Establishment of a radiotelemetric recording technique in mice to investigate gastric slow waves: Modulatory role of putative neurotransmitter systems

Wang, Hui-Chuan; Lu, Zhonghua; Liu, Julia Y.H.; Sun, Yayi; Tu, Longlong; Ngan, Man P.; Yeung, Chun; Rudd, John A.

doi:10.1113/ep086815

Cited by 7 publications

(4 citation statements)

References 51 publications

(60 reference statements)

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“…To date, only limited information is available regarding DFA of GMA, probably because the time series of EGG recordings in humans have a short-duration. With the advent of radiotelemetric technology, long-term GMA from the antrum has been recorded in different species (Percie du Sert et al, 2010;Rudd et al, 2018;Wang et al, 2018). Theoretically, the observed periodic trend in GMA may invalidate the power-law scaling behavior (Hu et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

“…Using state-of-the-art radiotelemetric technology, our previous studies successfully recorded the GMA from freely moving ferrets, shrews and mice (Percie du Sert et al, 2010;Lu et al, 2014;Tu et al, 2017;Wang et al, 2018). In the ferrets, analysis of the DF and power partition of GMA revealed that GMA could be disrupted by emetics such as apomorphine (Percie du Sert et al, 2009a), cisplatin (Percie du Sert et al, 2009a), and exendin-4 (Lu et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

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Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Zhou

et al. 2021

Front. Physiol.

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Nausea and emesis resulting from disease or drug treatment may be associated with disrupted gastric myoelectric activity (GMA). Conventional analytical techniques can determine the relative degrees of brady-, normo-, and tachygastric power, but lose information relative to the basic slow wave shape. The aim of the present study was to investigate the application of advanced analytical techniques in the analysis of disrupted GMA recorded after administration of sulprostone, a prostaglandin E3/1 agonist, in ferrets. Ferrets were implanted with radiotelemetry devices to record GMA, blood pressure, heart rate (HR) and core body temperature 1 week before the administration of sulprostone (30 μg/kg) or vehicle (saline, 0.5 mL/kg). GMA was initially analyzed using fast Fourier transformations (FFTs) and a conventional power partitioning. Detrended fluctuation analysis (DFA) was also applied to the GMA recordings to reveal information relative to the fluctuation of signals around local trends. Sample entropy (SampEn) analysis was used for examining the regularity of signals. Conventional signal processing techniques revealed that sulprostone increased the dominant frequency (DF) of slow waves, with an increase in the percentage power of the tachygastric range and a decrease in the percentage power of the normogastric range. DFA revealed that sulprostone decreased the fluctuation function, indicative of a loss of the variability of GMA fluctuations around local trends. Sulprostone increased SampEn values, indicating a loss of regularity in the GMA data. Behaviorally, sulprostone induced emesis and caused defecation. It also increased blood pressure and elevated HR, with an associated decrease in HR variability (HRV). Further analysis of HRV revealed a decrease in both low-frequency (LF) and high-frequency (HF) components, with an overall increase in the LF/HF ratio. Sulprostone did not affect core body temperature. In conclusion, DFA and SampEn permit a detailed analysis of GMA, which is necessary to understand the action of sulprostone to modulate gastric function. The action to decrease HRV and increase the LF/HF ratio may be consistent with a shift toward sympathetic nervous system dominance, commonly seen during nausea.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Zhou

et al. 2021

Front. Physiol.

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“…In the acute and chronic experiments, the existence of a DF was used as a criterion to include or reject recorded GI myoelectric signals. This criterion was based on prior studies involving awake behaving ferrets, dogs and mice [14, 20–22] that described the DF as a characteristic and consistent peak in the power spectrum of myoelectric signals recorded across the stomach. Five animals in the acute study displayed a DF in the GI myoelectric signals for at least one gastric segment and the intra-subject variability of the DF (across paddles) was low.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of emesis and gastrointestinal state in ferrets

Nanivadekar¹,

Miller

Fulton

et al. 2019

PLoS ONE

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Although electrogastrography (EGG) could be a critical tool in the diagnosis of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived from abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus-to-duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k-nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>75%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching, which could provide a methodology to diagnose GI diseases and symptoms such as nausea and vomiting.

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“…In the acute and chronic experiments, the existence of a DF was used as a criterion to include or reject recorded GI myoelectric signals. This criterion was based on prior studies involving awake behaving ferrets, dogs and mice [14,[19][20][21] that described the DF as a characteristic and consistent peak in the power spectrum of myoelectric signals recorded across the stomach. Five animals in the acute study displayed a DF in the GI myoelectric signals for at least one gastric segment and the intra-subject variability of the DF (across paddles) was low.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of emesis and gastrointestinal state in ferrets

Nanivadekar

Miller

Fulton

et al. 2019

Preprint

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Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus to duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features.Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

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Establishment of a radiotelemetric recording technique in mice to investigate gastric slow waves: Modulatory role of putative neurotransmitter systems

Cited by 7 publications

References 51 publications

Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Machine learning prediction of emesis and gastrointestinal state in ferrets

Machine learning prediction of emesis and gastrointestinal state in ferrets

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