Association between gut microbiota and Hirschsprung disease: a bidirectional two-sample Mendelian randomization study.

Background: Several studies have pointed to the critical role of gut microbiota (GM) and their metabolites in Hirschsprung disease (HSCR) pathogenesis. However, the detailed causal relationship between GM and HSCR remains unknown. Methods: In this study, we used two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between GM and HSCR, based on the MiBioGen Consortium's genome-wide association study (GWAS) and the GWAS Catalog's HSCR data. Reverse MR analysis was performed subsequently, and the sensitivity analysis, Cochran's Q-test, MR pleiotropy residual sum, outlier (MR-PRESSO), and the MR-Egger intercept were used to analyze heterogeneity or horizontal pleiotropy. 16S rDNA sequencing and targeted mass spectrometry were developed for initial validation. Results: In the forward MR analysis, inverse-variance weighted (IVW) estimates suggested that Eggerthella (OR: 2.66, 95%CI: 1.23-5.74, p = 0.01) was a risk factor for HSCR, while Peptococcus (OR: 0.37, 95%CI: 0.18-0.73, p = 0.004), Ruminococcus2 (OR: 0.32, 95%CI: 0.11-0.91, p = 0.03), Clostridiaceae1 (OR: 0.22, 95%CI: 0.06-0.78, p = 0.02), Mollicutes RF9 (OR: 0.27, 95%CI: 0.09-0.8, p = 0.02), Ruminococcaceae (OR: 0.16, 95%CI: 0.04-0.66, p = 0.01), and Paraprevotella (OR: 0.45, 95%CI: 0.21-0.98, p = 0.04) were protective factors for HSCR, which had no heterogeneity or horizontal pleiotropy. However, reverse MR analysis showed that HSCR (OR: 1.02, 95%CI: 1-1.03, p = 0.049) is the risk factor for Eggerthella. Furthermore, some of the above microbiota and short-chain fatty acids (SCFAs) were altered in HSCR, showing a correlation. Conclusion: Our analysis established the relationship between specific GM and HSCR, identifying specific bacteria as protective or risk factors. Significant microbiota and SCFAs were altered in HSCR, underlining the importance of further study and providing new insights into the pathogenesis and treatment.

Unveiling intra-person fingerprint similarity via deep contrastive learning.

Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.

Development and evaluation of visualizations of smoking data for integration into the Sense2Quit app for tobacco cessation.

IMPORTANCE: Due to insufficient smoking cessation apps for persons living with HIV, our study focused on designing and testing the Sense2Quit app, a patient-facing mHealth tool which integrated visualizations of patient information, specifically smoking use. OBJECTIVES: The purpose of this paper is to detail rigorous human-centered design methods to develop and refine visualizations of smoking data and the contents and user interface of the Sense2Quit app. The Sense2Quit app was created to support tobacco cessation and relapse prevention for people living with HIV. MATERIALS AND METHODS: Twenty people living with HIV who are current or former smokers and 5 informaticians trained in human-computer interaction participated in 5 rounds of usability testing. Participants tested the Sense2Quit app with use cases and provided feedback and then completed a survey. RESULTS: Visualization of smoking behaviors was refined through each round of usability testing. Further, additional features such as daily tips, games, and a homescreen were added to improve the usability of the app. A total of 66 changes were made to the Sense2Quit app based on end-user and expert recommendations. DISCUSSION: While many themes overlapped between usability testing with end-users and heuristic evaluations, there were also discrepancies. End-users and experts approached the app evaluation from different perspectives which ultimately allowed us to fill knowledge gaps and make improvements to the app. CONCLUSION: Findings from our study illustrate the best practices for usability testing for development and refinement of an mHealth-delivered consumer informatics tool for improving tobacco cessation yet further research is needed to fully evaluate how tools informed by target user needs improve health outcomes.

Visible-Light-Driven Iron-Catalyzed Decarboxylative C-N Coupling Reaction of Alkyl Carboxylic Acids with NaNO2.

An efficient visible-light-driven iron-catalyzed decarboxylative C-N coupling reaction of alkyl carboxylic acids with NaNO2 under mild conditions was developed. The reaction proceeds under photosensitizer-free conditions and features good to excellent yields, broad functional group tolerance, and an easy operation procedure. Preliminary mechanistic investigations showed that visible-light-driven iron catalysis not only achieved oxidative decarboxylation of alkyl carboxylic acids to alkyl radicals but also promoted the reduction of NO2- to NO, thus leading to the C-N radical coupling reaction.

Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: An estimated 40% of people living with HIV smoke cigarettes. Although smoking rates in the United States have been declining in recent years, people living with HIV continue to smoke cigarettes at twice the rate of the general population. Mobile health (mHealth) technology is an effective tool for people living with a chronic illness, such as HIV, as currently 84% of households in the United States report that they have a smartphone. Although many studies have used mHealth interventions for smoking cessation, few studies have recruited people living with HIV who smoke. OBJECTIVE: The objective of the pilot randomized controlled trial (RCT) is to examine the feasibility, acceptability, and preliminary efficacy of the Sense2Quit App as a tool for people living with HIV who are motivated to quit smoking. METHODS: The Sense2Quit study is a 2-arm RCT for people living with HIV who smoke cigarettes (n=60). Participants are randomized to either the active intervention condition, which consists of an 8-week supply of nicotine replacement therapy, standard smoking cessation counseling, and access to the Sense2Quit mobile app and smartwatch, or the control condition, which consists of standard smoking cessation counseling and a referral to the New York State Smokers' Quitline. The Sense2Quit app is a mobile app connected through Bluetooth to a smartwatch that tracks smoking gestures and distinguishes them from other everyday hand movements. In the Sense2Quit app, participants can view their smoking trends, which are recorded through their use of the smartwatch, including how often or how much they smoke and the amount of money that they are spending on cigarettes, watch videos with quitting tips, information, and distractions, play games, set reminders, and communicate with a study team member. RESULTS: Enrollment of study participants began in March 2023 and is expected to end in October 2023. All data collection is expected to be completed by the end of January 2024. This RCT will test the difference in outcomes between the control and intervention arms. The primary outcome will be the percentage of participants with biochemically verified 7-day point prevalence smoking or tobacco abstinence at their 12-week follow-up. Results from this pilot study will be disseminated to the research community following the completion of all data collection. CONCLUSIONS: The Sense2Quit study leverages mHealth so that it can help smokers improve their efforts at smoking cessation. Our research has the potential to not only increase quitting rates among people living with HIV who may need a prolonged, tailored intervention but also inform further development of mHealth for people living with HIV. This mHealth study will contribute significant findings to the greater mHealth research community, providing evidence as to how mHealth should be developed and tested among the target population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05609032; https://clinicaltrials.gov/study/NCT05609032. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49558.

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human.

The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully-dense (3DFD) U-net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U-net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot.

Chronic leg ulcers are affecting approximately 6.5 million Americans, and they are associated with significant mortality, reduced quality of life, and high treatment costs. Since many chronic ulcers have underlying vascular insufficiency, accurate assessment of tissue perfusion is critical to treatment planning and monitoring. This study introduces a dual-scan photoacoustic (PA) tomography (PAT) system that can simultaneously image the dorsal and plantar sides of the foot to reduce imaging time. To account for the unique shape of the foot, the system employs height-adjustable and articulating baseball stages that can scan along the foot's contour. In vivo results from healthy volunteers demonstrate the system's ability to acquire clear images of foot vasculature, and results from patients indicate that the system can image patients with various ulcer conditions. We also investigated various PA features and examined their correlation with the foot condition. Our preliminary results indicate that vessel sharpness, occupancy, intensity, and density could all be used to assess tissue perfusion. This research demonstrated the potential of PAT for routine clinical tissue perfusion assessment.

Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets.

Objective: Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and involves a complex regulatory mechanism. Transcriptional regulatory network (TRN) has been commonly reported in regulating gene expression and enteric nervous system development but remains to be investigated in HSCR. This study aimed to identify the potential TRN implicated in the pathogenesis and diagnosis of HSCR. Methods: Based on three microarray datasets from the Gene Expression Omnibus database, the multiMiR package was used to investigate the microRNA (miRNA)-target interactions, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF-miRNA-mRNA regulatory network and used cytoHubba to identify the key modules. Finally, the receiver operating characteristic (ROC) curve was determined and the integrated diagnostic models were established based on machine learning by the support vector machine method. Results: We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell-substrate adhesion, PI3K-Akt, Ras/mitogen-activated protein kinase and Rho/ROCK signaling. Moreover, 2 TFs (TP53 and TWIST1), 4 miRNAs (has-miR-107, has-miR-10b-5p, has-miR-659-3p, and has-miR-371a-5p), and 4 mRNAs (PIM3, CHUK, F2RL1, and CA1) were identified to construct the TF-miRNA-mRNA regulatory network. ROC analysis revealed a strong diagnostic value of the key TRN regulons (all area under the curve values were more than 0.8). Conclusion: This study suggests a potential role of the TF-miRNA-mRNA network that can help enrich the connotation of HSCR pathogenesis and diagnosis and provide new horizons for treatment.

Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV.

The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.

Engaging in a home-based exercise program: a mixed-methods approach to identify motivators and barriers for individuals with stroke.

Community-dwelling individuals with chronic stroke used a novel, portable rehabilitation system, mRehab, that uses a smartphone app coupled with 3D printed objects resembling daily use items. The objectives of this study include evaluating participant's approach and nature of engagement with mRehab and identifying factors that influenced the users' engagement with mRehab. An explanatory mixed-method approach was used. In the first phase, 16 participants used mRehab at home for six weeks; six participants were recruited from the first phase for in-depth interviews. Participants were categorized into High, Moderate, and Low Frequency groups based on their frequency of mRehab use. High frequency of use was not related to improved performance on clinical assessments; instead, High Frequency users more commonly initiated performance of new activities after the mRehab program compared to participants with lower frequency of use. Useful activities that are challenging and meaningful to the participants, and availability of objective feedback for self-monitoring were some of the motivators for mRehab use. Difficulty with time management, lack of caregiver availability, and difficulties with the design of the system posed as barriers to mRehab use. Tailoring home programs to the recipients' needs including perceived meaningfulness of the activities is key for long-term rehabilitation.

Fecal microbiota transplantation enhances cell therapy in a rat model of hypoganglionosis by SCFA-induced MEK1/2 signaling pathway.

Hirschsprung disease (HSCR), one of several neurocristopathies in children, is characterized by nerve loss in the large intestine and is mainly treated by surgery, which causes severe complications. Enteric neural crest-derived cell (ENCC) transplantation is a potential therapeutic strategy; however, so far with poor efficacy. Here, we assessed whether and how fecal microbiota transplantation (FMT) could improve ENCC transplantation in a rat model of hypoganglionosis; a condition similar to HSCR, with less intestinal innervation. We found that the hypoganglionosis intestinal microenvironment negatively influenced the ENCC functional phenotype in vitro and in vivo. Combining 16S rDNA sequencing and targeted mass spectrometry revealed microbial dysbiosis and reduced short-chain fatty acid (SCFA) production in the hypoganglionic gut. FMT increased the abundance of Bacteroides and Clostridium, SCFA production, and improved outcomes following ENCC transplantation. SCFAs alone stimulated ENCC proliferation, migration, and supported ENCC transplantation. Transcriptome-wide mRNA sequencing identified MAPK signaling as the top differentially regulated pathway in response to SCFA exposure, and inhibition of MEK1/2 signaling abrogated the SCFA-mediated effects on ENCC. This study demonstrates that FMT improves cell therapy for hypoganglionosis via short-chain fatty acid metabolism-induced MEK1/2 signaling.

Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach.

Background and Objectives: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. Research Design and Methods: Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015-2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]). Results: IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers' age, and the person living with dementia's income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). Discussion and Implications: Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study-that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care.

Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging.

Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.

An explainable COVID-19 detection system based on human sounds.

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Smoking Cessation System for Preemptive Smoking Detection.

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user's smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.

A fitness training optimization system based on heart rate prediction under different activities.

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction.

Exploring racial and gender disparities in voice biometrics.

Systemic inequity in biometrics systems based on racial and gender disparities has received a lot of attention recently. These disparities have been explored in existing biometrics systems such as facial biometrics (identifying individuals based on facial attributes). However, such ethical issues remain largely unexplored in voice biometric systems that are very popular and extensively used globally. Using a corpus of non-speech voice records featuring a diverse group of 300 speakers by race (75 each from White, Black, Asian, and Latinx subgroups) and gender (150 each from female and male subgroups), we explore and reveal that racial subgroup has a similar voice characteristic and gender subgroup has a significant different voice characteristic. Moreover, non-negligible racial and gender disparities exist in speaker identification accuracy by analyzing the performance of one commercial product and five research products. The average accuracy for Latinxs can be 12% lower than Whites (p < 0.05, 95% CI 1.58%, 14.15%) and can be significantly higher for female speakers than males (3.67% higher, p < 0.05, 95% CI 1.23%, 11.57%). We further discover that racial disparities primarily result from the neural network-based feature extraction within the voice biometric product and gender disparities primarily due to both voice inherent characteristic difference and neural network-based feature extraction. Finally, we point out strategies (e.g., feature extraction optimization) to incorporate fairness and inclusive consideration in biometrics technology.

Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography.

Linear-array-based photoacoustic tomography has shown broad applications in biomedical research and preclinical imaging. However, the elevational resolution of a linear array is fundamentally limited due to the weak cylindrical focus of the transducer element. While several methods have been proposed to address this issue, they have all handled the problem in a less time-efficient way. In this work, we propose to improve the elevational resolution of a linear array through Deep-E, a fully dense neural network based on U-net. Deep-E exhibits high computational efficiency by converting the three-dimensional problem into a two-dimension problem: it focused on training a model to enhance the resolution along elevational direction by only using the 2D slices in the axial and elevational plane and thereby reducing the computational burden in simulation and training. We demonstrated the efficacy of Deep-E using various datasets, including simulation, phantom, and human subject results. We found that Deep-E could improve elevational resolution by at least four times and recover the object's true size. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.

Stress prediction using micro-EMA and machine learning during COVID-19 social isolation.

Accurately predicting users' perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1-2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day's PSS scores, and higher than 81% accuracy for predicting the next 7 day's stress labels.

Phenotypic and functional comparison of rat enteric neural crest-derived cells during fetal and early-postnatal stages.

In our previous study, we showed that with increasing time in culture, the growth characteristics of enteric neural crest-derived cells (ENCCs) change, and that the proliferation, migration and neural differentiation potential of these cells in vitro notably diminish. However, there are no studies on the developmental differences in these characteristics between fetal and early-postnatal stages in vitro or in vivo. In this study, we isolated fetal (embryonic day 14.5) and postnatal (postnatal day 2) ENCCs from the intestines of rats. Fetal ENCCs had greater maximum cross-sectional area of the neurospheres, stronger migration ability, and reduced apoptosis, compared with postnatal ENCCs. However, fetal and postnatal ENCCs had a similar differentiation ability. Fetal and postnatal ENCCs both survived after transplant into a rat model of Hirschsprung's disease. In these rats with Hirschsprung's disease, the number of ganglionic cells in the myenteric plexus was higher and the distal intestinal pressure change was greater in animals treated with fetal ENCCs compared with those treated with postnatal ENCCs. These findings suggest that, compared with postnatal ENCCs, fetal ENCCs exhibit higher survival and proliferation and migration abilities, and are therefore a more appropriate seed cell for the treatment of Hirschsprung's disease. This study was approved by the Animal Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University (approval No. 2016086) on March 3, 2016.