Biology subjects

Horn, C. C.

Publications and source records attributed to Horn, C. C..

3 recordsLinked to original sources

bioRxiv2023

Anesthesia suppressed gastric myoelectric power in the ferret

BackgroundGastrointestinal myoelectric signals have been the focus of extensive research; although it is unclear how general anesthesia affects these signals, studies have often been conducted under general anesthesia. Here, we explore this issue directly by recording gastric myoelectric signals during awake and anesthetized states in the ferret and also explore the contribution of behavioral movement to observed changes in signal power. MethodsFerrets were surgically implanted with electrodes to record gastric myoelectric activity from the serosal surface of the stomach, and, following recovery, were tested in awake and isoflurane-anesthetized conditions. Video recordings were also analyzed during awake experiments to compare myoelectric activity during behavioral movement and rest. Key ResultsA significant decrease in gastric myoelectric signal power was detected under isoflurane anesthesia compared to the awake condition. Moreover, a detailed analysis of the awake recordings indicates that behavioral movement is associated with increased signal power compared to rest. Conclusions & InferencesThese results suggest that both general anesthesia and behavioral movement can affect the amplitude of gastric myoelectric. In summary, caution should be taken in studying myoelectric data collected under anesthesia. Further, behavioral movement could have an important modulatory role on these signals, affecting their interpretation in clinical settings.

neuroscience↗

bioRxiv2022

Physiological Changes Associated with Copper Sulfate-Induced Emesis in Felines

Nausea is a common disease symptom, yet there is no consensus regarding its physiological markers. In contrast, the process of vomiting is well documented as sequential muscular contractions of the diaphragm and abdominal muscles and esophageal shortening. Nausea, like other self-reported perceptions, is difficult to distinguish in preclinical models, but based on human experience emesis is usually preceded by nausea. Here we focused on measuring gastrointestinal and cardiorespiratory changes prior to emesis to provide additional insights into markers for nausea. Felines were instrumented to chronically record heart rate, respiration, and electromyographic (EMG) activity from the stomach and duodenum before and after intragastric delivery of saline or copper sulfate (CuSO4, from 83 to 322 mg). CuSO4 is a prototypical emetic test agent that triggers vomiting primarily by action on GI vagal afferent fibers when administered intragastrically. CuSO4 infusion elicited a significant increase in heart rate, decrease in respiratory rate, and a disruption of gastric and intestinal EMG activity several minutes prior to emesis. The change in EMG activity was most consistent in the duodenum. Administration of saline did not induce these effects. Increasing the dose of CuSO4 did not alter the physiologic changes induced by the treatment. It is postulated that the intestinal EMG activity was related to a retrograde movement of chyme from the intestine to the stomach. These findings suggest that monitoring of intestinal EMG activity, perhaps in combination with heart rate, may provide the best indicator of the onset of nausea following treatments and in disease conditions, including GI disease, associated with emesis.

physiology↗

bioRxiv2019

Machine learning prediction of emesis and gastrointestinal state in ferrets

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.

neuroscience↗