutic development.

PMID:39446541 | DOI:10.1109/TCBB.2024.3486216

Arch Public Health. 2024 Oct 23;82(1):188. doi: 10.1186/s13690-024-01414-1.

ABSTRACT

BACKGROUND: The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.

PURPOSE: This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients' rights and safety.

METHODS: We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.

RESULTS: We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.

CONCLUSIONS: Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

PMID:39444019 | DOI:10.1186/s13690-024-01414-1

Int J Pediatr. 2024 Oct 5;2024:4213025. doi: 10.1155/2024/4213025. eCollection 2024.

ABSTRACT

Background: Various initiatives are underway to improve maternal satisfaction with the vaccination of children in developing nations. Governments, international organizations, and nongovernmental organizations are actively working to improve healthcare infrastructure, expand service accessibility, improve communication, and foster community engagement. However, despite these efforts, maternal satisfaction with child vaccination services continues to be a significant issue. Objective: This systematic review and meta-analysis is aimed at assessing the pooled prevalence of maternal satisfaction with the child's vaccination service and its predictors in Ethiopia. Methods: Scopus, Embase, Web of Science, Google Scholar, PubMed, African Journals Online, and Semantic Scholar were searched to access the included articles. A weighted inverse-variance random effect model was used to estimate the prevalence of maternal satisfaction with vaccination of children. Variations in pooled prevalence estimates were adjusted by subgroup analysis according to the specific region where the study was conducted. Funnel plot and Egger's regression test were used to check publication bias. STATA version 14 statistical software was used for meta-analysis. Results: The combined prevalence of maternal satisfaction with vaccination of children was found to be 73% (95% CI: 72-75; I 2 = 0.00%, p value < 0.001). Based on the subgroup analysis, the result revealed that the prevalence of maternal satisfaction with vaccination of children was 63% in SNNPR, 79% in Oromia, and 74% in Amhara. Conclusions: A meta-analysis of mothers' satisfaction with vaccination services for their children in Ethiopia found a low level of satisfaction. Therefore, provide regular training and capacity-building programs for healthcare workers involved in the delivery of vaccination services.

PMID:39411518 | PMC:PMC11479785 | DOI:10.1155/2024/4213025

Sci Rep. 2024 Oct 15;14(1):24190. doi: 10.1038/s41598-024-74539-6.

ABSTRACT

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study's methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

PMID:39406791 | DOI:10.1038/s41598-024-74539-6

J Int Med Res. 2024 Oct;52(10):3000605241282116. doi: 10.1177/03000605241282116.

ABSTRACT

OBJECTIVES: This review aimed to summarize the existing data on the contribution of four single nucleotide polymorphisms (SNPs) in the cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) genes to pemphigus susceptibility.

METHODS: An electronic literature search for eligible studies among those published prior to 30 April 2024 was conducted through the PubMed, EMBASE, Web of Science, and Scopus databases. To minimize publication bias, an additional search was performed via the Google Scholar and Semantic Scholar search engines. Meta-analyses, together with subgroup analyses and meta-regressions, were performed for the following four CTLA-4 SNPs: rs231775, rs5742909, rs3087243, and rs733618.

RESULTS: Combined analyses revealed a significant increase in pemphigus risk conferred by the CTLA-4 rs5742909*C and rs733618*C alleles. Conversely, there was no evidence of any significant association between the rs231775*G and rs3087243*G alleles and susceptibility to pemphigus. Subgroup analyses by ethnicity and pemphigus type (vulgaris or foliaceus) and meta-regressions did not reveal any significant difference.

CONCLUSION: This meta-analysis suggested that two of the four investigated CTLA-4 SNPs were significantly associated with increased pemphigus risk.Registration: This review has been registered on PROSPERO: CRD42024550668; available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024550668.

PMID:39397428 | DOI:10.1177/03000605241282116

Genomics Inform. 2024 Oct 10;22(1):17. doi: 10.1186/s44342-024-00021-4.

ABSTRACT

This report presents the findings of a project from the 8th Biomedical Linked Annotation Hackathon (BLAH) to explore lightweight technology stacks to enhance assistive linked annotations. Using modern JavaScript frameworks and edge functions, in-browser Named Entity Recognition (NER), serverless embedding and vector search within web interfaces, and efficient serverless full-text search were implemented. Through this experimental approach, a proof of concept to demonstrate the feasibility and performance of these technologies was demonstrated. The results show that lightweight stacks can significantly improve the efficiency and cost-effectiveness of annotation tools and provide a local-first, privacy-oriented, and secure alternative to traditional server-based solutions in various use cases. This work emphasizes the potential of developing annotation interfaces that are more responsive, scalable, and user-friendly, which would benefit bioinformatics researchers, practitioners, and software developers.

PMID:39390526 | PMC:PMC11468380 | DOI:10.1186/s44342-024-00021-4

Inquiry. 2024 Jan-Dec;61:469580241288346. doi: 10.1177/00469580241288346.

ABSTRACT

The "Leave No One Behind" (LNOB) principle, a fundamental commitment of the United Nations' Sustainable Development Goals, emphasizes the urgent need to address and reduce global health inequalities. As global health initiatives strive to uphold this principle, they face significant ethical challenges in balancing equity, resource allocation, and diverse health priorities. This narrative review critically examines these ethical dilemmas and their implications for translating LNOB into actionable global health strategies. A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Semantic Scholar, covering publications from January 1990 to April 2024. The review included peer-reviewed articles, gray literature, and official reports that addressed the ethical dimensions of LNOB in global health contexts. A thematic analysis was employed to identify and synthesize recurring ethical issues, dilemmas, and proposed solutions. The thematic analysis identified 4 primary ethical tensions that complicate the operationalization of LNOB: (1) Universalism versus Targeting, where the challenge lies in balancing broad health improvements with targeted interventions for the most disadvantaged; (2) Resource Scarcity versus Equity; highlighting the ethical conflicts between maximizing efficiency and ensuring fairness; (3) Top-down versus Bottom-up Approaches, reflecting the tension between externally driven initiatives and local community needs; and (4) Short-term versus Long-term Sustainability, addressing the balance between immediate health interventions and sustainable systemic changes. To navigate these ethical challenges effectively, global health strategies must adopt a nuanced, context-sensitive approach incorporating structured decision-making processes and authentic community participation. The review advocates for systemic reforms that address the root causes of health disparities, promote equitable collaboration between health practitioners and marginalized communities, and align global health interventions with ethical imperatives. Such an approach is essential to truly operationalize the LNOB principle and foster sustainable health equity.

PMID:39385394 | PMC:PMC11465308 | DOI:10.1177/00469580241288346