Decompression Illness in Divers With or Without Patent Foramen Ovale : A Cohort Study.

BACKGROUND: In previous studies, the prevalence of patent foramen ovale (PFO) has been reported to be higher in scuba divers who experienced decompression illness (DCI) than in those who did not. OBJECTIVE: To assess the association between PFO and DCI in scuba divers. DESIGN: Prospective cohort study. SETTING: Tertiary cardiac center in South Korea. PARTICIPANTS: One hundred experienced divers from 13 diving organizations who did more than 50 dives per year. MEASUREMENTS: Participants had transesophageal echocardiography with a saline bubble test to determine the presence of a PFO and were subsequently divided into high- and low-risk groups. They were followed using a self-reported questionnaire while blinded to their PFO status. All of the reported symptoms were adjudicated in a blinded manner. The primary end point of this study was PFO-related DCI. Logistic regression analysis was done to determine the odds ratio of PFO-related DCI. RESULTS: Patent foramen ovale was seen in 68 divers (37 at high risk and 31 at low risk). Patent foramen ovale-related DCI occurred in 12 divers in the PFO group (non-PFO vs. high-risk PFO vs. low-risk PFO: 0 vs. 8.4 vs. 2.0 incidences per 10 000 person-dives; P = 0.001) during a mean follow-up of 28.7 months. Multivariable analysis showed that high-risk PFO was independently associated with an increased risk for PFO-related DCI (odds ratio, 9.34 [95% CI, 1.95 to 44.88]). LIMITATION: The sample size was insufficient to assess the association between low-risk PFO and DCI. CONCLUSION: High-risk PFO was associated with an increased risk for DCI in scuba divers. This finding indicates that divers with high-risk PFO are more susceptible to DCI than what has been previously reported and should consider either refraining from diving or adhering to a conservative diving protocol. PRIMARY FUNDING SOURCE: Sejong Medical Research Institute.

Fetuin as a potential serum biomarker to detect subclinical shedder of bovine paratuberculosis.

Paratuberculosis (PTB) is a chronic contagious granulomatous enteritis of wild and domestic ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP). PTB causes considerable economic losses to the dairy industry through decreased milk production and premature culling. PTB-affected cattle undergo a subclinical stage without clinical signs and initiate fecal shedding of MAP into the environment. Current diagnostic tools have low sensitivity for the detection of subclinical PTB infection. Therefore, alternative diagnostic tools are required to improve the diagnostic sensitivity of subclinical PTB infection. In this study, we performed ELISA for three previously identified host biomarkers (fetuin, alpha-1-acid glycoprotein, and apolipoprotein) and analyzed their diagnostic performance with conventional PTB diagnostic methods. We observed that serum fetuin levels were significantly lowered in the subclinical shedder and clinical shedder groups than in the healthy control group, indicating its potential utility as a diagnostic biomarker for bovine PTB. Also, fetuin showed an excellent discriminatory power with an AUC = 0.949, a sensitivity of 92.6%, and a specificity of 94.4% for the detection of subclinical MAP infection. In conclusion, our results demonstrated that fetuin could be used as a diagnostic biomarker for enhancing the diagnostic sensitivity for the detection of subclinical MAP infections that are difficult to detect based on current diagnostic methods.

Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection.

BACKGROUND: The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection. METHODS: The predictive performance of qSOFA and MEWS for in-hospital cardiac arrest (IHCA) or unexpected intensive care unit (ICU) transfer was compared with the areas under the receiver operating characteristic curve (AUC) analysis using the databases of vital signs collected from consecutive hospitalized adult patients over 12 months in five participating hospitals in Korea. RESULTS: Of 173,057 hospitalized patients included for analysis, 668 (0.39%) experienced the composite outcome. The discrimination for the composite outcome for MEWS (AUC, 0.777; 95% confidence interval [CI], 0.770-0.781) was higher than that for qSOFA (AUC, 0.684; 95% CI, 0.676-0.686; P < 0.001). In addition, MEWS was better for prediction of IHCA (AUC, 0.792; 95% CI, 0.781-0.795 vs. AUC, 0.640; 95% CI, 0.625-0.645; P < 0.001) and unexpected ICU transfer (AUC, 0.767; 95% CI, 0.760-0.773 vs. AUC, 0.716; 95% CI, 0.707-0.718; P < 0.001) than qSOFA. Using the MEWS at a cutoff of ≥ 5 would correctly reclassify 3.7% of patients from qSOFA score ≥ 2. Most patients met MEWS ≥ 5 criteria 13 hours before the composite outcome compared with 11 hours for qSOFA score ≥ 2. CONCLUSION: MEWS is more accurate that qSOFA score for predicting IHCA or unexpected ICU transfer in patients outside the ICU. Our study suggests that qSOFA should not replace MEWS for identifying patients in the general wards at risk of poor outcome.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. METHODS AND RESULTS: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs. METHODS: We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables. RESULTS: The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods. CONCLUSIONS: The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods.

Beneficial Effect of Left Ventricular Remodeling after Early Change of Sacubitril/Valsartan in Patients with Nonischemic Dilated Cardiomyopathy.

Background and Objectives: Evidence for effectiveness of early change from angiotensin II receptor blockers (ARBs) or angiotensin-converting enzyme inhibitors (ACEIs) to sacubitril/valsartan is lacking. We aimed to investigate whether early changes to sacubitril/valsartan could improve outcomes in patients with nonischemic dilated cardiomyopathy (DCM) in real-world practice. Materials and Methods: A total of 296 patients with nonischemic DCM who were treated with ARB or ACEI continuously (group A, n = 150) or had their medication switched to sacubitril/valsartan (group S, n = 146) were included. The sacubitril/valsartan group was divided into early change (within 60 days, group S/E, n = 59) and late change (group S/L, n = 87) groups. Changes in echocardiographic parameters from the time of initial diagnosis to the last follow-up were analyzed. Results: Patients in group S showed greater left ventricular (LV) end-diastolic dimension (EDD) (group A vs. S, 61.7 ± 7.4 vs. 66.5 ± 8.0, p < 0.001) and lower LV ejection fraction (LVEF) (28.9 ± 8.2% vs. 23.9 ± 7.5%, p < 0.001) than those in group A at initial diagnosis. During a median follow-up of 76 months, patients in group S/E, ∆ LVEF (%) and ∆ LVESD (mm) were significantly improved compared with those in patients in group A (group A vs. S/E, ∆ LVEF, p = 0.036; ∆ LVESD, p = 0.023) or S/L (group S/E vs. S/L, ∆ LVEF, p = 0.05; ∆ LVESD, p = 0.005). Among patients whose medications were switched to sacubitril/valsartan, those with an earlier change showed a significant correlation with greater LVEF improvement (r = -0.367, p < 0.001) and LV reverse remodeling (r = 0.277, p < 0.001). Conclusions: in patients with nonischemic DCM, an early switch to sacubitril/valsartan was associated with greater improvement in LV function. Patients might benefit in terms of LV function by early switching to sacubitril/valsartan.

Identification of Nontuberculous Mycobacteria in Patients with Pulmonary Diseases in Gyeongnam, Korea, Using Multiplex PCR and Multigene Sequence-Based Analysis.

BACKGROUND: Nontuberculous mycobacteria (NTM) are widely present in environments, such as soil and water, and have recently been recognized as important pathogenic bacteria. The incidence of NTM-related infections is steadily increasing. As the diagnosis and treatment of NTM infection should be distinguished from tuberculosis, and the treatment should be specific to the species of NTM acquired, accurate species identification is required. METHODS: In this study, two-step multiplex PCR (mPCR) and multigene sequence-based analysis were used to accurately identify NTM species in 320 clinical isolates from Gyeongsang National University Hospital (GNUH). In particular, major mycobacterial strains with a high isolation frequency as well as coinfections with multiple species were diagnosed through two-step mPCR. Multigene sequencing was performed to accurately identify other NTM species not detected by mPCR. Variable regions of the genes 16S rRNA, rpoB, hsp65, and 16S-23S rRNA internal transcribed spacer were included in the analysis. RESULTS: Two-step mPCR identified 234 (73.1%) cases of M. intracellulare, 26 (8.1%) cases of M. avium subsp. avium, and 13 (4.1%) cases of M. avium subsp. hominissuis infection. Additionally, 9 (2.8%) M. fortuitum, 9 (2.8%) M. massiliense, 2 (0.6%) M. abscessus, and 4 (1.2%) M. kansasii isolates were identified. Coinfection was identified in 7 (2.2%) samples. The sixteen samples not classified by two-step mPCR included 6 (1.9%) cases of M. chimaera, 4 (1.3%) M. gordonae, 1 (0.3%) M. colombiense, 1 (0.3%) M. mageritense, and 1 (0.3%) M. persicum identified by sequence analysis. CONCLUSIONS: The results of this study suggest a strategy for rapid detection and accurate identification of species using two-step mPCR and multigene sequence-based analysis. To the best of our knowledge, this study is the first to report the identification of NTM species isolated from patients in Gyeongnam/Korea.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.

AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. METHODS AND RESULTS: This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877-0.883) and 0.868 (0.865-0.871) during the internal and external validations. These results significantly outperformed the cardiologist's clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist's assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. CONCLUSION: An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques.

Artificial intelligence for detecting mitral regurgitation using electrocardiography.

BACKGROUND: Screening and early diagnosis of mitral regurgitation (MR) are crucial for preventing irreversible progression of MR. In this study, we developed and validated an artificial intelligence (AI) algorithm for detecting MR using electrocardiography (ECG). METHODS: This retrospective cohort study included data from two hospital. An AI algorithm was trained using 56,670 ECGs from 24,202 patients. Internal validation of the algorithm was performed with 3174 ECGs of 3174 patients from one hospital, while external validation was performed with 10,865 ECGs of 10,865 patients from another hospital. The endpoint was the diagnosis of significant MR, moderate to severe, confirmed by echocardiography. We used 500 Hz ECG raw data as predictive variables. Additionally, we showed regions of ECG that have the most significant impact on the decision-making of the AI algorithm using a sensitivity map. RESULTS: During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm using a 12-lead ECG for detecting MR was 0.816 and 0.877, respectively, while that using a single-lead ECG was 0.758 and 0.850, respectively. In the 3157 non-MR individuals, those patients that the AI defined as high risk had a significantly higher chance of development of MR than the low risk group (13.9% vs. 2.6%, p < 0.001) during the follow-up period. The sensitivity map showed the AI algorithm focused on the P-wave and T-wave for MR patients and QRS complex for non-MR patients. CONCLUSIONS: The proposed AI algorithm demonstrated promising results for MR detecting using 12-lead and single-lead ECGs.

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during initial treatment or screening. This study aimed to derive and validate an echocardiography-based mortality prediction model for HD using deep learning (DL). METHODS: In this multicenter retrospective cohort study, the subjects were admitted adult (age ≥ 18 years) HD patients who underwent echocardiography. The outcome was in-hospital mortality. We extracted predictor variables from echocardiography reports using text mining. We developed deep learning-based prediction model using derivation data of a hospital A. And we conducted external validation using echocardiography report of hospital B. We conducted subgroup analysis of coronary heart disease (CHD) and heart failure (HF) patients of hospital B and compared DL with the currently used predictive models (eg, Global Registry of Acute Coronary Events (GRACE) score, Thrombolysis in Myocardial Infarction score (TIMI), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and Get With The Guidelines-Heart Failure (GWTG-HF) score). RESULTS: The study subjects comprised 25 776 patients with 1026 mortalities. The areas under the receiver operating characteristic curve (AUROC) of the DL model were 0.912, 0.898, 0.958, and 0.913 for internal validation, external validation, CHD, and HF, respectively, and these results significantly outperformed other comparison models. CONCLUSIONS: This echocardiography-based deep learning model predicted in-hospital mortality among HD patients more accurately than existing prediction models and other machine learning models.

Clinical outcomes of patients with coronary artery aneurysm after the first generation drug-eluting stent implantation.

OBJECTIVES: We sought to investigate the long-term clinical outcomes of patients with coronary artery aneurysm (CAA) after drug-eluting stent (DES) implantation, compared with patients without CAA. BACKGROUND: CAA developed after DES implantation is a rare but associated with poor clinical outcome. METHODS: We retrospectively compared 78 patients with CAA after DES implantation with 269 patients without CAA who underwent DES implantation for complex lesions (controls). The primary endpoint was defined as major adverse cardiac events (MACE), the composite of all-cause death, nonfatal myocardial infarction (MI), and target lesion revascularization (TLR). RESULTS: Morphologically, CAAs were saccular (32%), fusiform (13%), or microform (55%). The stent types involved were Cypher (n = 56, 71.8%) and Taxus (n = 22, 28.2%). During a median follow-up period of 1164 days, the incidence of MACE was significantly higher in the CAA group (26.9 vs. 2.2%, P < 0.001); the difference was driven mainly by nonfatal MI (11.5 vs. 0%, P < 0.001) and TLR (20.5 vs. 1.9%, P < 0.001). The incidence of stent thrombosis was higher in the CAA group (12.8 vs. 0.74%, P < 0.001), irrespective of the maintenance of dual antiplatelet therapy. In the CAA group, Cox regression analysis showed significantly higher hazard ratios of CAA for MACE during the follow-up period. Further analyses after propensity-score matching of 65 pairs also showed similar results. CONCLUSIONS: The incidence of MACE was higher in patients with CAA compared with patients without CAA after DES implantation. This difference was driven by TLR and nonfatal MI and widened over time.

Efficacy and safety of endovascular treatment for femoropopliteal lesions of TASC II type C and D compared with TASC II type A and B in Korea.

Objective To compare the long-term safety and clinical efficacy of endovascular treatment for TASC-II type C/D femoropopliteal lesion compared with TASC-II type A/B femoropopliteal lesion in Korea. Methods A total of 179 limbs [TASC-II A/B femoropopliteal lesion (group I, n = 105 limbs) and TASC-II C/D (group II, n = 74 limbs)] were retrospectively analyzed from patients who underwent angioplasty with or without primary stent implantation between February 2008 and November 2012 at two medical centers in South Korea. The major adverse limb event was defined as a composite of target lesion revascularization, symptom relapse with abnormal ankle brachial index, and major amputation. Results Immediate procedural success rates were not significantly different (96.2% vs. 95.7%, p = 0.450). Although major adverse limb event, mainly driven by symptom relapse with abnormal ankle brachial index, were significantly higher in group II ( p = 0.013), the incidence of major amputation was very low and similar in both groups. Conclusion Even though there were higher incidences of overall procedural complication and major adverse limb event, the technical success rate of endovascular treatment for TASC-II C/D femoropopliteal lesion was comparable to endovascular treatment for TASC-II A/B FPL without an increase in major procedural complications or serious clinical events during follow-up.

The effects of a bridge exercise with vibration training and an unstable base of support on lumbar stabilization.

[Purpose] The aim of this study was to examine the effects of a bridge exercise with vibration training and an unstable base of support on lumbar stabilization. [Subjects] This study assigned healthy adults in their 20s to a bridge exercise with a sling and vibration group (BESV, n=20) and a bridge exercise with a sling group (BESG, n=20). [Methods] Electromyography was used to comparatively analyze the activity of the internal obliques (IO), external obliques (EO), and rectus abdominis (RA) when local vibration was applied during a bridge exercise that used a sling as an unstable base of support. [Results] There were statistically significant increases in the activity of the IO and EO within each group after the intervention. The activity of the IO and the EO was significantly higher in the BESV group than in the BES group after the intervention. [Conclusion] The bridge exercise performed using vibration training on an unstable base of support increased the activity of the IO and the EO, which improved lumbar stabilization.

Effects of closed chain exercises for the lumbar region performed with local vibration applied to an unstable support surface on the thickness and length of the transverse abdominis.

[Purpose] This study examined the effects of closed chain exercises performed with local vibration applied to an unstable support surface on the thickness and length of the transverse abdominis. [Subjects] The subjects were 64 healthy university students who were randomly assigned to a bridge exercise with sling and vibration group (BESVG, n=30) and a bridge exercise with sling group (BESG, n=34). [Methods] The bridge exercise was repeated four times per set and a total of 18 sets were performed: 9 sets in a supine position and 9 sets in a prone position. In both the BESVG and the BESG groups, the thickness and length of the transverse abdominis (TrA) were measured using ultrasonography with the abdomen "drawn-in" and the pressure of a biofeedback unit maintained at 40 mmHg, both before and after the intervention. [Results] In intra-group comparisons, the BESVG showed significant increases in the thickness of the TrA and significant decreases in the length of the TrA. The BESG showed significant increases in the thickness of the TrA. The BESVG showed significant increases in the thickness of the TrA and significant decreases in the length of the TrA compared to BESG. [Conclusion] Closed chain exercises for the lumbar region performed with local vibration applied to slings, which are unstable support surfaces, are an effective intervention for altering the thickness and length of the TrA.

A novel repeat sequence-based PCR (rep-PCR) using specific repeat sequences of Mycobacterium intracellulare as a DNA fingerprinting.

Repetitive sequence-based PCR (rep-PCR) is a potential epidemiological technique that can provide high-throughput genotype fingerprints of heterogeneous Mycobacterium strains rapidly. Previously published rep-PCR primers, which are based on nucleotide sequences of Gram-negative bacteria may have low specificity for mycobacteria. Moreover, it was difficult to ensure the continuity of the study after the commercial rep-PCR kit was discontinued. Here, we designed a novel rep-PCR for Mycobacterium intracellulare, a major cause of nontuberculous mycobacterial pulmonary disease with frequent recurrence. We screened the 7,645 repeat sequences for 200 fragments from the genome of M. intracellulare ATCC 13950 in silico, finally generating five primers with more than 90% identity for a total of 226 loci in the genome. The five primers could make different band patterns depending on the genome of three different M. intracellulare strains using an in silico test. The novel rep-PCR with the five primers was conducted using 34 bacterial samples of 7 species containing 25 M. intracellulare clinical isolates, compared with previous published rep-PCRs. This shows distinguished patterns depending on species and blotting assay for 6 species implied the sequence specificity of the five primers. The Designed rep-PCR had a 95-98% of similarity value in the reproducibility test and showed 7 groups of fingerprints in M. intracellulare strains. Designed rep-PCR had a correlation value of 0.814 with VNTR, reference epidemiological method. This study provides a promising genotype fingerprinting method for tracing the recurrence of heterogeneous M. intracellulare.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. METHODS: This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m2). RESULTS: The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851-0.866) and 0.906 (0.900-0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI. CONCLUSION: The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Antigenic Determinant of Helicobacter pylori FlaA for Developing Serological Diagnostic Methods in Children.

The early diagnosis of Helicobacter pylori infection is important for gastric cancer prevention and treatment. Although endoscopic biopsy is widely used for H. pylori diagnosis, an accurate biopsy cannot be performed until a lesion becomes clear, especially in pediatric patients. Therefore, it is necessary to develop convenient and accurate methods for early diagnosis. FlaA, an essential factor for H. pylori survival, shows high antigenicity and can be used as a diagnostic marker. We attempted to identify effective antigens containing epitopes of high diagnostic value in FlaA. Full-sized FlaA was divided into several fragments and cloned, and its antigenicity was investigated using Western blotting. The FlaA fragment of 1345-1395 bp had strong immunogenicity. ELISA was performed with serum samples from children by using the 1345-1395 bp recombinant antigen fragment. IgG reactivity showed 90.0% sensitivity and 90.5% specificity, and IgM reactivity showed 100% sensitivity and specificity. The FlaA fragment of 1345-1395 bp discovered in the present study has antigenicity and is of high value as a candidate antigen for serological diagnosis. The FlaA 1345-1395 bp epitope can be used as a diagnostic marker for H. pylori infection, thereby controlling various gastric diseases such as gastric cancer and peptic ulcers caused by H. pylori.

Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features.

Aims: Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods and results: This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave. Conclusion: The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.