Clinical medical education in rural and underserved areas and eventual practice outcomes: A systematic review and meta-analysis.

BACKGROUND: Undergraduate medical students are enrolled in clinical education programs in rural and underserved urban areas to increase the likelihood that they will eventually practice in those areas and train in a primary care specialty to best serve those patient populations. METHODS: MEDLINE and Cochrane Library online databases were searched to identify articles that provide a detailed description of the exposure and outcome of interest. A qualitative review of articles reporting outcome data without comparison or control groups was completed using the Medical Education Research Study Quality Instrument (MERSQI). A meta-analysis of articles reporting outcome data with comparison or control groups was completed with statistical and graphical summary estimates. RESULTS: Seven hundred and nine articles were retrieved from the initial search and reviewed based on inclusion and exclusion criteria. Of those, ten articles were identified for qualitative analysis and five articles included control groups and thus were included in the quantitative analysis. Results indicated that medical students with clinical training in underserved areas are almost three times as likely to practice in underserved areas than students who do not train in those areas (relative risk [RR] = 2.94; 95% confidence interval [CI]: 2.17, 4.00). Furthermore, medical students training in underserved areas are about four times as likely to practice primary care in underserved areas than students who do not train in those locations (RR = 4.35; 95% CI: 1.56, 12.10). DISCUSSION: These estimates may help guide medical school administrators and policymakers to expand underserved clinical training programs to help relieve some of the problems associated with access to medical care among underserved populations.

ß-arrestin1-biased ß1-adrenergic receptor signaling regulates microRNA processing.

RATIONALE: MicroRNAs (miRs) are small, noncoding RNAs that function to post-transcriptionally regulate gene expression. First transcribed as long primary miR transcripts (pri-miRs), they are enzymatically processed in the nucleus by Drosha into hairpin intermediate miRs (pre-miRs) and further processed in the cytoplasm by Dicer into mature miRs where they regulate cellular processes after activation by a variety of signals such as those stimulated by ß-adrenergic receptors (ßARs). Initially discovered to desensitize ßAR signaling, ß-arrestins are now appreciated to transduce multiple effector pathways independent of G-protein-mediated second messenger accumulation, a concept known as biased signaling. We previously showed that the ß-arrestin-biased ßAR agonist, carvedilol, activates cellular pathways in the heart. OBJECTIVE: Here, we tested whether carvedilol could activate ß-arrestin-mediated miR maturation, thereby providing a novel potential mechanism for its cardioprotective effects. METHODS AND RESULTS: In human cells and mouse hearts, carvedilol upregulates a subset of mature and pre-miRs, but not their pri-miRs, in ß1AR-, G-protein-coupled receptor kinase 5/6-, and ß-arrestin1-dependent manner. Mechanistically, ß-arrestin1 regulates miR processing by forming a nuclear complex with hnRNPA1 and Drosha on pri-miRs. CONCLUSIONS: Our findings indicate a novel function for ß1AR-mediated ß-arrestin1 signaling activated by carvedilol in miR biogenesis, which may be linked, in part, to its mechanism for cell survival.

Case study of invalid to valid shift in cognitive performance following successful treatment of psychogenic nonepileptic seizure events.

Patients with psychogenic nonepileptic seizure (PNES) who fail performance validity testing (PVT) may appear to produce non-valid cognitive profiles. Consequently, they may not get referred to treatment and events persist, with worsening disability and high resource utilization. As a result, we report pre- and post-treatment neuropsychological evaluation findings in a 59-year-old woman with a confirmed diagnosis of PNES established using video-EEG monitoring. At pre-treatment baseline neuropsychological evaluation, PNES events occurred weekly to daily. Performance was impaired across PVTs and across multiple cognitive domains. After behavioral intervention specific to PNES, these events substantially reduced in frequency to rare stress-induced flares. Post-treatment neuropsychological evaluation revealed marked improvement of most cognitive and behavioral scores from baseline, and valid PVT scores. We review predisposing, precipitating, and perpetuating factors for PNES and cognitive impairment in this case and discuss the patient's outcome from treatment. Effectively managing PNES events and dissociative tendencies while reducing unnecessary pharmacological interventions appears to have allowed this patient to function closer to her optimal state. This case illustrates the complexity of Functional Neurologic Disorder (FND) clinical presentation and challenges the assumption that suboptimal neuropsychological performance predicts poor treatment engagement and outcome. We showcase the reversibility of PNES and cognitive manifestations of FND using targeted psychotherapeutic interventions, which resulted in reduced disability and associated healthcare costs, as well as re-engagement in life.

ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics.

BACKGROUND: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. RESULTS: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. CONCLUSIONS: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.