In vivo characterization of ischemic small intestine using bioimpedance measurements

Advances in TEER measurements of biological barriers in microphysiological systems

Biological barriers are multicellular structures that precisely regulate the transport of ions, biomolecules, drugs, cells, and other organisms. Transendothelial/epithelial electrical resistance (TEER) is a label-free method for predicting the properties of biological barriers. Understanding the mechanisms that control TEER significantly enhances our knowledge of the physiopathology of different diseases and aids in the development of new drugs. Measuring TEER values within microphysiological systems called organ-on-a-chip devices that simulate the microenvironment, architecture, and physiology of biological barriers in the body provides valuable insight into the behavior of barriers in response to different drugs and pathogens. These integrated systems should increase the accuracy, reproducibility, sensitivity, resolution, high throughput, speed, cost-effectiveness, and reliable predictability of TEER measurements. Implementing advanced micro and nanoscale manufacturing techniques, surface modification methods, biomaterials, biosensors, electronics, and stem cell biology is necessary for integrating TEER measuring systems with organ-on-chip technology. This review focuses on the applications, advantages, and future perspectives of integrating organ-on-a-chip technology with TEER measurement methods for studying biological barriers. After briefly reviewing the role of TEER in the physiology and pathology of barriers, standard techniques for measuring TEER, including Ohm's law and impedance spectroscopy, and commercially available devices are described. Furthermore, advances in TEER measurement are discussed in multiple barrier-on-a-chip system models representing different organs. Finally, we outline future trends in implementing advanced technologies to design and fabricate nanostructured electrodes, complicated microfluidic chips, and membranes for more advanced and accurate TEER measurements.

Assessment of Intestinal Ischemia-Reperfusion Injury Using Diffuse Reflectance VIS-NIR Spectroscopy and Histology

A porcine model was used to investigate the feasibility of using VIS-NIR spectroscopy to differentiate between degrees of ischemia-reperfusion injury in the small intestine. Ten pigs were used in this study and four segments were created in the small intestine of each pig: (1) control, (2) full arterial and venous mesenteric occlusion for 8 h, (3) arterial and venous mesenteric occlusion for 2 h followed by reperfusion for 6 h, and (4) arterial and venous mesenteric occlusion for 4 h followed by reperfusion for 4 h. Two models were built using partial least square discriminant analysis. The first model was able to differentiate between the control, ischemic, and reperfused intestinal segments with an average accuracy of 99.2% with 10-fold cross-validation, and the second model was able to discriminate between the viable versus non-viable intestinal segments with an average accuracy of 96.0% using 10-fold cross-validation. Moreover, histopathology was used to investigate the borderline between viable and non-viable intestinal segments. The VIS-NIR spectroscopy method together with a PLS-DA model showed promising results and appears to be well-suited as a potentially real-time intraoperative method for assessing intestinal ischemia-reperfusion injury, due to its easy-to-use and non-invasive nature.

Bio-impedance method to monitor colon motility response to direct distal colon stimulation in anesthetized pigs

Electrical stimulation has been demonstrated as an alternative approach to alleviate intractable colonic motor disorders, whose effectiveness can be evaluated through colonic motility assessment. Various methods have been proposed to monitor the colonic motility and while each has contributed towards better understanding of colon motility, a significant limitation has been the spatial and temporal low-resolution colon motility data acquisition and analysis. This paper presents the study of employing bio-impedance characterization to monitor colonic motor activity. Direct distal colon stimulation was undertaken in anesthetized pigs to validate the bio-impedance scheme simultaneous with luminal manometry monitoring. The results indicated that the significant decreases of bio-impedance corresponded to strong colonic contraction in response to the electrical stimulation in the distal colon. The magnitude/power of the dominant frequencies of phasic colonic contractions identified at baseline (in the range 2-3 cycles per minute (cpm)) were increased after the stimulation. In addition, positive correlations have been found between bio-impedance and manometry. The proposed bio-impedance-based method can be a viable candidate for monitoring colonic motor pattern with high spatial and temporal resolution. The presented technique can be integrated into a closed-loop therapeutic device in order to optimize its stimulation protocol in real-time.

Small intestinal viability assessment using dielectric relaxation spectroscopy and deep learning

Intestinal ischemia is a serious condition where the surgeon often has to make important but difficult decisions regarding resections and resection margins. Previous studies have shown that 3 h (hours) of warm full ischemia of the small bowel followed by reperfusion appears to be the upper limit for viability in the porcine mesenteric ischemia model. However, the critical transition between 3 to 4 h of ischemic injury can be nearly impossible to distinguish intraoperatively based on standard clinical methods. In this study, permittivity data from porcine intestine was used to analyze the characteristics of various degrees of ischemia/reperfusion injury. Our results show that dielectric relaxation spectroscopy can be used to assess intestinal viability. The dielectric constant and conductivity showed clear differences between healthy, ischemic and reperfused intestinal segments. This indicates that dielectric parameters can be used to characterize different intestinal conditions. In addition, machine learning models were employed to classify viable and non-viable segments based on frequency dependent dielectric properties of the intestinal tissue, providing a method for fast and accurate intraoperative surgical decision-making. An average classification accuracy of 98.7% was obtained using only permittivity data measured during ischemia, and 96.2% was obtained with data measured during reperfusion. The proposed approach allows the surgeon to get accurate evaluation from the trained machine learning model by performing one single measurement on an intestinal segment where the viability state is questionable.

Automatic Prediction of Ischemia-Reperfusion Injury of Small Intestine Using Convolutional Neural Networks: A Pilot Study

Acute intestinal ischemia is a life-threatening condition. The current gold standard, with evaluation based on visual and tactile sensation, has low specificity. In this study, we explore the feasibility of using machine learning models on images of the intestine, to assess small intestinal viability. A digital microscope was used to acquire images of the jejunum in 10 pigs. Ischemic segments were created by local clamping (approximately 30 cm in width) of small arteries and veins in the mesentery and reperfusion was initiated by releasing the clamps. A series of images were acquired once an hour on the surface of each of the segments. The convolutional neural network (CNN) has previously been used to classify medical images, while knowledge is lacking whether CNNs have potential to classify ischemia-reperfusion injury on the small intestine. We compared how different deep learning models perform for this task. Moreover, the Shapley additive explanations (SHAP) method within explainable artificial intelligence (AI) was used to identify features that the model utilizes as important in classification of different ischemic injury degrees. To be able to assess to what extent we can trust our deep learning model decisions is critical in a clinical setting. A probabilistic model Bayesian CNN was implemented to estimate the model uncertainty which provides a confidence measure of our model decisions.

Bioelectric, tissue, and molecular characteristics of the gastric mucosa at different times of ischemia

Gastrointestinal ischemia may be presented as a complication associated with late shock detection in patients in critical condition. Prolonged ischemia can cause mucosal integrity to lose its barrier function, triggering alterations that can induce organ dysfunction and lead to death. Electrical impedance spectroscopy has been proposed to identify early alteration in ischemia-induced gastric mucosa in this type of patients. This work analyzed changes in impedance parameters, and tissue and molecular alterations that allow us to identify the time of ischemia in which the gastric mucosa still maintains its barrier function. The animals were randomly distributed in four groups: Control, Ischemia 60, 90, and 120 min. Impedance parameters were measured and predictive values were determined to categorize the degree of injury using a receiver operating characteristic curve. Markers of inflammatory process and apoptosis (iNOS, TNFα, COX-2, and Caspase-3) were analyzed. The largest increase in impedance parameters occurred in the ischemia 90 and 120 min groups, with resistance at low frequencies (RL) and reactance at high frequencies (XH) being the most related to damage, allowing prediction of the occurrence of reversible and irreversible tissue damage. Histological analysis and apoptosis assay showed progressive mucosal deterioration with irreversible damage (p < 0.001) starting from 90 min of ischemia. Furthermore, a significant increase in the expression of iNOS, TNFα, and COX-2 was identified in addition to apoptosis in the gastric mucosa starting from 90 min of ischemia. Tissue damage generated by an ischemia time greater than 60 min induces loss of barrier function in the gastric mucosa.

A bioimpedance-based monitor for real-time detection and identification of secondary brain injury

Secondary brain injury impacts patient prognosis and can lead to long-term morbidity and mortality in cases of trauma. Continuous monitoring of secondary injury in acute clinical settings is primarily limited to intracranial pressure (ICP); however, ICP is unable to identify essential underlying etiologies of injury needed to guide treatment (e.g. immediate surgical intervention vs medical management). Here we show that a novel intracranial bioimpedance monitor (BIM) can detect onset of secondary injury, differentiate focal (e.g. hemorrhage) from global (e.g. edema) events, identify underlying etiology and provide localization of an intracranial mass effect. We found in an in vivo porcine model that the BIM detected changes in intracranial volume down to 0.38 mL, differentiated high impedance (e.g. ischemic) from low impedance (e.g. hemorrhagic) injuries (p < 0.001), separated focal from global events (p < 0.001) and provided coarse 'imaging' through localization of the mass effect. This work presents for the first time the full design, development, characterization and successful implementation of an intracranial bioimpedance monitor. This BIM technology could be further translated to clinical pathologies including but not limited to traumatic brain injury, intracerebral hemorrhage, stroke, hydrocephalus and post-surgical monitoring.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text], F[Formula: see text] and F[Formula: see text]-score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Assessing Ischemic Injury in Human Intestine Ex Vivo with Electrical Impedance Spectroscopy

Electrical impedance spectroscopy is a well-established tool for monitoring changes in the electrical properties of tissue. Most tissue and organ types have been investigated in various studies. As for the small intestine, there are several published studies conducted on pig and rat models. This study investigates the changes in passive electrical properties of the complete wall of the human intestine non-invasively during ischemia. We aim to use the passive electrical properties to assess intestinal viability. The bioimpedance measurements were performed using a two-electrode set-up with a Solartron 1260 Impedance/gain-phase analyser. The small intestinal samples were resected from patients who underwent pancreaticoduodenectomy. Impedance measurements were conducted following resection by placing the electrodes on the surface of the intestine. A voltage was applied across the intestinal sample and the measured electrical impedance was obtained in the ZPlot software. Impedance data were further fitted into a Cole model to obtain the Cole parameters. The P_y value was calculated from the extracted Cole parameters and used to assess the cell membrane integrity, thus evaluate the intestinal viability. Eight small intestinal segments from different patients were used in this study and impedance measurements were performed once an hour for a ten-hour period. One hour after resection, the impedance decreased, then increased the next two hours, before decreasing until the end of the experiment. For all the intestinal segments, the P_y values first increased and reached a plateau which lasted for 1 - 2 hours, before it decreased irreversibly. The time interval where P_y value reached the maximum is consistent with reported viable/non-viable limits from histological analysis.

Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning

The incorporation of sensors onto the stapling platform has been investigated to overcome the disconnect in our understanding of tissue handling by surgical staplers. The goal of this study was to explore the feasibility of in vivo porcine tissue differentiation using bioimpedance data and machine learning methods. In vivo electrical impedance measurements were obtained in 7 young domestic pigs, using a logarithmic sweep of 50 points over a frequency range of 100 Hz to 1 MHz. Tissues studied included lung, liver, small bowel, colon, and stomach, which was further segmented into fundus, body, and antrum. The data was then parsed through MATLAB's classification learner to identify the best algorithm for tissue type differentiation. The most effective classification scheme was found to be cubic support vector machines with 86.96% accuracy. When fundus, body and antrum were aggregated together as stomach, the accuracy improved to 88.03%. The combination of stomach, small bowel, and colon together as GI tract improved accuracy to 99.79% using fine k nearest neighbors. The results suggest that bioimpedance data can be effectively used to differentiate tissue types in vivo. This study is one of the first that combines in vivo bioimpedance tissue data across multiple tissue types with machine learning methods.

Evaluation of the NMR-MOUSE as a new method for continuous functional monitoring of the small intestine during different perfusion states in a porcine model

Objective

The study aim was to evaluate a small low-field NMR (nuclear magnetic resonance) scanner, the NMR-MOUSE^®, for detecting changes in intestinal diffusion under different (patho-) physiological perfusion states.

Methods

Laparotomy was performed on 8 female landrace pigs (body weight 70±6 kg) and the feeding vessels of several intestinal loops were dissected. Successively, the intestinal loops were examined using O2C (oxygen to see, LEA Medizintechnik GmbH, Giessen, Germany) for microcirculatory monitoring and the NMR-MOUSE^® for diffusion measurement (fast and slow components). On each loop the baseline measurement (physiological perfusion) was followed by one of the following main procedures: method 1 –ischemia; method 2 –flow reduction; method 3 –intraluminal glucose followed by ischemia; method 4 –intraluminal glucose followed by flow reduction. Additionally, standard perioperative monitoring (blood pressure, ECG, blood gas analyses) and histological assessment of intestinal biopsies was performed.

Results

There was no statistical overall time and method effect in the NMR-MOUSE measurement (fast component: p_time = 0.6368, p_method = 0.9766, slow component: p_time = 0.8216, p_method = 0.7863). Yet, the fast component of the NMR-MOUSE measurement showed contrary trends during ischemia (increase) versus flow reduction (decrease). The slow-to-fast diffusion ratio shifted slightly towards slow diffusion during flow reduction. The O2C measurement showed a significant decrease of oxygen saturation and microcirculatory blood flow during ischemia and flow reduction (p < .0001). The local microcirculatory blood amount (rHb) showed a significant mucosal increase (p_{Clamping(method 1)} = 0.0007, p_{Clamping(method 3)} = 0.0119), but a serosal decrease (p_{Clamping(method 1)} = 0.0119, p_{Clamping(method 3)} = 0.0078) during ischemia. The histopathological damage was significantly higher with increasing experimental duration and at the end of methods 3 and 4 (p < .0001,Fisher-test).

Conclusion

Monitoring intestinal diffusion changes due to different perfusion states using the NMR-MOUSE is feasible under experimental conditions. Despite the lack of statistical significance, this technique reflects perfusion changes and therefore seems promising for the evaluation of different intestinal perfusion states in the future. Beforehand however, an optimization of this technology, including the optimization of the penetration depth, as well as further validation studies under physiological conditions and including older animals are required.

Machine learning for intraoperative prediction of viability in ischemic small intestine

OBJECTIVE

Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30%-93%) of patients with acute mesenteric ischemia (AMI) since the 1980s. As the accuracy from the first laparotomy alone is 50%, the gold standard is a second-look laparotomy, increasing the accuracy to 87%-89%. This study investigates the use of machine learning to classify intestinal viability and histological grading in pig jejunum, based on multivariate time-series of bioimpedance sensor data.

APPROACH

We have previously used a bioimpedance sensor system to acquire electrical parameters from perfused, ischemic and reperfused pig jejunum (7  +  15 pigs) over 1-16 h of ischemia and 1-8 h of reperfusion following selected durations of ischemia. In this study we compare the accuracy of using end-point bioimpedance measurements with a feedforward neural network (FNN), versus the accuracy when using a recurrent neural network with long short-term memory units (LSTM-RNN) with bioimpedance data history over different periods of time.

MAIN RESULTS

Accuracies in the range of what has been reported clinically can be achieved using FNN's on a single bioimpedance measurement, and higher accuracies can be achieved when employing LSTM-RNN on a sequence of data history.

SIGNIFICANCE

Intraoperative bioimpedance measurements on intestine of suspect viability combined with machine learning can increase the accuracy of intraoperative assessment of intestinal viability. Increased accuracy in intraoperative assessment of intestinal viability has the potential to reduce the high mortality and morbidity rate of the patients.

Performance assessment of dry electrodes for wearable long term cardiac rhythm monitoring: Skin-electrode impedance spectroscopy

The use of wearable dry sensors for recording long term ECG signals is a requirement for certain studies of heart rhythm. Knowledge of the skin-electrode electrical performance of dry electrodes is necessary when seeking to improve various processing stages for signal quality enhancement. In this paper, methods for the assessment of dry skin-electrode impedance (Z_SE) and its modelling are presented. Measurements were carried out on selected electrode materials such as silver, stainless steel, AgCl (dry) and polyurethane. These had Z_SE values between 500 kΩ and 1 MΩ within the main ECG frequency range (1 Hz - 100 Hz); in contrast to plain iron material which had a significantly higher impedance. However, in spite of the high Z_SE values, open bandwidth ECG traces were of acceptable quality and stability; with dry AgCl material offering the best ECG trace performance.

Ischemia/reperfusion injury in porcine intestine - Viability assessment

AIM

To investigate viability assessment of segmental small bowel ischemia/reperfusion in a porcine model.

METHODS

In 15 pigs, five or six 30-cm segments of jejunum were simultaneously made ischemic by clamping the mesenteric arteries and veins for 1 to 16 h. Reperfusion was initiated after different intervals of ischemia (1-8 h) and subsequently monitored for 5-15 h. The intestinal segments were regularly photographed and assessed visually and by palpation. Intraluminal lactate and glycerol concentrations were measured by microdialysis, and samples were collected for light microscopy and transmission electron microscopy. The histological changes were described and graded.

RESULTS

Using light microscopy, the jejunum was considered as viable until 6 h of ischemia, while with transmission electron microscopy the ischemic muscularis propria was considered viable until 5 h of ischemia. However, following ≥ 1 h of reperfusion, only segments that had been ischemic for ≤ 3 h appeared viable, suggesting a possible upper limit for viability in the porcine mesenteric occlusion model. Although intraluminal microdialysis allowed us to closely monitor the onset and duration of ischemia and the onset of reperfusion, we were unable to find sufficient level of association between tissue viability and metabolic markers to conclude that microdialysis is clinically relevant for viability assessment. Evaluation of color and motility appears to be poor indicators of intestinal viability.

CONCLUSION

Three hours of total ischemia of the small bowel followed by reperfusion appears to be the upper limit for viability in this porcine mesenteric ischemia model.

Small intestinal ischemia and reperfusion-bioimpedance measurements

OBJECTIVE

Trans-intestinal bioimpedance measurements have previously been used to investigate changes in electrical parameters during 6 h of ischemia in the small intestine. Knowledge is lacking regarding the time course of trans-intestinal bioimpedance parameters during reperfusion. As reperfusion is an important part in the clinical treatment of intestinal ischemia, we need to know how it affects the bioimpedance measurements.

APPROACH

We performed bioimpedance measurements, using a two-electrode setup on selected segments of the jejunum in 15 pigs. A controlled voltage signal was applied while measuring the resulting current. In each pig, five or six 30 cm segments of the jejunum were made ischemic by clamping the mesenteric arteries and veins creating segments with ischemia from 1-16 h duration. Reperfusion was initiated at selected time intervals of ischemia, and measured for 5-15 h afterwards.

MAIN RESULTS

The tan δ parameter (loss tangent) was different (p  <  0.016) comparing ischemic and control tissue for the duration of the experiment (16 h). Comparing the control tissue 30 cm from the ischemic area with the control tissue 60 cm from the ischemic tissue, we found that the mean tan δ amplitude in the frequency range (3900-6300 Hz) was significantly higher (p  <  0.036) in the proximal control after 10 h of experiment duration. After reperfusion, the time development of tanδm (loss tangent maximum over a frequency range) amplitude and frequency overlapped and periodically increased above the tanδm in the ischemic intestine. Dependent on the ischemic duration pre-reperfusion, the initial increase in tan δ stabilizes or increases drastically over time, compared to the tan δ amplitude of the ischemic tissue.

SIGNIFICANCE

As during ischemia, the electrical parameters during reperfusion also follow a characteristic time-course, depending on the ischemic exposure before pre-reperfusion. The temporal changes in electrical parameters during small intestinal ischemia followed by reperfusion provides important information for assessment of tissue injury.

Applications of Bioimpedance Measurement Techniques in Tissue Engineering

Rapid development in the field of tissue engineering necessitates implementation of monitoring methods for evaluation of the viability and characteristics of the cell cultures in a real-time, non-invasive and non-destructive manner. Current monitoring techniques are mainly histological and require labeling and involve destructive tests to characterize cell cultures. Bioimpedance measurement technique which benefits from measurement of electrical properties of the biological tissues, offers a non-invasive, label-free and real-time solution for monitoring tissue engineered constructs. This review outlines the fundamentals of bioimpedance, as well as electrical properties of the biological tissues, different types of cell culture constructs and possible electrode configuration set ups for performing bioimpedance measurements on these cell cultures. In addition, various bioimpedance measurement techniques and their applications in the field of tissue engineering are discussed.