Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.

We aimed to determine the feasibility, efficacy, success, and safety of intracardiac echocardiography (ICE) in transcatheter multiple atrial septal defect (ASD) closure. Of 185 patients with multiple ASDs who underwent transcatheter closure, 140 (76%) patients who weighed <30kg with a narrow distance between defects or in whom single device closure was anticipated were guided by ICE and 45 patients were guided by three-dimensional (3D) transesophageal echocardiography (TEE) with or without ICE. Patients in the ICE group were relatively younger and weighed less than those in the 3D TEE group (p < 0.0001). The ratio of the distance between defects >7 mm was high, and more cases required ≥2 devices in the 3D TEE group than those in the ICE group (p < 0.0001). All patients in the 3D TEE group and seven patients (5%) in the ICE group were operated on under general anesthesia (p < 0.0001). The fluoroscopic time was shorter in the ICE group (13.98 ± 6.24 min vs. 24.86 ± 16.47 min, p = 0.0005). No difference in the complete closure rate and complications was observed. ICE-guided transcatheter and 3D TEE were feasible, safe, and effective in successful multiple ASD device closures, especially for young children and patients at high risk under general anesthesia.

Background/Objectives: The concept of the “smoker’s paradox” in prior research posits that smoking could potentially offer neuroprotective effects in cases of acute carbon monoxide (CO) poisoning. This study aimed to determine the validity of this hypothesis by minimizing selection bias and confounding variables in a comparison of neurocognitive outcomes between smokers and non-smokers following acute CO poisoning. Methods: A total of 1150 patients were included in this retrospective study. Propensity Score Matching (PSM) was used to control for variables such as age, initial Glasgow Coma Scale (GCS) score, co-morbidities, and HBO₂ therapy application. Neurocognitive outcomes were assessed and compared between smokers and non-smokers. Results: In the initial analysis, 1150 patients were divided into non-smoking (61.7%) and smoking (38.3%) groups. Before PSM, smokers had a lower rate of poor outcomes (12.1% vs. 6.8%, p = 0.004). However, baseline differences emerged, with non-smokers being older with longer CO exposure, while smokers had more males and higher rates of intentional CO poisoning and drug ingestion. Smokers had a 0.56 times lower relative risk of poor outcomes compared to non-smokers (95% CI: 0.38–0.84, p = 0.004). After meticulous 1:1 PSM with 15 covariates, 317 patient pairs were matched, creating balanced cohorts. Neurocognitive outcomes at 1 month post-CO exposure showed no significant differences between matched non-smoking and smoking groups (9.8% vs. 7.9%, p = 0.461). Post-PSM, the relative risk for poor outcomes remained approximately 0.81 times lower in smokers compared to non-smokers, but with no statistical significance [0.81 (95% CI: 0.49–1.33, p = 0.402)]. Conclusions: Our findings do not support the idea of a protective effect from smoking in the context of acute CO poisoning. After accounting for potential confounders through PSM, we found that smoking status was not significantly associated with more favorable neurocognitive outcomes.

To evaluate the health impacts of PM_2.5 exposure on sensitive and vulnerable populations in South Korea and to identify flexion points—concentration levels at which the risk of environmental diseases increases significantly. A retrospective cohort study using nationwide health insurance claims data linked with environmental monitoring data. Statistical analysis included Cox proportional hazards regression and piecewise linear regression to estimate disease risks and identify flexion points. South Korea, using data from the National Health Insurance Service–National Sample Cohort (NHIS-NSC) and national air quality monitoring stations. A total of 1,031,517 individuals aged 0–79 years who were covered by the national health insurance system between 2017 and 2019 and had no prior diagnosis of the target diseases during the 2016 washout period. Primary outcomes were the incidence and exacerbation of four environmental diseases: chronic obstructive pulmonary disease (COPD), asthma, stroke, and heart failure. Exacerbation was defined as hospitalization or emergency department visits due to these diseases. Higher PM_2.5 exposure was significantly associated with increased incidence and exacerbation of all four diseases. Flexion points were identified at 30 µg/m³ for incidence and between 24 and 26 µg/m³ for exacerbation. Subgroup analyses showed lower flexion points among elderly participants. The findings suggest that current PM_2.5 standards in South Korea may not sufficiently protect vulnerable populations. Operational flexion points identified in this study may serve as evidence-based thresholds to guide targeted public health interventions and air quality regulations.