Changes in sensorimotor regions of the cerebral cortex in congenital amusia: a case-control study.

Perceiving pitch is a central function of the human auditory system; congenital amusia is a disorder of pitch perception. The underlying neural mechanisms of congenital amusia have been actively discussed. However, little attention has been paid to the changes in the motor rain within congenital amusia. In this case-control study, 17 participants with congenital amusia and 14 healthy controls underwent functional magnetic resonance imaging while resting with their eyes closed. A voxel-based degree centrality method was used to identify abnormal functional network centrality by comparing degree centrality values between the congenital amusia group and the healthy control group. We found decreased degree centrality values in the right primary sensorimotor areas in participants with congenital amusia relative to controls, indicating potentially decreased centrality of the corresponding brain regions in the auditory-sensory motor feedback network. We found a significant positive correlation between the degree centrality values and the Montreal Battery of Evaluation of Amusia scores. In conclusion, our study identified novel, hitherto undiscussed candidate brain regions that may partly contribute to or be modulated by congenital amusia. Our evidence supports the view that sensorimotor coupling plays an important role in memory and musical discrimination. The study was approved by the Ethics Committee of the Second Xiangya Hospital, Central South University, China (No. WDX20180101GZ01) on February 9, 2019.

Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas.

BACKGROUND: The grading and pathologic biomarkers of glioma has important guiding significance for the individual treatment. In clinical, it is often necessary to obtain tumor samples through invasive operation for pathological diagnosis. The present study aimed to use conventional machine learning algorithms to predict the tumor grades and pathologic biomarkers on magnetic resonance imaging (MRI) data. METHODS: The present study retrospectively collected a dataset of 367 glioma patients, who had pathological reports and underwent MRI scans between October 2013 and March 2019. The radiomic features were extracted from enhanced MRI images, and three frequently-used machine-learning models of LC, Support Vector Machine (SVM), and Random Forests (RF) were built for four predictive tasks: (1) glioma grades, (2) Ki67 expression level, (3) GFAP expression level, and (4) S100 expression level in gliomas. Each sub dataset was split into training and testing sets at a ratio of 4:1. The training sets were used for training and tuning models. The testing sets were used for evaluating models. According to the area under curve (AUC) and accuracy, the best classifier was chosen for each task. RESULTS: The RF algorithm was found to be stable and consistently performed better than Logistic Regression and SVM for all the tasks. The RF classifier on glioma grades achieved a predictive performance (AUC: 0.79, accuracy: 0.81). The RF classifier also achieved a predictive performance on the Ki67 expression (AUC: 0.85, accuracy: 0.80). The AUC and accuracy score for the GFAP classifier were 0.72 and 0.81. The AUC and accuracy score for S100 expression levels are 0.60 and 0.91. CONCLUSION: The machine-learning based radiomics approach can provide a non-invasive method for the prediction of glioma grades and expression levels of multiple pathologic biomarkers, preoperatively, with favorable predictive accuracy and stability.

Developing an evidence-based public health informatics course.

OBJECTIVES: This study assessed the need to develop a public health informatics (PHI) introductory course and determine contents of such a course. METHODS: Community assessments employing focus group interviews and an online survey were utilized to determine course need and content. RESULTS: Results revealed a need to provide PHI training to graduate public health students and suggested broad course content requirements. Results indicated lack of awareness of libraries and librarians as sources of public health information. CONCLUSIONS: A graduate PHI course was developed and delivered. Additionally, implementation of a subject guide increased the library's profile.

Identifying the Clinical Laboratory Tests from Unspecified "Other Lab Test" Data for Secondary Use.

Clinical laboratory results are stored in electronic health records (EHRs) as structured data coded with local or standard terms. However, laboratory tests that are performed at outside laboratories are often simply labeled "outside test" or something similar, with the actual test name in a free-text result or comment field. After being aggregated into clinical data repositories, these ambiguous labels impede the retrieval of specific test results. We present a general multi-step solution that can facilitate the identification, standardization, reconciliation, and transformation of such test results. We applied our approach to data in the NIH Biomedical Translational Research Information System (BTRIS) to identify laboratory tests, map comment values to the LOINC codes that will be incorporated into our Research Entities Dictionary (RED), and develop a reference table that can be used in the EHR data extract-transform-load (ETL) process.

Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures.

Clinicians and clinical researchers often seek information in electronic health records (EHRs) that are relevant to some concept of interest, such as a disease or finding. The heterogeneous nature of EHRs can complicate retrieval, risking incomplete results. We frame this problem as the presence of two gaps: 1) a gap between clinical concepts and their representations in EHR data and 2) a gap between data representations and their locations within EHR data structures. We bridge these gaps with a knowledge structure that comprises relationships among clinical concepts (including concepts of interest and concepts that may be instantiated in EHR data) and relationships between clinical concepts and the database structures. We make use of available knowledge resources to develop a reproducible, scalable process for creating a knowledge base that can support automated query expansion from a clinical concept to all relevant EHR data.

Quality Health Information on the Internet: Developing a Diabetes Pathfinder for the Chinese Population.

A Web-based bilingual diabetes information pathfinder was created to help the Chinese population access quality health information on the Internet as part of a collaborative outreach project in the Dallas-Fort Worth area. A survey was conducted to identify the demographics, Internet usage, health information needs, and preferences for training sessions of the Chinese population. Breast cancer, diabetes, and breast cancer were the top three diseases of interest. The process of developing the pathfinder is described from start to finish, and it can serve as a model for the development of others. Pathfinder training sessions were held.

An innovative partnership to recruit and educate biomedical information professionals.

There is a growing need for biomedical sciences information professionals who have strong backgrounds in the biomedical sciences. This project pilots an innovative model for recruiting and educating students with these backgrounds. The project includes recruiting students from undergraduate biomedical programs, creating and teaching a basic course in biomedical information management, and awarding ten fellowships for advanced study. The University of North Texas (UNT) and Texas A&M University (TAMU) administer the project.