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:

Journal: MethodsX

Article Title: Best practices for physiological data collection in youth with autism and co-occurring mental health diagnoses: Implications for human-animal intervention research

doi: 10.1016/j.mex.2025.103284

Figure Lengend Snippet: Representation of the percentage of usable data from n = 60 youth with ASD that completed the 10 week THR intervention. Baseline intervention data was collected in weeks one through Mid-point, intervention data was collected at weeks five through seven, and post-intervention data at weeks 8–10.

Article Snippet: Reagents/tools: , Electrocardiogram Devices: The primary measure that needs to be captured are the R-R intervals for calculating heart rate variability measures (i.e., RMSSD). Multiple devices meet these needs and are research friendly which we have listed below. • BioPac BioNomadix Wireless ECG Monitor with a Physiology Data Logger (optional) • Shimmer3 ECG Unit • AdInstruments Equivital Wearable ECG • iWorx ROAM Wireless ECG • Zephyr Bioharness • Equivital Electrodermal Activity: Electrodermal sensors that allow the user to place gelled electrode patches in select locations should be utilized. Those that wrap the fingers should be avoided. • BioPac BioNomadix Wireless PPG and EDA Monitor with Physiologial Data Logger (optional) • Shimmer3 GSR/EDA Unit • AdInstruments Equivital Galvanic Skin Response Sensor • iWorx ROAM Wireless Biopotential & GSR Recorder Software: The analysis of dynamic ECG or EDA data should be performed on software that allows for appropriate cleaning of the data with ≤5 % artifact. • BioPac AcqKnowledge Software (ECG & EDA) • MATLAB Based Software (ECG & EDA) • LabView Based Software (ECG & EDA) • ADInstruments LabChart & HRV Analysis Software (ECG & EDA) • Kubios (ECG).

Techniques:

Journal: MethodsX

Article Title: Best practices for physiological data collection in youth with autism and co-occurring mental health diagnoses: Implications for human-animal intervention research

doi: 10.1016/j.mex.2025.103284

Figure Lengend Snippet:

Article Snippet: Reagents/tools: , Electrocardiogram Devices: The primary measure that needs to be captured are the R-R intervals for calculating heart rate variability measures (i.e., RMSSD). Multiple devices meet these needs and are research friendly which we have listed below. • BioPac BioNomadix Wireless ECG Monitor with a Physiology Data Logger (optional) • Shimmer3 ECG Unit • AdInstruments Equivital Wearable ECG • iWorx ROAM Wireless ECG • Zephyr Bioharness • Equivital Electrodermal Activity: Electrodermal sensors that allow the user to place gelled electrode patches in select locations should be utilized. Those that wrap the fingers should be avoided. • BioPac BioNomadix Wireless PPG and EDA Monitor with Physiologial Data Logger (optional) • Shimmer3 GSR/EDA Unit • AdInstruments Equivital Galvanic Skin Response Sensor • iWorx ROAM Wireless Biopotential & GSR Recorder Software: The analysis of dynamic ECG or EDA data should be performed on software that allows for appropriate cleaning of the data with ≤5 % artifact. • BioPac AcqKnowledge Software (ECG & EDA) • MATLAB Based Software (ECG & EDA) • LabView Based Software (ECG & EDA) • ADInstruments LabChart & HRV Analysis Software (ECG & EDA) • Kubios (ECG).

Techniques: Activity Assay, Software