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Background: Therapeutic hypothermia (TH) represents a critical therapeutic intervention for patients with cardiac arrest, although treatment efficacy and prognostic factors may vary between individuals. Precise, personalized outcome predictions can empower better clinical decisions. Methods: In this multi-center retrospective cohort study involving nine medical centers in Taiwan, we developed machine learning algorithms to predict neurological outcomes in patients who experienced cardiac arrest with shockable rhythms and underwent TH. The study cohort comprised 209 patients treated between January 2014 and September 2019. The models were trained on patients’ pre-treatment characteristics collected during this study period. The optimal artificial neural network (ANN) model was interpretable using the SHapley Additive exPlanations (SHAP) method. Results: Among the 209 enrolled patients, 79 (37.80%) demonstrated favorable neurological outcomes at discharge. The ANN model achieved an area under the curve value of 0.9089 (accuracy = 0.8330, precision = 0.7984, recall = 0.7492, specificity = 0.8846) for outcome prediction. SHAP analysis identified vital predictive features, including the dose of epinephrine during resuscitation, diabetes status, body temperature at return of spontaneous circulation (ROSC), whether the cardiac arrest was witnessed, and diastolic blood pressure at ROSC. Using real-life case examples, we demonstrated how the ANN model provides personalized prognostic predictions tailored to individuals’ distinct profiles. Conclusion: Our machine learning approach delivers personalized forecasts of TH outcomes in cardiac arrest patients with shockable rhythms. By accounting for each patient’s unique health history and cardiac arrest event details, the ANN model empowers more precise risk stratification, tailoring clinical decision-making regarding TH prognostication and optimizing personalized treatment planning.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Background: Extracorporeal shockwave therapy (ESWT) and adipose-derived mesenchymal stem cells (ADSCs) have been used clinically for the treatment of osteonecrosis of the femoral head (ONFH). The study elucidated that ESWT, ADSCs, and combination therapy modulated pro-inflammatory cytokines in the articular cartilage and subchondral bone of early rat ONFH. Methods: ESWT and ADSCs were prepared and isolated for treatment. Micro-CT, pathological analysis, and immunohistochemistry were performed and analysed. Results: After treatments, subchondral bone of ONFH was improved in trabecular bone volume (BV/TV) (p < 0.001), thickness (Tb.Th) (p < 0.01 and 0.001), and separation (Tb.Sp) (p < 0.001) and bone mineral density (BMD) (p < 0.001) using micro-CT analysis. The articular cartilage was protected and decreased apoptosis markers after all the treatments. The expression of IL33 (p < 0.001), IL5 (p < 0.001), IL6 (p < 0.001), and IL17A (p < 0.01) was significantly decreased in the ESWT, ADSCs, and Combination groups as compared with ONFH group. The IL33 receptor ST2 was significantly increased after treatment (p < 0.001) as compared with ONFH group. The Combination group (p < 0.01) decreased the expression of IL6 better than the ESWT and ADSCs groups. Conclusion: ESWT, ADSCs and combination therapy significantly protected articular cartilage and subchondral bone of early rat ONFH by modulating the expression of pro-inflammatory cytokines including, IL33 and its receptor ST2, IL5, IL6, and IL17A.

Abstract Automated ischemic stroke detection and classification according to its vascular territory is an essential step in stroke image evaluation, especially at hyperacute stage where mechanical thrombectomy may improve patients’ outcome. This study aimed to evaluate the performance of various convolutional neural network (CNN) models on hyperacute staged diffusion-weighted images (DWI) for detection of ischemic stroke and classification into anterior circulation infarct (ACI), posterior circulation infarct (PCI) and normal image slices. In this retrospective study, 253 cases of hyperacute staged DWI were identified, downloaded and reviewed. After exclusion, DWI from 127 cases were used and we created a dataset containing total of 2119 image slices, and separates it into three groups, namely ACI (618 slices), PCI (149 slices) and normal (1352 slices). Two transfer learning based CNN models, namely Inception-v3, EfficientNet-b0 and one self-derived modified LeNet model were used. The performance of the models was evaluated and activation maps using gradient-weighted class activation mapping (Grad-Cam) technique were made. Inception-v3 had the best overall accuracy (86.3%), weighted F1 score (86.2%) and kappa score (0.715), followed by the modified LeNet (85.2% accuracy, 84.7% weighted F1 score and 0.693 kappa score). The EfficientNet-b0 had the poorest performance of 83.6% accuracy, 83% weighted F1 score and 0.662 kappa score. The activation map showed that one possible explanation for misclassification is due to susceptibility artifact. A sufficiently high performance can be achieved by using CNN model to detect ischemic stroke on hyperacute staged DWI and classify it according to vascular territory.

IntroductionRib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure. Point of care ultrasound offers an alternative but has drawbacks in terms of procedural time and operator expertise. Therefore, this study aims to explore the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs.MethodsWe assembled a comprehensive retrospective dataset of chest radiographs with formal image reports documenting rib fractures from a single medical center over the last five years. The DCNN models were trained using 2000 region-of-interest (ROI) slices for each category, which included fractured ribs, non-fractured ribs, and background regions. To optimize training of the deep learning models (DLMs), the images were segmented into pixel dimensions of 128 × 128.ResultsThe trained DCNN models demonstrated remarkable validation accuracies. Specifically, AlexNet achieved 92.6%, GoogLeNet achieved 92.2%, EfficientNetb3 achieved 92.3%, DenseNet201 achieved 92.4%, and MobileNetV2 achieved 91.2%.DiscussionBy integrating DCNN models capable of rib fracture recognition into clinical decision support systems, the incidence of missed rib fracture diagnoses can be significantly reduced, resulting in tangible decreases in morbidity and mortality rates among trauma patients. This innovative approach holds the potential to revolutionize the diagnosis and treatment of chest trauma, ultimately leading to improved clinical outcomes for individuals affected by these injuries. The utilization of DCNNs in rib fracture detection on chest radiographs addresses the limitations of other imaging modalities, offering a promising and practical solution to improve patient care and management.

Background: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). Methods: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. Conclusion: The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.

BackgroundAttention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results.ResultsWe collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.ConclusionThis proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.

Abstract Purpose Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation. Materials and methods The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation. Results The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided. Conclusions Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Abstract Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.

BackgroundDuring the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan’s National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile apps with which insured individuals can retrieve their MHB data. However, the NHIA MHB service has its limitations, and the participation rate among insured individuals is low. ObjectiveWe aimed to integrate the MHB SDK with our developed blockchain-enabled PHR mobile app, which enables patients to access, store, and manage their cross-hospital PHR data. We also collected and analyzed the app’s log data to examine patients’ MHB use during the COVID-19 pandemic. MethodsWe integrated our existing blockchain-enabled mobile app with the MHB SDK to enable NHIA MHB data retrieval. The app utilizes blockchain technology to encrypt the downloaded NHIA MHB data. Existing and new indexes can be synchronized between the app and blockchain nodes, and high security can be achieved for PHR management. Finally, we analyzed the app’s access logs to compare patients’ activities during high and low COVID-19 infection periods. ResultsWe successfully integrated the MHB SDK into our mobile app, thereby enabling patients to retrieve their cross-hospital medical data, particularly those related to COVID-19 rapid and polymerase chain reaction testing and vaccination information and progress. We retrospectively collected the app’s log data for the period of July 2019 to June 2021. From January 2020, the preliminary results revealed a steady increase in the number of people who applied to create a blockchain account for access to their medical data and the number of app subscribers among patients who visited the outpatient department (OPD) and emergency department (ED). Notably, for patients who visited the OPD and ED, the peak proportions with respect to the use of the app for OPD and ED notes and laboratory test results also increased year by year. The highest proportions were 52.40% for ED notes in June 2021, 88.10% for ED laboratory test reports in May 2021, 34.61% for OPD notes in June 2021, and 41.87% for OPD laboratory test reports in June 2021. These peaks coincided with Taiwan’s local COVID-19 outbreak lasting from May to June 2021. ConclusionsThis study developed a blockchain-enabled mobile app, which can periodically retrieve and integrate PHRs from the NHIA MHB's cross-hospital data and the investigated hospital's self-pay medical data. Analysis of users’ access logs revealed that the COVID-19 pandemic substantially increased individuals’ use of PHRs and their health awareness with respect to COVID-19 prevention.

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.

Avascular necrosis (AVN) of the femoral head (AVNFH) is a disease caused by injury to the blood supply of the femoral head, resulting in a collapse with osteonecrosis and damage to the articular cartilage. Extracorporeal shockwave therapy (ESWT) has been demonstrated to improve AVNFH owing to its anti-inflammation activity, angiogenesis effect, and tissue regeneration in clinical treatment. However, there are still so many pieces of the jigsaw that need to be fit into place in order to ascertain the mechanism of ESWT for the treatment of AVNFH. The study demonstrated that ESWT significantly protected the trabecular bone volume fraction BV/TV (P

Radiofrequency ablation (RFA) provides an effective treatment for patients who exhibit early hepatocellular carcinoma (HCC) stages or are waiting for liver transplantation. It is important to assess patients after RFA. The goal of this study was to build artificial neural network models with HCC-related variables to predict the 1-year and 2-year disease-free survival (DFS) of HCC patients receiving RFA treatments. Methods: This study was a retrospective study that tracked HCC patients who received computer tomography-guided percutaneous RFA between January 2009 and April 2012. The numbers of total patients with 1-year and 2-year DFS were 252 and 179, respectively. A total of 15 HCC clinical variables were collected for the construction of artificial neural network models for DFS prediction. Internal validation and validation conducted using simulated prospective data were performed. Results: The results showed that the model with 15 inputs showed better performance compared with the models including only significant features. Parameters for performance assessment of 1-year DFS prediction were as follows: accuracy 85.0% (70.0%), sensitivity 75.0% (63.3%), specificity 87.5% (71.8%), and area under the curve 0.84 (0.77) for internal validation (simulated prospective validation). For 2-year DFS prediction, the values of accuracy, sensitivity, specificity, and area under the curve were 67.9% (63.9%), 50.0% (56.3%), 85.7% (70.0%), and 0.75 (0.72), respectively, for internal validation (simulated prospective validation). Conclusion: This study revealed that the proposed artificial neural network models constructed with 15 clinical HCC relevant features could achieve an acceptable prediction performance for DFS. Such models can support clinical physicians to deal with clinical decision-making processes on the prognosis of HCC patients receiving RFA treatments.

In recent years, many types of research have continued to improve the environment of human speech and emotion recognition. As facial emotion recognition has gradually matured through speech recognition, the result of this study provided more accurate recognition of complex human emotional performance, and speech emotion identification will be derived from human subjective interpretation into the use of computers to automatically interpret the speaker’s emotional expression. Focused on use in medical care, which can be used to understand the current feelings of physicians and patients during a visit, and improve the medical treatment through the relationship between illness and interaction. By transforming the voice data into a single observation segment per second, the first to the thirteenth dimensions of the frequency cestrum coefficients are used as speech emotion recognition eigenvalue vectors. Vectors for the eigenvalue vectors are maximum, minimum, average, median, and standard deviation, and there are 65 eigenvalues in total for the construction of an artificial neural network. The sentiment recognition system developed by the hospital is used as a comparison between the sentiment recognition results of the artificial neural network classification, and then use the foregoing results for a comprehensive analysis to understand the interaction between the doctor and the patient. Using this experimental module, the emotion recognition rate is 93.34%, and the accuracy rate of facial emotion recognition results can be 86.3%.

Bone marrow-derived mesenchymal cells (BM-MSCs) are able to differentiate into adipocytes, which can secrete adipokines to affect BM-MSC proliferation and differentiation. Recent evidences indicated that adipocytes can secrete fatty acid metabolites, such as palmitic acid methyl ester (PAME), which is able to cause vasorelaxation and exerts anti-inflammatory effects. However, effects of PAME on BM-MSC proliferation remain unclear. The aim of this study was to investigate the effect of PAME on human BM-MSC (hBM-MSC) proliferation and its underlying molecular mechanisms. hBM-MSCs were treated with PAME for 48 h and then subjected to various analyses. The results from the present study show that PAME significantly reduced the levels of G2/M phase regulatory proteins, cyclin-dependent kinase 1 (Cdk1), and cyclin B1 and inhibited proliferation in hBM-MSCs. Moreover, the level of Mdm2 protein decreased, while the levels of p21 and p53 protein increased in the PAME-treated hBM-MSCs. However, PAME treatment did not significantly affect apoptosis/necrosis, ROS generation, and the level of Cdc25C protein. PAME also induced intracellular acidosis and increased intracellular Ca2+ levels. Cotreatment with PAME and Na+/H+ exchanger inhibitors together further reduced the intracellular pH but did not affect the PAME-induced decreases of cell proliferation and increases of the cell population at the G2/M phase. Cotreatment with PAME and a calcium chelator together inhibited the PAME-increased intracellular Ca2+ levels but did not affect the PAME-induced cell proliferation inhibition and G2/M cell cycle arrest. Moreover, the half-life of p53 protein was prolonged in the PAME-treated hBM-MSCs. Taken together, these results suggest that PAME induced p53 stabilization, which in turn increased the levels of p53/p21 proteins and decreased the levels of Cdk1/cyclin B1 proteins, thereby preventing the activation of Cdk1, and eventually caused cell cycle arrest at the G2/M phase. The findings from the present study might help get insight into the physiological roles of PAME in regulating hBM-MSC proliferation.

Slowing of average electroencephalography (EEG) frequency in Alzheimer's disease (AD) is well established, but whether EEG changes are able to reflect the severity of AD is uncertain. We attempt to establish quantitative EEG parameters that are suitable for evaluating AD in clinical practice. Methods: Ninety-five patients with newly diagnosed AD at different stages from four neurologic institutes were enrolled for the study. Standard scalp resting EEG data were collected for quantitative analysis. Global band power ratio and interhemispheric alpha band coherence were calculated. Results: Patients with advanced AD had a greater slow-to-fast wave power ratio. Among several power ratio parameters, global theta and delta to alpha and beta band power ratio showed the best correlation with stages of AD (p

Background/Aims: Intermittent hypoxia (IH) may exert pre-conditioning-like cardioprotective effects and alter Ca2+ regulation; however, the exact mechanism of these effects remains unclear. Thus, we examined Ca2+-handling mechanisms induced by IH in rat neonatal cardiomyocytes. Methods: Cardiomyocytes were exposed to repetitive hypoxia-re-oxygenation cycles for 1-4 days. Mitochondrial reactive oxygen species (ROS) generation was determined by flow cytometry, and intracellular Ca2+ concentrations were measured using a live-cell fluorescence imaging system. Protein kinase C (PKC) isoforms and Ca2+-handling proteins were analysed using immunofluorescence and western blotting. Results: After IH exposure for 4 days, the rate of Ca2+ extrusion from the cytosol to the extracellular milieu during 40-mM KCl-induced Ca2+ mobilization increased significantly, whereas ROS levels increased mildly. IH activated PKC isoforms, which translocated to the membrane from the cytosol, and Na+/Ca2+ exchanger-1, leading to enhanced Ca2+ efflux capacity. Simultaneously, IH increased sarcoplasmic reticulum (SR) Ca2+-ATPase and ryanodine receptor 2 (RyR-2) activities and RyR-2 expression, resulting in improved Ca2+ uptake and release capacity of SR in cardiomyocytes. Conclusions: IH-induced mild elevations in ROS generation can enhance Ca2+ efflux from the cytosol to the extracellular milieu and Ca2+-mediated SR regulation in cardiomyocytes, resulting in enhanced Ca2+-handling ability.

Irritable bowel syndrome (IBS) manifests as chronic abdominal pain. One pathophysiological theory states that the brain-gut axis is responsible for pain control in the intestine. Although several studies have discussed the structural changes in the brain of IBS patients, most of these studies have been conducted in Western populations. Different cultures and sexes experience different pain sensations and have different pain responses. Accordingly, we aimed to identify the specific changes in the cortical thickness of Asian women with IBS and to compare these data to those of non-Asian women with IBS.Thirty Asian female IBS patients (IBS group) and 39 healthy individuals (control group) were included in this study. Brain structural magnetic resonance imaging was performed. We used FreeSurfer to analyze the differences in the cortical thickness and their correlations with patient characteristics.The left cuneus, left rostral middle frontal cortex, left supramarginal cortex, right caudal anterior cingulate cortex, and bilateral insula exhibited cortical thinning in the IBS group compared with those in the controls. Furthermore, the brain cortical thickness correlated negatively the severity as well as duration of abdominal pain.Some of our findings differ from those of Western studies. In our study, all of the significant brain regions in the IBS group exhibited cortical thinning compared with those in the controls. The differences in cortical thickness between the IBS patients and controls may provide useful information to facilitate regulating abdominal pain in IBS patients. These findings offer insights into the association of different cultures and sexes with differences in cortical thinning in patients with IBS.

Type 1 diabetes is commonly associated with autonomic neuropathy. The present study investigated the influences of glycemic control, disease duration (DD), and exercise on autonomic nervous function in children with type 1 diabetes by analysis of their heart rate variability (HRV). Methods: Seventy-nine type 1 diabetic children were recruited and categorized into four groups by HbA1c of 8% and DD of 4.5 years. HRV parameters as determined by separate frequency domain components (low frequency: LnLF, 0.04–0.15 Hz; high frequency: LnHF, 0.15–0.5 Hz; total power: LnTP, 0.04–0.5 Hz) were measured both at rest and during exercise. Pearson's correlation, one-way ANOVA, and multiple regressions with stepwise method were used for statistical analysis. Results: While at rest, HbA1c and DD were negatively correlated with all HRV parameters. Both HbA1c and DD were significant predictors in LnTP. However, only HbA1c was a significant predictor in LnLF and LnHF. Type 1 diabetes patients with HbA1c > 8% and DD > 4.5 years had a significantly lower HRV than the other patients. During exercise, HRV reduced significantly and no significant correlation between HbA1c and HRV or between DD and HRV was observed. Also, a significant difference in HRV among the four groups was not demonstrated. The smallest decrement in HRV from resting to exercise were in subjects with HbA1c > 8% and DD > 4.5 years. Conclusion: HbA1c was a more dominant predictor for LnTP, LnHF and LnLF than DD in children with type 1 diabetes at rest. HRV reduced significantly from resting to exercise. However, the responses of HRV during exercise differ from the responses of HRV at rest.