A group of researchers discovered a way to detect fasciculation in muscles, which serves to detect early signs of motor neuron disease (MND). The article, Ultrasound-Based Detection of Fasciculations in Healthy and Diseased Muscles, were published by IEEE Xplore. This article was peer reviewed and can be found in Transactions on Biomedical Engineering or online, where they also publish other articles monthly. The intended audience of their article is other researchers, engineers, and scientists, and even students who search for articles to analyze to complete their assignments. The four authors, Peter John Harding, Ian D. Loram, Nicholas Combes, and Emma Hodson-Tole, lives in the United Kingdom, where all of them obtained their degrees. Harding is a member of the Cognitive Motor Function research group at Manchester Metropolitan University, where he works with two of his co-authors, Loram and Hodson-Tole. Some of his academic interests include biomedical imaging, medical diagnostics, and computational optimization and parallelism. In addition, he is a STEM ambassador. Aside from this article, he has made many other publications with other authors, such as Mutual Information Based Gesture Recognition, and Automated Measurement of Human Skeletal Calf Muscle Contraction. Loram got his Ph.D. from the University of Birmingham and currently instructs neuromuscular control of human movement at Manchester Metropolitan University. His academic interests involve optimization of human performance, fear of falling, stress, and human performance, and muscle coordination and synergies. His research interests range from visual manual tracking, postural control, to muscle proprioception, muscle tendon interactions, and real-time ultrasonography of muscle. He received the Leverhulme Early Career Fellowship award in 2004 and was appointed Reader in Neuromuscular Control of Human Movement, Institute for Biomedical Research into Human Movement and Health. Hodson-Tole is a member of many professional associations, of the Editorial Board, and received her Ph.D. in skeletal muscle physiology and biomechanics. The Wellcome Trust awarded her a Sir Henry Wellcome Postdoctoral Research Fellowship. She’s also a principal investigator for two projects: MND Diagnosis: the utility of standard frame rate B-mode ultrasound imaging and Imaging Motor Unit Recruitment Patterns. Her academic interests include diagnosis neurodegenerative disease, motor unit structure and function, and spatial and temporal dynamics of skeletal muscle activation. Combes received his Bachelor of Medicine and Bachelor of Surgery from University of Birmingham and consults in neurophysiology at Royal Preston Hospital.
Motor neuron disease (MND) is a neurodegenerative disease in which the motor neuron begins to die or become unstable, causing involuntary twitches known as fasciculation (Harding, 2016). The process to identify these fasciculations is called electromyography (EMG). It involves inserting needles into several different places on the body, making the process invasive and painful. Furthermore, the electrodes detect these muscle movements in small portion of the muscles, which leads to inaccuracy of the detector, causing the practitioner to potentially ‘miss’ the fasciculation (Harding, 2016). As an alternative, ultrasound imaging (US) was proposed for the detection of MND. US can assess multiple layers and areas of the skin and is highly sensitive to movement as small as 5 micrometers (Harding, 2016). To prove this hypothesis, the article provides the process of collecting the data collected from the US on fasciculation in muscles and compares that data to those taken from EMG. The article’s organization, diction, use of visuals, and sentence structure contributed to the understanding of its contents. Yet, there are also sentence structure errors and repetitions that made the reading confusing. However, the validity of the methodology used showed that the experiment practiced real science: measurement, formulation, and modification of hypotheses. The methodology proved the author’s thesis that ultrasound imaging provides more accurate detection of fasciculation in MND.
The article has strong points as well as weak ones. First, to analyze the organization of the contents: the authors construct the article in chronological order, which is the best option for the article. It shows the stages of their experiment, which steps were taken, how data was analyzed, etc. The article was divided into subsections, and those subsections were further divided into sections with subheadings. The subsections helped readers identify the stages of the experiment, while the subheadings added details to the stages. These subparts allow readers to easily navigate through the article because they know what each section is about. Secondly, the use of professional diction is suitable to the intended audience: engineers, scientists, and researchers. The article used words pertaining to their intended audience such as “hypothesize,” “magnitude,” identified,” “accuracy,” and “dataset.” Furthermore, they gave adequate detail of the process and used precise words. The article gave very specific details to about their experiment, such as age, gender, and health condition of the muscles of their participants. To judge the quality of the experiment, such details are important for the knowledge of readers. The word “operator” was chosen instead of “machine” or “instrument,” which specifies what kind of equipment was used in the experiment. Additionally, the article used nonsexist language. For instance, in the phrase “To assess the correctness and level of agreement between operators, the operator assessments were combined in several ways,” (Harding, 2016) no gender roles were imposed on the object. Thus, the operator wasn’t sexualized. Thirdly, the use of visuals help readers understand the content more clearly; they used colors effectively, chose appropriate graphs, and created effective tables. Since the experiment compares two areas of muscles between two different type of muscles (healthy and diseased), the article used line graphs to help readers visualize the data. As shown below, two line graphs are presented to compare the data of the two muscles that are being tested in the experiment (Figure 1). The placement of the graphs shows the visible difference between the data. Legends and colors help readers identify the data on the graphs as well. Aside from line graphs, tables were created to display the data, which was also an effective visual. As shown in Figure 2, the data between the different muscle area and their health conditions are shown numerically. Unlike the line graphs which shows the increase and decrease, the table shows the difference between the data.
Lastly, the article contains overly long sentences that are repetitive. For instance, the phrase “When evaluating the agreement between observer identified muscle twitches and MI…” (Harding, 2016) were repeated twice in the article. Even though the contents of the sentence following the phrase are different, the phrase is repetitive.
The researchers believe the current equipment being used to test for detection of MND through fasciculation is invasive, painful, and lack accuracy. Hence, they experimented on ultrasound’s ability to detect fasciculation to show that it provides a more efficient way to test for MND. The method that was used in the experiment is primarily data collection and comparison. The data is collected from different test subjects, in which the two areas of muscles are the bicep brachii in the upper arms and medial gastrocnemius in the lower leg. In other words, the researchers proved their hypothesis scientifically. A scientific method has characterized natural science and consists of observation, measurement, experiment, formulation, testing, and modification of hypotheses. Since the experiment tests the hypotheses via data collection and comparison, the experiment uses scientific methodology and is practicing real science, not pseudoscience. Pseudoscience is a collection of beliefs and practices mistakenly regarded as being based on scientific method. In other words, beliefs that can be scientifically proven wrong, and doesn’t have supporting evidence. The experiment didn’t practice any pseudoscience methods as its process is scientific.
The article helped me understood how to test the usefulness of an equipment in bioengineering. It also showed me how to correlate different equipment to existing tools to improve the efficiency of the tools’ function or to even replace it. Since biomedical engineering is a growing field, new ideas can stem from existing tools to improve it, and not just new ideas to create something more advanced. However, the article could be improved by giving readers details about the data rather than the data itself. Since it’s an article, displaying the data points create a mess in the article, causing the reader to easily get confused with the numbers given. More tables can be created to replace the written data in the subsections. There should be follow-ups to explain and clarify the data. By doing so, readers will be able to see the results more visually and understand it more.
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