A multi-task CNN learning model for taxonomic assignment of human viruses

10.1186/s12859-021-04084-wBMC Bioinformatics2219

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria

Development of a clinical decision support system for diabetes care: A pilot study

<div><p>Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.</p></div

Calcium modulation of doxorubicin cytotoxicity in yeast and human cells

10.1111/gtc.12346GENES TO CELLS213226-240UNITED KINGDO

A screenshot of Computerized Patient Support System 2 showing the HbA_1c result of a patient as an example.

<p>The left panel displays a list of all the historical laboratory and radiological tests ordered for a patient. The right panel displays the results of the individual test order on the left panel.</p

Flowchart summarizing the overall design of the survey.

<p>Flowchart summarizing the overall design of the survey.</p

Treatment targets for each laboratory marker related to diabetes care, and their assigned color codes.

<p>Treatment targets for each laboratory marker related to diabetes care, and their assigned color codes.</p

An example of the lipid panel displayed in the mock CPSS2 interface.

<p>An example of the lipid panel displayed in the mock CPSS2 interface.</p

Box plots showing the participants’ (A) ability to determine if the adjustment of treatment was required, (B) average time (in minutes) spent on each case, (C) perceived confidence before and after completing the survey using the mock CPSS2 interface, (D) perceived confidence before and after completing the survey using the mock CPSS2 interface, using either interface.

<p>The exact question asked in the survey is shown in the title of the graphs. ** denotes <i>p</i><0.05, **** denotes <i>p</i><0.0001.</p