Objective To investigate the association of serum total testosterone(TT),free testosterone(FT),sex hormone-binding globulin(SHBG),and estradiol(E_2)with all-cause mortality rate(AMR)in males and to evaluate their predictive value. Methods Based on data from the National Health and Nutrition Examination Survey(NHANES)cycles(1999—2004 and 2013—2016),the relationships between different sex hormones and AMR was analyzed with Cox proportional hazards regression models. The dose-response relationships between different sex hormones and male AMR were assessed with restricted cubic splines(RCS). The predictive performances of different sex hormones for male AMR were compared with receiver operating characteristic(ROC)curves and the area under the curve(AUC). The survival differences across varying sex hormone levels were evaluated with Kaplan-Meier survival curves and log-rank tests. Results This study involved 3632 adult male participants with a median follow-up of 6.5 years,during which 320 AMR occurred(8.8%). Cox univariate analysis indicated that TT,FT and SHBG were risk factors for AMR(P＜0.001). Cox multivariate analysis revealed that FT and SHBG were independent risk factors for AMR(FT:HR=0.90,95%CI:0.86-0.94; SHBG:HR=1.01,95%CI:1.01-1.02). RCS revealed a U-shaped nonlinear relationship between TT,FT and AMR(P＜0.001); SHBG showed a positive dose-response relationship with AMR(P＜0.001). The AUCs of FT predicting the 5-year,10-year,and 15-year AMR were 0.78(95%CI:0.73-0.82),0.80(95%CI:0.76-0.84),and 0.84(95%CI: 0.80-0.88),respectively,significantly outperforming other indicators. Subgroup analysis revealed that FT was negatively correlated with male AMR,but this association was modulated by age,race,smoking status and diabetes status(P_(interaction)＜0.05). Kaplan-Meier survival analysis showed that different levels of TT and FT(Q1-Q4)were significantly positively correlated with survival rate(P＜0.001),while different levels of SHBG were significantly negatively correlated with survival rate,with patients in the Q4 group having the highest risk of death(P＜0.001). Conclusion Based on US NHANES,studies have shown that the FT and SHBG are independent risk factors for male AMR,and FT demonstrates greater predictive value for male AMR than TT.

Abstract The cooccurrence of multi disease among children and adolescents has become increasingly prominent, necessitating a shift from "single disease separate treatment" to "multi disease co prevention". Focusing on four common health problems in children and adolescents, namely, myopia, depression, overweight/obesity, and spinal abnormalities, the paper systematically reviews the association between four categories of factors, society, environment, family, and individual behavior, and the co occurrence of the aforementioned health problems. The findings indicate that particulate matter 2.5 exposure, light at night, physical inactivity, excessive screen time, and unhealthy diet constitute the primary cross dimensional risk network. Based on these findings, the paper proposes a collaborative action framework involving family, school, and society, emphasizing systematic strategies ranging from upstream environmental governance to downstream behavioral intervention, so as to provide evidence for the practice and policy making of multi disease co-prevention in children and adolescents.

To investigate the genetic relatedness between carbapenem-resistant Klebsiella pneumoniae(CRKP) strains isolated from intestinal colonization and subsequent bloodstream infections.

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.

ObjectiveTo investigate the possible mechanism of Shufeng Jiedu capsules （SFJD） in alleviating influenza A （H1N1） virus pneumonia and focus on its effect on Toll-like receptor （TLR） signaling pathway in respiratory epithelial cells. MethodsA mouse model of viral pneumonia was established via the A/PR/8/34 （PR8） strain of influenza A virus. Mice were randomly divided into a normal group， a PR8 infection （PR8） group， and an SFJD group （8.4 g·kg^-1）， with 10 mice in each group. The day of infection was designated as day 1. The SFJD group was administered intragastrically at a volume of 20 mL·kg^-1 daily， while the normal and PR8 groups were given an equal volume of deionized water. Micro-computed tomography （Micro-CT） was performed on day 5， and the mice were dissected to collect their lungs， after which the lung index was calculated to verify the therapeutic effect of SFJD. Single-cell sequencing was used to analyze the differentially expressed genes in respiratory epithelial cells. Multiplex fluorescence immunohistochemistry was employed to detect the expression of TLR， tumor necrosis factor receptor-associated factor 6 （TRAF6）， and myeloid differentiation factor 88 （MyD88） proteins in epithelial cell adhesion molecule （EpCAM）-positive cells， and the proportion of respiratory epithelial cells expressing TLR pathway proteins was calculated. Respiratory epithelial cells were then sorted by flow cytometry， and Western blot was used to detect the expression of TLR， MyD88， TRAF6， Toll-interleukin receptor domain-containing adaptor inducing interferon-β （TRIF）， inhibitor of κB kinase α （IKKα）， and nuclear factor-κB （NF-κB） in the sorted epithelial cells. Enzyme-linked immunosorbent assay （ELISA） was used to measure the levels of interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α） in lung tissue. ResultsAt the transcriptional level， SFJD reversed the expression of TLR signaling pathway genes in respiratory epithelial cells， downregulating multiple TLR signaling pathway-related genes （P<0.01）. At the protein level， SFJD significantly reduced the proportion of respiratory epithelial cells expressing TLR3 （P<0.05）， the expression levels of TLR2， TLR3， TLR4， TRIF， TRAF6， IKKα， and NF-κB in epithelial cells（P<0.05， P<0.01）， as well as the levels of pro-inflammatory cytokines IL-1β and TNF-α in lung tissue （P<0.01）. ConclusionSFJD may alleviate viral pneumonia by suppressing the expression of TLR in respiratory epithelial cells and their subsequent signaling cascades.

This study aimed to investigate the effect of saltwater stir-fried Plantaginis Semen(SPS) on renal fibrosis in rats and decipher the underlying mechanism. Thirty-six Sprague-Dawley rats were randomly assigned into control, model, losartan potassium, and low-, medium-, and high-dose(15, 30, and 60 g·kg~(-1), respectively) SPS groups. Rats in other groups except the control group were subjected to unilateral ureteral obstruction(UUO) to induce renal fibrosis, and the modeling and gavage lasted for 14 days. After 14 consecutive days of treatment, the levels of serum creatinine(Scr) and blood urea nitrogen(BUN) in rats of each group were determined by an automatic biochemical analyzer. Hematoxylin-eosin(HE) and Masson staining were used to evaluate pathological changes in the renal tissue. Western blot and immunofluorescence assay were conducted to determine the protein levels of fibronectin(FN), collagen Ⅰ, vimentin, and α-smooth muscle actin(α-SMA) in the renal tissue. The mRNA levels of epithelial-mesenchymal transition(EMT)-associated transcription factors including twist family bHLH transcription factor 1(TWIST1), snail family transcriptional repressor 1(SNAI1), and zinc finger E-box binding homeobox 1(ZEB1), as well as inflammatory cytokines such as interleukin-1β(IL-1β), interleukin-6(IL-6), and tumor necrosis factor-α(TNF-α), were determined by RT-qPCR. Human renal proximal tubular epithelial(HK2) cells exposed to transforming growth factor-β(TGF-β) for the modeling of renal fibrosis were used to investigate the inhibitory effect of SPS on EMT. Network pharmacology and Western blot were employed to explore the molecular mechanism of SPS in alleviating renal fibrosis. The results showed that SPS significantly reduced Scr and BUN levels and alleviated renal injury and collagen deposition in UUO rats. Moreover, SPS notably down-regulated the protein levels of FN, collagen Ⅰ, vimentin, and α-SMA as well as the mRNA levels of SNAI1, ZEB1, TWIST1, IL-1β, IL-6, and TNF-α in the kidneys of UUO rats and TGF-β-treated HK-2 cells. In addition, compared with Plantaginis Semen without stir-frying with saltwater, SPS showed increased content of specific compounds, which were mainly enriched in the mitogen-activated protein kinase(MAPK) signaling pathway. SPS significantly inhibited the phosphorylation of extracellular signal-regulated kinase(ERK) and p38 MAPK in the kidneys of UUO rats and TGF-β-treated HK2 cells. In conclusion, SPS can alleviate renal fibrosis by attenuating EMT through inhibition of the MAPK signaling pathway.
Animals ; Epithelial-Mesenchymal Transition/drug effects* ; Rats, Sprague-Dawley ; Male ; Rats ; Fibrosis/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Kidney Diseases/pathology* ; Kidney Tubules/pathology* ; Humans

OBJECTIVE: To investigate the impact of early antimicrobial therapy on the prognosis of patients with suspected sepsis in emergency and outpatient settings. METHODS: A prospective cohort study was conducted. Patients with suspected sepsis admitted to the emergency department of Taizhou Hospital, Zhejiang Province, from May 1, 2022, to July 31, 2023, were enrolled. Participants were divided into an early group (0-1 hour) and a delayed group (> 1 hour) according to duration from admission to antimicrobial administration. General information, initial vital signs, laboratory parameters within 24 hours after admission, disease severity scores, vasoactive drug usage, and clinical outcomes of the patient were collected. Kaplan-Meier survival curve was used to analyze 28-day survival. Multivariate Cox proportional hazards regression was performed to identify independent risk factors for prognosis of the patients with suspected sepsis in emergency and outpatient settings. Sensitivity analyses were conducted through subgroup analyses. RESULTS: A total of 143 patients with suspected sepsis were enrolled in the analysis, with 66 patients in the early group and 77 in the delayed group. No statistically significant differences were observed in baseline characteristics (age, gender, vital signs, laboratory parameters, disease severity scores) or clinical outcomes [vasoactive drug usage rate, mechanical ventilation duration, length of intensive care unit (ICU) stay, total hospitalization duration] between the two groups. The 28-day mortality, multidrug resistance rate and sepsis confirmation rate did not differ significantly between the early group and delay group [28-day mortality: 18.2% (12/66) vs. 20.8% (16/77), multidrug resistance rate: 3.0% (2/66) vs. 2.6% (2/77), sepsis confirmation rate: 87.9% (58/66) vs. 88.3% (68/77), all P > 0.05]. Kaplan-Meier survival curve analysis showed no difference in 28-day cumulative survival between the two groups (Log-Rank test: χ² = 2.528, P = 0.112). Multivariate Cox proportional hazards regression identified vasoactive drug usage [hazard ration (HR) = 2.465, 95% confidence interval (95%CI) was 1.019-5.961, P = 0.045] and endotracheal intubation (HR = 5.516, 95%CI was 2.195-13.858, P < 0.001) as independent risk factors for 28-day death of the patients with suspected sepsis in emergency and outpatient settings. Further exploration of the impact of early antimicrobial therapy on 28-day death in different subgroups of the patients with suspected sepsis in emergency and outpatient settings was conducted through subgroup analysis. The results showed that in the patients with different ages (< 60 years old: HR = 1.214, 95%CI was 0.535-2.751, P = 0.643; ≥ 60 years old: HR = 2.085, 95%CI was 0.233-18.668, P = 0.511), sequential organ failure assessment (SOFA) scores (< 6: HR = 1.411, 95%CI was 0.482-4.128, P = 0.530; ≥ 6: HR = 0.869, 95%CI was 0.292-2.587, P = 0.801), shock indexes (< 1: HR = 1.095, 95%CI was 0.390-3.077, P = 0.863; ≥ 1: HR = 1.364, 95%CI was 0.458-4.059, P = 0.577) and whether diagnosed with sepsis or not (yes: HR = 0.943, 95%CI was 0.059-15.091, P = 0.967; no: HR = 1.207, 95%CI was 0.554-2.628, P = 0.636) subgroups, early usage of antibiotics had not shown any advantage in improving prognosis compared with delayed usage. CONCLUSION Early antimicrobial therapy does not improve the prognosis of patients with suspected sepsis in emergency and outpatient settings.
Humans ; Sepsis/drug therapy* ; Prospective Studies ; Prognosis ; Emergency Service, Hospital ; Outpatients ; Female ; Male ; Anti-Infective Agents/therapeutic use* ; Middle Aged ; Aged ; Proportional Hazards Models ; Treatment Outcome

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