In vivo electrochemical lipolysis of fat in a Yucatan pig model: A proof of concept study.

OBJECTIVES: Traditional fat contouring is now regularly performed using numerous office- based less invasive techniques. However, some limitations of these minimally invasive techniques include high cost or limited selectivity with performing localized contouring and reduction of fat. These shortcomings may potentially be addressed by electrochemical lipolysis (ECLL), a novel approach that involves the insertion of electrodes into tissue followed by application of a direct current (DC) electrical potential. This results in the hydrolysis of tissue water creating active species that lead to fat necrosis and apoptosis. ECLL can be accomplished using a simple voltage-driven system (V-ECLL) or a potential-driven feedback cell (P-ECLL) both leading to water electrolysis and the creation of acid and base in situ. The aim of this study is to determine the long-lasting effects of targeted ECLL in a Yucatan pig model. METHODS: A 5-year-old Yucatan pig was treated with both V-ECLL and P-ECLL in the subcutaneous fat layer using 80:20 platinum:iridium needle electrodes along an 8 cm length. Dosimetry parameters included 5 V V-ECLL for 5, 10, and 20 minutes, and -1.5 V P-ECLL, -2.5 V P-ECLL, -3.5 V P-ECLL for 5 minutes. The pig was assessed for changes in fat reduction over 3 months with digital photography and ultrasound. After euthanasia, tissue sections were harvested and gross pathology and histology were examined. RESULTS: V-ECLL and P-ECLL treatments led to visible fat reduction (12.1%-27.7% and 9.4%-40.8%, respectively) and contour changes across several parameters. An increased reduction of the superficial fat layer occurred with increased dosimetry parameters with an average charge transfer of 12.5, 24.3, and 47.5 C transferred for 5 V V-ECLL for 5, 10, and 20 minutes, respectively, and 2.0, 11.5, and 24.0 C for -1.5 V P-ECLL, -2.5 V P-ECLL, -3.5 V P-ECLL for 5 minutes, respectively. These dose-dependent changes were also evidenced by digital photography, gross pathology, ultrasound imaging, and histology. CONCLUSIONS: ECLL results in selective damage and long-lasting changes to the adipose layer in vivo. These changes are dose-dependent, thus allowing for more precise contouring of target areas. P-ECLL has greater efficiency and control of total charge transfer compared to V-ECLL, suggesting that a low-voltage potentiostat treatment can result in fat apoptosis equivalent to a high-voltage DC system.

Exploring feedback-controlled versus open-circuit electrochemical lipolysis in ex vivo and in vivo porcine fat: A feasibility study.

OBJECTIVES: Minimally invasive fat sculpting techniques are becoming more widespread with the development of office-based devices and therapies. Electrochemical lipolysis (ECLL) is a needle-based technology that uses direct current (DC) to electrolyze tissue water creating acid and base in situ. In turn, fat is saponified and adipocyte cell membrane lysis occurs. The electrolysis of water can be accomplished using a simple open-loop circuit (V-ECLL) or by incorporating a feedback control circuit using a potentiostat (P-ECLL). A potentiostat utilizes an operational amplifier with negative feedback to allow users to precisely control voltage at specific electrodes. To date, the variation between the two approaches has not been studied. The aim of this study was to assess current and charge transfer variation and lipolytic effect created by the two approaches in an in vivo porcine model. METHODS: Charge transfer measurements from ex vivo V-ECLL and P-ECLL treated porcine skin and fat were recorded at -1 V P-ECLL, -2 V P-ECLL, -3 V P-ECLL, and -5 V V-ECLL each for 5 min to guide dosimetry parameters for in vivo studies. In follow-up in vivo studies, a sedated female Yorkshire pig was treated with both V-ECLL and P-ECLL across the dorsal surface over a range of dosimetry parameters, including -1.5 V P-ECLL, -2.5 V P-ECLL, -3.5 V P-ECLL, and 5 V V-ECLL each treated for 5 min. Serial biopsies were performed at baseline before treatment, 1, 2, 7, 14, and 28 days after treatment. Tissue was examined using fluorescence microscopy and histology to compare the effects of the two ECLL approaches. RESULTS: Both V-ECLL and P-ECLL treatments induced in-vivo fat necrosis evident by adipocyte membrane lysis, adipocyte denuclearization, and an acute inflammatory response across a 28-day longitudinal study. However, -1.5 V P-ECLL produced a smaller spatial necrotic effect compared to 5 V V-ECLL. In addition, 5 V V-ECLL produced a comparable necrotic effect to that of -2.5 V and -3.5 V P-ECLL. CONCLUSIONS: V-ECLL and P-ECLL at the aforementioned dosimetry parameters both achieved fat necrosis by adipocyte membrane lysis and denuclearization. The -2.5 V and -3.5 V P-ECLL treatments created spatially similar fat necrotic effects when compared to the 5 V V-ECLL treatment. Quantitatively, total charge transfer between dosimetry parameters suggests that -2.5 V P-ECLL and 5 V V-ECLL produce comparable electrochemical reactions. Such findings suggest that a low-voltage closed-loop potentiostat-based system is capable of inducing fat necrosis to a similar extent compared to that of a higher voltage direct current system.

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques.

BACKGROUND: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations. OBJECTIVE: In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians' interpretation. METHODS: A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA. RESULTS: An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA. CONCLUSIONS: We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.