Electrocardiogram (ECG) signals capture cardiac electrical activity and serve as a fundamental tool for diagnosing cardiovascular diseases. A standard ECG waveform is composed of P waves, QRS complexes, and T waves, each reflecting different aspects of cardiac function through their morphological characteristics. However, precise delineation of these waveform boundaries is often complicated due to overlapping signals between adjacent heartbeats and inherent variability in waveform morphology. Therefore, this study systematically investigates the critical role of R-peak prior information in guiding ECG delineation and proposes a stepwise segmentation strategy that leverages the quasi-periodic structure of cardiac cycles. Specifically, each wave point is classified into P, QRS, T, or background categories, with heartbeat segmentation anchored by R-peak prior to enhance waveform boundary localization accuracy. To further improve performance, we develop an encoder-decoder model designed to capture associations between ECG sub-waves by fusing fine-grained local morphological features with global temporal dependencies via a dedicated integration mechanism. Experimental results on the LUDB and QTDB datasets demonstrate that the proposed method achieves superior delineation performance, with average F1-scores of 97.60% and 94.59%, respectively, surpassing those of existing state-of-the-art approaches. These results highlight the promise of incorporating structural cardiac priors to achieve more reliable and robust automated ECG analysis in clinical practice.

10-Hydroxyl mesaconitine (10-OH MA) is a new potential toxicity marker of Radix Aconiti Lateralis Preparata (Fuzi), which is widely used for the treatment of cardiovascular diseases, rheumatoid arthritis, and other illnesses. Recently, we found that 10-OH MA concentrations in the plasma and urine of outpatients are comparable to those of aconitine, one of the most important toxicity markers of Fuzi. Published studies have shown that CYP3A plays important roles in the disposition and toxicity of Fuzi. This study aims to elucidate the role of cytochrome P450 enzymes in the metabolism and toxicity of 10-OH MA. 10-OH MA was transformed to 14 and 12 metabolites when incubated with human and mouse liver microsomes, respectively. The major metabolic pathways included hydroxylation, dehydrogenation, and demethylation. Human recombinant enzyme metabolic assays revealed that CYP3A5 was mainly involved in 10-OH MA metabolism, followed by CYP3A4, CYP3A7, CYP1A2, and CYP2C8. CYP3A inhibitor ketoconazole significantly inhibited the metabolism of 10-OH MA in vitro. The dose-normalized area under the plasma concentration-time curve from time 0 to t (AUC_0-t/Dose) and tissue 10-OH MA concentrations were significantly increased in Cyp3a-inhibited C57BL/6J mice administered with 10-OH MA, which was accompanied by a sharp decrease in the LD₅₀ value and enhanced cardiotoxicity and neurotoxicity. Moreover, diltiazem markedly increased the AUC_0-t/Dose of 10-OH MA by 17.6-fold. Additionally, 10-OH MA competitively inhibited CYP3A5 and CYP3A4 in vitro, with apparent K_i values of 5.0 and 13.6 μmol/L, respectively. In conclusion, CYP3A5 and CYP3A4 are the pivotal enzymes responsible for the metabolism and toxicity of 10-OH MA. Coexposure to 10-OH-MA and CYP3A inhibitors likely significantly increased 10-OH MA toxicity. SIGNIFICANCE STATEMENT: 10-Hydroxyl mesaconitine (10-OH MA) is a potential toxicity marker of Radix Aconiti Lateralis Preparata (Fuzi), the metabolism of which is predominantly mediated by CYP3A5 and CYP3A4. CYP3A inhibition markedly increases systemic exposure and cardiotoxicity/neurotoxicity of 10-OH MA. In addition, 10-OH MA competitively inhibits CYP3A, indicating the risks of coadministration with CYP3A inhibitors.

Large language models (LLMs) are increasingly embedded in conversational agents for cardiometabolic care. These systems could support self-management, but their behavior change content, delivery mechanisms, and implementation transparency are poorly understood.

This scoping review mapped behavior change techniques (BCTs) used in LLM-driven conversational agents for cardiometabolic prevention and management, described how these techniques are delivered across static, rule-based, and generative mechanisms, examined LLM design, personalization, and safety reporting, and summarized user experience and behavioral or clinical outcomes.

We searched PubMed, Web of Science, Embase, CINAHL, APA PsycInfo, IEEE Xplore, ACM Digital Library, arXiv, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform for records published from January 1, 2020, to November 30, 2025. The final search was run on March 25, 2026, using this publication-date limit. Eligible studies reported a patient-facing text- or voice-based cardiometabolic conversational agent using an LLM or other transformer-based generative model. Two reviewers independently screened records and extracted data. BCTs were coded using the Behavior Change Technique Taxonomy v1; selected self-management BCTs were classified as static, rule-based or templated, or generative or context-aware. Empirical human-participant- or evaluator-based studies were appraised with the Mixed Methods Appraisal Tool, and a study-specific checklist assessed LLM implementation reporting transparency.

Thirty-eight studies were included; 19 involved empirical human-participant- or evaluator-based assessments, whereas 19 were technical and system-level evaluations, including framework-development, simulated-output, and proof-of-concept studies. Studies were concentrated in 2024-2025. Instruction on how to perform behavior was identified in 30 of 38 (79%) studies, information about health consequences in 27 of 38 (71%) studies, and feedback and monitoring techniques in 19 of 38 (50%) studies. Most agents were positioned as educators or coaches targeting type 2 diabetes, obesity, or related cardiometabolic risk, and GPT-family models embedded in hybrid architectures with retrieval-augmented generation or rule-based components predominated. Generative outputs were used mainly for tailored explanations, risk information, and socioemotional responses, whereas self-monitoring, reminders, and structured interactions were more often rule-based or mixed-mode. Only 13 of 38 (34%) studies fully reported prompts or system messages, and 16 of 38 (42%) studies fully reported safety or oversight mechanisms. User evaluations reported good usability and perceived helpfulness, but behavioral or physiological outcomes were sparse and usually limited to pilot, short-term, or single-case designs.

LLM-driven conversational agents for cardiometabolic care are proliferating but remain early-stage and methodologically heterogeneous. Current systems primarily use LLMs as educational and explanatory layers with "synthetic empathy" over rule-based data capture and safety functions, while behavior change content remains dominated by information provision and simple feedback. More rigorous comparative studies with longer follow-up are needed before firm conclusions can be drawn about sustained behavioral or clinical benefit.

Electrocardiogram (ECG) interpretation is fundamental to cardiovascular care, yet persistent human variability and escalating diagnostic demands hinder its reliability and scalability. Despite the remarkable scientific advancements of artificial intelligence (AI) in health care - including its demonstrated capacity to outperform humans in certain predictive tasks - few systems have successfully bridged the gap between technical potential and sustainable real-world clinical transformation. To address these challenges, this article presents the development and implementation of the Synergistic Human-AI Partnership for ECG analysis system (SHAPE), an AI-enabled ECG system at Fuwai Hospital - a leading cardiovascular center located in Beijing, China. SHAPE is designed to enhance ECG interpretation accuracy and efficiency and also pioneers a model for meaningful human-AI collaboration in clinical practice. SHAPE comprises five interconnected platforms: the Benchmark Labeling Platform generates gold-standard reference data for model validation through a rigorous two-stage expert adjudication process based on national consensus guidelines; the Model Development Platform enables iterative model development and deployment; the Clinical Collaboration Platform embeds AI seamlessly into routine clinical workflows; the Education and Evaluation Platform benchmarks performance across human and AI interpreters; and the Visualization and Management Platform guides oversight and continuous improvement. Together, these components form a closed-loop architecture designed to evolve through real-world feedback and clinician engagement. Since its launch in 2022, SHAPE has yielded substantial clinical and operational gains. Even with a 50% reduction in dedicated reporting staff (from six to three between 2022 and 2025), per-physician productivity nearly doubled, driving the average daily interpretation volume from 272 to 504 reports. Concurrently, diagnostic accuracy - evaluated against independent gold-standard samples - improved significantly, from 96.76% pre implementation (2021) to 98.58% post implementation (2024) (P<0.001). Clinical reliance on AI grew robustly, with the proportion of reports incorporating AI outputs increasing from 84.79% in the second year (2023) to over 95% by mid-2025, a statistically significant increase (P<0.001). These achievements were enabled not only by technical advances, but by deliberate workflow design that nurtured human-AI coevolution - from AI as a learner to AI as a catalyst for system-level improvement. Based on their experience with SHAPE, the authors suggest that AI's greatest potential lies not in automation alone, but in thoughtful human-AI collaboration, underscoring the importance of shared goals and feedback-driven design. As the platform scales beyond Fuwai Hospital, it offers a replicable blueprint for AI integration to enhance care quality and clinician capacity.

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality in patients with insulin resistance, and new therapies are urgently needed. We previously developed an orally administered formulation of 2,4-dinitrophenol, here termed controlled-release mitochondrial protonophore (CRMP), and showed that it safely reversed hypertriglyceridemia, hepatic steatosis, and insulin resistance in dysmetabolic rodents and nonhuman primates. Here, we investigated the therapeutic utility of CRMP for treating atherogenesis in a murine model of cardiometabolic syndrome [high-fat cholesterol diet (HFCD)-fed low-density lipoprotein receptor-deficient (Ldlr^-/-) mice]. In both early and late disease stages, CRMP treatment diminished total plaque burden and lesion size compared with HFCD. Morphometric analysis of the aortic root revealed that CRMP also decreased neutral lipid and lesional macrophage content and increased plaque stability. Reductions in atheroprogression were associated with lower plasma and hepatic triglyceride levels and improved whole-body insulin sensitivity, as assessed by hyperinsulinemic-euglycemic clamps. Furthermore, CRMP markedly limited lesional macrophage inflammasome activation and IL-1β release, changes that are consistent with a local immune-dampening effect. Mechanistically, CRMP-mediated reductions in inflammasome activation were driven by mild increases in macrophage mitochondrial inefficiency and lower mitochondrial reactive oxygen species (ROS) production. These effects were context dependent because CRMP failed to curtail lesional IL-1β content and atheroprogression in chow-fed apolipoprotein E-deficient (Apoe^-/-) mice. Collectively, these data show that CRMP exerted antiatherogenic effects through uncoupling of oxidative phosphorylation in hepatocytes and macrophages, highlighting the therapeutic potential of mitochondrial uncouplers for treating ASCVD.

The relationship between sleep disordered breathing (SDB), bone mineral density (BMD) and risk of osteoporosis is unclear. Obesity, a condition often comorbid with SDB, has been associated with high BMD yet its impact on bone health remains controversial. The purpose of this study was to evaluate the implications of obesity in the relationship between SDB and BMD.

Two hundred and twenty-five adults aged 20-65 years enrolled in this study. Participants were nonsmokers, free of major cardiovascular diseases and not receiving treatment for SDB. Participants completed overnight polysomnography and whole-body dual-energy X-ray absorptiometry (DEXA) scans. SDB was defined as apnea-hypopnea index (AHI) ≥5 event/hour. Total body BMD (g/cm2), T-scores and Z-scores were obtained from DEXA scans. Height and weight measures were collected for computation of body mass index (BMI, kg/m2).

Participants with SDB (n=74, 32.9% of the sample) had higher total-body BMD (P=0.003), T-score (P=0.017), and Z-score (P=0.011) compared to non-SDB controls. In multivariable models adjusted for demographic characteristics, SDB was not significantly associated with total-body bone measures. However, when including BMI, significant inverse relationships between SDB and total body BMD (P=0.046), T-score (P=0.049), and Z-score (P=0.049) became apparent. Such associations persisted after controlling for lifestyle, clinical and sleep variables (fully adjusted models, total BMD, P=0.036; T-score, P=0.037; Z-score, P=0.034). In sensitivity analysis, there were no associations between SDB and bone measures in models adjusted for body fat in lieu of BMI.

An inverse, albeit modest, association between SDB and BMD exists, and this relationship emerges when considering BMI but not body fat. Hence, our data suggests that elevated body mass rather than adiposity may mask the negative association between SDB and bone health, thus possibly explaining prior conflicting evidence. Longitudinal investigations corroborating these findings and evaluating their implications for osteoporosis risk in patients with SDB are needed.

Doxorubicin (DOX)-induced cardiotoxicity remains a major limitation of cancer therapy. Incretin-based therapies are established cardiometabolic agents with pleiotropic cardiovascular effects; however, their potential role in mitigating DOX-related cardiac injury has not been systematically synthesized. We conducted a PRISMA 2020-compliant systematic review to evaluate the effects of incretin-based therapies on DOX-induced cardiotoxicity, with a focus on functional, structural, biomarker, and mechanistic outcomes. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched through 20 October 2025. Eligible studies included in vivo rodent models of DOX cardiotoxicity and any clinical studies directly evaluating incretin-based therapy during DOX exposure; in vitro studies, non-DOX models, combination-treatment studies, studies not focused on cardiotoxicity, gene therapy-based interventions, and reviews/editorials were excluded. Risk of bias and certainty of evidence were assessed using the SYRCLE tool and GRADE adapted for preclinical research. Thirteen rodent studies were included, and no eligible human study was identified. Investigated agents included liraglutide (n = 4), exenatide/exendin-4 (n = 4), semaglutide (n = 2), and tirzepatide (n = 3). In chronic cumulative-dose DOX models, incretin-based therapies were generally associated with preservation of left ventricular systolic function, with between-study improvements of approximately 7-20 percentage points in left ventricular ejection fraction, along with reductions in injury biomarkers. These effects were accompanied by attenuation of oxidative stress, inflammation, apoptosis, and, in some studies, ferroptosis. In contrast, findings were less consistent in acute single-dose models. Co-treatment during DOX exposure showed the most reproducible protective signal, whereas isolated pretreatment with liraglutide or tirzepatide and post-treatment with exenatide did not show a clear additional benefit. The evidence base is limited by exclusive reliance on small heterogeneous animal studies, predominantly male models, variable dosing/timing protocols, and low-to-very-low certainty of evidence. Overall, incretin-based therapies show biologically plausible cardioprotective effects in preclinical DOX cardiotoxicity, but these findings should be regarded as hypothesis-generating until confirmed in carefully designed clinical studies.

Isoproterenol-induced cardiac hypertrophy is a well-established experimental model that mimics environmental stress exposure and the pathological consequences of chronic sympathetic overstimulation on the heart. This model suggests critical understanding in to molecular and cellular mechanisms underlying maladaptive cardiac remodeling, a precursor to heart failure. At the mechanistic level, isoproterenol-induced cardiac hypertrophy is driven by multiple interconnected signaling pathways and triggered by sustained mechanical load, neurohormonal stimulation, inflammatory responses and environmental stressors, that often preceding fibrosis, ventricular dysfunction, and heart failure. Concurrently, excessive production of reactive oxygen species (ROS) generates oxidative stress, which damages cellular macromolecules and disrupts functions of cellular organelles. Inflammatory responses are also triggered, largely mediated by transcription factors such as the NF-kB pathway, resulting in the upregulation of pro-inflammatory cytokines and profibrotic factors. Additional signaling cascades, including the MAPK pathway, further amplify hypertrophic gene expression and cardiac remodeling. Environmental stressors like chronic psychological stress, air pollution, thermal strain and unhealthy lifestyle habits tend to trigger overlapping neurohormonal and molecular responses. These effects closely resemble those seen in Isoproterenol-induced cardiac hypertrophy models. Such conditions lead to prolonged sympathetic stimulations, increased oxidative stress and persistent-low grade inflammation, all of which contribute to the onset and progression of cardiac hypertrophy which makes Isoproterenol-induced cardiac injury often used as a practical model to study cardiovascular damage driven by environmental factors. Current molecular cardioprotective strategies largely target oxidative stress and inflammatory pathways. In this context, enhancing endogenous antioxidant defence mechanisms particularly through activation of Nrf2 pathway has gained attention as an effective means to limit ROS-mediated damage. Pharmacological interventions such as β-blockers and angiotensin-converting enzyme inhibitors help to reduce β-adrenergic overstimulation and are known to improve cardiac outcomes. In contrast, naturally derived bioactive compounds including polyphenols and flavonoids have demonstrated significant antioxidant and anti-inflammatory potential thereby attenuating hypertrophic signaling and preserving myocardial structure and function. Therefore, this review focuses on Isoproterenol-induced cardiac hypertrophy as an experimental model to examine the interplay between neurohormonal activation, environmental stressors and the molecular mechanisms underlying cardiac remodeling. A clearer understanding of these interconnected pathways is expected to not only provide insight into disease progression but also support the development of more targeted cardioprotective strategies aimed at reducing the global burden of cardiovascular disease.

Synonymous variants are often overlooked during genetic screening, however current reports forecasted their significant biological impact and inevitably considered pathogenic. These silent changes in genome significantly affect the mRNA structure and stability and hence, alter the protein expression and function. IRX4 is an essential transcription factor for cardiogenesis and reported to be associated with congenital heart disease (CHD).

We have performed genetic screening of IRX4 in 205 isolated cases of CHD. Five synonymous variants c.90A > C; Gly30=, c.240G > A; Ser80=, c.381A > G; Pro127=, c.1281G > A; Ala427=, and c.1509C > T; Gly503=, six intronic variants c.1-139G > A, c.21-107G > C, c.46-107G > C, c.297 + 6T > G, c.815-130 C > A, c.1638 + 62 C > T were identified. A computed analysis by diverse tools namely RNAfold, MutaRNA, Human Splicing Finder (HSF), and RNA22 was applied to predict the substantial effect on downstream function. RNAfold analysis indicated that all five variants impacted RNA structure and stability. Further, notable changes in the base-pairing probability and RNA accessibility were induced by c.90A > C, c.240G > A, c.381A > G, c.1281G > A, and c.1509C > T variants as shown by MutaRNA. Moreover, the effect on the cis-acting regulatory element of splicing was speculated due to c.1281G > A variant only. Likewise, various modes of the RNA22 tool indicated changes in miRNA binding sites, showing that 61.5% of targets were altered and 38.5% were completely lost as a result of the c.1281G > A variant.

Our findings provide an insight into the molecular effect on mRNA structure and stability, splicing and miRNA target binding sites that potentially impair the transcription and translation and consequently might be associated with the pathogenesis of CHD.