Chronic heart failure （CHF） is a complex clinical syndrome caused by ventricular dysfunction， with mitochondrial energy metabolism disorder being a critical factor in disease progression. Heart-Yang deficiency syndrome， as the core pathogenesis of CHF， persists throughout the disease course. Insufficiency of heart-Yang leads to weakened warming and propelling functions， resulting in the accumulation of phlegm-fluid， blood stasis， and dampness. This eventually causes Qi stagnation with phlegm obstruction and blood stasis with water retention， forming a vicious cycle that exacerbates disease progression. According to the theory of reinforcing Yang， the clinical experience of the traditional Chinese medicine （TCM） master Tang Zuxuan in treating CHF with heart-Yang deficiency syndrome， and achievements from molecular biological studies， this study innovatively proposes an integrated research framework of "TCM syndrome differentiation and staging-mitochondrial metabolism mechanisms-intervention with Yang-reinforcing prescriptions" which is characterized by the integration of traditional Chinese and Western medicine. Heart-Yang deficiency syndrome is classified into mild （Stage Ⅰ-Ⅱ）， severe （Stage Ⅲ）， and critical （Stage Ⅳ） stages. The study elucidates the precise correlations between the pathogenesis of each stage and mitochondrial metabolism disorders from theoretical， pathophysiological， and therapeutic perspectives. The mild stage is characterized by impaired biogenesis and substrate-utilization imbalance， corresponding to heart-Yang deficiency and phlegm-fluid aggregation. Linggui Zhugantang and similar prescriptions can significantly improve the expression of peroxisome proliferator-activated receptor gamma co-activator-1α（PGC-1α）/silent information regulator 2 homolog 1 （SIRT1） and ATPase activity. The severe stage centers on oxidative stress and structural damage， reflecting Yang deficiency with water overflow and phlegm-blood stasis intermingling. At this stage， Zhenwu Tang and Qiangxin Tang can effectively mitigate oxidative stress damage， increase adenosine triphosphate （ATP） content， and repair mitochondrial structure. The critical stage arises from calcium overload and mitochondrial disintegration， leading to the collapse of Yin-Yang equilibrium. At this stage， Yang-restoring and crisis-resolving prescriptions such as Fuling Sini Tang and Qili Qiangxin capsules can inhibit abnormal opening of the mitochondrial permeability transition pore （MPTP）， reduce cardiomyocyte apoptosis rate， and protect mitochondrial function. By summarizing the characteristics of mitochondrial energy metabolism disorders at different stages of CHF， this study explores the application of the theory of reinforcing Yang in treating heart-Yang deficiency syndrome and provides new insights for the clinical diagnosis and treatment of CHF.

Chronic heart failure （CHF） is a group of complex clinical syndromes caused by abnormal changes in the structure and/or function of the heart due to various reasons， resulting in disorders of ventricular contraction and/or diastole. CHF is a condition where primary diseases such as coronary heart disease， hypertension and pulmonary heart disease recur frequently and persist for a long time， presenting blood stasis in meridians and collaterals， stagnation of water and dampness， and accumulation of Qi in collaterals. Its pathogenesis is complex and may involve myocardial energy metabolism disorders， oxidative stress responses， myocardial cell apoptosis， autophagy， inflammatory responses， etc. According to the theory of restraining hyperactivity to acquire harmony， we believe that under normal circumstances， the adenosine monophosphate-activated protein kinase （AMPK） signaling pathway functions normally， maintaining human physiological activities and energy metabolism. Under pathological conditions， the AMPK signaling pathway is abnormal， causing energy metabolism disorders， inflammatory responses， and myocardial fibrosis. Traditional Chinese medicine （TCM） can regulate the AMPK signaling pathway through multiple mechanisms， targets， and effects， effectively curbing the occurrence and development of CHF. It has gradually become a research hotspot in the prevention and treatment of this disease. Guided by the theory of TCM， our research group， through literature review， summarized the relationship between the AMPK pathway and CHF and reviewed the research progress in the prevention and control of CHF with TCM active ingredients， TCM compound prescriptions， and Chinese patent medicines via regulating the AMPK pathway. The review aims to clarify the mechanism and targets of TCM in the treatment of CHF by regulating the AMPK pathway and guide the clinical treatment and drug development for CHF.

Chronic heart failure （CHF） refers to a clinical syndrome in which the function or structure of the heart is changed due to damage to the original myocardium， resulting in reduced pumping and/or filling functions of the heart. In recent years， the mechanisms， pathways， and targets of traditional Chinese medicine （TCM） in the treatment of CHF have been continuously confirmed， and the application of TCM theories in guiding the syndrome differentiation and precise treatment of CHF is currently a research hotspot. On the basis of the syndrome differentiation and treatment in TCM， Professor LI Candong innovatively proposed the thinking of five differentiation： Disease differentiation， syndrome differentiation， pathogenesis differentiation， symptom differentiation， and individual differentiation. This article explores the clinical diagnosis and treatment of CHF from this thinking， emphasizing comprehensive syndrome differentiation， objective analysis， dynamic assessment， and individualized treatment. In terms of diagnosis， the first is to identify the disease name， cause， location， severity， and type of CHF， determine the type and its evolution， and clarify the process of transmission and transformation between deficiency and excess. Secondly， it is necessary to distinguish the authenticity， severity， primary and secondary， urgency and complexity of CHF syndromes， providing scientific guidance for syndrome differentiation and treatment. Thirdly， according to the symptoms and the principles of deficiency and excess， the physician should identify the core pathogenesis of CHF from the perspectives of Qi， blood， Yin， Yang， deficiency， stasis， phlegm， water， and toxins. Fourthly， from the macro， meso and micro levels， the physician should carefully distinguish the presence or absence， severity， authenticity， and completeness of the symptoms to guide the diagnosis and treatment process of CHF. Finally， personalized medication for CHF should be promoted based on the patient's gender， age， constitution， and living habits. In terms of treatment， based on the thinking of five differentiation， we propose that the treatment of CHF should integrate the disease and syndrome， clarify the pathogenesis， and apply precise treatment. The treatment should be people-oriented， staged， and typed， and the medication should be adjusted according to symptoms. This diagnostic and therapeutic approach is based on the holistic concept and syndrome differentiation and treatment， and combines the three causes for appropriate treatment， providing new ideas and insights for the diagnosis and treatment of CHF.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective:To present a set of clinically representative quality assurance (QA) test cases for stereotactic radiosurgery (SRT) plans of multiple intracranial metastases, in order to assess the plan quality and machine execution capabilities.Methods:Based on the clinical characteristics of multiple brain metastases, four groups of test cases with three target volumes (TVs), six TVs, nine TVs, and TVs near organs at risk (OARs) were designed. For these cases, SRT plans were developed, and plan quality was assessed using metrics including the Radiation Therapy Oncology Group conformality index (RTOG CI), gradient index (GI), homogeneity index (HI), and the volume of normal brain tissue receiving a dose of 24 Gy ( V24 Gy), which was defined as the volume enclosed by the 24 Gy isodose line around the Brain-PTV ( V24 Gy of Brain-PTV). Verification plans were generated for each test case, including the verification of point doses, planar doses (PD), and SRS MapCHECK (SMC) semiconductor matrix planar doses. Compared with the calculated result of the treatment planning system (TPS), the criteria for the γ analysis of planar doses were set at 1 mm/2% and 2 mm/2%. Results:For the four groups of test cases, the mean CI, GI, HI, and V24 Gy of Brain-PTV were 1.04±0.03, 3.79±0.40, 0.73±0.01 and (7.46±3.80) cm 3, respectively. The mean deviations of the point doses were 0.88%±0.98%, 1.47%±0.79%, 1.52%± 0.76%, and 1.17% ± 0.38%, respectively. The mean γ passing rates of the single fields for PDs were greater than 98% at 2 mm/2% and exceeding 96% at 1 mm/2%, and the mean γ pass rates of the SMC semiconductor matrix for PDs were 97.75% ± 2.31% and 99.33% ± 0.62%, at 1 mm/2% and 2 mm/2% respectively. Conclusions:The proposed QA test cases for SRT of multiple intracranial metastases allow for the effective assessments of the plan quality and machine execution capabilities and, thus, can assist various centers in clinical applications.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

OBJECTIVE To analyze the blood components of Suanzaoren Decoction after oral administration using UHPLC-Q Exactive Orbitrap-MS technology.METHODS Female Sprague-Dawley(SD)rats were used as experimental subjects,and Suanza-oren Decoction was administered orally.Serum samples were collected,and the aqueous extract of Suanzaoren Decoction and the serum were analyzed using UHPLC-Q Exactive Orbitrap-MS technology to identify the prototype components and metabolites absorbed into the blood by comparing and analyzing with the LuMet-TCM database.RESULTS It showed that a total of 458 components were iden-tified in the aqueous extract of Suanzaoren Decoction,and 26 chemical components were identified in the blood,including 23 prototype components and 3 metabolites.CONCLUSION The prototype components absorbed into the blood discovered in this study may be the active ingredients of Suanzaoren Decoction,providing a reference for the research on the pharmacodynamic material basis of Suanza-oren Decoction.

Objective To develop a virtual reality rehabilitation support system and evaluate its effects on improving discharge readiness in caregivers of laryngectomy patients.Methods The system is equipped with a virtual reality-based home nursing skills guidance module for post-laryngectomy patients and an evidence-based artificial intelligence Q&A module.A convenience sampling method was adopted to select 64 caregivers of patients who underwent laryngectomy for the first time in the otorhinolaryngology head and neck surgery department of a tertiary A hospital in Beijing from July 2024 to January 2025 for a systematic study on application effects.To avoid cross-contamination between an experimental group and a control group,ward-based grouping was applied.A coin toss was used to assign Ward 1 to an experimental group and Ward 2 to a control group,with 32 cases in each group.The experimental group received discharge guidance based on the virtual reality technology,while the control group was provided with conventional verbal and written discharge instructions.The readiness for hospital discharge and the quality of discharge teaching scores of caregivers were compared between the 2 groups after the intervention.Results No sample detachment.After the intervention,the patient caregivers in the experimental group scored significantly higher than those in the control group in terms of discharge readiness and quality of discharge guidance,with a statistically significant difference(P＜0.001).Conclusion This study developed the virtual reality rehabilitation system for laryngeal cancer patients demonstrated good feasibility and effectiveness.The application of this system for discharge education significantly improved discharge readiness of caregivers and quality of discharge guidance for post-laryngectomy patients.It also helped with the optimization of health education models,enhancement of nursing resource utilization efficiency,and improvement of transitional care.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

Objective:To investigate the predictive value of color Doppler ultrasound combined with electrocardio-gram for right heart dys function in patients with pulmonary heart disease(PHD).Methods:A total of 100 PHD patients admitted in Dongcheng Branch of First Affiliated Hospital of Anhui Medical University between January 2020 and December 2023 were retrospectively analyzed.According to results of 6min walking test(6MWT),pa-tients were divided into good right heart function group(n=64,≥350m)and right heart dysfunction group(n=36,＜350m).The indexes of cardiac color ultrasound[isovolumic relaxation time(IVRT),isovolumetric contraction time(IVCT)and right ventricular Tei index],ECG[24h mean R-R interval standard deviation(SDNN),normal R-R interval standard deviation per 5min(SDANN)and the ratio of low frequency components to high frequency components(LF/HF)]were compared between two groups.Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of color Doppler ultrasound,ECG and their combination for right heart dys-function in PHD patients.Spearman correlation coefficient was used to analyze the association of color Doppler ul-trasound,ECG and their combination with right heart dysfunction in PHD patients.Results:Compared with those in good right heart function group,patients in right heart dysfunction group had significant higher IVRT[(120.64±14.08)ms vs.(97.87±10.93)ms],IVCT[(84.28±12.33)ms vs.(71.92±10.61)ms]and Tei index[(0.85±0.11)vs.(0.63±0.07)](P＜0.001 all),and significant lower SDNN[(75.52±12.58)ms vs.(85.58±11.75)ms],SDANN[(63.86±10.92)ms vs.(76.75±11.71)ms]and LF/HF[(1.33±0.19)vs.(1.84±0.27)](P＜0.001 all).ROC curve indicated that the AUC of color Doppler ultrasound combined ECG in diagnosing right heart dysfunction in PHD patients was 0.911(95％CI 0.838～0.959),which was significantly higher than those of color Doppler ultrasound[0.775(95％CI 0.681～0.853),Z=2.404,P=0.016]and ECG[0.688(95％CI 0.588～0.777),Z=3.968,P=0.001]alone.Spearman correlation analysis indicated that there was a significant positive correlation of color Doppler ultrasound(r=0.547),ECG(r=0.375)and their combination(r=0.810)with right heart dysfunction in PHD patients(P＜0.001 all),and the correlation between combined detection and right heart dysfunction in PHD patients was significantly higher.Conclusion:Color Doppler ultrasound combined with ECG possesses high diagnostic performance for right heart dysfunction in PHD patients.