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Background: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. Methods: We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Results: Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Conclusions: Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender, and via a Web service API). [ABSTRACT FROM AUTHOR]

Introduction: Radiation therapy (RT) is effective treatment for curing and palliating cancer, yet concern exists that not all Canadians for whom RT is indicated receive it. Many factors may contribute to suboptimal use of RT. A review of recent Canadian literature was undertaken to identify such barriers. Methods: MEDLINE, CINAHL, and EMBase databases were used to search keywords relating to barriers to accessing or utilizing RT in Canada. Collected abstracts were reviewed independently. Barriers identified in relevant articles were categorized as relating to the health systems, patient socio-demographic, patient factors, or provider factors contexts and thematic analysis performed for each context. Results: 535 unique abstracts were collected. 75 met inclusion criteria. 46 (61.3%) addressed multiple themes. The most cited barriers to accessing RT when indicated were patient age (n = 26, 34.7%), distance to treatment centre (n = 23, 30.7%), wait times (n = 22, 29.3%), and lack of physician understanding about the use of RT (n = 16, 21.6%). Conclusions: Barriers to RT are reported in many areas. The role of provider factors and the lack of attention to patient fears and mistrust as potential barriers were unexpected findings demanding further attention. Solutions should be sought to overcome identified barriers facilitating more effective cancer care for Canadians. [ABSTRACT FROM AUTHOR]

Background: Controversy exists with regard to the impact that the different components of diagnosis delay may have on the degree of invasion and prognosis in patients with colorectal cancer. The follow-up strategies after treatment also vary considerably. The aims of this study are: a) to determine if the symptoms-to-diagnosis interval and the treatment delay modify the survival of patients with colorectal cancer, and b) to determine if different follow-up strategies are associated with a higher survival rate. Methods/Design: Multi-centre study with prospective follow-up in five regions in Spain (Galicia, Balearic Islands, Catalonia, Aragón and Valencia) during the period 2010-2012. Incident cases are included with anatomopathological confirmation of colorectal cancer (International Classification of Diseases 9th revision codes 153-154) that formed a part of a previous study (n = 953). At the time of diagnosis, each patient was given a structured interview. Their clinical records will be reviewed during the follow-up period in order to obtain information on the explorations and tests carried out after treatment, and the progress of these patients. Symptoms-to-diagnosis interval is defined as the time calculated from the diagnosis of cancer and the first symptoms attributed to cancer. Treatment delay is defined as the time elapsed between diagnosis and treatment. In non-metastatic patients treated with curative intention, information will be obtained during the follow-up period on consultations performed in the digestive, surgery and oncology departments, as well as the endoscopies, tumour markers and imaging procedures carried out. Local recurrence, development of metastases in the follow-up, appearance of a new tumour and mortality will be included as outcome variables. Actuarial survival analysis with Kaplan-Meier curves, Cox regression and competitive risk survival analysis will be performed. [ABSTRACT FROM AUTHOR]

For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling., (© 2024. The Author(s).)

Background: Controversy exists with regard to the impact that the different components of diagnosis delay may have on the degree of invasion and prognosis in patients with colorectal cancer. The follow-up strategies after treatment also vary considerably. The aims of this study are: a) to determine if the symptoms-to-diagnosis interval and the treatment delay modify the survival of patients with colorectal cancer, and b) to determine if different follow-up strategies are associated with a higher survival rate., Methods/design: Multi-centre study with prospective follow-up in five regions in Spain (Galicia, Balearic Islands, Catalonia, Aragón and Valencia) during the period 2010-2012. Incident cases are included with anatomopathological confirmation of colorectal cancer (International Classification of Diseases 9th revision codes 153-154) that formed a part of a previous study (n = 953).At the time of diagnosis, each patient was given a structured interview. Their clinical records will be reviewed during the follow-up period in order to obtain information on the explorations and tests carried out after treatment, and the progress of these patients.Symptoms-to-diagnosis interval is defined as the time calculated from the diagnosis of cancer and the first symptoms attributed to cancer. Treatment delay is defined as the time elapsed between diagnosis and treatment. In non-metastatic patients treated with curative intention, information will be obtained during the follow-up period on consultations performed in the digestive, surgery and oncology departments, as well as the endoscopies, tumour markers and imaging procedures carried out.Local recurrence, development of metastases in the follow-up, appearance of a new tumour and mortality will be included as outcome variables.Actuarial survival analysis with Kaplan-Meier curves, Cox regression and competitive risk survival analysis will be performed., Discussion: This study will make it possible to verify if the different components of delay have an impact on survival rate in colon cancer and rectal cancer. In consequence, this multi-centre study will be able to detect the variability present in the follow-up of patients with colorectal cancer, and if this variability modifies the prognosis. Ideally, this study could determine which follow-up strategies are associated with a better prognosis in colorectal cancer.