Lin Xu

Low-intensity pulsed ultrasound (LIPUS) is a non-invasive mechanical stimulation modality with potential effects on tissue repair and mechanotransduction. However, whether LIPUS protects against exhaustive exercise-induced oxidative skeletal muscle damage through mechanosensitive channel-associated antioxidant signaling remains unclear. In this study, male Sprague-Dawley rats were subjected to an exhaustive swimming exercise protocol and treated with LIPUS, with or without GsMTx4, a broad inhibitor of mechanosensitive ion channels. Exercise performance, serum biochemical indicators, tissue oxidative stress, glycogen storage, skeletal muscle histology, and molecular markers related to mechanosensitive channel-associated Nrf2/HO-1 signaling were evaluated. LIPUS improved swimming exhaustion time, reduced serum markers of tissue injury, attenuated lipid peroxidation, increased SOD activity, preserved hepatic and skeletal muscle glycogen content, and improved skeletal muscle histological integrity. These protective effects were accompanied by increased Piezo1 and HO-1 expression and enhanced Nrf2 nuclear accumulation. GsMTx4 partially attenuated the LIPUS-associated improvements in exercise endurance, oxidative stress, glycogen preservation, histological injury, and Nrf2/HO-1 signaling. These findings suggest that LIPUS alleviates exhaustive exercise-induced oxidative muscle damage, at least in part, through GsMTx4-sensitive mechanosensitive channel-associated Nrf2/HO-1 antioxidant signaling. Because GsMTx4 is not Piezo1-specific, further genetic and electrophysiological studies are required to confirm the precise mechanosensitive channel subtype involved.

Enhanced external counterpulsation (EECP) is a safe, non-invasive, economic and effective assisted circulation technique. It not only can improves multisystem disorders, but also reduces adverse event rates. EECP utilizes electrocardiogram-gated sequential inflation of cuffs wrapped around patient's lower extremities to increase blood flow perfusion of heart, which can improve myocardial ischemia of patients with coronary heart disease (CHD). Patients with CHD or heart failure or sleep disorder are suitable to receive EECP. But there are few reports on super elderly people. This is the first reported case of a 98-year-old patient with CHD, who accepted EECP. Patient was presented to hospital with complaints of “intermittent chest tightness for 12 years, worsening for 20 days.” His medical history included lacunar infarction, as well as type 2 diabetes mellitus and hypertension. For the elderly patients reported in this case, the efficacy of medicine was limited and the surgical risk was high. After performing a course of EECP, The patient's symptoms of CHD adverse psychological conditions and sleep quality were improved. This report proved the effectiveness and safety of EECP to treat such super elderly patients with CHD, which provided evidence for the clinical application of EECP in such patients.

Background: The triglyceride-glucose index serves as a dependable biomarker for gauging insulin resistance linked to cardiovascular disease. Our study was designed to investigate how the trajectory of the triglyceride-glucose index relates to the risk of worsening heart failure and overall mortality in patients aged 60 years and older with chronic heart failure and type 2 diabetes. Methods: This study enrolled 466 patients who had ≥ 3 medical exams. The formula for calculating the triglyceride-glucose index was ln (fasting triglycerides [mg/dL] × fasting blood glucose [mg/dL]/2). The trajectory of the triglyceride-glucose index in longitudinal analysis was analyzed via linear mixed models. The relationships between the trajectory of the TyG index and the risk of worsening heart failure and overall mortality were analyzed via competing Cox regression analysis and mixed-effects Cox regression analysis. Results: After the variables adjustment, compared with the first quartile group, the adjusted hazard ratios for worsening heart failure in top quartile group were 2.40 (1.35–3.28) for 10-year follow-up, and 2.09 (1.22-3.58) for overall follow-up duration. The adjusted hazard ratios for overall mortality in top quartile group were 1.99 (1.56–3.14) for 10-year follow-up, and 1.87 (1.22–2.88) for overall follow-up duration. Compared with the low decreasing trajectory, adjusted hazard ratios for worsening heart failure of high decreasing trajectory were 1.37 (1.10–1.71) for the 5-year follow-up, 1.78 (1.10–2.88) for 10-year follow-up, and 1.67 (1.04–2.68) for overall follow-up duration. The adjusted hazard ratios for overall mortality were 2.16 (1.39–3.35) for 10-year follow-up, and 2.23 (1.46–3.40) for overall follow-up duration. Conclusion: During follow-up, a higher baseline level of TyG index and a high decreasing trajectory were independently associated with long-term worsening heart failure and an increased risk of overall mortality.

The rapid development of smart wearable devices has significantly advanced noninvasive, continuous health monitoring, enabling real-time collection of vital biosignals. Electrocardiographic imaging (ECGI), a noninvasive technique that reconstructs transmembrane potential (TMP) from body surface potential, has emerged as a promising method for reflecting cardiac electrical activity. However, the ECG inverse problem’s inherent instability has hindered its practical application. To address this, we introduce a novel Priori-to-Attention Network (P2AN) that enhances the stability of ECGI solutions. By leveraging the one-dimensional nature of electrical signals and the body’s electrical propagation properties, P2AN uses small-scale convolutions for attention computation, integrating a priori physiological knowledge via cross-attention mechanisms. This approach eliminates the need for clinical TMP measurements and improves solution accuracy through normalization constraints. We evaluate the method’s effectiveness in diagnosing myocardial ischemia and ventricular hypertrophy, demonstrating significant improvements in TMP reconstruction and lesion localization. Moreover, P2AN exhibits high robustness in noisy environments, making it highly suitable for integration with wearable electrocardiographic clothing. By improving spatiotemporal accuracy and noise resilience, P2AN offers a promising solution for noninvasive, real-time cardiovascular monitoring using AI-powered wearable devices.

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management.

The Electrocardiogram (ECG) is a crucial tool for cardiac health monitoring, but existing ECG monitoring systems often lack effective signal conditioning coordination between hardware (HW) and software (SW), resulting in poor signal quality and monitoring effectiveness. To address this issue, this study presents a novel single-arm ECG monitoring system based on the Internet of Things that comprehensively considers both hardware and software aspects. The system mainly consists of an ECG measurement device and a signal reconstruction algorithm. For the device, impedance matching circuits and pre-amplifier circuits were specially designed to measure the weak single-arm ECG signal. Additionally, to improve wearing comfort, a metal dry electrode and an armband were developed. On the software side, an ECG signal reconstruction algorithm based on a diffusion model is proposed, and the reverse process of the diffusion model and the noise prediction model are improved according to the characteristics of the ECG signal. The feasibility of the system was verified through monitoring experiments conducted in real environments on healthy subjects and heart disease patients. The experimental results show that the proposed measurement device is capable of long-term ECG monitoring and that the signal reconstruction algorithm effectively removes various real-world noises. The R-wave detection precision, sensitivity, and F1-score of the reconstructed signal improved from 75.90% to 85.87%, 69.16% to 83.40%, and 72.37% to 84.62%, respectively. This demonstrates that the system provides a new solution for efficient cardiac monitoring in daily life. The open-source code is available at https://github.com/CHENJIAR3/ECG_Denoising/tree/Linfei.