Dataset Information

Biomedical Imaging in Experimental Models of Cardiovascular Disease.

ABSTRACT: Major advances in biomedical imaging have occurred over the last 2 decades and now allow many physiological, cellular, and molecular processes to be imaged noninvasively in small animal models of cardiovascular disease. Many of these techniques can be also used in humans, providing pathophysiological context and helping to define the clinical relevance of the model. Ultrasound remains the most widely used approach, and dedicated high-frequency systems can obtain extremely detailed images in mice. Likewise, dedicated small animal tomographic systems have been developed for magnetic resonance, positron emission tomography, fluorescence imaging, and computed tomography in mice. In this article, we review the use of ultrasound and positron emission tomography in small animal models, as well as emerging contrast mechanisms in magnetic resonance such as diffusion tensor imaging, hyperpolarized magnetic resonance, chemical exchange saturation transfer imaging, magnetic resonance elastography and strain, arterial spin labeling, and molecular imaging.

SUBMITTER: Sosnovik DE

PROVIDER: S-EPMC9202007 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Biomedical Imaging in Experimental Models of Cardiovascular Disease.

Sosnovik David E DE Scherrer-Crosbie Marielle M

Circulation research 20220609 12

Major advances in biomedical imaging have occurred over the last 2 decades and now allow many physiological, cellular, and molecular processes to be imaged noninvasively in small animal models of cardiovascular disease. Many of these techniques can be also used in humans, providing pathophysiological context and helping to define the clinical relevance of the model. Ultrasound remains the most widely used approach, and dedicated high-frequency systems can obtain extremely detailed images in mice ...[more]

PMID: 35679370

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Project description:Current animal protection laws require replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially relevant methods, e.g. organ or cell culture models, or computer simulations, can be supported with text mining tools specifically built for this purpose. Such tools are trained (or fine tuned) on relevant data sets labeled by human experts. We developed the GoldHamster corpus, composed of 1,600 PubMed (Medline) articles (titles and abstracts), in which we manually identified the used experimental model according to a set of eight labels, namely: "in vivo", "organs", "primary cells", "immortal cell lines", "invertebrates", "humans", "in silico" and "other" (models). We recruited 13 annotators with expertise in the biomedical domain and assigned each article to two individuals. Four additional rounds of annotation aimed at improving the quality of the annotations with disagreements in the first round. Furthermore, we conducted various machine learning experiments based on supervised learning to evaluate the corpus for our classification task. We obtained more than 7,000 document-level annotations for the above labels. After the first round of annotation, the inter-annotator agreement (kappa coefficient) varied among labels, and ranged from 0.42 (for "others") to 0.82 (for "invertebrates"), with an overall score of 0.62. All disagreements were resolved in the subsequent rounds of annotation. The best-performing machine learning experiment used the PubMedBERT pre-trained model with fine-tuning to our corpus, which gained an overall f-score of 0.83. We obtained a corpus with high agreement for all labels, and our evaluation demonstrated that our corpus is suitable for training reliable predictive models for automatic classification of biomedical literature according to the used experimental models. Our SMAFIRA - "Smart feature-based interactive" - search tool ( https://smafira.bf3r.de ) will employ this classifier for supporting the retrieval of alternative methods to animal experiments. The corpus is available for download ( https://doi.org/10.5281/zenodo.7152295 ), as well as the source code ( https://github.com/mariananeves/goldhamster ) and the model ( https://huggingface.co/SMAFIRA/goldhamster ).

Dataset Information

Biomedical Imaging in Experimental Models of Cardiovascular Disease.

Publications

Biomedical Imaging in Experimental Models of Cardiovascular Disease.

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