The electrocardiogram (ECG) records heartbeats and is potentially life-saving. However, the ECG signals (e.g., recorded by the standard ECG monitoring system or the Holter ECG monitoring system) heavily suffered from the noises. Thus, the recorded signals involve meaningful cardiac deflections, other biological waves (e.g., caused by the muscle or electrode movements), and even some noises from the monitoring devices (e.g., the power cables). These noise sources would result in inaccurate analyses of cardiac diseases, thus requiring the ECG de-noising methods in data pre-processing phase before the diagnoses. Previous work provided various ECG de-noising approaches, typically based on some filter algorithms or some wave decomposition algorithms. Most of these approaches did not profoundly consider the ECG signals’ specific data structure, and were not adaptive to the signals recorded by various devices or different skin electrodes. Inspired by the ECG recording theory, we find it available to extract noise information from noisy ECG signals directly. We propose a new ECG de-noising method implemented by the neural network, which de-noise the ECG signals without the supervision of the clean signals. The procedure in the self-supervision is straightforward: we first estimate and simulate the noise signals according to the given noisy ECG signals, and then “subtract” the simulated noises to obtain the de-noised ECG signals by the neural network. Experiments on a public dataset verify that our approach is adaptive to ECG signals from different patients and devices. Also, it is proven that the classification on the ECG signals de-noised by the proposed de-noising methods outperforms those with the traditional de-noising methods.

Rationale: We present a case of a 43-year-old female patient diagnosed with hyperthyroidism. This study aims to demonstrate the rare association between hyperthyroidism and severe cholestatic jaundice, and the effectiveness of methimazole treatment. Patient concerns: The patient developed severe jaundice, a typically mild symptom in most hyperthyroidism cases. Diagnosis: The severe jaundice was suspected to be a result of cholestasis induced by hyperthyroidism, with other potential causes such as drug-induced or autoimmune liver dysfunction being ruled out. Outcomes: The patient was effectively treated with methimazole. Outcomes: Treatment with methimazole alleviated the severe cholestatic jaundice and restored normal thyroid function. Lessons: The specific mechanism of cholestasis as a secondary complication of hyperthyroidism remains unclear, and there are no specific biochemical markers for cholestasis caused by this hormonal disease. This case underscores the possibility of severe jaundice as a clinical manifestation of hyperthyroidism, and highlights antithyroid drug treatment as an effective strategy for managing severe cholestatic jaundice.

Objective: This study evaluates the relationship between the Life's Essential 8 (LE8) scoring system and all-cause and cause-specific mortality among obese individuals using National Health and Nutrition Examination Survey data. Methods: Data from 9143 obese participants (BMI >= 30 kg/m(2)) collected between 2005 and 2018 were analyzed. Participants were categorized based on their LE8 scores: low cardiovascular health (Low CVH, n = 2264), moderate cardiovascular health (Moderate CVH, n = 6541), and high cardiovascular health (High CVH, n = 338). Associations between LE8 scores and mortality were assessed using Kaplan-Meier survival analysis and Cox proportional hazards models. Results: Over a median follow-up of 7.3 years, there were 867 all-cause deaths (9.5%), including 246 cardiovascular disease (CVD) deaths (2.7%) and 621 non-CVD deaths (6.8%). In multivariable Cox regression analysis, compared to the Low CVH group, the Moderate CVH group had an adjusted hazard ratio (HR) for all-cause mortality of 0.63 (95% CI: 0.55-0.72), and the High CVH group had an HR of 0.25 (95% CI: 0.10-0.60). For CVD mortality, the HRs were 0.61 (95% CI: 0.47-0.78) for Moderate CVH and 0.19 (95% CI: 0.03-1.38) for High CVH. For non-CVD mortality, the HRs were 0.64 (95% CI: 0.54-0.75) for Moderate CVH and 0.27 (95% CI: 0.10-0.72) for High CVH. Each 10-point increase in LE8 score was associated with a 20% reduction in all-cause mortality (P < .001), 21% reduction in CVD mortality (P < .001), and 20% reduction in non-CVD mortality (P < .001). Conclusion: Higher LE8 scores are significantly associated with lower rates of all-cause, CVD, and non-CVD mortality among obese individuals. These findings support the LE8 scoring system as an effective predictor of health status and mortality risk. (c) 2024 AACE. Published by Elsevier Inc. This is an open access article under the CC BY license (http://

The microbiota-gut-brain axis is a key conduit linking metabolism, mood, and cognition; however, its position within the continuous functional hierarchy of the cortex and the underlying mechanisms are unclear. In this study, a cross-modal brain-gut dataset from 88 healthy male participants was utilized. By integrating functional magnetic resonance imaging (fMRI) gradient analysis, microbiome sequencing, and dietary behavioral information, this study systematically evaluated the relationships between the abundance of unclassified Ruminococcaceae (Ruminococcaceae_unc), cerebral functional hierarchy, network topology, and emotional symptoms. The results demonstrated that increased abundance of this bacterial group was associated with a functional shift in the brain from unimodal to transmodal hubs, accompanied by a drift of the small-world network toward randomization. Functional gradient values were significantly negatively correlated with depression and anxiety scores and were tightly coupled with latent components in the dietary behavioral dimension, including education, physical activity, and nutrient intake. Transcriptomic analysis further revealed that the GPCR-Rho/integrin-vesicular trafficking pathway may serve as the key molecular mechanism. In conclusion, this study proposes a multiscale coupling framework encompassing the gut microbiota, functional gradients, and emotional health, thereby providing a theoretical basis for the development of microbiota-targeted intervention strategies for modulating transmodal emotions and cognition.

Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the advent of deep learning, numerous innovative models have been introduced for diagnosing arrhythmias using electrocardiogram (ECG) signals. However, recent studies solely focus on the performance of models, neglecting the interpretation of their results. This leads to a considerable lack of transparency, posing a significant risk in the actual diagnostic process. To solve this problem, this article introduces MambaCapsule, a deep neural network for ECG arrhythmias classification, which increases the explainability of the model while enhancing the accuracy. Our model utilizes Mamba for feature extraction and Capsule networks for prediction, providing not only a confidence score but also signal features. Akin to the processing mechanism of human brain, the model learns signal features and their relationship between them by reconstructing ECG signals in the predicted selection. The model evaluation was conducted on Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTB) datasets, following the AAMI standard. MambaCapsule has achieved a total accuracy of 99.54% and 99.59% on the test sets, respectively. These results demonstrate the promising performance under the standard test protocol.

Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the advent of deep learning, numerous innovative models have been introduced for diagnosing arrhythmias using Electrocardiogram (ECG) signals. However, recent studies solely focus on the performance of models, neglecting the interpretation of their results. This leads to a considerable lack of transparency, posing a significant risk in the actual diagnostic process. To solve this problem, this paper introduces MambaCapsule, a deep neural networks for ECG arrhythmias classification, which increases the explainability of the model while enhancing the accuracy.Our model utilizes Mamba for feature extraction and Capsule networks for prediction, providing not only a confidence score but also signal features. Akin to the processing mechanism of human brain, the model learns signal features and their relationship between them by reconstructing ECG signals in the predicted selection. The model evaluation was conducted on MIT-BIH and PTB dataset, following the AAMI standard. MambaCapsule has achieved a total accuracy of 99.54% and 99.59% on the test sets respectively. These results demonstrate the promising performance of under the standard test protocol.