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.

BACKGROUND: Temozolomide (TMZ) resistance is a significant challenge in treating glioblastoma (GBM). Collagen remodeling has been shown to be a critical factor for therapy resistance in other cancers. This study aimed to investigate the mechanism of TMZ chemoresistance by GBM cells reprogramming collagens. METHODS: Key extracellular matrix components, including collagens, were examined in paired primary and recurrent GBM samples as well as in TMZ-treated spontaneous and grafted GBM murine models. Human GBM cell lines (U251, TS667) and mouse primary GBM cells were used for in vitro studies. RNA-sequencing analysis, chromatin immunoprecipitation, immunoprecipitation-mass spectrometry, and co-immunoprecipitation assays were conducted to explore the mechanisms involved in collagen accumulation. A series of in vitro and in vivo experiments were designed to assess the role of the collagen regulators prolyl 4-hydroxylase subunit alpha 1 (P4HA1) and yes-associated protein (YAP) in sensitizing GBM cells to TMZ. RESULTS: This study revealed that TMZ exposure significantly elevated collagen type I (COL I) expression in both GBM patients and murine models. Collagen accumulation sustained GBM cell survival under TMZ-induced stress, contributing to enhanced TMZ resistance. Mechanistically, P4HA1 directly binded to and hydroxylated YAP, preventing ubiquitination-mediated YAP degradation. Stabilized YAP robustly drove collagen type I alpha 1 ( COL1A1) transcription, leading to increased collagen deposition. Disruption of the P4HA1-YAP axis effectively reduced COL I deposition, sensitized GBM cells to TMZ, and significantly improved mouse survival. CONCLUSION P4HA1 maintained YAP-mediated COL1A1 transcription, leading to collagen accumulation and promoting chemoresistance in GBM.
Temozolomide ; Humans ; Glioblastoma/drug therapy* ; Animals ; Mice ; Cell Line, Tumor ; Drug Resistance, Neoplasm/genetics* ; YAP-Signaling Proteins ; Hydroxylation ; Dacarbazine/pharmacology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Transcription Factors/metabolism* ; Collagen/biosynthesis* ; Collagen Type I/metabolism* ; Prolyl Hydroxylases/metabolism* ; Antineoplastic Agents, Alkylating/therapeutic use*

OBJECTIVE: To explore the functional roles and underlying mechanisms of neferine in the context of angiotensin II (Ang II)-induced hypertension and vascular dysfunction. METHODS: Male mice were infused with Ang II to induce hypertension and randomly divided into treatment groups receiving neferine or a control vehicle based on baseline blood pressure using a random number table method. The hypertensive mouse model was constructed by infusing Ang II via a micro-osmotic pump (500 ng/kg per minute), and neferine (0.1, 1, or 10 mg/kg), valsartan (10 mg/kg), or double distilled water was administered intragastrically once daily for 6 weeks. A non-invasive blood pressure system, ultrasound, and hematoxylin and eosin staining were performed to assess blood pressure and vascular changes. RNA sequencing and network pharmacology were employed to identify differentially expressed transcripts (DETs) and pathways. Vascular ring tension assay was used to test vascular function. A7R5 cells were incubated with neferine for 24 h and then treated with Ang II to record the real-time Ca²⁺ concentration by confocal microscope. Immunohistochemistry (IHC) and Western blot were used to evaluate vasorelaxation, calcium, and the extracellular signal-regulated kinase (ERK)1/2 pathway. RESULTS: Neferine treatment effectively mitigated the elevation in blood pressure, pulse wave velocity, aortic thickening in the abdominal aorta of Ang II-infused mice (P<0.05). RNA sequencing and network pharmacology analysis identified 355 DETs that were significantly reversed by neferine treatment, along with 25 potential target genes, which were further enriched in multiple pathways and biological processes, such as ERK1 and ERK2 cascade regulation, calcium pathway, and vascular smooth muscle contraction. Further investigation revealed that neferine treatment enhanced vasorelaxation and reduced Ca²⁺-dependent contraction of abdominal aortic rings, independent of endothelium function (P<0.05). The underlying mechanisms were mediated, at least in part, via suppression of receptor-operated channels, store-operated channels, or voltage-operated calcium channels. Neferine pre-treatment demonstrated a reduction in intracellular Ca²⁺ release in Ang II stimulated A7R5 cells. IHC staining and Western blot confirmed that neferine treatment effectively attenuated the upregulation of p-ERK1/2 both in vivo and in vitro, which was similar with treatment of ERK1/2 inhibitor PD98059 (P<0.05). CONCLUSIONS Neferine remarkably alleviates Ang II-induced elevation of blood pressure, vascular dysfunction, and pathological changes in the abdominal aorta. This beneficial effect is mediated by the modulation of multiple pathways, including calcium and ERK1/2 pathways.
Animals ; Angiotensin II ; Male ; Benzylisoquinolines/therapeutic use* ; Network Pharmacology ; Blood Pressure/drug effects* ; Sequence Analysis, RNA ; Mice ; Hypertension/chemically induced* ; Mice, Inbred C57BL ; Calcium/metabolism*

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.

A new method was developed for simultaneous and efficient determination of linear perfluorooctanoic acid(n-PFOA)and linear perfluorooctane sulfonate(n-PFOS),and their typical branched isomers in seawater by online solid phase extraction-liquid chromatography-tandem mass spectrometry(Online SPE-LC-MS/MS).Only centrifugation of the seawater sample was required to remove the particulate matter,and then the seawater sample was directly injected and analyzed by online SPE-LC-MS/MS.An Eclipse Plus-C18 guard column was selected as SPE column for online enrichment of linear and branched isomers,and a F5 PFP column(150 mm×2.1 mm,2.7 μm)was used as the analytical column.Under the optimized experimental conditions,the separation and detection of all PFOA and PFOS linear and branched isomers could be completed within 20 min.The spiked recoveries of various target compounds ranged from 82.9%to 107.7%with detection limits and limits of quantification of 0.10-1.05 ng/L and 0.30-2.11 ng/L,respectively.The method was characterized by good precision(RSD≤9.10%)and linearity(R2≥0.990).Subsequently,linear and branched isomers of PFOA and PFOS in surface and bottom seawater samples collected from the Laizhou Bay of China were determined.The results showed that the detection rate of all the four branched PFOA isomers were 100%,with the highest average concentration of 25.85 ng/L found for 6m-PFOA,which accounted for 11.79%of the∑PFOA.For the five branched isomers of PFOS,the highest detection rate of 90.84%was found for 5m-PFOS.The highest average concentration of 0.64 ng/L was observed for 3m-PFOS,accounting for 19.88%of ∑PFOS.The proposed method provided an effective detection tool for qualitative and quantitative detection of PFOA and PFOS isomers in the marine aquatic environment.

Paclitaxel(PTX)is a first-line chemotherapy drug for breast cancer,but its resistance issues significantly impact clinical treatment efficacy.Fucosylation,especially core fucosylation,is closely related to tumor chemoresistance,resulting in poor chemotherapy responses and poor prognosis in patients.In this study,we investigated the effect and mechanism of the fucosylation inhibitor 2-fluorofucose(2-F-Fuc)on the chemosensitivity of paclitaxel-resistant breast cancer MCF-7/PTX cells.The drug resistanceindex(RI)of MCF-7/PTX cells was 8.49 by MTT assays.Western blotting,real-time PCR,enzyme-linked immu-nosorbent assay(ELISA)and Lens Culinaris Agglutinin(LCA)lectin imprinting showed that compared with MCF-7 cells,the expression of FUT8,MDR1and core fucosylation in MCF-7/PTX cells was high.Western blotting showed that 2-F-Fuc had a significant inhibitory effect on the growth of MCF-7/PTX cells,and the expression levels of FUT8 and MDR1 were significantly down-regulated after 2-F-Fuc treatment,and the down-regulation was more pronounced in the PTX and 2-F-Fuc combination group(P＜0.05).Compared to the control,expression of PCNA in MCF-7/PTX cells in the PTX and the 2-F-Fuc group were down-regulated,and the apoptosis-related proteins,such as cleaved caspase-3 and Bax/Bcl-2 were in-creased.The level of p-PI3K and p-AKT were down-regulated,and the changes in the combination of 2-F-Fuc and PTX were more robust(P＜0.05).The above results showed that the core fucosylation level of MCF-7/PTX cells was significantly increased,and 2-F-Fuc could reduce the core fucosylation level of MCF-7/PTX cells by inhibiting the expression of FUT8,and enhance the sensitivity of drug-resistant cells to PTX,which may correlate with the downregulation of PI3K/AKT signaling pathway proteins.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.