Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

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PESI-MS enables with its greatly simplified handling and fast result delivery the application field for high-throughput use in routine settings. In health care and research, pre-analytical errors often remain undetected and disrupt diagnosis, treatment, clinical studies and biomarker validations incurring high costs. This proof-of-principle study investigates the suitability of PESI-MS for robust, routine sample quality evaluation.One of the most common pre-analytical quality issues in blood sampling are prolonged transportations times from bedside to laboratory promptly changing the metabolome. Here, human blood (n=50) was processed immediately or with a time delay of 3 h. The developed sample preparation method delivers ready-to-measure extracts in <8 min. PESI-MS spectra were measured in both ionization modes in 2 min from as little as 2 µl plasma allowing 3 replicate measurements. The mass spectra contained 1200 stable features covering a broad chemical space covering major metabolic classes (e.g. fatty acids, lysolipids, lipids). The time delay of 3 h was predictable by using 18 features with AUC > 0.95 with various machine learning and was robust against loss of single features.Our results serve as first proof of principle for the unique advantages of PESI-MS in sample quality assessments. The results pave the way towards a fully automated, cost-efficient, user-friendly, robust and fast quality assessment of human blood samples from minimal sample amounts.Graphical abstract

BackgroundIn biomedical applications where the size and complexity of SNOMED CT become problematic, using a smaller subset that can act as a reasonable substitute is usually preferred. In a special class of use cases—like ontology-based quality assurance, or when performing scaling experiments for real-time performance—it is essential that modules show a similar shape than SNOMED CT in terms of concept distribution per sub-hierarchy. Exactly how to extract such balanced modules remains unclear, as most previous work on ontology modularization has focused on other problems. In this study, we investigate to what extent extracting balanced modules that preserve the original shape of SNOMED CT is possible, by presenting and evaluating an iterative algorithm.MethodsWe used a graph-traversal modularization approach based on an input signature. To conform to our definition of a balanced module, we implemented an iterative algorithm that carefully bootstraped and dynamically adjusted the signature at each step. We measured the error for each sub-hierarchy and defined convergence as a residual sum of squares <1.ResultsUsing 2000 concepts as an initial signature, our algorithm converged after seven iterations and extracted a module 4.7 % the size of SNOMED CT. Seven sub-hierarhies were either over or under-represented within a range of 1–8 %.ConclusionsOur study shows that balanced modules from large terminologies can be extracted using ontology graph-traversal modularization techniques under certain conditions: that the process is repeated a number of times, the input signature is dynamically adjusted in each iteration, and a moderate under/over-representation of some hierarchies is tolerated. In the case of SNOMED CT, our results conclusively show that it can be squeezed to less than 5 % of its size without any sub-hierarchy losing its shape more than 8 %, which is likely sufficient in most use cases.