OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

Sjögren's syndrome (SS) is an autoimmune disease characterized primarily by oral and periocular dryness. Astragalus-Salvia (AS) and Ophiopogon-Dendrobium (OD) represent two frequently utilized herb pairs in SS treatment. While the combination of AS-OD herb pairs demonstrates clinical efficacy in alleviating SS symptoms, its underlying mechanism remains unclear. This investigation sought to assess the therapeutic effects and elucidate the potential mechanisms of AS-OD in non-obese diabetic (NOD)/Ltj mice with SS. The study utilized NOD/Ltj mice as SS models, administering AS-OD treatment for 10 weeks at doses of 113.1, 226.2, and 339.3 mg·d^-1·20 g^-1. Results demonstrated that AS-OD improved SS symptoms, evidenced by enhanced salivary flow rate, decreased anti-SSA/Ro and anti-SSB/La antibody levels, increased swimming duration, and reduced lactate (LA) and blood urea nitrogen (BUN) levels in NOD/Ltj mice. AS-OD reduced lymphocyte infiltration, enhanced Aquaporin-5 (AQP5) expression in the submandibular gland, decreased inflammatory cytokine levels in the submandibular gland, and reduced the T helper type 17/regulatory T lymphocyte (Th17/Treg) cell ratio in the spleen. Transcriptomic and proteomic analyses indicated AS-OD's involvement in regulating phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) and Janus kinase 3/signal transducer and activator of transcription 3 (JAK1/STAT3) pathways, with inhibitory effects validated in both NOD/Ltj mice submandibular gland and A-253 cells. Furthermore, AS-OD enhanced cell viability and reduced A-253 cell apoptosis through the PI3K/AKT pathway. In A-253 cells, AS-OD reduced inflammatory cytokine levels, CXC chemokine ligand 9/10 (CXCL9/10), and T-cell chemotaxis by inhibiting the JAK1/STAT3 pathway. AS-OD mitigates SS by suppressing inflammation and immune responses through the PI3K/AKT and JAK1/STAT3 pathways.
Animals ; STAT3 Transcription Factor/genetics* ; Sjogren's Syndrome/immunology* ; Mice, Inbred NOD ; Proto-Oncogene Proteins c-akt/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Signal Transduction/drug effects* ; Janus Kinase 1/genetics* ; Humans ; Female ; Astragalus Plant/chemistry* ; Male

OBJECTIVE Aimd to investigate the prevalence of olfactory dysfunction among healthy elderly individuals in China using a large sample size and to explore its correlations with demographic and other factors.METHODS A total of 5 258 participants who aged 60 and above underwent the T&T olfactometer test and completed questionnaires.The prevalence of olfactory dysfunction was reported,and logistic regression analyses were performed to assess the associations between olfactory dysfunction and other factors.RESULTS The overall prevalence of olfactory dysfunction in the study was 26.5%.Independent risk factors for olfactory dysfunction included advanced age,rural residence(OR=1.88,95%CI:1.60-2.22,P<0.01),current smoking(OR=1.45,95%CI:1.15-1.83,P<0.01),and self-reported olfactory dysfunction(OR=10.12,95%CI:8.44-12.14,P<0.01).In contrast,female gender(OR=0.70,95%CI:0.58-0.85,P<0.01)and high educational attainment were independently associated with a lower prevalence of olfactory dysfunction.CONCLUSION The prevalence of olfactory dysfunction among individuals aged 60 and above increased with age.Male gender,rural residence,low educational attainment,current smoking,and self-reported decline in olfactory function were independently associated with olfactory dysfunction.

Decabromodiphenyl ether(BDE-209)and decabromodiphenyl ethane(DBDPE)are widely used brominated flame retardants,which have been detected in the atmosphere,water,soil,and various organisms.In this study,a method based on high-performance liquid chromatography-inductively coupled plasma-mass spectrometry(HPLC-ICP-MS)was developed for determination of BDE-209 and DBDPE in sediment.Firstly,the target compounds in the sediments were extracted by accelerated solvent extraction(ASE),and the extraction solvent was hexane/dichloromethane(1∶1,V/V).The extract was concentrated by rotary evaporation and purified by a composite silica gel column(6 g neutral silica gel,8 g acidic silica gel,and 4 g anhydrous sodium sulfate),concentrated by nitrogen blowing,and then re-dissolved with 1 mL of toluene for instrumental determination.The chromatographic separation was carried out on a TC-C18(2)column(250 mm×4.6 mm)with isocratic elution using methanol-isopropanol-water(89∶6∶5,V/V)as the mobile phase,and the samples were separated within 20 min.Further,the Br element was quantified by ICP-MS to realize the detection of the target.The results showed that the method established in this study exhibited good linearity(R2>0.999)in the range of 100-10000 ng/mL,and the limits of quantification(LOQs)of the method were 2.0 ng/g for BDE-209 and 10.0 ng/g for DBDPE,with the relative standard deviations(RSDs,n=3)lower than 10%,and the recoveries were in the acceptable range(80.9%-120.7%).The matrix effect was effectively controlled within 10%.In addition,by analyzing the actual sediment samples from Guangxi,a background point,and Taizhou,Zhejiang,a typical contaminated area,it was found that neither BDE-209 nor DBDPE was detected in the sediment from Guangxi,while the concentrations of BDE-209 and DBDPE in the sediment from Zhejiang ranged from 1591.8 to 3362.9 ng/g,which further demonstrated the applicability and reliability of the method for analyzing actual environmental samples.This study provided a strong technical support for the accurate detection of POPs in the environment.

Microbial detection and control of traditional Chinese medicine(TCM) decoction pieces are crucial for the quality control of TCM preparations. It is also a key area of research in the measurement technology and equipment development for TCM manufacturing. Guided by TCM manufacturing measurement methodologies, this study presented a design of a novel portable microbial detection device, using Atractylodis Macrocephalae Rhizoma decoction pieces as a demonstration. Immunomagnetic separation technology was employed for specific isolation and labeling of target microorganisms. Enzymatic signal amplification was utilized to convert weak biological signals into colorimetric signals, constructing an optical biosensor. A self-developed smartphone APP was further applied to analyze the colorimetric signals and quantify target concentrations. A portable and automated detection system based on Arduino microcontroller was developed to automatically perform target microbial separation/extraction, as well as mimetic enzyme labeling and catalytic reactions. The developed equipment specifically focuses on the rapid and quantitative microbial analysis of TCM active pharmaceutical ingredients, intermediates in TCM manufacturing, and final TCM products. Experimental results demonstrate that the equipment could detect Salmonella in samples within 2 h, with a detection limit as low as 5.1 × 10~3 CFU·mL~(-1). The equipment enables the rapid detection of microorganisms in TCM decoction pieces, providing a potential technical solution for on-site rapid screening of microbial contamination indicators in TCM. It has broad application prospects in measurement technology for TCM manufacturing and offers strong technical support for the modernization, industrialization, and intelligent development of TCM.
Drugs, Chinese Herbal/analysis* ; Atractylodes/microbiology* ; Rhizome/microbiology* ; Biosensing Techniques/methods* ; Medicine, Chinese Traditional ; Colorimetry/instrumentation* ; Quality Control

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.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.