Journal: International Journal of Cardiology. Cardiovascular Risk and Prevention

Article Title: Prevalence, associated factors, and prognostic value of P wave abnormality in patients with coronary artery disease

doi: 10.1016/j.ijcrp.2025.200533

Figure Lengend Snippet: (A) Representative electrocardiogram for determining PTFV1. PTFV1 (mm∙s) was calculated by the duration (s) of the terminal negative part of the P wave in lead V1 multiplied by the absolute value of its amplitude (mm). (B) Distribution of PTFV1 in patients with CAD who underwent PCI. The red bars indicate abnormal PTFV1, and the blue bars show normal PTFV1. CAD = coronary artery disease, PCI = percutaneous coronary intervention, and PTFV1 = P-wave terminal force in V1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Article Snippet: These parameters were also automatically analyzed by the MBF-1000 ECG Data Management System (Fukuda Denshi Co., Ltd., Tokyo, Japan) in 1179 patients.

Techniques:

Journal: Frontiers in Cardiovascular Medicine

Article Title: ZnO nanoparticles induce acute arrhythmia and heart failure in mice by disturbing cardiac ion channels

doi: 10.3389/fcvm.2025.1569265

Figure Lengend Snippet: Effects of ZnO NPs on mice ECG. (A) Representative images showed the effects of ZnO NPs-40 at different doses and exposure duration on ECG in mice. (B) Representative images showed the effects of ZnO NPs-100 at different doses and exposure duration on ECG in mice. (C) Changes of PR-interval after different doses of ZnO NPs-40 for 0-60 min. (D) Changes of PR-interval after different doses of ZnO NPs-100 for 0-60 min. P, P wave; R, R wave; QRS-T, QRS complex and T wave; PVC, premature ventricular contractions; AVB, atrioventricular conduction block. * P < 0.05, ** P < 0.01, *** P < 0.001. n = 6 for control group; n = 6 for ZnO NPs-40 at 5 mg/kg group and 8 mg/kg group; n = 11 for ZnO NPs-40 at 10 mg/kg group; n = 7 for ZnO NPs-40 at 20 mg/kg group; n = 6 for ZnO NPs-100 at 5 mg/kg group, 10 mg/kg group and 20 mg/kg group; n = 10 for ZnO NPs-100 at 30 mg/kg group.

Article Snippet: 8–10 weeks male C57BL/6J mice were anesthetized with isoflurane, and ECGs were collected using a BIOPAC MP150 ECG data acquisition module (BIOPAC Systems, Inc., Goleta, CA, USA) before and after exposure to ZnO NPs for up to 60 min. ZnO NPs-40 were intravenously injected at doses of 5, 8, 10, and 20 mg/kg, while ZnO NPs-100 were administered at doses of 5, 10, 20, and 30 mg/kg.

Techniques: Blocking Assay, Control

Journal: European Heart Journal. Digital Health

Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study

doi: 10.1093/ehjdh/ztaf025

Figure Lengend Snippet: Performance of the artificial intelligence model based on various electrocardiogram signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).

Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

Techniques: Diagnostic Assay, Comparison

Journal: European Heart Journal. Digital Health

Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study

doi: 10.1093/ehjdh/ztaf025

Figure Lengend Snippet: Forest plot for subgroup analysis. The forest plot demonstrating the efficacy of the six-lead artificial intelligence model based on electrocardiogram and its possible different efficacy for predicting left ventricular systolic dysfunction by the prespecified characteristics. The plot highlights that although area under the receiver operating characteristic curve values are generally consistent across subgroups, the presence of heart failure significantly affects the model’s performance.

Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

Techniques: