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  1. Article ; Online: Oogonial stem cells: the unexpected superheroes.

    Sousa, Rita L

    Reproduction & fertility

    2024  Volume 5, Issue 2

    MeSH term(s) Animals ; Female ; Oogonial Stem Cells ; Oocytes ; Ovary
    Language English
    Publishing date 2024-04-12
    Publishing country England
    Document type Journal Article
    ISSN 2633-8386
    ISSN (online) 2633-8386
    DOI 10.1530/RAF-24-0004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Medicina à Cabeceira do Doente & Comunicação Clínica

    Sousa, Rita / Silva, Rui / Almeida, Marta / Pereira, Vitor Hugo / Sousa, Nuno

    2020  

    Keywords Doctor/patient relationship ; Medicina clínica ; comunicação clínica
    Size 1 electronic resource (62 pages)
    Publishing place Braga
    Document type Book ; Online
    Note Portuguese ; Open Access
    HBZ-ID HT021233278
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article ; Online: Explaining protein-protein interactions with knowledge graph-based semantic similarity.

    Sousa, Rita T / Silva, Sara / Pesquita, Catia

    Computers in biology and medicine

    2024  Volume 170, Page(s) 108076

    Abstract: The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of ... ...

    Abstract The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data. Given the complexity of the biomedical domain, explanations should be grounded in domain knowledge which can be achieved by using ontologies and knowledge graphs. These knowledge graphs express knowledge about a domain by capturing different perspectives of the representation of real-world entities. However, the most popular way to explore knowledge graphs with machine learning is through using embeddings, which are not explainable. As an alternative, knowledge graph-based semantic similarity offers the advantage of being explainable. Additionally, similarity can be computed to capture different semantic aspects within the knowledge graph and increasing the explainability of predictive approaches. We propose a novel method to generate explainable vector representations, KGsim2vec, that uses aspect-oriented semantic similarity features to represent pairs of entities in a knowledge graph. Our approach employs a set of machine learning models, including decision trees, genetic programming, random forest and eXtreme gradient boosting, to predict relations between entities. The experiments reveal that considering multiple semantic aspects when representing the similarity between two entities improves explainability and predictive performance. KGsim2vec performs better than black-box methods based on knowledge graph embeddings or graph neural networks. Moreover, KGsim2vec produces global models that can capture biological phenomena and elucidate data biases.
    MeSH term(s) Artificial Intelligence ; Semantics ; Pattern Recognition, Automated ; Neural Networks, Computer ; Machine Learning
    Language English
    Publishing date 2024-02-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2024.108076
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 653: Show issues Location:
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  4. Article: Ceftriaxone-Induced Encephalopathy in a Patient With Chronic Kidney Disease.

    Martins, Ana Filipa / Dias, Mónica / Matos Sousa, Rita / Regadas, Maria João

    Cureus

    2024  Volume 16, Issue 2, Page(s) e54476

    Abstract: Neurotoxicity is an acknowledged side effect of third and fourth-generation cephalosporins, but its occurrence with ceftriaxone is not widely recognized. This article presents a case involving a 56-year-old woman with multiple comorbidities who sought ... ...

    Abstract Neurotoxicity is an acknowledged side effect of third and fourth-generation cephalosporins, but its occurrence with ceftriaxone is not widely recognized. This article presents a case involving a 56-year-old woman with multiple comorbidities who sought medical attention after experiencing lipothymia. The initial diagnosis suggested a urinary tract infection with acute kidney failure, leading to the initiation of ceftriaxone and hemodialysis. Subsequently, the patient exhibited a progressive deterioration of her neurological state, characterized by agitation and chorea. Metabolic encephalopathy, seizure/nonconvulsive status epilepticus, and acute central nervous system lesions were considered primary differential diagnoses, all of which were subsequently ruled out through thorough investigations. Days later, a remarkable recovery of the patient's neurological state was observed. A retrospective analysis revealed a correlation between the improvement and the fourth day of antimicrobial suspension. Consequently, a presumptive diagnosis of ceftriaxone-induced encephalopathy was made. This unusual case underscores the importance of recognizing the potential for pharmacological encephalopathy, particularly with ceftriaxone, and emphasizes its reversibility upon discontinuation of the implicated drug. Clinicians should remain vigilant to this uncommon adverse effect, promoting timely intervention and improved patient outcomes.
    Language English
    Publishing date 2024-02-19
    Publishing country United States
    Document type Case Reports
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.54476
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Multi-domain knowledge graph embeddings for gene-disease association prediction.

    Nunes, Susana / Sousa, Rita T / Pesquita, Catia

    Journal of biomedical semantics

    2023  Volume 14, Issue 1, Page(s) 11

    Abstract: Background: Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of ... ...

    Abstract Background: Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art knowledge graph embeddings are produced over a single ontology or multiple but disconnected ones, ignoring the impact that considering multiple interconnected domains can have on complex tasks such as gene-disease association prediction.
    Results: We propose a novel approach to predict gene-disease associations using rich semantic representations based on knowledge graph embeddings over multiple ontologies linked by logical definitions and compound ontology mappings. The experiments showed that considering richer knowledge graphs significantly improves gene-disease prediction and that different knowledge graph embeddings methods benefit more from distinct types of semantic richness.
    Conclusions: This work demonstrated the potential for knowledge graph embeddings across multiple and interconnected biomedical ontologies to support gene-disease prediction. It also paved the way for considering other ontologies or tackling other tasks where multiple perspectives over the data can be beneficial. All software and data are freely available.
    MeSH term(s) Pattern Recognition, Automated ; Biological Ontologies ; Algorithms ; Machine Learning
    Language English
    Publishing date 2023-08-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2548651-2
    ISSN 2041-1480 ; 2041-1480
    ISSN (online) 2041-1480
    ISSN 2041-1480
    DOI 10.1186/s13326-023-00291-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Anti-interleukin-6 receptor antibody for the treatment of remitting seronegative symmetrical synovitis with pitting oedema: a new outlook?

    Noversa de Sousa, Rita / Marques Rocha, Diana / Nair Simões, Marisa / Rosário, Cristina

    BMJ case reports

    2024  Volume 17, Issue 3

    Abstract: We present the case of an elderly man with a small-joint polyarthritis, accompanied by pitting oedema, involving hands and feet, raising clinical suspicion of remitting seronegative symmetrical synovitis with pitting oedema (RS3PE). Treatment with ... ...

    Abstract We present the case of an elderly man with a small-joint polyarthritis, accompanied by pitting oedema, involving hands and feet, raising clinical suspicion of remitting seronegative symmetrical synovitis with pitting oedema (RS3PE). Treatment with corticosteroids was initiated with significant improvement, but unacceptable iatrogeny ensued, and tapering was not possible without disease flare-up. A trial of tocilizumab allowed disease activity control, slow weaning of corticosteroids and, ultimately, its suspension. RS3PE is a rare rheumatological entity, initially thought to be a variant of rheumatoid arthritis (RA), with shared traits with polymyalgia rheumatica (PMR), and other seronegative spondyloarthropathies, thereby implying a shared pathophysiological background. Elevated levels of interleukin 6 (IL-6) are found in patients with RA, have shown to mirror disease activity in PMR and have also been described in the serum and synovial fluid of patients with RS3PE. Tocilizumab, an anti-IL-6 receptor antibody, shows auspicious results in several other rare rheumatic diseases other than RA.
    MeSH term(s) Male ; Humans ; Aged ; Synovitis/diagnosis ; Synovitis/drug therapy ; Synovitis/complications ; Polymyalgia Rheumatica/complications ; Arthritis, Rheumatoid/complications ; Arthritis, Rheumatoid/drug therapy ; Adrenal Cortex Hormones/therapeutic use ; Edema/drug therapy ; Edema/complications
    Chemical Substances Adrenal Cortex Hormones
    Language English
    Publishing date 2024-03-15
    Publishing country England
    Document type Case Reports ; Journal Article
    ISSN 1757-790X
    ISSN (online) 1757-790X
    DOI 10.1136/bcr-2023-257645
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Scalp Eschar and Neck Lymphadenopathy Associated with Rickettsial Infection After a Tick Bite: A Case Report.

    Quadros Flores, Maria Ana / Cruz Carvalho, Isabel / Alves, Mariana / Paulo, Sérgio Eduardo / De Sousa, Rita

    Acta medica portuguesa

    2024  Volume 37, Issue 4, Page(s) 312–314

    MeSH term(s) Humans ; Tick Bites/complications ; Scalp ; Rickettsia Infections/complications ; Lymphadenopathy/complications ; Skin Diseases
    Language English
    Publishing date 2024-04-01
    Publishing country Portugal
    Document type Case Reports ; Letter
    ZDB-ID 603078-6
    ISSN 1646-0758 ; 0870-399X
    ISSN (online) 1646-0758
    ISSN 0870-399X
    DOI 10.20344/amp.20914
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2343: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: Dietary exposure to heavy metals and iodine intake via consumption of seaweeds and halophytes in the European population.

    Dujardin, Bruno / Ferreira de Sousa, Rita / Gómez Ruiz, Jose Ángel

    EFSA journal. European Food Safety Authority

    2023  Volume 21, Issue 1, Page(s) e07798

    Abstract: EFSA assessed the relevance of seaweed and halophyte consumption to the dietary exposure to heavy metals (arsenic, cadmium, lead and mercury) and the iodine intake in the European population. Based on sampling years 2011-2021, there were 2,093 analytical ...

    Abstract EFSA assessed the relevance of seaweed and halophyte consumption to the dietary exposure to heavy metals (arsenic, cadmium, lead and mercury) and the iodine intake in the European population. Based on sampling years 2011-2021, there were 2,093 analytical data available on cadmium, 1,988 on lead, 1,934 on total arsenic, 920 on inorganic arsenic (iAs), 1,499 on total mercury and 1,002 on iodine. A total of 697 eating occasions on halophytes, seaweeds and seaweed-related products were identified in the EFSA Comprehensive European Food Consumption Database (468 subjects, 19 European countries). From seaweed consumption, exposure estimates for cadmium in adult 'consumers only' are within the range of previous exposure estimates considering the whole diet, while for iAs and lead the exposure estimates represent between 10% and 30% of previous exposures from the whole diet for the adult population. Seaweeds were also identified as important sources of total arsenic that mainly refers, with some exceptions, to organic arsenic. As regards iodine, from seaweed consumption, mean intakes above 20 μg/kg body weight per day were identified among 'consumers only' of Kombu and Laver algae. The impact of a future increase in seaweed consumption ('per capita') on the dietary exposure to heavy metals and on iodine intake will strongly depend on the seaweeds consumed. The exposure estimates of heavy metals and iodine intakes in 'consumers only' of seaweeds were similar to those estimated in a replacement scenario with selected seaweed-based foods in the whole population. These results underline the relevance of the current consumption of seaweeds in the overall exposure to different heavy metals and in the intake of iodine. Recommendations are provided for further work needed on different areas to better understand the relationship between seaweed consumption and exposure to heavy metals and iodine intake.
    Language English
    Publishing date 2023-01-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2540248-1
    ISSN 1831-4732 ; 1831-4732
    ISSN (online) 1831-4732
    ISSN 1831-4732
    DOI 10.2903/j.efsa.2023.7798
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Benchmark datasets for biomedical knowledge graphs with negative statements

    Sousa, Rita T. / Silva, Sara / Pesquita, Catia

    2023  

    Abstract: Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, they have been demonstrated to ... ...

    Abstract Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, they have been demonstrated to improve the performance of several applications, namely in the biomedical domain. However, no benchmark dataset supports the evaluation of the methods that consider these negative statements. We present a collection of datasets for three relation prediction tasks - protein-protein interaction prediction, gene-disease association prediction and disease prediction - that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements. These datasets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, enriched with negative statements. We also generate knowledge graph embeddings for each dataset with two popular path-based methods and evaluate the performance in each task. The results show that the negative statements can improve the performance of knowledge graph embeddings.
    Keywords Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-07-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Explainable Representations for Relation Prediction in Knowledge Graphs

    Sousa, Rita T. / Silva, Sara / Pesquita, Catia

    2023  

    Abstract: Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent ... ...

    Abstract Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourhood properties, but sacrifice explainability. However, in tasks such as link or relation prediction, understanding which specific features better explain a relation is crucial to support complex or critical applications. We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects (i.e., subgraphs) between entities and learning representations for each subgraph, producing a multi-faceted and explainable representation. We evaluate SEEK on two real-world highly complex relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction. Our extensive analysis using established benchmarks demonstrates that SEEK achieves significantly better performance than standard learning representation methods while identifying both sufficient and necessary explanations based on shared semantic aspects.

    Comment: 16 pages, 3 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006 ; 004
    Publishing date 2023-06-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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