Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the idea “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” stays controversial up to now in several fields. For instance, it’s outlined as (1) quantitative scientific idea in pure science and engineering, (2) knowledge level in the subject of training analysis, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, gives a primary knowledge infrastructure for measuring medical knowledge. In normal, the research of extracting SPO triples as computable knowledge unit from unstructured scientific textual content has been overwhelmingly specializing in scientific knowledge per se.

Since the SPO triples could be probably extracted from hypothetical, speculative statements and even conflicting and contradictory assertions, the knowledge standing (i.e., the uncertainty), which serves as an integral and crucial a part of scientific knowledge has been largely missed. This article goals to place ahead a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung most cancers publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its standing evolves over time not directly mirror the energy of competing knowledge claims, and the likelihood of certainty for a given SPO triple.

We attempt to focus on the new insights using the uncertainty-centric approaches to detect analysis fronts, and establish knowledge claims with excessive certainty degree, in an effort to enhance the efficacy of knowledge-driven resolution help. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes is one in every of the most generally used cell-based fashions that resulted from the discovery of how non-embryonic stem cells might be differentiated into a number of cell sorts. In only one decade, iPSC-derived cardiomyocytes went from a analysis lab to widespread use in biomedical analysis and preclinical security analysis for medication and different chemical compounds.

Insulin: A Pacesetter for the Shape of Modern Biomedical Science and the Nobel Prize

The 100^th anniversary of the discovery of insulin in Toronto in 1921 is a crucial second in medical and scientific historical past. The demonstration that an extract of canine pancreas reproducibly lowered blood glucose, initially in diabetic canines and then in people with sort 1 diabetes, was a medical breakthrough that modified the course of what was till then a largely deadly illness. The discovery of the “exercise” – quickly named “insulin” – was extensively celebrated, garnering a Nobel Prize for Banting and McLeod in 1923. Over the ensuing 100 years, analysis on insulin has superior on many fronts, producing insights which have remodeled our understanding of diabetes and our strategy to its therapy.

But it’s too usually unappreciated that analysis on insulin had one other consequence of far broader scientific significance, serving as a pacesetter and catalyst to bioscience analysis throughout many fields. Some of this was straight insulin-related, and like its discovery was additionally acknowledged by the Nobel Prize. Equally vital, nonetheless, was analysis stimulated by the discovery of insulin that has profoundly influenced biomedical analysis, generally additionally acknowledged by the Nobel Prize and generally with out this recognition, however maybe equally vital. In this paper, we evaluation a few of the most notable examples of each insulin-related and insulin-stimulated analysis as an instance the impression of this discovery on the course of contemporary bioscience.