Organoids and cells in organ-on-chip platforms replicate higher-level anatomical, physiological, or pathological states of tissues and organs. These technologies are widely regarded by academia,... Show moreOrganoids and cells in organ-on-chip platforms replicate higher-level anatomical, physiological, or pathological states of tissues and organs. These technologies are widely regarded by academia, the pharmacological industry and regulators as key biomedical developments. To map advances in this emerging field, a meta-analysis based on a quality-controlled text-mining algorithm is performed. The analysis covers titles, keywords, and abstracts of categorized academic publications in the literature and preprint databases published after 2010. The algorithm identifies and tracks 149 and 107 organs or organ substructures modeled as organoids and organ-on-chip, respectively, stem cell sources, as well as 130 diseases, and 16 groups of organisms other than human and mouse in which organoid/organ-on-chip technology is applied. The meta-analysis illustrates changing diversity and focus in organoid/organ-on-chip research and captures its geographical distribution. The downloadable dataset provided is a robust framework for researchers to interrogate with their own questions. Show less
Simple Summary: Recently, nivolumab, pembrolizumab, both immune-checkpoint inhibitors (ICIs) and the combination of dabrafenib plus trametinib (D + T) were registered as adjuvant melanoma... Show moreSimple Summary: Recently, nivolumab, pembrolizumab, both immune-checkpoint inhibitors (ICIs) and the combination of dabrafenib plus trametinib (D + T) were registered as adjuvant melanoma treatments, to prevent recurrence. The aim of this paper was to retrospectively review the benefits and risks of these treatments in clinical practice, by extracting data from electronic health records with a text-mining tool. In a population of 122 patients, 55 used nivolumab, 48 used pembrolizumab and 20 used D + T, and we found that the ICIs were better tolerated than D + T. However, the frequent adverse events of D + T are reversible and include pyrexia and fatigue. ICIs show immune-related chronic adverse events, and chronic thyroid-related adverse events occurred frequently. The efficacy results, including the recurrence-free survival, are promising; however, the follow-up was too short for conclusions. This study furthermore showed that the application of text-mining is a valuable method to collect data for the evaluation of adjuvant melanoma treatments.Abstract - Introduction: Nivolumab (N), pembrolizumab (P), and dabrafenib plus trametinib (D + T) have been registered as adjuvant treatments for resected stage III and IV melanoma since 2018. Electronic health records (EHRs) are a real-world data source that can be used to review treatments in clinical practice. In this study, we applied EHR text-mining software to evaluate the real-world tolerability, safety, and efficacy of adjuvant melanoma treatments. Methods: Adult melanoma patients receiving adjuvant treatment between January 2019 and October 2021 at the Leiden University Medical Center, the Netherlands, were included. CTcue text-mining software (v3.1.0, CTcue B.V., Amsterdam, The Netherlands) was used to construct rule-based queries and perform context analysis for patient inclusion and data collection from structured and unstructured EHR data. Results: In total, 122 patients were included: 54 patients treated with nivolumab, 48 with pembrolizumab, and 20 with D + T. Significantly more patients discontinued treatment due to toxicity on D + T (N: 16%, P: 6%, D + T: 40%), and X-2 (6, n = 122) = 14.6 and p = 0.024. Immune checkpoint inhibitors (ICIs) mainly showed immune-related treatment-limiting adverse events (AEs), and chronic thyroid-related AE occurred frequently (hyperthyroidism: N: 15%, P: 13%, hypothyroidism: N: 20%, P: 19%). Treatment-limiting toxicity from D + T was primarily a combination of reversible AEs, including pyrexia and fatigue. The 1-year recurrence-free survival was 70.3% after nivolumab, 72.4% after pembrolizumab, and 83.0% after D + T. Conclusions: Text-mining EHR is a valuable method to collect real-world data to evaluate adjuvant melanoma treatments. ICIs were better tolerated than D + T, in line with RCT results. For BRAF+ patients, physicians must weigh the higher risk of reversible treatment-limiting AEs of D + T against the risk of long-term immune-related AEs. Show less
This squib briefly explores how contextualized embeddings – which are a type of compressed token-based semantic vectors – can be used as semantic retrieval and annotation tools for corpus-based... Show moreThis squib briefly explores how contextualized embeddings – which are a type of compressed token-based semantic vectors – can be used as semantic retrieval and annotation tools for corpus-based research into constructions. Focusing on embeddings created by the Bidirectional Encoder Representations from Transformer model, also known as ‘BERT’, this squib demonstrates how contextualized embeddings can help counter two types of retrieval inefficiency scenarios that may arise with purely form-based corpus queries. In the first scenario, the formal query yields a large number of hits, which contain a reasonable number of relevant examples that can be labeled and used as input for a sense disambiguation classifier. In the second scenario, the contextualized embeddings of exemplary tokens are used to retrieve more relevant examples in a large, unlabeled dataset. As a case study, this squib focuses on the INTO-INTEREST construction (e.g. I’m so into you). Show less
Brandsen, A.; Verberne, S.; Lambers, K.; Wansleeben, M. 2020
In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire need for semantic... Show moreIn this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire need for semantic search within archaeology, in order to allow archaeologists to find structured information in collections of Dutch excavation reports, currently totalling around 60,000 (658 million words) and growing rapidly. To guide this search task, NER is needed. We created rigorous annotation guidelines in an iterative process, then instructed five archaeology students to annotate a number of documents. The resulting dataset contains ~31k annotations between six entity types (artefact, time period, place, context, species & material). The inter-annotator agreement is 0.95, and when we used this data for machine learning, we observed an increase in F1 score from 0.51 to 0.70 in comparison to a machine learning model trained on a dataset created in prior work. This indicates that the data is of high quality, and can confidently be used to train NER classifiers Show less
Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual... Show moreMost methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data. Show less
