The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

Breast cancer， as one of the major cancers threatening women's health globally， is characterized by high aggressiveness， high malignancy， and poor prognosis. In 2022， according to the World Health Organization， breast cancer ranked second in the incidence of female cancers globally， accounting for 11.6% of all new cancer cases. Western medical doctors mainly use surgery， chemotherapy， radiotherapy， endocrine therapy， and molecular targeted therapy to treat breast cancer， which can effectively improve the recurrence rate and death rate of breast cancer patients and prolong the survival period of patients. However， its treatment process is often accompanied by a series of side effects， which bring challenges to patients' quality of life. However， traditional Chinese medicine （TCM） has demonstrated significant therapeutic effects in inhibiting the proliferation and metastasis of breast cancer cells， reducing toxic side effects produced by chemotherapy， and improving patients' survival rate and quality of life. It is therefore particularly necessary to investigate the pharmacological effects and mechanisms of TCM in breast cancer treatment. The authors combed the pharmacological effects and mechanisms of the etiology and pathogenesis of breast cancer， identification and treatment of breast cancer， TCM compound， TCM single medicine， TCM monomer， and external treatment of TCM to prevent and control breast cancer and found that TCM has a therapeutic effect on breast cancer. It can play a role in increasing the effectiveness， reducing the toxicity， and alleviating the adverse reactions. It can inhibit breast cancer cell proliferation， cell cycle， migration， invasion， immune escape， epithelial-mesenchymal transition （EMT）， aerobic glycolysis， mitochondrial biosynthesis， aminoacyl-tRNA biosynthesis， reduce drug resistance， promote apoptosis， ferroptosis， cell autophagy， and regulate the tumor immune microenvironment by regulating signaling pathways. This paper aims to provide new ideas and methods for experimental research and clinical treatment of breast cancer.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Lung cancer has the highest morbidity and mortality rate among all cancers. Because of the complex pathogenesis， there are limitations in the common Western medicine treatment methods. Clinical and experimental studies have proved that traditional Chinese medicine （TCM） can not only effectively treat lung cancer and alleviate the clinical symptoms of cancer patients but also reduce the adverse reactions and complications caused by surgery， chemotherapy， and radiotherapy to improve the quality of life of the patients. The biological behaviors of lung cancer cells， such as proliferation， invasion， and metastasis， are closely related to their metabolic reprogramming. Metabolic reprogramming in lung cancer involves a series of metabolic changes such as increased glucose uptake and consumption， enhanced glycolysis， increased amino acid uptake and catabolism， and enhanced lipid and protein synthesis. Studies have reported that TCM active components， extracts， and compound prescriptions can effectively inhibit the biological behaviors of lung cancer by regulating metabolic reprogramming. Therefore， this paper reviews the pharmacological mechanisms of TCM active components， extracts， and compound prescriptions in regulating metabolic reprogramming of lung cancer， with the aim of providing a new way of thinking for the treatment of lung cancer by TCM regulation of metabolic reprogramming of lung cancer cells. The available studies suggest that TCM mainly inhibits the extracellular signal-regulated protein kinase （ERK）/c-Myc， phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， and hypoxia-inducible factor-α （HIF-1α） pathways. Furthermore， the expression of monocarboxylate transporter 4 （MCT4）， glucose transporter 1 （GLUT1）， pyruvate dehydrogenase （PDH）， phosphofructokinase 1 （PFK1）， pyruvate dehydrogenase kinase 1 （PDK1）， pyruvate kinase M2 （PKM2）， hexokinase （HK）， lactate dehydrogenase （LDH）， and lactate dehydrogenase A （LDHA） are inhibited. In this way， TCM inhibits the glucose uptake by lung cancer cells and glycolysis in lung cancer cells to reduce the energy metabolism of tumor cells， ultimately achieving the therapeutic effect on lung cancer.

As the search for effective treatments for COVID-19 continues, the high mortality rate among critically ill patients in Intensive Care Units (ICU) presents a profound challenge. This study explores the potential benefits of traditional Chinese medicine (TCM) as a supplementary treatment for severe COVID-19. A total of 110 critically ill COVID-19 patients at the Intensive Care Unit (ICU) of Vulcan Hill Hospital between Feb., 2020, and April, 2020 (Wuhan, China) participated in this observational study. All patients received standard supportive care protocols, with a subset of 81 also receiving TCM as an adjunct treatment. Clinical characteristics during the treatment period and the clinical outcome of each patient were closely monitored and analysed. Our findings indicated that the TCM group exhibited a significantly lower mortality rate compared with the non-TCM group (16 of 81 vs 24 of 29; 0.3 vs 2.3 person/month). In the adjusted Cox proportional hazards models, TCM treatment was associated with improved survival odds (P < 0.001). Furthermore, the analysis also revealed that TCM treatment could partially mitigate inflammatory responses, as evidenced by the reduced levels of proinflammatory cytokines, and contribute to the recovery of multiple organic functions, thereby potentially increasing the survival rate of critically ill COVID-19 patients.
Humans ; COVID-19 ; Medicine, Chinese Traditional ; SARS-CoV-2 ; Critical Illness ; Treatment Outcome

Objective To investigate the application effects of cardiac resynchronization therapy(CRT)of[left bundle optimization(LOT)]and biventricular pacing(BiV)in the patients with chronic heart failure complicating left bundle branch block.Methods The single center,prospective and non-randomized controlled study method was used.Forty-two patients with heart failure meeting CRT in this center from April 2020 to April 2022 were consecutively included.Among them,32 cases adopted the BiV-CRT(BiV-CRT group)and 10 cases adopted LOT-CRT(LOT-CRT group).The pacing-making parameters,quality of life scale(SF-36)score,6-min walk test(6-MWT),ECG QRS width(QRSd),left ventricular end-diastolic diameter(LVEDD),left ventricular ejection fraction(LVEF)and New York cardiac function grade(NYHA)situation were collected before surgery,after surgery immediately and in postoperative 3,6,12 months.Their complica-tions and clinical outcomes were evaluated.Results The pacing threshold value in the LOT-CRT group was stable and lower than that in the BiV-CRT group(P＜0.05);QRSd in postoperative 12 months in the LOT-CRT group was shorter than that in the BiV-CRT group[(115.0±14.3)ms vs.(133.0±14.0)ms,P＜0.05]and 6-MWT was longer than that in the BiV-CRT group[(327.0±52.8)m vs.(274.0±52.8)m,P＜0.05],and the differences were statistically significant(P＜0.05);LVEF,LVEDD,NYHA cardiac grade and SF-36 score in postoperative 12 months were improved compared with those before implantation.The rehospitaliza-tion rate of heart failure in the LOT-CRT group was lower.Conclusion LOT-CRT could obtain a narrower QRS wave and longer 6-MWT than BiV-CRT.

Objective To explore the mechanisms of circadian rhythm disorder(CRD)on behavior and testicular spermatogenic capacity in adolescent mice.Methods Thirty SPF grade C57 mice were selected and randomly di-vided into the control and CRD groups with 15 mice in each group.The control group kept 12 h dark/12 h bright circulating light,and the CRD group kept 24 h light.The trial lasted for 61 days.The growth curves of mice in each group were counted;the elevated plus maze test and open field test were performed to detect mice behavior;neuronal morphology was visualized by Nissl staining.The distribution of ionized calcium-binding adapter molecule 1(Iba1)and neuron-specific nuclear protein(NeuN)in the hypothalamus were detected by immunofluorescence and Western blot.Expression of testosterone synthesis-related genes steroidogenic acute regulatory protein(StAR)and hydroxy-delta-5-steroid dehydrogenase,3 beta-and steroid delta-isomerase 1(HSD3B1)and spermatogenesis-related genes gametogenetin binding protein 2(GGNBP2)and deleted in azoospermia-like(DAZL)were deter-mined by RT-qPCR.Results The weight of the CRD group was significantly higher than that of control group at 61 days;in the elevated plus maze test,the time,frequency,and percentage of time in the open arm of the CRD group were significantly less than those of the control group;in the open field test,there was no significant differ-ence in movement distance between the two groups;however,the residence time of the central area in the CRD group was significantly less than that in the control group;the frequency of entering the central area in the CRD group was significantly less than that in the control group.Nissl staining results showed that the positive cells in the CRD group were significantly lower than the control group.Immunofluorescence and Western blot results showed that Iba1 protein expression was up-regulated and NeuN protein expression was down-regulated in the hypothalamus of the CRD group.In the RT-qPCR experiment,the expression of HSD3B1 in the CRD group was significantly low-er than that of the control group;the expression of GGNBP2 and DAZL in the CRD group was significantly lower than that in the control group.Conclusion The CRD treatment can not only lead to depressive behavior in adoles-cent mice but also reduce the development of reproductive system in male adolescent mice.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

Background::Bladder cancer, characterized by a high potential of tumor recurrence, has high lifelong monitoring and treatment costs. To date, tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer types. Nonetheless, the existence of soft tumor cells in bladder tumors remains elusive. Thus, our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer cells.Methods::The stiffness of bladder cancer cells was determined by atomic force microscopy (AFM). The modified microfluidic chip was utilized to separate soft cells, and the 3D Matrigel culture system was to maintain the softness of tumor cells. Expression patterns of integrin β8 (ITGB8), protein kinase B (AKT), and mammalian target of rapamycin (mTOR) were determined by Western blotting. Double immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59 (TRIM59). The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor models. Results::Using our newly designed microfluidic approach, we identified a small fraction of soft tumor cells in bladder cancer cells. More importantly, the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens, in which the number of soft tumor cells was associated with tumor relapse. Furthermore, we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor cells. Simultaneously, we detected a remarkable up-regulation in ITGB8, TRIM59, and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent counterparts.Conclusions::The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and stemness. Meanwhile, the soft tumor cells become more sensitive to chemotherapy after stiffening, that offers new insights for hampering tumor progression and recurrence.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.