Temporal change in repolarization parameters after surgical correction of valvular heart diseases.

BACKGROUND: Ventricular tachyarrhythmia is a potentially fatal outcome of cardiac surgery. Abrupt changes in the hemodynamics after surgical correction of valvular heart disease (VHD) can lead to alterations in ventricular repolarization. We compared the difference between temporal changes in repolarization parameters after correction of left-sided VHD. METHODS: We retrospectively analyzed the electrograms of patients who underwent surgical correction of isolated VHD between 2006 and 2015 at Asan Medical Center, including mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR). Ventricular repolarization parameters were measured at pre-specified time intervals after index surgery using a custom-made ECG analysis program. We compared repolarization parameters, including QT and corrected QT intervals, T peak-to-end interval, and corrected T peak-to-end interval. RESULTS: Analysis of 8265 ECGs from 2110 patients (266 MS, 1059 MR, 421 AS, and 364 AR) was performed. Patients with AS were characterized by older age and more comorbidities than other VHDs. The corrected QT interval showed a peak value immediately after surgery and decreased thereafter in the AS groups. However, a gradual increase over 1 month after surgery in AR, MS, and MR groups was observed. The corrected T peak-to-end interval increased in the MS and MR groups and was unchanged in the AS and AR groups. CONCLUSIONS: The repolarization parameters of surgery changed dynamically after left-sided valvular surgery. Understanding differential temporal change of repolarization parameters according to the type of VHD would help clinicians avoid fatal arrhythmias related to the repolarization changes.

Effect of Hospital Case-Volume on Mortality after Ovarian Cancer Surgery: A Population-Based Retrospective Cohort Study.

OBJECTIVES: The goal of ovarian cancer surgery has recently shifted from optimal cytoreduction to more complete resection. This study attempted to reassess and update the association between surgical case-volume and both in-hospital and long-term mortality after ovarian cancer surgery using recent data. DESIGN: This study is a population-based retrospective cohort study. Participants/Material: Data from all adult patients who underwent ovarian cancer surgery in Korea between 2005 and 2019 were obtained from the national database. A total of 24,620 patients underwent ovarian cancer surgery in 362 hospitals during the period. SETTING: In-hospital and 1-, 3-, 5-year mortality were set as primary and secondary outcomes. METHODS: Hospitals were categorized into high-volume (>90 cases/year), medium-volume (20-90 cases/year), and low-volume (<20 cases/year) centers considering overall distribution of case-volume. Postoperative in-hospital and long-term mortality were analyzed using logistic regression after adjusting for potential risk factors. RESULTS: Compared to high-volume centers (0.54%), in-hospital mortality was significantly higher in medium-volume (1.40%; adjusted odds ratio, 2.92; confidence interval, 1.82-3.73; p < 0.001) and low-volume (1.61%; adjusted odds ratio, 2.94; confidence interval, 2.07-4.17; p < 0.001) centers. In addition, 1-year mortality was 6.26%, 7.06%, and 7.94% for high-volume, medium-volume, and low-volume centers, respectively, and the differences among the groups were significant. However, case-volume effect was not apparent in 3- and 5-year mortality after ovarian cancer surgery. LIMITATIONS: Lacking clinical information such as staging or histologic diagnosis due to the nature of the administrative data should be considered in interpreting the data. CONCLUSIONS: Case-volume effect was observed for in-hospital and 1-year mortality after ovarian cancer surgery, while it was not clearly found in 3- or 5-year mortality. Dilution of the case-volume effect might be attributed to the high accessibility to care.

Long-term outcomes after sacrocolpopexy with or without transobturator tape.

INTRODUCTION AND HYPOTHESIS: The aim of this study was to report the long-term outcomes after sacrocolpopexy (SCP) with or without transobturator tape (TOT). METHODS: We conducted a planned secondary analysis of a prospective, observational study comparing urinary outcomes in women who underwent SCP with or without TOT based on the results of a prolapse-reduction stress test. Patients were enrolled between November 2008 and December 2011 and were followed up 5 years after surgery. The primary outcomes were 5-year success rates for stress urinary incontinence (SUI) and pelvic organ prolapse (POP) estimated using the Kaplan-Meier method. SUI success was defined as a negative cough stress test, no bothersome SUI symptoms, and no additional anti-incontinence surgery. POP success was defined as no vaginal bulge symptoms, no apical descent greater than one-third of the total vaginal length or anterior or posterior vaginal wall prolapse beyond the hymen, and no retreatment for prolapse. RESULTS: Of 240 women enrolled, 175 (73%) completed 5 years of follow-up. The estimated SUI success rate was 91.1% in the TOT group and 56.5% in the no TOT group (difference, 34.6%; 95% confidence interval, 24.1 to 45.1). The estimated POP success rate was 90.0% in the TOT group and 92.9% in the no TOT group (difference, -2.9%; 95% confidence interval, -10.7 to 4.9). CONCLUSIONS: The advantage of concomitant TOT for SUI after SCP that was seen at 2 years remained at 5 years. Long-term POP failure rates after SCP are low and not affected by concomitant TOT.

A simple apparatus for safety assessment of magnetically induced torque on active implantable medical devices (AIMDs) under 1.5 T and 3.0 T MRI.

OBJECTIVE: Our goal is to design and validate a simple apparatus for the safety assessments of magnetically induced torques by four active implantable medical devices (AIMDs) in 1.5 T and 3.0 T magnetic resonance imaging (MRI) environments. MATERIALS AND METHODS: A simple apparatus was designed to measure the magnetically induced torque. Before testing the apparatus, its effectiveness was first evaluated with a commercially available weights. The torque values of the four AIMDs, namely implantable cardioverter defibrillator (ICD), pacemaker, bone conduction implant, and cochlear implant, under 1.5 T and 3.0 T MRI environments were measured and compared with established international standard. RESULTS: The ICD, pacemaker, bone conduction implant, and cochlear implant had average torques of 1.405, 0.255, 9.460, and 5.490 N·cm under 1.5 T MRI and 2.950, 0.668, 14.618, and 11.484 N·cm under 3.0 T MRI, respectively. DISCUSSION: The apparatus developed in this study was verified to be feasible for measuring magnetically induced torques, with tests on four types of AIMDs under 1.5 T and 3.0 T MRI environments. Although the designed structure is simple, the apparatus was shown to measure torque values of AIMDs under MRI accurately.

Why Do Data Users Say Health Care Data Are Difficult to Use? A Cross-Sectional Survey Study.

BACKGROUND: There has been significant effort in attempting to use health care data. However, laws that protect patients' privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users' needs for data linking from practical perspectives. OBJECTIVE: The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users' data linking needs. METHODS: We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. RESULTS: In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants' responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. CONCLUSIONS: This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.

New Metrics in High-Resolution and High-Definition Anorectal Manometry.

PURPOSE OF REVIEW: To review recently published diagnostic methods that use high-resolution (HR-) or high-definition- (HD-) anorectal manometry (ARM) techniques. RECENT FINDINGS: The integrated pressurized volume (IPV) is a new measure based on spatiotemporal plots obtained from HR-ARM. The IPV may be clinically useful for improving the prediction of abnormal balloon expulsion test in patients with constipation and for discriminating patients with anorectal disorders from asymptomatic controls. Combination of IPV parameters was superior to conventional manometric parameters in predicting the responsiveness to biofeedback therapy. Moreover, several novel parameters including the HR-ARM resting integral, HR-ARM squeeze profile, and anorectal asymmetry index may each be useful as predictive factors for identifying patients with fecal incontinence. HR- and HD-ARM are increasingly performed worldwide for evaluation of anorectal function. Here, we describe new metrics whose clinical significance has not been fully established. Further standardization and validation of these metrics could provide clinically important new information and could help improve our understanding of the pathophysiology of anorectal disorders.

ß-Amyloid is transmitted via neuronal connections along axonal membranes.

OBJECTIVE: ß-amyloid plaque is a critical pathological feature of Alzheimer disease. Pathologic studies suggest that neurodegeneration may occur in a retrograde fashion from axon terminals near ß-amyloid plaques, and that plaque may spread through brain regions. However, there is no direct experimental evidence to show transmission of ß-amyloid. METHODS: Microscopic imaging data of ß-amyloid transmission was acquired in cortical neuron cultures from Sprague-Dawley rat embryos using polydimethylsiloxane (PDMS) microfluidic culture chambers and in brain sections from in vivo ß-amyloid injection. RESULTS: We present direct imaging evidence in cultured cortical neurons, using PDMS microfluidic culture chambers, that ß-amyloid is readily absorbed by axonal processes and retrogradely transported to neuronal cell bodies. Transmission of ß-amyloid via neuronal connections was also confirmed in mouse brain. ß-Amyloid absorbed by distal axons accumulates in axonal swellings, mitochondria, and lysosomes of the cell bodies. Interestingly, dynasore, an inhibitor of dynamin, which is a protein indispensable for endocytosis, did not prevent retrograde transport of ß-amyloid, indicating that ß-amyloid is absorbed onto axonal membranes and transmitted via them to the cell body. Dynasore did decrease the transneuronal transmission of ß-amyloid, suggesting that this requires the internalization and secretion of ß-amyloid. INTERPRETATION: Our findings provide direct in vitro and in vivo evidence for spreading of ß-amyloid through neuronal connections, and suggest possible therapeutic approaches to blocking this spread.

A smartphone-based system for the automated management of point-of-care test results in hospitals.

OBJECTIVE: Managing test results is an important issue in hospitals because of the increasing use of point-of-care testing (POCT). Here, we propose a smartphone-based system for automatically managing POCT test results. MATERIALS AND METHODS: We developed the system to provide convenience to the medical staffs. The system recognizes the patient identification or prescription number of the test by reading barcodes and provides a countdown to indicate when the results will be ready. When the countdown in finished, a picture of the test result is transferred to the electronic medical record server using the Health Level 7 protocol. Human immunodeficiency virus (HIV) kits were selected in this research because HIV is a life-threatening infectious virus, especially for the medical staff who treat undiagnosed patients. The performance of the system was verified from a survey of the users. RESULTS: The performance of the system was tested at the emergency room (ER) for 10 months using commercially available POCT kits for detecting HIV. The survey showed that, in total, 80% and 0% of users reported positive or negative feedback, respectively. The staff also reported that the system reduced total processing time by approximately 32 min, in addition to reducing workload. CONCLUSIONS: The developed automated management system was successfully tested at an ER for 10 months. The survey results show that the system is effective and that medical staff members who used the system are satisfied with using the system at the ER.

Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals.

Nowadays, Electrocardiogram (ECG) signals can be measured using wearable devices, such as smart watches. Most wearable devices provide only a few details; however, they have the advantage of recording data in real time. In this study, 12-lead ECG signals were generated from lead I and their feasibility was tested to obtain more details. The 12-lead ECG signals were generated using a U-net-based generative adversarial network (GAN) that was trained on ECG data obtained from the Asan Medical Center. Subsequently, unseen PTB-XL PhysioNet data were used to produce real 12-lead ECG signals for classification. The generated and real 12-lead ECG signals were then compared using a ResNet classification model; and the normal, atrial fibrillation (A-fib), left bundle branch block (LBBB), right bundle branch block (RBBB), left ventricular hypertrophy (LVH), and right ventricular hypertrophy (RVH) were classified. The mean precision, recall, and f1-score for the real 12-lead ECG signals are 0.70, 0.72, and 0.70, and that for the generated 12-lead ECG signals are 0.82, 0.80, and 0.81, respectively. In our study, according to the result generated 12-lead ECG signals performed better than real 12-lead ECG.

Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments.

Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.

Tele-monitoring system for intensive care ventilators in isolation rooms.

The COVID-19 pandemic and discovery of new mutant strains have a devastating impact worldwide. Patients with severe COVID-19 require various equipment, such as ventilators, infusion pumps, and patient monitors, and a dedicated medical team to operate and monitor the equipment in isolated intensive care units (ICUs). Medical staff must wear personal protective equipment to reduce the risk of infection. This study proposes a tele-monitoring system for isolation ICUs to assist in the monitoring of COVID-19 patients. The tele-monitoring system consists of three parts: medical-device panel image processing, transmission, and tele-monitoring. This system can monitor the ventilator screen with obstacles, receive and store data, and provide real-time monitoring and data analysis. The proposed tele-monitoring system is compared with previous studies, and the image combination algorithm for reconstruction is evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The system achieves an SSIM score of 0.948 in the left side and a PSNR of 23.414 dB in the right side with no obstacles. It also reduces blind spots, with an SSIM score of 0.901 and a PSNR score of 18.13 dB. The proposed tele-monitoring system is compatible with both wired and wireless communication, making it accessible in various situations. It uses camera and performs live data monitoring, and the two monitoring systems complement each other. The system also includes a comprehensive database and an analysis tool, allowing medical staff to collect and analyze data on ventilator use, providing them a quick, at-a-glance view of the patient's condition. With the implementation of this system, patient outcomes may be improved and the burden on medical professionals may be reduced during the COVID-19 pandemic-like situations.

High-resolution impedance manometry for comparing bolus transit between patients with non-obstructive dysphagia and asymptomatic controls.

BACKGROUND: Currently, there are no high-resolution impedance manometry (HRIM)-based diagnostic criteria for non-obstructive dysphagia (NOD). New impedance parameters, such as the esophageal impedance integral (EII) and volume of inverted impedance (VII) ratios, have shown strong correlations with bolus transit. This study compared the EII and VII ratios as diagnostic tools for NOD. METHODS: We analyzed 36 participants (12 patients with achalasia, 12 patients with NOD [7 with normal motility and 5 with ineffective esophageal motility], and 12 asymptomatic controls) who underwent HRIM with a maximum of 5 swallows per participant. The EII and VII ratios were calculated as Z2 (post-swallow)/Z1 (pre-swallow). Bolus transit was retrospectively evaluated using transluminal impedance analysis. KEY RESULTS: Both EII and VII ratios could effectively distinguish the achalasia group from the non-achalasia groups (area under the receiver operating characteristic curve [AUROC]: 0.83 for VII vs. 0.80 for EII; p = 0.73). However, the VII ratio was significantly better in discriminating asymptomatic controls from patients with dysphagia (NOD + achalasia) (AUROC: 0.81 vs. 0.68; p = 0.01). Moreover, the VII ratio was better in discriminating asymptomatic controls from patients with NOD (AUROC: 0.68 vs. 0.51; p = 0.06). In repeated swallows, the VII ratio was consistently the lowest in controls and the highest in patients with achalasia, whereas the EII ratio did not show a consistent pattern. CONCLUSIONS & INFERENCES: The VII ratio was more reliable than the EII ratio for describing bolus transit and distinguishing patients with NOD from asymptomatic controls, even during repeated measures of subsequent swallows.

In-channel electrochemical detection in the middle of microchannel under high electric field.

We propose a new method for performing in-channel electrochemical detection under a high electric field using a polyelectrolytic gel salt bridge (PGSB) integrated in the middle of the electrophoretic separation channel. The finely tuned placement of a gold working electrode and the PGSB on an equipotential surface in the microchannel provided highly sensitive electrochemical detection without any deterioration in the separation efficiency or interference of the applied electric field. To assess the working principle, the open circuit potentials between gold working electrodes and the reference electrode at varying distances were measured in the microchannel under electrophoretic fields using an electrically isolated potentiostat. In addition, "in-channel" cyclic voltammetry confirmed the feasibility of electrochemical detection under various strengths of electric fields (∼400 V/cm). Effective separation on a microchip equipped with a PGSB under high electric fields was demonstrated for the electrochemical detection of biological compounds such as dopamine and catechol. The proposed "in-channel" electrochemical detection under a high electric field enables wider electrochemical detection applications in microchip electrophoresis.

Multiple electrocardiogram generator with single-lead electrocardiogram.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is measured in various ways. The three main ECG measurement methods include resting ECG, Holter monitoring, and treadmill method. In standard ECG measurement methods, multiple electrodes are attached to the limb and chest. Limb and chest leads measure the frontal and sagittal planes of the heart, respectively. In this case, ECG signals are measured briefly up to 10 seconds. To measure ECG signals based on a single lead, wearable devices have been developed that could measure long-term ECG signals daily. ECG signals are vectors in the heart, which is a three-dimensional structure. Therefore, a single-lead measurement lacks detailed information. The objective of this study was to synthesize multiple ECGs from a single-lead ECG using a generative adversarial network (GAN). METHODS: We trained our model with two independent datasets and one combined dataset. For experiment 1, the PTB-XL dataset was used as the training set, and the China dataset was used as the test set. For experiment 2, the China dataset was used as the training set, and the PTB-XL was used as the test set. Optimized GAN models were obtained for each experiment and evaluated. RESULTS: The Fréchet distance (FD) score and mean squared error (MSE) were used for evaluation. The FD and MSE scores for experiments 1 and 2 were 7.237 and 0.024, and 8.055 and 0.011, respectively. CONCLUSION: We proposed a method to overcome the limitations of modern ECG measurement methods. Low FD and MSE scores not only indicate the possibility but also the similarity between synthesized ECG and reference ECG when compared in ECG paper format. This indicates that the proposed method can be applied to wearable devices that measure single-lead ECG.

Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model.

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs-cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models. We applied the proposed methods to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans two times, before and after the administration of human immunoglobulin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target VOIs were used as the label. We compared our CNN-based VOIs with conventional (template-based) VOIs in terms of the correlation of standardized uptake value ratio (SUVR) by both methods and two-sample t-tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.

The Predictive Value of Intraoperative Esophageal Functional Luminal Imaging Probe Panometry in Patients With Achalasia Undergoing Peroral Endoscopic Myotomy: A Single-center Experience.

Background/Aims: We evaluated the clinical significance and prognostic power of functional luminal imaging probe (FLIP) panometry in patients with achalasia treated with peroral endoscopic myotomy (POEM), and examined the clinical parameters associated with symptomatic improvement and the presence of contractility (POC) following POEM. Methods: We reviewed the electronic medical records of patients with achalasia treated with FLIP panometry and POEM at a tertiary teaching hospital in Seoul, Republic of Korea. Follow-up examination was composed of esophageal manometry and questionnaires on symptoms. We analyzed the FLIP data by interpolating using the cubic spline method in MATLAB. Results: We retrospectively analyzed 33 men and 35 women (mean age: 52 ± 17 years), of whom 14, 39, and 15 patients were diagnosed with achalasia types I, II, and III, respectively. The FLIP panometry diagnoses were reduced esophagogastric junction opening (REO) with a retrograde contractile response (n = 43); REO with an absent contractile response (n = 5); REO with a normal contractile response (n = 11); and a retrograde contractile response (n = 9). Overall, the patients showed improvements in Eckardt scores following POEM from 6.48 ± 2.20 to 1.16 ± 1.15 (P < 0.01). Post-POEM symptomatic improvement was not significantly associated with any of the clinical parameters, including panometry diagnosis. Conversely, post-POEM POC was significantly associated with the presence of repetitive antegrade contractions and achalasia subtypes (both P < 0.01). Conclusion: While FLIP panometry was not significantly associated with the clinical course of achalasia, FLIP panometry was associated with POC following POEM and may complement manometry in the functional evaluation of esophageal motility disorders.

Predicting Responsiveness to Biofeedback Therapy Using High-resolution Anorectal Manometry With Integrated Pressurized Volume.

Background/Aims: Biofeedback therapy is widely used to treat patients with chronic constipation, especially those with dyssynergic defecation. Yet, the utility of high-resolution manometry with novel parameters in the prediction of biofeedback response has not been reported. Thus, we constructed a model for predicting biofeedback therapy responders by applying the concept of integrated pressurized volume in patients undergoing high-resolution anorectal manometry. Methods: Seventy-one female patients (age: 48-68 years) with dyssynergic defecation who underwent initial high-resolution anorectal manometry and subsequent biofeedback therapy were enrolled. The manometry profiles were used to calculate the 3-dimensional integrated pressurized volumes by multiplying the distance, time, and amplitude during simulated evacuation. Partial least squares regression was performed to generate a predictive model for responders to biofeedback therapy by using the integrated pressurized volume parameters. Results: Fifty-five (77.5%) patients responded to biofeedback therapy. The responders and non-responders did not show significant differences in the conventional manometric parameters. The partial least squares regression model used a linear combination of eight integrated pressurized volume parameters and generated an area under the curve of 0.84 (95% confidence interval: 0.76-0.95, P < 0.01), with 85.5% sensitivity and 62.1% specificity. Conclusions: Integrated pressurized volume parameters were better than conventional parameters in predicting the responsiveness to biofeedback therapy, and the combination of these parameters and partial least squares regression was particularly promising. Integrated pressurized volume parameters can more effectively explain the physiology of the anorectal canal compared with conventional parameters.

ECG data dependency for atrial fibrillation detection based on residual networks.

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98-99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53-92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

Development of a Portable Respiratory Gas Analyzer for Measuring Indirect Resting Energy Expenditure (REE).

OBJECTIVE: A rapidly growing home healthcare market has resulted in the development of many portable or wearable products. Most of these products measure, estimate, or calculate physiologic signals or parameters, such as step counts, blood pressure, or electrocardiogram. One of the most important applications in home healthcare is monitoring one's metabolic state since the change of metabolic state could reveal minor or major changes in one's health condition. A simple and noninvasive way to measure metabolism is through breath monitoring. With breath monitoring by breath gas analysis, two important indicators like the respiratory quotient (RQ) and resting energy exposure (REE) can be calculated. Therefore, we developed a portable respiratory gas analyzer for breath monitoring to monitor metabolic state, and the performance of the developed device was tested in a clinical trial. Approach. The subjects consisted of 40 healthy men and women. Subjects begin to measure exhalation gas using Vmax 29 for 15 minutes. After that, subjects begin to measure exhalation gas via the developed respiratory gas analyzer. Finally, the recorded data on the volume of oxygen (VO2), volume of carbon dioxide (VCO2), RQ, and REE were used to validate correlations between Vmax 29 and the developed respiratory gas analyzer. RESULTS: The results showed that the root-mean-square errors (RMSE) values of VCO2, VO2, RQ, and REE are 0.0315, 0.0417, 0.504, and 0.127. Bland-Altman plots showed that most of the VCO2, VO2, RQ, and REE values are within 95% of the significance level. CONCLUSIONS: We have successfully developed and tested a portable respiratory gas analyzer for home healthcare. However, there are limitations of the clinical trial; the number of subjects is small in size, and the age and race of subjects are confined. The developed portable respiratory gas analyzer is a cost-efficient method for measuring metabolic state and a new application of home healthcare.

SERS decoding of micro gold shells moving in microfluidic systems.

In this study, in situ surface-enhanced Raman scattering (SERS) decoding was demonstrated in microfluidic chips using novel thin micro gold shells modified with Raman tags. The micro gold shells were fabricated using electroless gold plating on PMMA beads with diameter of 15 microm. These shells were sophisticatedly optimized to produce the maximum SERS intensity, which minimized the exposure time for quick and safe decoding. The shell surfaces produced well-defined SERS spectra even at an extremely short exposure time, 1 ms, for a single micro gold shell combined with Raman tags such as 2-naphthalenethiol and benzenethiol. The consecutive SERS spectra from a variety of combinations of Raman tags were successfully acquired from the micro gold shells moving in 25 microm deep and 75 microm wide channels on a glass microfluidic chip. The proposed functionalized micro gold shells exhibited the potential of an on-chip microfluidic SERS decoding strategy for micro suspension array.