Artificial intelligence (AI) has significantly improved the screening and diagnostic efficiency of ventricular arrhythmia (VA) through electrocardiogram (ECG) interpretation and wearable device monitoring. In the treatment field, AI has made significant progress in assisting the localization of premature ventricular contraction origins and optimizing strategies for implantable cardioverter defibrillator therapies. AI models excel in predicting the risk of fatal VA, providing robust support for individualized prevention and treatment. This review further discusses challenges such as data quality, model interpretability, and clinical translation, as well as the role of multimodal data integration and interdisciplinary collaboration in advancing the precision medicine for VA. In the future, AI is expected to play a more pivotal role in remote healthcare, and individualized diagnosis and treatment, comprehensively enhancing diagnostic accuracy, treatment efficiency, and quality of life for VA patients.

Objective  To investigate the impact of long non-coding RNA ZFAS1 (lncZFAS1) on the initiation and progression of cardiomyocyte fibrosis. Methods  HL-1 mouse cardiac myocytes were induced by transforming growth factor-β (TGF-β) to establish a cardiomyocyte fibrosis model. LncZFAS1 overexpression vectors or small interfering RNA (siRNA) were transfected into the model. Protein levels of α-smooth muscle actin (α-SMA) and collagen Ⅰ were detected by western blotting; lncZFAS1 overexpression and interference fragments were transfected into the cardiomyocyte fibrotic model. The transcriptional level of lncZFAS1 was measured via real-time quantitative PCR. Cell proliferation viability was assessed using the CCK-8 assay, apoptosis rate was analyzed by flow cytometry, and LC3 autophagy puncta formation was examined using cellular immunofluorescence. Results  Compared with the control group, the TGF-β induction group showed significantly increased protein expression of α-SMA, collagen Ⅰ and LC3 B, significantly decreased p62 protein expression, elevated lncZFAS1 expression, enhanced cell viability and autophagy puncta formation, and reduced apoptosis rate (all P<0.05). After interfering with lncZFAS1 in the model, it showed a decrease in the expression level of lncZFAS1, a decrease in cell viability and the number of autophagy puncta, an increase of apoptosis rate and expression of p62 protein, and a decrease in the expression of LC3 B protein, all with statistically significant differences (all P<0.05). However, after overexpression of lncZFAS1, the opposite results were presented. Conclusion  LncZFAS1 promotes the initiation and progression of cardiomyocyte fibrosis by regulating cellular autophagy. Targeted inhibition of lncZFAS1 may provide a novel therapeutic strategy for delaying the progression of cardiovascular diseases.

Objective  To investigate the regulatory role and underlying mechanism of circular RNA circHECTD1, a potential diagnostic biomarker for coronary heart disease (CHD), in macrophage polarization. Methods  A lipopolysaccharide-induced M1 macrophage polarization model was used to simulate a low-grade inflammatory response process. Quantitative real-time PCR (qRT-PCR) was employed to detect the expression levels of circHECTD1 and N6-methyladenosine (m6A) regulatory factors. Western blotting was performed to measure the expression of M1 phenotype marker proteins and autophagyrelated proteins. Intracellular reactive oxygen species (ROS) levels were assessed by flow cytometry. Results  Silencing circHECTD1 significantly reduced the expression of M1 phenotype-related proteins and inflammatory cytokines, decreased ROS levels, and enhanced cellular autophagy. Disordered expression of multiple m6A regulatory factors was observed in both the cellular model and peripheral blood mononuclear cells (PBMCs) from CHD patients. Significant differences in the expression levels of multiple m6A methylation regulators were identified between CHD and control groups, suggesting their potential value in CHD diagnosis. Univariate and multivariate Logistic regression analysis revealed that the m6A regulators of METTL3, METTL14, ALKBH5, and YTHDC2 were significantly associated with CHD risk. Further analysis showed that the expression of METTL14 and ALKBH5 were significantly upregulated in advanced stages of atherosclerosis. The expression level of circHECTD1 significantly correlated with FTO, YTHDF3, ALKBH5, METTL3, METTL14, and YTHDF2. In vitro experiment results demonstrated that silencing METTL14 and ALKBH5 markedly downregulated circHECTD1 expression, indicating that METTL14 and ALKBH5 positively regulated circHECTD1 expression. Conclusion  Dysregulation of m6A regulatory factors may be closely associated with the occurrence and development of CHD, potentially mediated through the regulation of circHECTD1 expression and function.

Objective  To explore the relationship between epicardial fat thickness (EFT), serum secreted frizzled-related protein 5 (SFRP5), wingless-type MMTV integration site family member 5a (Wnt5a), and in-stent restenosis (ISR) in patients with acute ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI). MethodsA total of 128 STEMI patients who had undergone emergency PCI were enrolled. After a 12-month follow-up, they were divided into a non-ISR group (80 cases) and an ISR group (48 cases) based on coronary angiography results. The general data between the two groups were compared, as well as the EFT before discharge and 1 month after surgery, and the serum levels of SFRP5 and Wnt5a measured before surgery and 1 month postoperatively. The differences in the levels of these indicators among patients with varying degrees of coronary artery lesions were also examined. Pearson correlation analysis was used to evaluate the association between EFT and serum levels of SFRP5 and Wnt5a 1 month after surgery. Multivariate Logistic regression analysis was applied to identify the risk factors of ISR in STEMI patients after PCI. ROC curve analysis was used to assess the predictive value of EFT, and serum SFRP5 and Wnt5a levels for ISR. ResultsThe EFT values of the ISR group before discharge and 1 month after surgery were both greater than those of the non-ISR group (all P<0.01). The serum SFRP5 level of the ISR group was lower than that of the non-ISR group 1 month after surgery, while the serum Wnt5a level was higher (all P<0.01). Compared to pre-operative levels, serum SFRP5 levels increased 1 month after surgery in the non-ISR group but decreased in the ISR group (both P<005). Serum Wnt5a levels showed a mild increase 1 month after surgery compared to pre-operative levels in the non-ISR group, but increased significantly in the ISR group (both P<0.05). The serum SFRP5 level 1 month after surgery was significantly lower in the three-vessel disease group than that in the single- or two-vessel disease groups (P<0.05). EFT 1 month after surgery showed a negative correlation with SFRP5 and a positive correlation with Wnt5a. Both univariate Logistic regression analysis and Logistic regression analysis adjusted for factors such as age and BMI demonstrated that EFT, and serum SFRP5 and Wnt5a levels 1 month after surgery were independent influencing factors for ISR. ROC curve analysis revealed that the combination of these three markers yielded an AUC value of 0.906, which was higher than that of detection with any single indicator. Conclusion  Increased EFT, decreased serum SFRP5, and elevated serum Wnt5a 1 month after surgery are closely related to ISR in STEMI patients after PCI. Early detection of these markers facilitates the prediction of ISR, and the combined detection offers superior predictive value.

This article focuses on the application of artificial intelligence (AI) technology in the diagnosis and treatment of arrhythmias, and briefly introduces the latest relevant research findings from both domestic and international sources. It not only outlines the capability of AI in significantly improving the detection efficiency and accuracy of arrhythmia screening and diagnosis, but also emphasizes its value in the prediction of arrhythmia and the risk of its related complications, and guidance of its clinical treatment. Furthermore, this article summarizes the challenges faced by AI in arrhythmia management, including data quantity and quality issues, the “black box” effect in AI learning process, model generalization issues, ethical issues in technology, and the clinical acceptance of AI models. Finally, it looks forward to the prospect of AI technology deeply integrating with medicine, and comprehensive application in all aspects of arrhythmia diagnosis and treatment, so as to optimize medical resource allocation and medical service workflows, increase medical efficiency and quality, and improve patients’ prognoses.

Objective  To investigate the application of dexmedetomidine via programmed intermittent epidural bolus (PIEB) for labor analgesia in hypertensive disorder of pregnancy (HDP) parturients, and its effect on umbilical artery flow and maternal-fetal outcomes. Methods  This study was a prospective, and double-blind clinical study. A hundred HDP parturients who had undergone labor analgesia were selected, and randomly divided into group A and B, each with 50 cases by random number table method. Group A received continuous epidural infusion, while group B received PIEB. Based on the visual analogue scale (VAS), the degree of pain before analgesia (T0), 1 h after analgesia (T1), 2 h after analgesia (T2), and at the time of complete cervical dilation (T3) were compared between the two groups of parturients. We simultaneously recorded the changes of umbilical artery flow at T0-T2 ［umbilical artery resistance index (RI), pulsation index (PI) and the ratio of maximum systolic blood velocity to diastolic blood velocity (S/D)］, maternal vital signs at T0 and T1 ［heart rate (HR) and mean arterial pressure (MAP)］, and the occurrence of adverse maternalfetal outcomes and adverse reactions in the two groups. ResultsComparison of VAS scores at different time points within each group showed statistically significant differences (all P<0.01). VAS scores at T1, T2 and T3 were all lower than that at T0(all P<0.05). VAS score of the group B at T2 was lower than that at T1(P<0.05). VAS score of the group B at T3 was lower than that at T2(P<0.05), while the VAS score of the group B at T2 or T3 were both lower than that of the group A (both P<0.01). There was no statistically significant difference in VAS score between the two groups at T0 and T1(P>0.05). Statistically significant differences were observed in RI, PI, and S/D values across different time points within both groups (all P<0.01). At T1 and T2, the RI, PI, and S/D ratio of fetuses in both groups all decreased compared to T0(all P<0.05). At T2, the RI, PI and S/D ratio of fetuses in both groups were all lower than those at T1(all P<0.05). There were no statistically significant differences in fetal RI, PI, and S/D ratio between the two groups at T0, T1 or T2(all P>0.05). At T1, HR and MAP in the two groups were both lower than those at T0(both P<0.05), while HR and MAP of the group B were both lower than those of the group A (both P<0.05). There were no statistically significant differences in the total incidences of adverse maternal-fetal outcomes or drug adverse reactions between the two groups (all P>0.05). Conclusion  Dexmedetomidine via PIEB could improve the effect of labor analgesia in HDP parturients, and maintain stability of fetal umbilical hemodynamics and maternal vital signs, with a high safety.

Objective  To explore the value of ABC-X model-based nursing intervention applied in patients with mild cognitive impairment (MCI). Methods  A total of 100 MCI patients were selected, and randomly divided into an observation group and a control group using a random number table method, with 50 cases in each group. The observation group received the ABC-X model-based nursing intervention, while the control group received conventional nursing care. The self-rating anxiety scale (SAS), self-rating depression scale (SDS), Montreal cognitive assessment (MoCA) scale, and 36-item short form health survey (SF-36) scores were compared between the two groups before the intervention and after 4 weeks of intervention. Results  Before the nursing intervention, there were no statistically significant differences in SAS score, SDS score, total MoCA scale score, or the average SF-36 score between the two groups (all P>0.05). After 4 weeks of intervention, the SAS and SDS scores in the observation group were both significantly lower than those in the control group (all P<0.01), while the total MoCA scale score and the average SF-36 score were both significantly higher in the observation group than those in the control group (both P<0.01). Conclusion  The application of the ABC-X model-based nursing intervention in MCI patients could effectively alleviate negative emotions, improve cognitive function, and further enhance their quality of life, thereby making it highly recommended for widespread use.

Objective To investigate the application value of transesophageal echocardiography ( TEE) in diagnosing left atrial appendage thrombus (LAAT) among patients with atrial fibrillation (AF), and to analyze the impact of echocardiographic parameters and clinical factors on the diagnosis of LAAT by TEE. Methods Clinical data of 326 AF patients who had undergone TEE examination were retrospectively analyzed. Patients were divided into LAAT positive group ( n = 52) and negative group ( n = 274) according to TEE results. Patients' general information, AF type and duration, echocardiographic parameters, and TEE findings were recorded. Univariate analysis was performed. ROC curves were used to evaluate the predictive efficacy of left atrial appendage peak flow velocity (LAAV) and left atrial appendage area (LAAA) for LAAT. Independent influencing factors for LAAT in AF patients were identified via Logistic regression analysis. The Hosmer-Lemeshow test and decision curve analysis were employed to assess the goodness-of-fit and clinical utility of the predictive model. Results TEE detected LAAT in 52 (16. 0%) AF patients. Compared with the LAAT negative group, patients in the LAAT positive group were older, had longer AF duration, higher CHA2DS2-VASc scores, larger left atrial diameter (LAD) and LAAA,lower LAAV, and a higher proportion of persistent or permanent AF (all P<0. 05). For predicting thrombus, the area under the curve (AUC) was 0. 893 for LAAV, with an optimal cutoff value of 25. 6 cm / s, sensitivity of 82. 7%, and specificity of 84. 3%. The AUC value for LAAA was 0. 812, with an optimal cutoff value of 5. 2 cm2,sensitivity of 76. 9%, and specificity of 75. 5%. The combined prediction of LAAV and LAAA increased the AUC value to 0. 921, which was significantly higher than the predictive efficacy of LAAV or LAAA used alone (both P<0. 05). Logistic regression analysis identified longer AF duration (OR = 1. 089), decreased LAAV (OR = 0. 876),and larger LAAA (OR = 1. 626) as independent risk factors for LAAT (all P<0. 05). The Hosmer-Lemeshow test indicated good model fit ( P = 0. 587 ), and decision curve analysis confirmed its good clinical utility.Conclusion TEE is an effective tool for diagnosing LAAT in AF patients. Longer AF duration, decreased LAAV,and larger LAAA are independent risk factors for LAAT formation. The combined application of LAAV and LAAA could significantly enhance the predictive value for LAAT, and may serve as an important screening indicator inTEE examinations.

Objective  To review the research status and hotspots in the artificial intelligence (AI) application of the hypertension field over the past decade using bibliometric methods, and to provide references for future related research in the future. Methods  The Web of Science Core Collection database was used as the data source to retrieve literature published between January 1, 2015 and December 31, 2024, on the application of AI in the field of hypertension. CiteSpace 6.3.R1 software was employed to conduct visual mapping analysis of annual publication volume, countries/regions, research institutions, authors, co-citation networks, and key words. Results  A total of 654 articles were included. Between 2015 and 2024, although there were fluctuations in parts of these years, the annual publication volume in the field of AI applied to hypertension showed a general upward trend, with particularly notable growth after 2020. China had the highest volume of publications, while the United States showed the highest centrality index. The Chinese Academy of Sciences was the institution with the highest publication volume. Acharya UR, Mulatero P, and Li Y were the authors with the highest publication outputs. The top three authors ranked by co-citation frequency, Kachuee M, O Brien E, and Whelton PK, made significant contribution to this field. The most frequently co-cited literature was “Cuffless blood pressure estimation algorithms for continuous health-care monitoring”. Hypertension, J Hypertens, and Circulation were the top three journals in terms of influence. Research hotspots primarily focused on the prediction and early identification of hypertension, non-invasive continuous blood pressure monitoring, diagnosis, risk assessment, and intelligent clinical decision support. Conclusion  The application of AI in the field of hypertension shows broad prospects. Future research could focus on multimodal data fusion, optimization of non-invasive continuous blood pressure monitoring technology, improvement of AI-assisted clinical decision support systems, integrated application of digital health technologies, and in-depth studies on specific hypertensive populations.

The electrocardiogram (ECG) examination, as a commonly used diagnostic tool for cardiovascular diseases, holds significant importance in clinical application. However, its interpretation relies heavily on the expertise of cardiologists. In recent years, remote ECG technology has flourished under the dual impetus of policy and technology, demonstrating immense potential in aspects such as promoting the reallocation of high-quality medical resources to grassroots areas, executing the graded diagnosis and treatment system, and enhancing both pre-hospital emergency response and out-of-hospital monitoring frameworks. Particularly, breakthroughs in artificial intelligence and the widespread adoption of wearable devices have significantly energized the application of telemetry ECG. This paper provides a systematic overview of the present state and future prospects of telemetry ECG, focusing on its clinical value and technological advancements.

Objective  To investigate the predictors of severe arrhythmia in patients with acute inferior wall myocardial infarction (IWMI) during hospitalization. Methods  A total of 82 patients with acute IWMI were enrolled, and divided into an arrhythmia group (n=28) and a non-arrhythmia group (n=54) based on the occurrence of severe arrhythmia during hospitalization. Baseline characteristics, laboratory results, electrocardiogram (ECG), and coronary angiography indexes were collected from both groups. These data included sex, age, diabetes, hypertension, smoking history, Killip classification, time from symptom onset to hospital admission, systolic blood pressure and heart rate on admission, as well as serum potassium, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), neutrophil-to-lymphocyte ratio (NLR), troponin I, left ventricular ejection fraction (LVEF), Gensini score, presence of pathological Q waves, ST-segment elevation amplitude, QTc dispersion, right dominant coronary artery supply, and pre-procedural TIMI flow grade. The relationship between the above parameters and the occurrence of arrhythmia was analyzed. Results  Univariate analysis revealed no statistically significant differences between the two groups in terms of sex, age, comorbidities (diabetes and hypertension), smoking history, time from symptom onset to hospital admission, heart rate on admission, LDL-C level, Gensini score, pathological Q waves, ST-segment elevation amplitude, or pre-procedural TIMI flow grade (all P>0.05). However, statistically significant differences were observed in Killip classification, systolic blood pressure on admission, serum potassium, FBG, NLR, troponin I, LVEF, QTc dispersion, and right dominant coronary artery supply (all P<0.05). Multivariate Logistic regression analysis identified Killip class (Ⅲ-Ⅳ), NLR≥5.0, QTc dispersion≥80 ms, and right dominant coronary artery supply as independent risk factors for severe arrhythmia (all P<0.05), while serum potassium ≥3.8 mmol/L was an independent protective factor (P<0.05). Conclusion  High Killip classification, elevated NLR, increased QTc dispersion, right dominant coronary artery supply, and low serum potassium level (<3.8 mmol/L) are strong independent predictors of severe arrhythmia in patients with acute IWMI. These factors facilitate early identification of high-risk patients and guide clinical intervention.

Objective  To investigate the predictive value of electrocardiogram morphology-voltage-P-wave duration (MVP) score in hypertension complicated by atrial fibrillation (AF). Methods  A total of 198 hospitalized patients with hypertension were enrolled, and divided into an AF group (n=99) and a hypertension-only group (n=99) based on the presence of paroxysmal AF. Indicators such as left atrial diameter (LAD), left ventricular end-diastolic diameter (LVEDD), LVEF, P-wave duration, P-wave voltage in lead Ⅰ, MVP score, PtfV1, RV5, and RV5+SV1 were compared between the two groups. The predictive efficacy of MVP score was analyzed using ROC curve analysis. Results  The LAD in the AF group was significantly larger than that in the hypertension-only group (P<0.001), while there were no statistically significant differences in LVEDD or LVEF between the two groups (both P>0.05). P-wave duration, positive rate of PtfV1, RV5, RV5+SV1, and MVP score were all significantly higher in the AF group, whereas the P-wave voltage in lead Ⅰ was significantly lower compared with the hypertension-only group (P<0.05). The area under the ROC curve for an MVP score≥4 points in predicting AF was 0.818, with a sensitivity of 0.838 and a specificity of 0.798. Conclusion  The MVP score is an independent predictor of AF in patients with hypertension.