Presence of atrial natriuretic factor prohormone in enterochromaffin cells of the human large intestine

Atrial natriuretic factor is a hormone intimately involved in water and salt homeostasis. The heart constitutes the major but not exclusive site of synthesis of this hormone. Among other functions, the gastrointestinal tract has endocrine functions, plays an important role in volume regulation of the body, and seems to be a target organ for atrial natriuretic factor. Therefore, the presence of atrial natriuretic factor was investigated in the human gut. Immunoreactive atrial natriuretic factor was found in intraoperatively obtained samples of normal human colon. Acidic extracts of human large intestine contained about 0.4 pmol/g wet wt of atrial natriuretic factor. Analysis of atrial natriuretic factor immunoreactivity by gel-filtration and reverse-phase high-performance liquid chromatography showed that about 65% of the immunoreactivity corresponded to the atrial natriuretic factor phohormone and about 35% corresponded to the C-terminal ANF99-126. Immunohistochemistry showed atrial natriuretic factor prohormone location in enterochromaffin cells of the colon mucosa. Altogether, these findings show the presence of atrial natriuretic factor prohormone in enterochromaffin cells of the human large intestine and may suggest this organ as a site of atrial natriuretic factor synthesis in humans

Framework for Guiding the Development of High-Quality Conversational Agents in Healthcare

Evaluating conversational agents (CAs) that are supposed to be applied in healthcare settings and ensuring their quality is essential to avoid patient harm and ensure efficacy of the CA-delivered intervention. However, a guideline for a standardized quality assessment of health CAs is still missing. The objective of this work is to describe a framework that provides guidance for development and evaluation of health CAs. In previous work, consensus on categories for evaluating health CAs has been found. In this work, we identify concrete metrics, heuristics, and checklists for these evaluation categories to form a framework. We focus on a specific type of health CA, namely rule-based systems that are based on written input and output, have a simple personality without any kind of embodiment. First, we identified relevant metrics, heuristics, and checklists to be linked to the evaluation categories through a literature search. Second, five experts judged the metrics regarding their relevance to be considered within evaluation and development of health CAs. The final framework considers nine aspects from a general perspective, five aspects from a response understanding perspective, one aspect from a response generation perspective, and three aspects from an aesthetics perspective. Existing tools and heuristics specifically designed for evaluating CAs were linked to these evaluation aspects (e.g., Bot usability scale, design heuristics for CAs); tools related to mHealth evaluation were adapted when necessary (e.g., aspects from the ISO technical specification for mHealth Apps). The resulting framework comprises aspects to be considered not only as part of a system evaluation, but already during the development. In particular, aspects related to accessibility or security have to be addressed in the design phase (e.g., which input and output options are provided to ensure accessibility?) and have to be verified after the implementation phase. As a next step, transfer of the framework to other types of health CAs has to be studied. The framework has to be validated by applying it during health CA design and development

Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?

In the medical domain, multiple ontologies and terminology systems are available. However, existing classification and prediction algorithms in the clinical domain often ignore or insufficiently utilize semantic information as it is provided in those ontologies. To address this issue, we introduce a concept for augmenting embeddings, the input to deep neural networks, with semantic information retrieved from ontologies. To do this, words and phrases of sentences are mapped to concepts of a medical ontology aggregating synonyms in the same concept. A semantically enriched vector is generated and used for sentence classification. We study our approach on a sentence classification task using a real world dataset which comprises 640 sentences belonging to 22 categories. A deep neural network model is defined with an embedding layer followed by two LSTM layers and two dense layers. Our experiments show, classification accuracy without content enriched embeddings is for some categories higher than without enrichment. We conclude that semantic information from ontologies has potential to provide a useful enrichment of text. Future research will assess to what extent semantic relationships from the ontology can be used for enrichment

Concept-Based Retrieval from Critical Incident Reports

Background: Critical incident reporting systems (CIRS) are used as a means to collect anonymously entered information of incidents that occurred for example in a hospital. Analyzing this information helps to identify among others problems in the workflow, in the infrastructure or in processes. Objectives: The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval of relevant reports and their analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this work is to develop a method for retrieving reports from the CIRS related to a specific user query. Methods: atural language processing (NLP) and information retrieval (IR) methods are exploited for realizing the retrieval. We compare standard retrieval methods that rely upon frequency of words with an approach that includes a semantic mapping of natural language to concepts of a medical ontology. Results: By an evaluation, we demonstrate the feasibility of semantic document enrichment to improve recall in incident reporting retrieval. It is shown that a combination of standard keyword-based retrieval with semantic search results in highly satisfactory recall values. Conclusion: In future work, the evaluation should be repeated on a larger data set and real-time user evaluation need to be performed to assess user satisfactory with the system and results. Keywords. Information Retrieval, Data Mining, Natural Language Processing, Critical Incidents Reporting