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.

Objective:To analyze the association of peripheral blood systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR)with disease severity and progno-sis in elderly patients with chronic pulmonary heart disease(CPHD).Methods:A total of 180 elderly CPHD pa-tients admitted in Xuancheng Central Hospital between September 2021 and January 2023 were enrolled as case group.Healthy volunteers who simultaneously underwent physical examinations in our hospital were selected as con-trol group(n=50).According to the 28d prognosis,the case group was divided into death group(n=45)and sur-vival group(n=135).Levels of peripheral blood SII,NLR and PLR were compared among above-mentioned groups;Spearman correlation analysis was used to analyze the association of above indexes with cardiac function class and prognosis in these patients.Multivariate Logistic regression analysis was used to analyze risk factors for death in these patients.The predictive value of SII,NLR,and PLR for death in elderly CPHD patients was ana-lyzed using receiver operating characteristic(ROC)curve.Results:Compared with those in control group,those in the case group had significant higher levels of peripheral blood SII,NLR and PLR(P＜0.001 all).Compared with NYHA class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher levels of peripheral blood SII[(1759.87±179.43)vs.(1148.33±121.57)vs.(1392.44±146.36)],NLR[(8.65±0.89)vs.(7.14±0.75)vs.(7.76±0.81)],PLR[(152.45±16.79)vs.(125.29±13.46)vs.(138.77±13.58)];and levels of peripheral blood SII,NLR,PLR in class Ⅲ group were significantly higher than those of class Ⅱ group(P＜0.001 all).Com-pared with patients in survival group,those in death group had significant higher levels of peripheral blood SII[(1723.86±189.65)vs.(1296.81±142.33)],NLR[(8.24±0.89)vs.(7.63±0.78)],PLR[(148.75±15.26)vs.(134.41±14.58)](P＜0.001 all).Spearman correlation analysis indicated that the levels of peripheral blood SII,NLR and PLR were significant positively correlated with the severity and poor prognosis(r=0.336～0.432,P＜0.05 or＜0.01;r=0.319～0.504,P＜0.05 or＜0.01)in elderly CPHD patients.Multivariate Logistic regression analy-sis indicated that peripheral blood SII,NLR,PLR and smoking were independent risk factors for death(OR=1.024～9.514,P＜0.05 or＜0.01)in elderly CPHD patients.ROC curve indicated that area under curve(AUC)of combination of SII,NLR and PLR predicting death in elderly CPHD patients was 0.979(95％CI 0.946～0.995),significantly higher than those of each single detection[SII:0.847(95％CI 0.786～0.896),NLR:0.832(95％CI 0.769～0.883),PLR:0.881(95％CI 0.825～0.925),Z=3.988,4.386,4.217,P＜0.01 all].The nomogram calibration curve and decision curve showed good consistency and net benefit of the model.Conclusion:Peripheral blood SII,NLR and PLR are associat-ed with the severity and prognosis of elderly CPHD patients,and have certain predictive value for patient's prognosis.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection,with a high mortality rate.Sirtuin 3,a deacetylase within mitochondria,plays an important regulatory role in cellular metabolism,oxidative stress,and inflammatory responses.In recent years,significant progress has been made in the study of the function and regulatory role of sirtuin 3 in sepsis-related diseases.Research has shown that sirtuin 3 can alleviate organ damage caused by sepsis by regulating mitochondrial function,reducing oxidative stress,and inhibiting inflammatory responses.The specific mechanisms include the regulation of mitochondrial bioenergetics,activation of antioxidant enzyme systems,and inhibition of inflammatory mediator expression.In addition,sirtuin 3 plays a protective role in the pathological process of sepsis by interacting with multiple signaling pathways.This article summarizes the functions and regulatory mechanisms of sirtuin 3 in various sepsis-related diseases,aiming to provide new targets and strategies for the prevention and treatment of sepsis in the future.
Sepsis/metabolism* ; Sirtuin 3/physiology* ; Humans ; Animals ; Oxidative Stress ; Mitochondria/metabolism* ; Signal Transduction

Objective:To analyze the association of peripheral blood systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR)with disease severity and progno-sis in elderly patients with chronic pulmonary heart disease(CPHD).Methods:A total of 180 elderly CPHD pa-tients admitted in Xuancheng Central Hospital between September 2021 and January 2023 were enrolled as case group.Healthy volunteers who simultaneously underwent physical examinations in our hospital were selected as con-trol group(n=50).According to the 28d prognosis,the case group was divided into death group(n=45)and sur-vival group(n=135).Levels of peripheral blood SII,NLR and PLR were compared among above-mentioned groups;Spearman correlation analysis was used to analyze the association of above indexes with cardiac function class and prognosis in these patients.Multivariate Logistic regression analysis was used to analyze risk factors for death in these patients.The predictive value of SII,NLR,and PLR for death in elderly CPHD patients was ana-lyzed using receiver operating characteristic(ROC)curve.Results:Compared with those in control group,those in the case group had significant higher levels of peripheral blood SII,NLR and PLR(P＜0.001 all).Compared with NYHA class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher levels of peripheral blood SII[(1759.87±179.43)vs.(1148.33±121.57)vs.(1392.44±146.36)],NLR[(8.65±0.89)vs.(7.14±0.75)vs.(7.76±0.81)],PLR[(152.45±16.79)vs.(125.29±13.46)vs.(138.77±13.58)];and levels of peripheral blood SII,NLR,PLR in class Ⅲ group were significantly higher than those of class Ⅱ group(P＜0.001 all).Com-pared with patients in survival group,those in death group had significant higher levels of peripheral blood SII[(1723.86±189.65)vs.(1296.81±142.33)],NLR[(8.24±0.89)vs.(7.63±0.78)],PLR[(148.75±15.26)vs.(134.41±14.58)](P＜0.001 all).Spearman correlation analysis indicated that the levels of peripheral blood SII,NLR and PLR were significant positively correlated with the severity and poor prognosis(r=0.336～0.432,P＜0.05 or＜0.01;r=0.319～0.504,P＜0.05 or＜0.01)in elderly CPHD patients.Multivariate Logistic regression analy-sis indicated that peripheral blood SII,NLR,PLR and smoking were independent risk factors for death(OR=1.024～9.514,P＜0.05 or＜0.01)in elderly CPHD patients.ROC curve indicated that area under curve(AUC)of combination of SII,NLR and PLR predicting death in elderly CPHD patients was 0.979(95％CI 0.946～0.995),significantly higher than those of each single detection[SII:0.847(95％CI 0.786～0.896),NLR:0.832(95％CI 0.769～0.883),PLR:0.881(95％CI 0.825～0.925),Z=3.988,4.386,4.217,P＜0.01 all].The nomogram calibration curve and decision curve showed good consistency and net benefit of the model.Conclusion:Peripheral blood SII,NLR and PLR are associat-ed with the severity and prognosis of elderly CPHD patients,and have certain predictive value for patient's prognosis.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective To quantitatively assess the influence of various factors on the pharmacokinetic parameters of propofol and to develop a propofol population pharmacokinetic model tailored for Chinese patients.Methods Thirty patients scheduled for selective abdominal surgeries received an anesthesia dose of propofol at 2.0 mg·kg-1.The concentration of propofol in collected venous blood samples was measured using liquid chromatography-tandem mass spectrometry.Polymorphisms in metabolizing enzyme genes were detected through Sanger sequencing technology.Pharmacokinetic parameters were computed,and models were constructed and evaluated using Phoenix Winnonlin software.Results Through software analysis,the drug's in vivo process was best described by a three-compartment model.The population mean values for the central compartment clearance rate(CL),shallow peripheral compartment clearance rate(Q2),deep peripheral compartment clearance rate(Q3),central compartment volume of distribution(V),shallow peripheral compartment volume of distribution(V2),and deep peripheral compartment volume of distribution(V3)were 1.71 L·min-1,1.31 L·min-1,1.51 L·min-1,5.92 L,19.86 L and 99.06 L,respectively.Body weight was identified as a significant covariate affecting CL and V,and was incorporated into the model.Conclusion The evaluation of the final model demonstrates its substantial predictive capability,offering directional guidance for the clinical administration of propofol.

Objective To develop an MRI-based habitat radiomics model for the preoperative prediction of endometrial cancer(EC)molecular subtypes.Methods Patients with pathologically proven EC from two hospitals were included in the training(n=270)and testing(n=70)cohorts.All patients had preoperative MRI and histological and molecular diagnoses.First,the tumor was divided into habitat subregions based on diffusion-weighted imaging(DWI)and contrast-enhanced(CE)images.Subsequently,habitat radiomic features were extracted from different subregions of T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),DWI,and CE images.Three machine learning classifiers,including logistic regression,support vector machines,and random forests,were applied to develop predictive models for p53-abnormal endometrial cancer,with model performance validated.The model demonstrating the best overall predictive performance was selected as the habitat radiomics model.Using the same procedure,a whole-region radiomics model based on T1WI,T2WI,DWI,and CE sequences and a clinical model were constructed.The performance of the models was evaluated using receiver operating characteristic curves,and DeLong's test was employed to compare differences between the models.Decision curve analysis was used to assess the clinical benefits of the models'application.Results After feature selection,eight habitat radiomic features were retained to construct the habitat radiomics model,ten features for the whole-region radiomics model,and three clinical features for the clinical model.The habitat radiomics model achieved the highest area under the curve(AUC),with 0.855(0.788-0.922)in the training cohort and 0.769(0.631-0.907)in the testing cohort.DeLong's test showed that the habitat radiomics model outperformed the whole-region radiomics model in the training cohort(P=0.001),but there was no significant difference in the testing cohort(P=0.543).In both cohorts,the habitat radiomics model outperformed the clinical model(P=0.007,training cohort;P=0.038,testing cohort).Decision curve analysis(DCA)demonstrated that this model provided clinical benefit for diagnosis within a threshold probability range of approximately 0.2-0.8.Conclusion The MRI-based habitat radiomics model can accurately predict p53-abnormal EC,outperforming both the whole-region radiomics model and the clinical model,and is useful for the non-invasive molecular subtyping of endometrial cancer before surgery.

ObjectiveIn recent years, the negative impact of microgravity on astronauts’ nervous systems has received widespread attention. The repetitive transcranial magnetic stimulation (rTMS) technology has shown significant positive effects in the treatment of neurological and psychiatric disorders. The potential benefits of combined frequency stimulation (CFS) which combines different frequency stimulation patterns in ameliorating neurological dysfunctions induced by the microgravity environment, still require in-depth investigation. Exploring the therapeutic effects and electrophysiological mechanisms of CFS in improving various neurological disorders caused by microgravity holds significant importance for neuroscience and the clinical application of magnetic stimulation. MethodsThis study employed 40 C57BL/6 mice, randomly divided into 5 groups: sham group, hindlimb unloading (HU) group, 10 Hz group, 20 Hz group, and combined frequency stimulation (10 Hz+20 Hz, CFS) group. Mice in all groups except the sham group received 14 d of simulated microgravity conditions along with 14 d of repetitive transcranial magnetic stimulation. The effects of CFS on negative emotions and spatial cognitive abilities were assessed through sucrose preference tests and water maze experiments. Finally, patch-clamp techniques were used to record action potentials, resting membrane potentials, and ion channel dynamics of granule neurons in the hippocampal dentate gyrus (DG) region. ResultsCompared to the single-frequency stimulation group, behavioral results indicated that the combined frequency stimulation (10 Hz+20 Hz) significantly improved cognitive impairments and negative emotions in simulated microgravity mice. Electrophysiological experiments revealed a decrease in excitability of granule neurons in the hippocampal DG region after HU manipulation, whereas the combined frequency stimulation notably enhanced neuronal excitability and improved the dynamic characteristics of voltage-gated Na⁺ and K⁺ channels. ConclusionThe repetitive transcranial magnetic stimulation with combined frequencies (10 Hz+20 Hz) effectively ameliorates cognitive impairments and negative emotions in simulated microgravity mice. This improvement is likely attributed to the influence of combined frequency stimulation on neuronal excitability and the dynamic characteristics of Na⁺ and K⁺ channels. Consequently, this study holds the promise to provide a theoretical basis for alleviating cognitive and emotional disorders induced by microgravity environments.