A novel application of real-time RT-LAMP for body fluid identification: using HBB detection as the model.

We report on a novel application of real-time reverse transcription-loop-mediated isothermal amplification (real-time RT-LAMP) to identify the presence of a specific body fluid using blood as a proof-of-concept model. By comparison with recently developed methods of body fluid identification, the RT-LAMP assay is rapid and requires only one simple heating-block maintained at a single temperature, circumventing the need for dedicated equipment. RNA was extracted from different body fluids (blood, semen, saliva, menstrual blood, sweat, and urine) for use in real-time RT-LAMP reaction. The 18S rRNA locus was used as the internal control and hemoglobin beta (HBB) as the blood-specific marker. Reverse transcription and LAMP reaction were performed in the same tube using a turbidimeter for real-time monitoring the reaction products within a threshold of 60 min. HBB LAMP products were only detected in blood and not in any of the other body fluid, but products from the 18S rRNA gene were detected in all the tested body fluids as expected. The limit of detection was a minimum of 10(-5) ng total RNA for detection of both 18S rRNA and HBB. Augmenting the detection of RT-LAMP products was performed by separation of the products using gel electrophoresis and collecting the fluorescence of calcein. The data collected indicated complete concordance with the body fluid tested regardless of the method of detection used. This is the first application of real-time RT-LAMP to detect body fluid specific RNA and indicates the use of this method in forensic biology.

Mining context-aware resource profiles in the presence of multitasking.

Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS). This can be used to, e.g., discover resource profiles which are groups of resources performing similar activity instances, providing an extensive overview of resource behaviour within healthcare organisations. Healthcare managers can employ these insights to allocate their resources efficiently, e.g., by improving the scheduling and staffing of nurses. Existing resource profiling algorithms are limited in their ability to apprehend the complex relationship between processes and resources because they do not take into account the context in which activities were executed, particularly in the context of multitasking. Therefore, this paper introduces ResProMin-MT to discover context-aware resource profiles in the presence of multitasking. In contrast to the state-of-the-art, ResProMin-MT is capable of taking into account more complex contextual activity dimensions, such as activity durations and the degree of multitasking by resources. We demonstrate the feasibility of our method within a real-life healthcare context, validated by medical domain experts.