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2022 ◽  
Vol 12 ◽  
Tom Maudrich ◽  
Susanne Hähner ◽  
Rouven Kenville ◽  
Patrick Ragert

BackgroundSomatosensory-evoked potentials (SEP) represent a non-invasive tool to assess neural responses elicited by somatosensory stimuli acquired via electrophysiological recordings. To date, there is no comprehensive evaluation of SEPs for the diagnostic investigation of exercise-induced functional neuroplasticity. This systematic review aims at highlighting the potential of SEP measurements as a diagnostic tool to investigate exercise-induced functional neuroplasticity of the sensorimotor system by reviewing studies comparing SEP parameters between athletes and healthy controls who are not involved in organized sports as well as between athlete cohorts of different sport disciplines.MethodsA systematic literature search was conducted across three electronic databases (PubMed, Web of Science, and SPORTDiscus) by two independent researchers. Three hundred and ninety-seven records were identified, of which 10 cross-sectional studies were considered eligible.ResultsDifferences in SEP amplitudes and latencies between athletes and healthy controls or between athletes of different cohorts as well as associations between SEP parameters and demographic/behavioral variables (years of training, hours of training per week & reaction time) were observed in seven out of 10 included studies. In particular, several studies highlight differences in short- and long-latency SEP parameters, as well as high-frequency oscillations (HFO) when comparing athletes and healthy controls. Neuroplastic differences in athletes appear to be modality-specific as well as dependent on training regimens and sport-specific requirements. This is exemplified by differences in SEP parameters of various athlete populations after stimulation of their primarily trained limb.ConclusionTaken together, the existing literature suggests that athletes show specific functional neuroplasticity in the somatosensory system. Therefore, this systematic review highlights the potential of SEP measurements as an easy-to-use and inexpensive diagnostic tool to investigate functional neuroplasticity in the sensorimotor system of athletes. However, there are limitations regarding the small sample sizes and inconsistent methodology of SEP measurements in the studies reviewed. Therefore, future intervention studies are needed to verify and extend the conclusions drawn here.

2022 ◽  
Vol 12 ◽  
Guangying Cui ◽  
Shanshuo Liu ◽  
Zhenguo Liu ◽  
Yuan Chen ◽  
Tianwen Wu ◽  

Objective: The gut microecosystem is the largest microecosystem in the human body and has been proven to be linked to neurological diseases. The main objective of this study was to characterize the fecal microbiome, investigate the differences between epilepsy patients and healthy controls, and evaluate the potential efficacy of the fecal microbiome as a diagnostic tool for epilepsy.Design: We collected 74 fecal samples from epilepsy patients (Eps, n = 24) and healthy controls (HCs, n = 50) in the First Affiliated Hospital of Zhengzhou University and subjected the samples to 16S rRNA MiSeq sequencing and analysis. We set up a train set and a test set, identified the optimal microbial markers for epilepsy after characterizing the gut microbiome in the former and built a diagnostic model, then validated it in the validation group.Results: There were significant differences in microbial communities between the two groups. The α-diversity of the HCs was higher than that of the epilepsy group, but the Venn diagram showed that there were more unique operational taxonomic unit (OTU) in the epilepsy group. At the phylum level, Proteobacteria and Actinobacteriota increased significantly in Eps, while the relative abundance of Bacteroidota increased in HCs. Compared with HCs, Eps were enriched in 23 genera, including Faecalibacterium, Escherichia-Shigella, Subdoligranulum and Enterobacteriaceae-unclassified. In contrast, 59 genera including Bacteroides, Megamonas, Prevotella, Lachnospiraceae-unclassified and Blautia increased in the HCs. In Spearman correlation analysis, age, WBC, RBC, PLT, ALB, CREA, TBIL, Hb and Urea were positively correlated with most of the different OTUs. Seizure-type, course and frequency are negatively correlated with most of the different OTUs. In addition, twenty-two optimal microbial markers were identified by a fivefold cross-validation of the random forest model. In the established train set and test set, the area under the curve was 0.9771 and 0.993, respectively.Conclusion: Our study was the first to characterize the gut microbiome of Eps and HCs in central China and demonstrate the potential efficacy of microbial markers as a noninvasive biological diagnostic tool for epilepsy.

2022 ◽  
Vol 15 ◽  
Danilo Pena ◽  
Jessika Suescun ◽  
Mya Schiess ◽  
Timothy M. Ellmore ◽  
Luca Giancardo ◽  

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

2022 ◽  
Shalini Pandey ◽  
Subhayan Chakraborty ◽  
Rimilmandrita Ghosh ◽  
Divya Radhakrishnan ◽  
Saravanan Peruncheralathan ◽  

The effectiveness of MRI as a diagnostic tool have increased tremendously after the discovery of contrast agents (CA). Most of the clinically approved CAs at present are relaxation based and...

2022 ◽  
Vol 17 (01) ◽  
pp. C01033
J. Cerovsky ◽  
O. Ficker ◽  
V. Svoboda ◽  
E. Macusova ◽  
J. Mlynar ◽  

Abstract Scintillation detectors are widely used for hard X-ray spectroscopy and allow us to investigate the dynamics of runaway electrons in tokamaks. This diagnostic tool proved to be able to provide information about the energy or the number of runaway electrons. Presently it has been used for runaway studies at the GOLEM and the COMPASS tokamaks. The set of scintillation detectors used at both tokamaks was significantly extended and improved. Besides NaI(Tl) (2 × 2 inch) scintillation detectors, YAP(Ce) and CeBr3 were employed. The data acquisition system was accordingly improved and the data from scintillation detectors is collected with appropriate sampling rate (≈300 MHz) and sufficient bandwidth (≈100 MHz) to allow a pulse analysis. Up to five detectors can currently simultaneously monitor hard X-ray radiation at the GOLEM. The same scintillation detectors were also installed during the runaway electron campaign at the COMPASS tokamak. The aim of this contribution is to report progress in diagnostics of HXR radiation induced by runaway electrons at the GOLEM and the COMPASS tokamaks. The data collected during the 12th runaway electron campaign (2020) at COMPASS shows that count rates during typical low-density runaway electron discharges are in a range of hundreds of kHz and detected photon energies go up to 10 MeV (measured outside the tokamak hall). Acquired data from experimental campaigns from both machines will be discussed.

Kai-Fu Yang ◽  
Hao-Wei Yang ◽  
Chao-Hung Huang

This research attempts to focus on Return Merchandise Authorization (RMA) and the optimization of the RMA solutions. In the meantime, this research establishes RMA Diagnosis Checklist, Innovative RMA Process system, and a number of suggested solutions for Small and Medium Enterprises (SMEs). This research has important implications as it optimizes RMA solutions to B2B problems for SMEs, provides useful information on effective RMA service policies for B2B marketing decisions and creates a general awareness of the importance and rationale of RMA. Finally, this study provides RMA as a diagnostic tool and strategic tactic for SMEs.

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