Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer’s disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P < 0.05) with highest significance for miR-532-5p (Benjamini-Hochberg adjusted P = 4.8 × 10−30). Machine learning models reached an area under the curve (AUC) value of 87.6% in differentiating AD patients from controls. Further, ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted P = 0.0002). Interestingly, the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells, while those up-regulated in AD were enriched in serum, exosomes, cytotoxic t-cells, and B-cells. Our study represents the next important step in translational research for a miRNA-based AD test

Treatment-independent miRNA signature in blood of wilms tumor patients

Background Blood-born miRNA signatures have recently been reported for various tumor diseases. Here, we compared the miRNA signature in Wilms tumor patients prior and after preoperative chemotherapy according to SIOP protocol 2001. Results We did not find a significant difference between miRNA signature of both groups. However both, Wilms tumor patients prior and after chemotherapy showed a miRNA signature different from healthy controls. The signature of Wilms tumor patients prior to chemotherapy showed an accuracy of 97.5% and of patients after chemotherapy an accuracy of 97.0%, each as compared to healthy controls. Conclusion Our results provide evidence for a blood-born Wilms tumor miRNA signature largely independent of four weeks preoperative chemotherapy treatment

Treatment-independent miRNA signature in blood of wilms tumor patients

Abstract Background Blood-born miRNA signatures have recently been reported for various tumor diseases. Here, we compared the miRNA signature in Wilms tumor patients prior and after preoperative chemotherapy according to SIOP protocol 2001. Results We did not find a significant difference between miRNA signature of both groups. However both, Wilms tumor patients prior and after chemotherapy showed a miRNA signature different from healthy controls. The signature of Wilms tumor patients prior to chemotherapy showed an accuracy of 97.5% and of patients after chemotherapy an accuracy of 97.0%, each as compared to healthy controls. Conclusion Our results provide evidence for a blood-born Wilms tumor miRNA signature largely independent of four weeks preoperative chemotherapy treatment.</p

The revised Approved Instructional Resources score:An improved quality evaluation tool for online educational resources

BACKGROUND: Free Open-Access Medical education (FOAM) use among residents continues to rise. However, it often lacks quality assurance processes and residents receive little guidance on quality assessment. The Academic Life in Emergency Medicine Approved Instructional Resources tool (AAT) was created for FOAM appraisal by and for expert educators and has demonstrated validity in this context. It has yet to be evaluated in other populations.OBJECTIVES: We assessed the AAT's usability in a diverse population of practicing emergency medicine (EM) physicians, residents, and medical students; solicited feedback; and developed a revised tool.METHODS: As part of the Medical Education Translational Resources: Impact and Quality (METRIQ) study, we recruited medical students, EM residents, and EM attendings to evaluate five FOAM posts with the AAT and provide quantitative and qualitative feedback via an online survey. Two independent analysts performed a qualitative thematic analysis with discrepancies resolved through discussion and negotiated consensus. This analysis informed development of an initial revised AAT, which was then further refined after pilot testing among the author group. The final tool was reassessed for reliability.RESULTS: Of 330 recruited international participants, 309 completed all ratings. The Best Evidence in Emergency Medicine (BEEM) score was the component most frequently reported as difficult to use. Several themes emerged from the qualitative analysis: for ease of use-understandable, logically structured, concise, and aligned with educational value. Limitations include deviation from questionnaire best practices, validity concerns, and challenges assessing evidence-based medicine. Themes supporting its use include evaluative utility and usability. The author group pilot tested the initial revised AAT, revealing a total score average measure intraclass correlation coefficient (ICC) of moderate reliability (ICC = 0.68, 95% confidence interval [CI] = 0 to 0.962). The final AAT's average measure ICC was 0.88 (95% CI = 0.77 to 0.95).CONCLUSIONS: We developed the final revised AAT from usability feedback. The new score has significantly increased usability, but will need to be reassessed for reliability in a broad population.</p

The Social Media Index as an Indicator of Quality for Emergency Medicine Blogs: A METRIQ Study

Study objective: Online educational resources such as blogs are increasingly used for education by emergency medicine clinicians. The Social Media Index was developed to quantify their relative impact. The Medical Education Translational Resources: Indicators of Quality (METRIQ) study was conducted in part to determine the association between the Social Media Index score and quality as measured by gestalt and previously derived quality instruments. Methods: Ten blogs were randomly selected from a list of emergency medicine and critical care Web sites. The 2 most recent clinically oriented blog posts published on these blogs were evaluated with gestalt, the Academic Life in Emergency Medicine Approved Instructional Resources (ALiEM AIR) score, and the METRIQ-8 score. Volunteer raters (including medical students, emergency medicine residents, and emergency medicine attending physicians) were identified with a multimodal recruitment methodology. The Social Media Index was calculated in February 2016, November 2016, April 2017, and December 2017. Pearson's correlations were calculated between the Social Media Index and the average rater gestalt, ALiEM AIR score, and METRIQ-8 score. Results: A total of 309 of 330 raters completed all ratings (93.6%). The Social Media Index correlated moderately to strongly with the mean rater gestalt ratings (range 0.69 to 0.76) and moderately with the mean rater ALiEM AIR score (range 0.55 to 0.61) and METRIQ-8 score (range 0.53 to 0.57) during the month of the blog post's selection and for 2 years after. Conclusion: The Social Media Index's correlation with multiple quality evaluation instruments over time supports the hypothesis that it is associated with overall Web site quality. It can play a role in guiding individuals to high-quality resources that can be reviewed with critical appraisal techniques