Blending Gagne's instructional model with Peyton's approach to design an introductory bioinformatics lesson plan for medical students:Proof-of-concept study

Background: With the rapid integration of genetics into medicine, it has become evident that practicing physicians as well as medical students and clinical researchers need to be updated on the fundamentals of bioinformatics. To achieve this, the following gaps need to be addressed: a lack of defined learning objectives for “Bioinformatics for Medical Practitioner” courses, an absence of a structured lesson plan to disseminate the learning objectives, and no defined step-by-step strategy to teach the essentials of bioinformatics in the medical curriculum. Objective: The objective of this study was to address these gaps to design a streamlined pedagogical strategy for teaching basics of bioinformatics in the undergraduate medical curriculum. Methods: The established instructional design strategies employed in medical education-Gagne's 9 events of instruction-were followed with further contributions from Peyton's four-step approach to design a structured lesson plan in bioinformatics. Results: First, we defined the specifics of bioinformatics that a medical student or health care professional should be introduced to use this knowledge in a clinical context. Second, we designed a structured lesson plan using a blended approach from both Gagne's and Peyton's instructional models. Lastly, we delineated a step-by-step strategy employing free Web-based bioinformatics module, combining it with a clinical scenario of familial hypercholesterolemia to disseminate the defined specifics of bioinformatics. Implementation of Schon's reflective practice model indicated that the activity was stimulating for the students with favorable outcomes regarding their basic training in bioinformatics. Conclusions: To the best of our knowledge, the present lesson plan is the first that outlines an effective dissemination strategy for integrating introductory bioinformatics into a medical curriculum. Further, the lesson plan blueprint can be used to develop similar skills in workshops, continuing professional development, or continuing medical education events to introduce bioinformatics to practicing physicians.</p

Augmenting flexnerism via twitterism:Need for integrating social media application in blueprinting pedagogical strategies for undergraduate medical education

Background: Flexnerism, or “competency-based medical education,” advocates that formal analytic reasoning, the kind of rational thinking fundamental to the basic sciences, especially the natural sciences, should be the foundation of physicians' intellectual training. The complexity of 21st century health care requires rethinking of current (medical) educational paradigms. In this “Millennial Era,” promulgation of the tenets of Flexnerism in undergraduate medical education requires a design and blueprint of innovative pedagogical strategies, as the targeted learners are millennials (designated as generation-Y medical students). Objective: The aim of this proof-of-concept study was to identify the specific social media app platforms that are selectively preferred by generation-Y medical students in undergraduate medical education. In addition, we aimed to explore if these preferred social media apps can be used to design an effective pedagogical strategy in order to disseminate course learning objectives in the preclinical phase of a spiral curriculum. Methods: A cross-sectional survey was conducted by distributing a 17-item questionnaire among the first- and second-year medical students in the preclinical phase at the Mohammed Bin Rashid University of Medicine and Health Science. Results: The study identified YouTube and WhatsApp as the social media app platforms preferred by generation-Y medical students in undergraduate medical education. This study also identified the differences between female and male generation-Y medical students in terms of the use of social media apps in medical education, which we believe will assist instructors in designing pedagogical strategies to integrate social media apps. In addition, we determined the perceptions of generation-Y medical students on the implementation of social media apps in medical education. The pedagogical strategy designed using social media apps and implemented in the Biochemistry course was well accepted by generation-Y medical students and can be translated to any course in the preclinical phase of the medical curriculum. Moreover, the identified limitations of this study provide an understanding of the gaps in research in the integration of social media apps in a medical curriculum catering to generation-Y medical students. Conclusions: 21st century medical education requires effective use of social media app platforms to augment competency-based medical education: Augmentation of Flexnerism in the current scenario is possible only by the adaptation of Twitterism.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

OneG: A Computational Tool for Predicting Cryptic Intermediates in the Unfolding Kinetics of Proteins under Native Conditions

<div><p>Understanding the relationships between conformations of proteins and their stabilities is one key to address the protein folding paradigm. The free energy change (ΔG) of unfolding reactions of proteins is measured by traditional denaturation methods and native hydrogen-deuterium (H/D) exchange methods. However, the free energy of unfolding (ΔG_U) and the free energy of exchange (ΔG_HX) of proteins are not in good agreement, though the experimental conditions of both methods are well matching to each other. The anomaly is due to any one or combinations of the following reasons: (i) effects of <em>cis-trans</em> proline isomerisation under equilibrium unfolding reactions of proteins (ii) inappropriateness in accounting the baselines of melting curves (iii) presence of cryptic intermediates, which may elude the melting curve analysis and (iv) existence of higher energy metastable states in the H/D exchange reactions of proteins. Herein, we have developed a novel computational tool, OneG, which accounts the discrepancy between ΔG_U and ΔG_HX of proteins by systematically accounting all the four factors mentioned above. The program is fully automated and requires four inputs: three-dimensional structures of proteins, ΔG_U, ΔG_U^* and residue-specific ΔG_HX determined under EX2-exchange conditions in the absence of denaturants. The robustness of the program has been validated using experimental data available for proteins such as cytochrome c and apocytochrome b₅₆₂ and the data analyses revealed that cryptic intermediates of the proteins detected by the experimental methods and the cryptic intermediates predicted by the OneG for those proteins were in good agreement. Furthermore, using OneG, we have shown possible existence of cryptic intermediates and metastable states in the unfolding pathways of cardiotoxin III and cobrotoxin, respectively, which are homologous proteins. The unique application of the program to map the unfolding pathways of proteins under native conditions have been brought into fore and the program is publicly available at <a href="http://sblab.sastra.edu/oneg.html">http://sblab.sastra.edu/oneg.html</a></p> </div

OneG-Vali: a computational tool for detecting, estimating and validating cryptic intermediates of proteins under native conditions

Unfolding pathway of T4 lysozyme under native conditions as predicted by the OneG-Vali has been illustrated. Also, structural contexts of various states (native (N), cryptic intermediates (CIs) and unfolded (U) conformations) of the protein and the population of three CIs are depicted.</p

Possible existence of metastable states of CBTX.

<p>The overall backbone folding of CBTX (1COD) is shown in ribbon model using PyMol and the five β-strands of the protein are labelled (S1–S5). The residues, predicted by OneG program, constituting the metastable states of CBTX are shown in sticks model.</p

GdnHCl-induced changes in 222 nm ellipticity in ubiquitin in the far-UV region.

<p>Solid line through the data in ‘A’ was the fit to the equation-11 and in ‘B’ to the equation-16. Pre-transition baselines were extrapolated using fitted-parameters up to 7 M GdnHCl (refer text).</p