Thrombospondin 4 (THBS4; TSP4), a crucial component of the extracellular matrix (ECM), serves as an important regulator of tissue homeostasis and various pathophysiological processes. As a member of the evolutionarily conserved thrombospondin family, THBS4 is a multidomain adhesive glycoprotein characterized by six distinct structural domains that mediate its diverse biological functions. Through dynamic interactions with various ECM components, THBS4 plays pivotal roles in cell adhesion, proliferation, inflammation regulation, and tissue remodeling, establishing it as a key modulator of microenvironmental organization. The transcription and translation of THBS4 gene, as well as the activity of the THBS4 protein, are tightly regulated by multiple signaling pathways and extracellular cues. Positive regulators of THBS4 include transforming growth factor-β (TGF-β), interferon-γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), bone morphogenetic proteins (BMP12/13), and other regulatory factors (such as B4GALNT1, ITGA2/ITGB1, PDGFRβ, etc.), which upregulate THBS4 at the mRNA and/or protein level. Conversely, oxidized low-density lipoprotein (OXLDL) acts as a potent negative regulator of THBS4. This intricate regulatory network ensures precise spatial and temporal control of THBS4 expression in response to diverse physiological and pathological stimuli. Functionally, THBS4 acts as a critical signaling hub, influencing multiple downstream pathways essential for cellular behavior and tissue homeostasis. The best-characterized pathways include: (1) the PI3K/AKT/mTOR axis, which THBS4 modulates through both direct and indirect interactions with integrins and growth factor receptors; (2) Wnt/β-catenin signaling, where THBS4 functions as either an activator or inhibitor depending on the cellular context; (3) the suppression of DBET/TRIM69, contributing to its diverse regulatory roles. These signaling connections position THBS4 as a master regulator of cellular responses to microenvironmental changes. Substantial evidence links aberrant THBS4 expression to a range of pathological conditions, including neoplastic diseases, cardiovascular disorders, fibrotic conditions, neurodegenerative diseases, musculoskeletal disorders, and atopic dermatitis. In cancer biology, THBS4 exhibits context-dependent roles, functioning either as a tumor suppressor or promoter depending on the tumor type and microenvironment. In the cardiovascular system, THBS4 contributes to both adaptive remodeling and maladaptive fibrotic responses. Its involvement in fibrotic diseases arises from its ability to regulate ECM deposition and turnover. The diagnostic and therapeutic potential of THBS4 is particularly promising in oncology and cardiovascular medicine. As a biomarker, THBS4 expression patterns correlate significantly with disease progression and patient outcomes. Therapeutically, targeting THBS4-mediated pathways offers novel opportunities for precision medicine approaches, including anti-fibrotic therapies, modulation of the tumor microenvironment, and enhancement of tissue repair. This comprehensive review systematically explores three key aspects of THBS4 research(1) the fundamental biological functions of THBS4 in ECM organization; (2) its mechanistic involvement in various disease pathologies; (3) its emerging potential as both a diagnostic biomarker and therapeutic target. By integrating recent insights from molecular studies, animal models, and clinical investigations, this review provides a framework for understanding the multifaceted roles of THBS4 in health and disease. The synthesis of current knowledge highlights critical research gaps and future directions for exploring THBS4-targeted interventions across multiple disease contexts. Given its unique position at the intersection of ECM biology and cellular signaling, THBS4 represents a promising frontier for the development of novel diagnostic tools and therapeutic strategies in precision medicine.

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.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

OBJECTIVE: This study explored the job choice preferences of Center for Disease Prevention and Control (CDC) workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies. METHODS: A discrete choice experiment was conducted in nine provinces across China. Seven key attributes were identified to analyze the job preferences of CDC workers. Mixed logit models, latent class models, and policy simulation tools were used. RESULTS: A valid sample of 5,944 cases was included in the analysis. All seven attributes significantly influenced the job choices of CDC workers. Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility. Income-prioritizers were concerned with income and opportunities for career development, whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits. The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives. CONCLUSION Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers. Heterogeneity in job preferences was also identified. Based on the preference characteristics of different subgroups, policy content should be skewed to differentiate the importance of incentives.
China ; Humans ; Male ; Female ; Adult ; Centers for Disease Control and Prevention, U.S. ; Middle Aged ; Choice Behavior ; Career Choice ; Motivation

OBJECTIVE: The objective of our study was to evaluate the vaccine effectiveness (VE) of the pentavalent rotavirus vaccine (RV5) among < 5-year-old children in three provinces of China during 2020-2024 via a propensity score-matched test-negative case-control study. METHODS: Electronic health records and immunization information systems were used to obtain data on acute gastroenteritis (AGE) cases tested for rotavirus (RV) infection. RV-positive cases were propensity score matched with RV-negative controls for age, visit month, and province. RESULTS: The study included 27,472 children with AGE aged 8 weeks to 4 years at the time of AGE diagnosis; 7.98% (2,192) were RV-positive. The VE (95% confidence interval, CI) of 1-2 and 3 doses of RV5 against any medically attended RV infection (inpatient or outpatient) was 57.6% (39.8%, 70.2%) and 67.2% (60.3%, 72.9%), respectively. Among children who received the 3rd dose before turning 5 months of age, 3-dose VE decreased from 70.4% (53.9%, 81.1%) (< 5 months since the 3rd dose) to 63.0% (49.1%, 73.0%) (≥ 1 year since the 3rd dose). The three-dose VE rate was 69.4% (41.3%, 84.0%) for RVGE hospitalization and 57.5% (38.9%, 70.5%) for outpatient-only medically attended RVGE. CONCLUSION Three-dose RV5 VE against rotavirus gastroenteritis (RVGE) in children aged < 5 years was higher than 1-2-dose VE. Three-dose VE decreased with time since the 3rd dose in children who received the 3rd dose before turning five months of age, but remained above 60% for at least one year. VE was higher for RVGE hospitalizations than for medically attended outpatient visits.
Humans ; Rotavirus Vaccines/immunology* ; China/epidemiology* ; Case-Control Studies ; Child, Preschool ; Infant ; Rotavirus Infections/epidemiology* ; Male ; Propensity Score ; Female ; Vaccine Efficacy ; Gastroenteritis/virology* ; Vaccines, Attenuated ; Rotavirus

A microchannel-based electrochemiluminescence(ECL)sensor was developed for detection of tetracycline(TC)utilizing luminol/H2O2 as ECL reporting system.The low excitation potential of luminol/H2O2 effectively mitigated the impact of clamping voltage,thereby enhancing the detection performance of the microchannel-based ECL sensor.The microchannel modified with TC aptamer selectively recognized and captured target TC.The positively charged TC reduced the surface charge density within the microchannel,thereby increasing the ionic current in the microchannel,leading to change of ECL signal of system.The experimental conditions such as electrolyte concentration,TC-aptamer concentration,and reaction time between TC and TC-aptamer were optimized.Under optimal conditions,the difference of ECL signal in the absence and presence of TC(?ECL)exhibited a good linear relationship with TC concentration in the range from 1.00 ng/mL to 200 ng/mL,with a detection limit as low as 0.69 ng/mL.The sensor had good selectivity and was successfully used in detection of TC in milk samples.

Monoamine neurotransmitters mainly include serotonin,dopamine,epinephrine,and norepinephrine.They play an indispensable regulatory role in key physiological activities such as emotion,sleep,and memory within the central nervous system.Precise detection of these neurotransmitters holds great significance in the field of neuroscience research.Detection methods for monoamine neurotransmitters encompass high-performance liquid chromatography,mass spectrometry,capillary electrophoresis,fluorescence spectroscopy,and electrochemical methods,etc.Compared with other methods,electrochemical methods offer advantages such as high sensitivity,good selectivity,low cost,strong portability,convenient operation,and capability for in vivo real-time detection.This article reviewed recent research progress in electrochemical detection of monoamine neurotransmitters,focusing on a retrospective and summary from three aspects:sensor electrode materials,detection of various monoamine neurotransmitters,and in vivo real-time analysis.Furthermore,the future development of electrochemical sensors for monoamine neurotransmitters was prospected.

Objective To investigate the size variation in the first metatarsal of extant primates.Methods In this study,we analyzed 135 first metatarsal specimens across 12 primate genera,quantifying eight linear measurements,articular surface areas,mid-shaft cross-sectional area,total surface area,volume,and derived indices.Multivariate patterns were assessed through mean-based correspondence analysis and principal component analysis(PCA).Results Eulemur,Otolemur,Cebus,and Perodicticus exhibited a relatively high metatarsal surface-area-to-volume ratio.Perodicticus additionally showed a low articular facet index.Propithecus,Colobus,and Macaca displayed lower values for metatarsal shaft robusticity,the ratio of shaft cross-sectional area to base articular surface area,and the proximal articular facet index.Nasalis possessed a relatively high articular facet index.Pongo,Pan,Gorilla,and Homo sapiens were characterized by higher metatarsal shaft robusticity and a lower metatarsal surface-area-to-volume ratio.Principal component analysis revealed that the 12 extant primate genera could be broadly divided into two groups.Group 1 comprised Pongo,Pan,Gorilla and H.sapiens,although H.sapiens formed a distinct cluster relative to the extant great apes.Group 2 included Eulemur,Otolemur,Perodicticus,Propithecus,Cebus,Colobus,Macaca and Nasalis.Conclusion The relative sizedistribution of the first metatarsal provides some reference value for classifying extant primates.However,it demonstrates no clear correlations with specific locomotor patterns or foot grasping ability.