Machine learning prediction of emesis and gastrointestinal state in ferrets

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

Artificial Intelligence Tools for Improving Manometric Diagnosis of Esophageal Dysmotility

PURPOSE OF REVIEW

Artificial intelligence (AI) is a broad term that pertains to a computer's ability to mimic and sometimes surpass human intelligence in interpretation of large datasets. The adoption of AI in gastrointestinal motility has been slower compared to other areas such as polyp detection and interpretation of histopathology.

RECENT FINDINGS

Within esophageal physiologic testing, AI can automate interpretation of image-based tests, especially high resolution manometry (HRM) and functional luminal imaging probe (FLIP) studies. Basic tasks such as identification of landmarks, determining adequacy of the HRM study and identification from achalasia from non-achalasia patterns are achieved with good accuracy. However, existing AI systems compare AI interpretation to expert analysis rather than to clinical outcome from management based on AI diagnosis. The use of AI methods is much less advanced within the field of ambulatory reflux monitoring, where challenges exist in assimilation of data from multiple impedance and pH channels. There remains potential for replication of the AI successes within esophageal physiologic testing to HRM of the anorectum, and to innovative and novel methods of evaluating gastric electrical activity and motor function. The use of AI has tremendous potential to improve detection of dysmotility within the esophagus using esophageal physiologic testing, as well as in other regions of the gastrointestinal tract. Eventually, integration of patient presentation, demographics and alternate test results to individual motility test interpretation will improve diagnostic precision and prognostication using AI tools.

Care, management, and use of ferrets in biomedical research

The ferret (Mustela putorius furo) is a small domesticated species of the family Mustelidae within the order Carnivora. The present article reviews and discusses the current state of knowledge about housing, care, breeding, and biomedical uses of ferrets. The management and breeding procedures of ferrets resemble those used for other carnivores. Understanding its behavior helps in the use of environmental enrichment and social housing, which promote behaviors typical of the species. Ferrets have been used in research since the beginning of the twentieth century. It is a suitable non-rodent model in biomedical research because of its hardy nature, social behavior, diet and other habits, small size, and thus the requirement of a relatively low amount of test compounds and early sexual maturity compared with dogs and non-human primates. Ferrets and humans have numerous similar anatomical, metabolic, and physiological characteristics, including the endocrine, respiratory, auditory, gastrointestinal, and immunological systems. It is one of the emerging animal models used in studies such as influenza and other infectious respiratory diseases, cystic fibrosis, lung cancer, cardiac research, gastrointestinal disorders, neuroscience, and toxicological studies. Ferrets are vulnerable to many human pathogenic organisms, like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), because air transmission of this virus between them has been observed in the laboratory. Ferrets draw the attention of the medical community compared to rodents because they occupy a distinct niche in biomedical studies, although they possess a small representation in laboratory research.

Isoflurane anesthesia suppresses gastric myoelectric power in the ferret

BACKGROUND

Gastric myoelectric signals have been the focus of extensive research; although it is unclear how general anesthesia affects these signals, and 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 explore the contribution of behavioral movement to observed changes in signal power.

METHODS

Ferrets 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 RESULTS

A 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 & INFERENCES

These results suggest that both general anesthesia and behavioral movement can affect the signal power of gastric myoelectric recordings. 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.

Electrogastrography in patients with gastric motility disorders

Electrogastrography (EGG) is a novel diagnostic modality for assessing the gastrointestinal tract (GI) that generates spontaneous electrical activity and monitors gastric motility. The aim of this study was to compare patients with functional dyspepsia (FD) and diabetic gastroparesis (D-GP) with healthy controls (CT) to use established findings on abnormalities of gastric motility based on EGG characteristics. In this study, 50 patients with FD, 50 D-GP patients, and 50 CT subjects were studied to compare EGG with discrete wavelet transform models (DWT) to extract signal characteristics using a variety of different qualitative and quantitative metrics from pre-prandial and postprandial states. As a result, higher statistically significant (p < 0.05*) were found in the DWT models based on power spectral density (PSD) analysis in both states. We also present that the correlations between EGG metrics and the presence of FD, D-GP, and CT symptoms were inconsistent. This paper represents that EGG assessments of FD and D-GP patients differ from healthy controls in terms of abnormalities of gastric motility. Additionally, we demonstrate that diverse datasets showed adequate gastric motility responses to a meal. We anticipate that our method will provide a comprehensive understanding of gastric motility disorders for better treatment and monitoring in both clinical and research settings. In conclusion, we present potential future opportunities for precise gastrointestinal electrophysiological disorders.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

Anesthesia suppresses gastric myoelectric power in the ferret

BACKGROUND

Gastrointestinal 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.

METHODS

Ferrets 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 RESULTS

A 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 & INFERENCES

These 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.

Physiological changes associated with copper sulfate-induced nausea and retching 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 the same volume 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 the retrograde movement of chyme from the intestine to the stomach demonstrated to occur prior to emesis by other investigators. 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.

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-resolution Electrogastrogram

OBJECTIVE

Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysiology of pediatric functional nausea from healthy controls would be an invaluable aid to support clinical decision-making in diagnosis and management of patient treatment methodology. The purpose of this paper is to present an innovative approach for objectively classifying pediatric functional nausea using cutaneous high-resolution electrogastrogram data.

METHODS

We present an Automated Electrogastrogram Data Analytics Pipeline framework and demonstrate its use in a 3x8 factorial design to identify an optimal classification model according to a defined objective function. Low-fidelity synthetic high-resolution electrogastrogram data were generated to validate outputs and determine SOBI-ICA noise reduction effectiveness.

RESULTS

A 10 parameter support vector machine binary classifier with a radial basis function was selected as the overall top-performing model from a pool of over 1000 alternatives via maximization of an objective function. This resulted in a 91.6% test ROC AUC score.

CONCLUSION

Using an automated machine learning pipeline approach to process high-resolution electrogastrogram data allows for clinically significant objective classification of pediatric functional nausea.

SIGNIFICANCE

To our knowledge, this is the first study to demonstrate clinically significant performance in the objective classification of pediatric nausea patients from healthy control subjects using experimental high-resolution electrogastrogram data. These results indicate a promising potential for high-resolution electrogastrography to serve as a data-driven screening tool for the objective diagnosis of pediatric functional nausea.

Surgical placement of customized abdominal vagus nerve stimulating and gastrointestinal serosal surface recording electrodes

Bioelectronic medical approaches to control vagus nerve-to-organ signaling have the potential to treat cardiac, respiratory, gastrointestinal (GI) and metabolic diseases, such as obesity. Unlike cervical vagus nerve stimulation (VNS), abdominal VNS could provide specific therapeutic control of the GI tract without off-target effects on thoracic organs; however, surgical approaches for abdominal VNS electrode placement are not well established. Moreover, optimal device configurations and additional placement of GI recording electrodes for closed-loop control are largely unknown. We designed VNS cuff and GI planar serosal electrodes and tested placement of these devices in laparoscopic surgery in two cadavers. We determined that electrode positioning on the ventral abdominal vagus nerve and gastric antrum was feasible but other sites, such as the duodenum and proximal stomach, were more difficult. The current investigation can guide potential placement and design of VNS cuff and GI electrodes for development of closed-loop GI therapeutic devices.

Selective stimulation of the ferret abdominal vagus nerve with multi-contact nerve cuff electrodes

Dysfunction and diseases of the gastrointestinal (GI) tract are a major driver of medical care. The vagus nerve innervates and controls multiple organs of the GI tract and vagus nerve stimulation (VNS) could provide a means for affecting GI function and treating disease. However, the vagus nerve also innervates many other organs throughout the body, and off-target effects of VNS could cause major side effects such as changes in blood pressure. In this study, we aimed to achieve selective stimulation of populations of vagal afferents using a multi-contact cuff electrode wrapped around the abdominal trunks of the vagus nerve. Four-contact nerve cuff electrodes were implanted around the dorsal (N = 3) or ventral (N = 3) abdominal vagus nerve in six ferrets, and the response to stimulation was measured via a 32-channel microelectrode array (MEA) inserted into the left or right nodose ganglion. Selectivity was characterized by the ability to evoke responses in MEA channels through one bipolar pair of cuff contacts but not through the other bipolar pair. We demonstrated that it was possible to selectively activate subpopulations of vagal neurons using abdominal VNS. Additionally, we quantified the conduction velocity of evoked responses to determine what types of nerve fibers (i.e., Aδ vs. C) responded to stimulation. We also quantified the spatial organization of evoked responses in the nodose MEA to determine if there is somatotopic organization of the neurons in that ganglion. Finally, we demonstrated in a separate set of three ferrets that stimulation of the abdominal vagus via a four-contact cuff could selectively alter gastric myoelectric activity, suggesting that abdominal VNS can potentially be used to control GI function.

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

OBJECTIVE

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG).

METHODS

Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features.

RESULTS

A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0 dB, horizontal shifts within 3 cm, vertical shifts within 6 cm, and abdominal tissue depth within 6 cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI.

CONCLUSION

This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data.

SIGNIFICANCE

These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.