Prolonged ventricular repolarization has been associated with cardiovascular disease. We sought to investigate the association of prolonged ventricular repolarization with mild cognitive impairment (MCI) and the potential underlying neuropathological mechanisms in older adults. This cross-sectional study included 4328 dementia-free participants (age ≥ 65 years; 56.8% female) in the baseline examination of the Multidomain INterventions to delay dementia and Disability in rural China; of these, 989 undertook structural brain magnetic resonance imaging (MRI) scans. QT, QTc, JT, JTc, and QRS intervals were derived from 12-lead electrocardiograph. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were defined following the Petersen\'s criteria. Volumes of gray matter (GM), white matter, cerebrospinal fluid, total white matter hyperintensities (WMH), periventricular WMH (PWMH), and deep WMH (DWMH) were automatically estimated. Data were analyzed using logistic and general linear regression models. Prolonged QT, QTc, JT, and JTc intervals were significantly associated with an increased likelihood of MCI and aMCI, but not naMCI (p < 0.05). In the MRI subsample, QT, QTc, JT, and JTc intervals were significantly associated with larger total WMH and PWMH volumes (p < 0.05), but not with DWMH volume. Statistical interactions were detected, such that prolonged QT and JT intervals were significantly associated with reduced GM volume only among participants with coronary heart disease or without APOE ε4 allele (p < 0.05). Prolonged ventricular repolarization is associated with MCI and cerebral microvascular lesions in a general population of older adults. This underlies the importance of cognitive assessments and brain MRI examination among older adults with prolonged QT interval.

Phosphoinositide 3-kinase (PI3K) inhibitors have shown synergistic anticancer effects with endocrine therapy against ER+/PIK3CA-mutated breast cancer. PI3K inhibitors for cancer therapy are becoming more common. There is an increasing need to understand their cardiac adverse events. In this report, we describe the features of near-fatal mixed arrhythmias in a patient who was undergoing a phase Ib clinical study of PI3Kα inhibitor with fulvestrant. Subsequently, the patient survived by cardiopulmonary resuscitation and therefore did not die. This case highlights that PI3K inhibitors can induce QT/QTc prolongation and predispose patients to TdP. The combination of QT/QTc prolongation in combination with prolonged cardiac repolarization, such as an AV block during treatment with PI3Kα inhibitor, may aggravate the occurrence of TdP. It is likely to be a safer strategy to adjust the standard of discontinuing drugs and continuing drugs (QTc interval was <500 and <60 ms at baseline) or choose other types of alternative treatment options. This report provided some ideas for clinicians to identify early and prevent the occurrence of fatal arrhythmias during anticancer treatment.

BACKGROUND: Pulmonary embolisms (PEs) exhibit clinical features similar to those of acute coronary syndrome (ACS), including electrocardiographic abnormalities and elevated troponin levels, which frequently lead to misdiagnoses in emergency situations.
METHODS: Here, we report a case of PE coinciding with chronic coronary syndrome in which the patient\'s condition was obscured by symptoms mimicking ACS. A 68-year-old female with syncope presented to the hospital. Upon admission, she was found to have elevated troponin levels and an electrocardiogram showing ST-segment changes across multiple leads, which initially led to a diagnosis of ACS. Emergency coronary arteriography revealed occlusion of the posterior branches of the left ventricle of the right coronary artery, but based on the complexity of the intervention, the occlusion was considered chronic rather than acute. On the 3rd day after admission, the patient experienced recurrent chest tightness and shortness of breath, which was confirmed as acute PE by emergency computed tomography pulmonary angiography. Following standardized anticoagulation treatment, the patient improved and was subsequently discharged.
CONCLUSIONS: This case report highlights the importance of recognizing the nonspecific features of PE. Clinicians should be vigilant when identifying other clinical features that are difficult to explain accompanying the expected disease, and it is necessary to carefully identify the causes to prevent missed diagnoses or misdiagnoses.

BACKGROUND: We aimed to assess the efficacy of the neutrophil elastase inhibitor, sivelestat, in the treatment of sepsis-induced acute respiratory distress syndrome (ARDS) and septic cardiomyopathy (SCM).
METHODS: Between January 2019 and December 2021, we conducted a randomized trial on patients who had been diagnosed with sepsis-induced acute respiratory distress syndrome (ARDS) and septic cardiomyopathy (SCM) at Wuhan Union Hospital. The patients were divided into two groups by random envelop method, the Sivelestat group and the Control group. We measured the serum concentrations of Interleukin (IL)-6, IL-8, Tumor necrosis factor-α (TNF-α), and High-mobility group box 1 (HMGB1) at five time points, which were the baseline, 12 h, 24 h, 48 h, and 72 h after admission to the ICU. We evaluated the cardiac function by sonography and the heart rate variability (HRV) with 24-hour Holter recording between the time of admission to the intensive care unit (ICU) and 72 h after Sivelestat treatment.
RESULTS: From January 2019 to December 2021, a total of 70 patients were included in this study. The levels of IL-6, IL-8, and TNF-α were significantly lower in the Sivelestat group at different time points (12 h, 24 h, 48 h, and 72 h). HMGB1 levels were significantly lower at 72 h after Sivelestat treatment (19.46 ± 2.63pg/mL vs. 21.20 ± 2.03pg/mL, P = 0.003). The stroke volume (SV), tricuspid annular plane systolic excursion (TAPSE), early to late diastolic transmitral flow velocity (E/A), early (e\') and late (a\') diastoles were significantly low in the Control group compared with the Sivelestat group. Tei index was high in the Control group compared with the Sivelestat group (0.60 ± 0.08 vs. 0.56 ± 0.07, P = 0.029). The result of HRV showed significant differences in standard deviation of normal-to-normal intervals (SDNN), low frequency (LF), and LF/HF (high frequency) between the two groups.
CONCLUSIONS: Sivelestat can significantly reduce the levels of serum inflammatory factors, improve cardiac function, and reduce heart rate variability in patients with Sepsis-induced ARDS and SCM.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

Heart disease is the world\'s leading cause of death. Diagnostic models based on electrocardiograms (ECGs) are often limited by the scarcity of high-quality data and issues of data imbalance. To address these challenges, we propose a conditional generative adversarial network (CECG-GAN). This strategy enables the generation of samples that closely approximate the distribution of ECG data. Additionally, CECG-GAN addresses waveform jitter, slow processing speeds, and dataset imbalance issues through the integration of a transformer architecture. We evaluated this approach using two datasets: MIT-BIH and CSPC2020. The experimental results demonstrate that CECG-GAN achieves outstanding performance metrics. Notably, the percentage root mean square difference (PRD) reached 55.048, indicating a high degree of similarity between generated and actual ECG waveforms. Additionally, the Fréchet distance (FD) was approximately 1.139, the root mean square error (RMSE) registered at 0.232, and the mean absolute error (MAE) was recorded at 0.166.

UNASSIGNED: Arrhythmia is an important indication of underlying cardiovascular diseases (CVD) and is prevalent worldwide. Accurate diagnosis of arrhythmia is crucial for timely and effective treatment. Electrocardiogram (ECG) plays a key role in the diagnosis of arrhythmia. With the continuous development of deep learning and machine learning processes in the clinical field, ECG processing algorithms have significantly advanced the field with timely and accurate diagnosis of arrhythmia.
UNASSIGNED: In this study, we combined the wavelet time-frequency maps with the novel Swin Transformer deep learning model for the automatic detection of cardiac arrhythmias. In specific practice, we used the MIT-BIH arrhythmia dataset, and to improve the signal quality, we removed the high-frequency noise, artifacts, electromyographic noise and respiratory motion effects in the ECG signals by the wavelet thresholding method; we used the complex Morlet wavelet for the feature extraction, and plotted wavelet time-frequency maps to visualise the time-frequency information of the ECG; we introduced the Swin Transformer model for classification and achieve high classification accuracy of ECG signals through hierarchical construction and self attention mechanism, and combines windowed multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) to comprehensively utilise the local and global information.
UNASSIGNED: To enhance the confidence of the experimental results, we evaluated the performance using intra-patient and inter-patient paradigm analyses, and the model classification accuracies reached 99.34% and 98.37%, respectively, which are better than the currently available detection methods.
UNASSIGNED: The results reveal that our proposed method is superior to currently available methods for detecting arrhythmia ECG. This provides a new idea for ECG based arrhythmia diagnosis.

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We described an 82-year-old man who was taken to our emergency department after being found unconscious. His electrocardiogram (ECG) showed ST-segment elevation in leads V4-V6 and cardiac troponin I (cTnI) was abnormally elevated. In addition to ECG and cTnI changes, this patient was combined with unconsciousness, high fever, abnormal liver function, acute renal failure, and rhabdomyolysis. The initial diagnosis was heat stroke, so cooling measures were initiated immediately, but a concurrent myocardial infarction was suspected. Meanwhile, emergency coronary angiography was performed, but no severe coronary stenosis or thrombosis was found. We first evaluated quantitative flow ratio (QFR) and coronary angiography-derived index of microvascular resistance (ca-IMR) in patients with heat stroke. Ca-IMR was 260 mmHg*s/m in the left circumflex artery, indicating the presence of coronary microvascular dysfunction (CMD). After several days of treatment, the patient recovered from multiple organ damage. Therefore, ECG and troponin results should be interpreted carefully in patients with high fever and coma during high temperature seasons.

When the electrocardiogram of acute pulmonary embolism is similar to that of acute myocardial infarction, it is difficult to distinguish between the two diseases quickly and effectively. We present the case of a 50-year-old man with acute pulmonary embolism. His electrocardiogram showed subtotal occlusion of the left main coronary artery with ST segment depression in I, II, aVF, V3 to V6, ST segment elevation in aVR, V1 and S1Q3T3. Invasive coronary angiography did not show coronary artery stenosis, then pulmonary angiography was performed quickly which showed massive bilateral acute pulmonary embolism. Electrocardiogram cannot effectively distinguish acute pulmonary embolism from subtotal occlusion of the left main coronary artery. For patients with hemodynamic instability, if ultrasound cannot be performed in time, the combination of invasive coronary angiography and pulmonary angiography can be an option to distinguish acute pulmonary embolism from subtotal occlusion of the left main coronary artery and to treat.