An Interventional Study Comparing the Memory Retention of Verbal & Pictorial Materials among MMMC Students

Aims: To compare the effectiveness of pictorial against verbal materials in memory retention among medical students. Study Design: Crossover randomized controlled trial. Place and Duration of Study: This study was conducted in Melaka-Manipal Medical College, Muar, Johor, Malaysia in April 2016. Methodology: 38 right-handed medical students of Melaka-Manipal Medical College were volunteers and participants were divided into two groups equally via simple random sampling. One group of participants were to recall pictures shown first followed by words while the other group of participants were to recall words first followed by pictures. All the pictures and words shown were of everyday objects. Data were analysed using Epi Info version 7. Results: There was a significant difference of memory retention between pictures and words (P-value =; p < 0.05) and of memory accuracy (P-value; p < 0.05). For memory retention, both groups were found to have higher scores for pictures than words as both groups obtained a mean score of 11.3 and 13.4 respectively for the pictures and 9.7 and 11.1 respectively for words. For memory accuracy, pictures were found to be recalled better than words as the mean scores for the pictures are higher than words in both groups. Conclusion: Information in the form of pictures should be more utilized in medical schools so that medical students can have better memory retention which in turn will lead to better academic performances

Towards a greener electrosynthesis: pairing machine learning and 3D printing for rapid optimisation of anodic trifluoromethylation

Applying electro-organic synthesis in flow configuration can potentially reduce the pharmaceutical industry\u27s carbon footprint and simplify the reaction scale-up. However, the optimisation of such reactions has remained challenging and resource consuming due to the convoluted interplay between the various input experimental parameters. Herein, we demonstrate the advantage of integrating a machine learning (ML) algorithm within an automated flow microreactor setup to assist in the optimisation of anodic trifluoromethylation. The ML algorithm is able to optimise six reaction parameters concurrently and increase the reaction yield of anodic trifluoromethylation by >270% within two iterations. Further, we discovered that electrode passivation and even higher reaction yields could be achieved by integrating 3D-printed metal electrodes into the microreactor. By coupling multiple analytical tools such as AC voltammetry, kinetic modelling, and gas chromatography, we gained holistic insights into the trifluoromethylation reaction mechanism, including potential sources of Faradaic efficiency and reactant losses. More importantly, we confirmed the multiple electrochemical and non-electrochemical steps involved in this reaction. Our findings highlight the potential of synergistically combining ML-assisted flow systems with advanced analytical tools to rapidly optimise complex electrosynthetic reaction sustainably