Assessing the effectiveness of virtual reality to promote empathy for patients through a mixed-methods study.

OBJECTIVE: To describe the implementation and outcomes of EmpathyVR, an immersive experience using virtual reality (VR) to promote empathy for patients with physical limitations due to chronic diseases. METHODS: First-year student pharmacists participated in a learning experience that incorporated VR and stiff gloves to simulate physical limitations associated with color-blindness and arthritis, respectively. Students completed pre- and post-intervention surveys that included the Kiersma-Chen Empathy Scale and measures of immersion, user enjoyment, perceived usefulness for learning, and adverse effects of the experience. A focus group was also conducted with 6 students to obtain additional feedback on the learning experience. RESULTS: Of the 132 students who were enrolled in the program, 131 completed the assigned tasks; 117 of these agreed to participate in the study, and their data were included in the analyses. There was a significant improvement in empathy scores in the cognitive domain from pre- to post-intervention. Post-intervention survey results also demonstrated a high degree of immersion in the experience, high levels of self-reported enjoyment, and high levels of perceived usefulness of the activity to support learning. There was a low to moderate incidence of minor self-reported adverse effects from the activity. Focus group feedback was also positive. One student stated, "… it really allowed you to think and put yourself in their shoes." CONCLUSION: Implementation of a VR-based learning activity into the first semester of the PharmD curriculum was successful and resulted in improved student empathy scores and a positive learning experience.

Implementation of an automated grading tool for phonetic transcription training.

Clinical phonetic transcription is regarded as a highly specialised skill requiring hours of practice for mastery. Although this skill is a critical part of students' clinical preparation to become speech-language pathologists, students often report feeling unprepared to apply the skill in clinical practice. Previous studies suggest that increased opportunities for practice and timely feedback on transcriptions are needed in order to develop skill confidence. However, providing more opportunities for practice can be impeded by the limited resources to manage the grading of additional assignments. The purpose of this study is to show the implementation of a web-based learning management system (LMS) designed in our labs for phonetics instruction. The Automated Phonetic Transcription Grading Tool (APTgt LMS) was developed to provide a platform for assignment delivery and automated grading of transcription assignments. The APTgt LMS has three embedded IPA keyboards (basic, advanced, and full IPA) and an automated edit distance algorithm modified by phonetic alignment principles, which allows for individualised scoring and visual course-level feedback in an interactive online environment. For pilot testing, student confidence was queried before and after practice opportunities using APTgt. A concurrent mixed methods research design was used to analyse four Likert scale and three open-ended questions. Student confidence in transcribing disordered speech was found to significantly increase (p <0.001) following additional practice. Students reported concerns related to accurate transcription of disordered speech and that additional practice is still needed. Tools like APTgt can aid in facilitating student learning and increasing student confidence in applied transcription.

Investigation of the chemical composition of antibacterial Psidium guajava extract and partitions against foodborne pathogens.

P. guajava was partitioned into aqueous and ethyl acetate fractions and studied for its antibacterial chemical constituents. The minimum inhibitory concentrations of the aqueous and ethyl acetate partitions against Escherichia coli, Salmonella Typhimurium, and Staphylococcus aureus were found to be 0.75, 0.75, 0.15, 0.5, 0.5, and 0.125%, respectively. Using LC-MS-based chemical fingerprinting, auto MS/MS fragmentation and bioactive molecular networking, 18 compounds of interest were detected. The top 10 bioactive compounds and eight additional non-bioactive compounds known to be found in P. guajava are highlighted. We report five compounds to be identified in P. guajava for the first time. Studies have indicated P. guajava to be a plant source of antibacterial compounds that could be useful in the food industry to prevent foodborne illnesses outbreaks, reduce food spoilage, and satisfy consumer demands for less synthetic chemical usage in the food industry.

Bioactive Natural Products Identification Using Automation of Molecular Networking Software.

Secondary metabolites from natural sources are promising starting points for discovering and developing drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. Recent advances in bioinformatics tools and molecular networking methods have made it possible to identify novel bioactive compounds. In this study, a workflow combining network-based methods for identifying bioactive compounds found in natural products was streamlined by innovating an automated bioinformatics software. The workflow relies on Global Natural Product Social Molecular Networking (GNPS), a web-based mass spectrometry ecosystem that aims to be an open-access knowledge base for community-wide organization and sharing of raw, processed, or annotated fragmentation mass spectrometry data. By combining computational tools including MZmine2, GNPS, and Cytoscape, the integrated dashboard quickly creates bioactive molecular networks with minimal user intervention and reduces the processing time of the original workflow by over 80%. This newly automated workflow quickens the process of discovering bioactive compounds from natural products. This study uses extracts from Psidium guajava leaves to demonstrate the application of our automated software.

An automated tool for comparing phonetic transcriptions.

Many computerized tools for comparing phonetic transcriptions have been proposed and shared in the past; however, previous tools are relatively difficult to access and incorporate into clinical and research practice, or require users to learn additional phonetic symbol systems. The purpose of this project was to develop and test a readily available web-based application for quantitatively comparing phonetic transcriptions that are input using International Phonetic Alphabet (IPA) symbols. A web-based computer application was developed to allow for IPA phonetic transcription comparison. A point-and-click keyboard was developed to provide support for character input of the full IPA, as well as most symbols in the extIPA set. The application compares phonetic transcriptions using a modified edit distance algorithm following phonologically informed alignment. Visualizations of the algorithm's optimal alignment and scoring operations are shown for each comparison input by a user. The application, named the Automated Phonetic Transcription Comparison Tool (APTct), was thoroughly tested for accurate implementation of the algorithm principles. Validity tests through two sample use cases were also performed by comparison of hand calculations to APTct calculations. In function testing, the APTct showed excellent agreement with an expert's hand scoring using the same algorithm principles. In a validity test, only minor differences between the APTct and hand calculations were observed, primarily due to errors inputting the transcriptions into the APTct. The web-based APTct is a validated and versatile tool for quantitatively comparing even complex IPA phonetic transcriptions. It is freely available to clinicians and researchers, who may find it useful for a variety of potential scenarios. We invite researchers, clinicians, and other individuals to use the tool in their clinical and research work. The permalink for the website is as follows: https://aptct.auburn.edu.