Cardiovascular magnetic resonance based tissue tracking (CMR-TT) was reported to provide detailed insight into left ventricular mechanical features. However, inadequate knowledge of the right ventricle (RV) mechanical deformation has been acquired by this advanced technique so far. It was the aim of this study to establish reference values of RV free wall (RVFW) global, regional and segmental longitudinal peak strain and strain rate (LS and LSR), and to investigate the gender- and age-related difference as well as the base-to-apex gradient of RVFW-LS and LSR with CMR-TT. 150 healthy volunteers (75 males/females) were retrospectively and continuously recruited and subdivided into three age groups (G_20–40, G_41–60 and G_61–80). RVFW global, regional (basal, middle-cavity and apical) and segmental LS (GLS, RLS, SLS) along with systolic and diastolic LSR were generated by post-hoc CMR-TT analysis of standard steady-state free precession long-axis four-chamber view cine images acquired at 1.5T field strength. The reference value of myocardial RVFW-GLS was − 24.9 ± 5.2%. We found that females showed more negative GLS than males except in the youngest group, and no age-related difference of GLS was observed in both gender groups. RLS and SLS presented with the same age-related tendency as GLS. The basal and middle-cavity LS were similar between each other and significantly larger than apical LS. RVFW-GLSR resulted as − 1.73 ± 0.58 s⁻¹ and 1.69 ± 0.65 s⁻¹ during systolic and diastolic phases, respectively. The diastolic GLSR of males tended to decline with the ageing and was significantly lower than that of females in G_61–80 group. Regional and segmental LSR showed significant gender-related differences in certain basal and apical region/segments without any age-related effects. CMR-TT overcomes the difficulty in measuring RV global and segmental deformation. The establishment of the vendor-, gender- and segment-specific reference values of RVFW-LS and LSR is essential for the rapid and efficient utilization of CMR-TT modality in the clinical routine.

Doxorubicin is one of the most common antitumor drugs. However, cardiotoxicity’s side effect limits its clinical applicability. In the present study, Gene Expression Omnibus (GEO) datasets were applied to reanalyze differentially expressed genes (DEGs) and construct weighted correlation network analysis (WGCNA) modules of doxorubicin-induced cardiotoxicity in wild-type mice. Several other bioinformatics analyses were performed to pick out the hub gene, and then the correlation between the hub gene and immune infiltration was evaluated. In total, 120 DEGs were discovered in a mouse model of doxorubicin-induced cardiotoxicity, and PF-04217903, propranolol, azithromycin, etc. were found to be potential drugs against this pathological condition. Among all the DEGs, 14 were further screened out by WGCNA modules, of which Limd1 was upregulated and finally regarded as the hub gene after being validated in other GEO datasets. Limd1 was upregulated in the peripheral blood mononuclear cell (PBMC) of the rat model, and the area under curve (AUC) of the receiver operating characteristic curve (ROC) in diagnosing cardiotoxicity was 0.847. The GSEA and PPI networks revealed a potential immunocyte regulatory role of Limd1 in cardiotoxicity. The proportion of “dendritic cells activated” in the heart was significantly elevated, while “macrophage M1” and “monocytes” declined after in vivo doxorubicin application. Finally, Limd1 expression was significantly positively correlated with “dendritic cells activation’ and negatively correlated with “monocytes” and “macrophages M1’. In summary, our results suggested that limd1 is a valuable biomarker and a potential inflammation regulator in doxorubicin-induced cardiotoxicity.

Myocardial strain is a well‐validated parameter for evaluating myocardial contraction. Cardiovascular magnetic resonance myocardial feature tracking (CMR‐FT) is a novel method for the quantitative measurements of myocardial strain from routine cine acquisitions. In this study, we investigated the influence of temporal resolution on tracking accuracy of CMR‐FT and the intraobserver, interobserver, and interstudy reproducibilities for biventricular strain analysis in mice from self‐gated CMR at 11.7 T. 12 constitutive nexilin knockout (Nexn‐KO) mice, heterozygous (Het, N = 6) and wild‐type (WT, N = 6), were measured with a well‐established self‐gating sequence twice within two weeks. CMR‐FT measures of biventricular global and segmental strain parameters were derived. Interstudy, intraobserver, and interobserver reproducibilities were investigated. For the assessment of the impact of the temporal resolution for the outcome in CMR‐FT, highly oversampled semi‐4 chamber and midventricular short‐axis data were acquired and reconstructed with 10 to 80 phases per cardiac cycle. A generally reduced biventricular myocardial strain was observed in Nexn‐KO Het mice. Excellent intraobserver and interobserver reproducibility was achieved in all global strains (ICC range from 0.76 to 0.99), where global right ventricle circumferential strain (RCS_SAX) showed an only good interobserver reproducibility (ICC 0.65, 0.11‐0.89). For interstudy reproducibility, left ventricle longitudinal strain (LLS_LAX) was the most reproducible measure of strain (ICC 0.90, 0.71‐0.97). The left ventricle radial strain (LRS_SAX) (ICC 0.50, 0.10‐0.83) showed fair reproducibility and RCS_SAX (ICC 0.36, 0.14‐0.74) showed only poor reproducibility. In general, compared with global strains, the segmental strains showed relatively lower reproducibility. A minimal temporal resolution of 20 phases per cardiac cycle appeared sufficient for CMR‐FT strain analysis. The analysis of myocardial strain from high‐resolution self‐gated cine images by CMR‐FT provides a highly reproducible method for assessing myocardial contraction in small rodent animals. Especially, global LV longitudinal and circumferential strain revealed excellent reproducibility of intra‐ and interobserver and interstudy measurements.

Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications.