Identification of shared molecular mechanisms and diagnostic biomarkers between heart failure and idiopathic pulmonary fibrosis.

Background: Heart failure (HF) and idiopathic pulmonary fibrosis (IPF) are global public health concerns. The relationship between HF and IPF is widely acknowledged. However, the interaction mechanisms between these two diseases remain unclear, and early diagnosis is particularly difficult. Through the integration of bioinformatics and machine learning, our work aims to investigate common gene features, putative molecular causes, and prospective diagnostic indicators of IPF and HF. Methods: The Gene Expression Omnibus (GEO) database provided the RNA-seq datasets for HF and IPF. Utilizing a weighted gene co-expression network analysis (WGCNA), possible genes linked to HF and IPF were found. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were then employed to analyze the genes that were shared by HF and IPF. Using the cytoHubba and iRegulon algorithms, a competitive endogenous RNA (ceRNA) network was built based on seven basic diagnostic indicators. Additionally, hub genes were identified using machine learning approaches. External datasets were used to validate the findings. Lastly, the association between the number of immune cells in tissues and the discovered genes was estimated using the CIBERSORT method. Results: In total, 63 shared genes were identified between HF- and IPF-related modules using WGCNA. Extracellular matrix (ECM)/structure organization, ECM-receptor interactions, focal, and protein digestion and absorption, were shown to be the most enrichment categories in GO and KEGG enrichment analysis of common genes. Furthermore, a total of seven fundamental genes, including COL1A1, COL3A1, THBS2, CCND1, ASPN, FAP, and S100A12, were recognized as pivotal genes implicated in the shared pathophysiological pathways of HF and IPF, and TCF12 may be the most important regulatory transcription factor. Two characteristic molecules, CCND1 and NAP1L3, were selected as potential diagnostic markers for HF and IPF, respectively, using a support vector machine-recursive feature elimination (SVM-RFE) model. Furthermore, the development of diseases and diagnostic markers may be associated with immune cells at varying degrees. Conclusions: This study demonstrated that ECM/structure organisation, ECM-receptor interaction, focal adhesion, and protein digestion and absorption, are common pathogeneses of IPF and HF. Additionally, CCND1 and NAP1L3 were identified as potential diagnostic biomarkers for both HF and IPF. The results of our study contribute to the comprehension of the co-pathogenesis of HF and IPF at the genetic level and offer potential biological indicators for the early detection of both conditions.

Entropy-Driven Phase Transition in Nanonucleation via Reciprocal Relations.

Depletion forces, commonly modeled with rigid body assumptions, inadequately describe nanoparticle behavior. This study introduces the roles of Soret and Dufour effects in nanoparticle systems, focusing on mass and energy gradient interchange. The Dufour effect drives energy flow through mass gradients, which implies the possibility of driving the energy flow near the nanoparticles to induce phase transitions via manipulating the monomer concentrations. Our experimental platform confirmed these theories, synthesizing HCP quantum dots from Au, Ag, Pd, and mixed halide perovskites under varied precursor concentrations. By analyzing energy flow and nucleation dynamics through the Dufour effect, we mapped phase transition thresholds, linking them to material entropy. This study provides insight into nanoparticle assembly and phase behavior, highlighting the significance of entropy in nucleation dynamics.

Mateverse, the Future Materials Science Computation Platform Based on Metaverse.

Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ultimate goal for many disciplines. In materials science, human intelligence is still irreplaceable from machine learning techniques, where humans can deal with complex correlations in the real world. We design the framework of Mateverse, a materials science computation platform based on Metaverse, which unifies human intelligence, experiment data, and theoretical simulations. In Mateverse, we intensively study the properties of H2O, including the liquid and solid phases. We show that we can optimize a new water force field (which we name TIP4P-Meta) directly from the interactions between human and visible properties of H2O. This force field is validated to be better than the conventional water model, and new ice polymorphs can be generated. We believe our platform can provide valuable hints in the paradigm upgrade in future computational materials science development.

A prognostic nomogram for papillary thyroid cancer lymph node metastasis based on immune score.

Background: Papillary thyroid cancer (PTC) is the most common subtype of thyroid cancer and is characterized by an overall good prognosis and early-stage lymph node metastasis. The immune microenvironment is believed to play a crucial role in PTC initiation, progression and metastasis. However, to our knowledge, prognostic tools for thyroid cancer metastasis based on immune scores have not been adequately explored. This study aimed to construct a clinical nomogram to predict lymph node metastasis in patients with PTC. Methods: The genomic data and clinical-pathological characteristics of 447 PTC subjects were obtained from TCGA (The Cancer Genome Atlas data). Logistic regression models were performed for univariate and multivariate analyses to identify significant prediction factors. A prognostic nomogram was built based on the multivariate analysis results. The concordance index (C-index) and calibration curve were used to assess the predictive accuracy and discriminative ability of the model. Results: The patients were divided into two subgroups based on immune scores. We found that patients with high immune scores had significantly higher lymph node metastasis risks (OR and 95% confidence interval [CI]: 1.774[1.130-2.784]) than those with low immune scores. The C-index for lymph node metastasis was 0.722 (95% CI, 0.671-0.774), which had a favorable performance for clinical prediction. The calibration curve for lymph node metastasis showed significant agreement between the nomogram prediction and actual observation. Conclusion: High immune scores are significantly correlated with higher lymph node metastasis risk in patients with PTC. Immune score-based prognostic nomograms may help to predict lymph node metastasis and have potential clinical application possibilities.

Positive association of fasting plasma glucose with bone mineral density in non-diabetic elderly females.

INTRODUCTION: Previous studies involving diabetics have shown different associations between fasting plasma glucose (FPG) and bone mineral density (BMD). The different effects of FPG on BMD are due to varying effects of antidiabetic drugs, glycemic control and diabetic complications in the diabetic patients. It is necessary to identify the association in subjects without diabetes. MATERIALS AND METHODS: A total of 2367 females over 65 were included in this cross-sectional study. Subjects were grouped by FPG quartile. BMD and the prevalence of osteoporosis were compared among different FPG quartiles. Multiple logistic regression was used to analyze the independent contribution of FPG to osteoporosis. RESULTS: Subjects in lower FPG quartile had lower BMD (P < 0.05). Subjects with osteoporosis had a lower FPG than the subjects of osteopenia, and both were lower than subjects with normal bone mass (P < 0.001 for all). Compared with the lowest FPG quartile, subjects in the 3rd and the 4th quartiles have a lower risk of osteoporosis in the lumbar spine (OR 0.77, 95% CI 0.59-0.98; OR 0.76, 95% CI 0.56-0.99, respectively), the total hip (OR 0.72, 95% CI 0.56-0.96; OR 0.75, 95% CI 0.53-0.99, respectively), and the femoral neck (OR 0.73, 95% CI 0.50-0.97; OR 0.71, 95% CI 0.54-0.92, respectively) after adjustment for age, BMI, education, physical activity and menopausal age. CONCLUSION: FPG was positively associated with BMD in non-diabetic elderly females. Low FPG may increase the risk of osteoporosis in the non-diabetic elderly females in China.

Physical activities mediate the correlations between serum creatinine and bone mineral density in Chinese.

BACKGROUND: The relationship between serum creatinine (CR) and osteoporosis under normal renal function and the possible mediating effects mediated by physical activity (PA) are largely unknown. METHODS: A total of 4,137 elderly Chinese subjects were recruited. Three models including different covariates were established. Correlation analysis and multiple linear regression analysis were used to evaluate the relationship between CR and bone mineral density (BMD) and also under different PA pattern. Logistic regression was used to investigate the interaction between CR*PA with osteoporosis. PA was used as a moderating variable to investigate the relationship between CR and BMD. RESULTS: As we expected, the association between CR and BMD remained significant after adjusting covariates in all models. The relationship between CR and BMD showed changeable pattern in case of different physical activity. Specially, the moderating effects of PA on serum creatinine and BMD were significant for all models only in the case of medium physical activity (PA3). CONCLUSIONS: In Chinese elderly under normal renal function, serum CR is positively correlated with BMD, and medium physical activity has mediation effect on such correlation.

A novel regQTL-SNP and the risk of lung cancer: a multi-dimensional study.

RegQTL, a novel concept, indicates that different genotypes of some SNPs have differential effects on the expression patterns of miRNAs and their target mRNAs. We aimed to identify the association between regQTL-SNPs and lung cancer risk and to explore the underlying mechanisms. The two-stage case-control study included the first stage in a Chinese population (626 lung cancer cases and 667 healthy controls) and the second stage in a European population (18,082 lung cancer cases and 13,780 healthy controls). Functional annotations were conducted based on the GTEx and the TCGA databases. Functional experiments were performed to explore the underlying biological mechanisms in vitro and vivo. After strict screening, five candidate regQTL-SNPs (rs7110737, rs273957, rs6593210, rs3768617, and rs6836432) were selected. Among them, the variant T allele of rs3768617 in LAMC1 was found to significantly increase the risk of lung cancer (first stage: P = 0.044; second stage: P = 0.007). The eQTL analysis showed that LAMC1 expression level was significantly higher in subjects with the variant T allele of rs3768617 (P = 1.10 × 10-14). In TCGA paired database, the regQTL annotation indicated the different expression patterns between LAMC1 and miRNA-548b-3p for the distinct genotypes of rs3768617. Additionally, LAMC1 knockdown significantly inhibited malignant phenotypes in lung cancer cell lines and suppressed tumor growth. A novel regQTL-SNP, rs3768617, might affect lung cancer risk by modulating the expression patterns of miRNA-548b-3p and LAMC1. RegQTL-SNPs could provide a new perspective for evaluating the regulatory function of SNPs in lung cancer development.

Robust block-matching algorithm for motion estimation using an anti-interference similarity criterion and the bilateral optimization scheme.

As an essential component in applications such as video coding, autonomous navigation, and surveillance cameras, efficient and robust motion estimation is always required. This paper proposes a robust block-matching algorithm consisting of a rough matching step and a fine matching step for motion estimation. In the coarse matching step, an improved adaptive rood pattern search strategy combined with an anti-interference similarity criterion is developed to improve the computational efficiency and robustness. In the fine matching step, after performing a subpixel estimation procedure, a bilateral verification scheme is demonstrated to decrease the motion estimation errors caused by environmental disturbances. Experiments are carried out over popular video and image sequences, and several measurement indexes are used to quantify the performance of the proposed method and other motion estimation methods. Comparative analysis and quantitative evaluation demonstrate that the proposed method exhibits strong robustness and can achieve a good balance between computational efficiency and complexity.

Optimization of the Radiation Dose of Digital Breast Tomosynthesis in Opportunistic Screening by Studying the Effect of Different Combinations of FFDM and DBT Views.

BACKGROUND: Full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) are used separately or in combination to identify small breast lesions. The dose of the examination depends on the density of the breast and the imaging (FFDM or DBT) performed. We have performed a retrospective review of FFDM and DBT in women with denser breasts in order to demonstrate how varying the combination of FFDM and DBT in CC and MLO views affects lesion detection and the average gland dose. METHODS: Eighty-one patients with dense breast received both full-field digital mammography (FFDM) and DBT bilateral screening. The recorded data included the display rates for small lesions or other positive lesions, the type of breast gland, the average gland dose (AGD), and the compression thickness of different collection methods. ANOVA was used to compare the AGD among different collection combinations, and t-test was used to perform pairwise comparison between groups with the same gland type. The relationship between AGD and compression thickness was analyzed by Pearson linear correlation, and the lesion display rates were compared using Chi-square test. RESULTS: We found that AGDs were significantly different among the 6 collection methods (F = 119.06, p<0.01), but were not obviously different between groups with the same gland type (F = 0.848, p>0.05). The types of dense glands were correlated with compression thickness, and the thickness was moderately to strongly correlated with AGD (r=0.596-0.736). The combination of single-view DBT(CC-DBT) and FFDM showed significantly higher mass display rates than the two-view FFDM (p<0.05), while the display rates for other positive lesions were similar (p>0.05). CONCLUSION: Our study showed that in opportunistic screening of patients with small breast masses that can be easily detected by ultrasound, MLO-FFDM+CC-DBT or CC-FFDM+MLO-DBT combinations can better balance the individual average gland dose and detection accuracy. The study result cannot be applied to the detection of non-mass lesions as the numbers are too small.

A robust edge-based template matching algorithm for displacement measurement of compliant mechanisms under scanning electron microscope.

This paper develops a robust edge-based template matching algorithm for displacement measurement of compliant mechanisms under a scanning electron microscope (SEM). The algorithm consists of three steps. First, the Sobel gradient operator and a self-adaptive segment strategy are used to establish the shape model in which the gradient directions of the object's edge points are calculated. Second, a similarity criterion based on image gradients that is robust to illumination change and image noise is utilized for template matching to obtain the coarse results. The third step is to refine the matching results by using an orientation-guided subpixel interpolation strategy. A series of simulations is conducted, and the results show that the proposed algorithm enjoys great robustness against strong image noise and gray-value fluctuation, as well as small rotations and background interferences, and thus is suitable for processing SEM images of compliant mechanisms. Finally, the application of the proposed algorithm in the measurement of the spring constant of the flexure hinges with a straight beam form under a SEM is demonstrated.

Integrating RNA-Seq With GWAS Reveals a Novel SNP in Immune-Related HLA-DQB1 Gene Associated With Occupational Pulmonary Fibrosis Risk: A Multi-Stage Study.

Objective: To evaluate the association between single-nucleotide polymorphisms (SNPs) in RNA-seq identified mRNAs and silicosis susceptibility. Methods: A comprehensive RNA-seq was performed to screen for differently expressed mRNAs in the peripheral blood lymphocytes of eight subjects exposed to silica dust (four silicosis cases and four healthy controls). Following this, the SNPs located on the shortlisted mRNAs, which may affect silicosis susceptibility, were screened through silicosis-related genome-wide association studies (GWAS) (155 silicosis cases and 141 healthy controls), whereas functional expression quantitative trait locus (eQTL)-SNPs were identified using the GTEx database. Finally, the association between functional eQTL-SNPs and silicosis susceptibility (194 silicosis cases and 235 healthy controls) was validated. Results: A total of 70 differentially expressed mRNAs (fold change > 2 or fold change < 0.5, P < 0.05) was obtained using RNA-seq. Furthermore, 476 SNPs located on the shortlisted mRNAs, which may affect silicosis susceptibility (P < 0.05) were obtained using GWAS, whereas subsequent six functional eQTL-SNPs were identified. The mutant A allele of rs9273410 in HLA-DQB1 indicated a potential increase in silicosis susceptibility in the validation stage (additive model: odds ratio (OR)= 1.31, 95% confidence interval (CI) = 0.99-1.74, P = 0.061), whereas the combination of GWAS and the validation results indicated that the mutant A allele of rs9273410 was associated with increased silicosis susceptibility (additive model: OR = 1.35, 95% CI =1.09-1.68, P = 0.006). Conclusion: The mutant A allele of rs9273410 was associated with increased silicosis susceptibility by modulating the expression of HLA-DQB1.

Association of Serum Uric Acid, Urea Nitrogen, and Urine Specific Gravity Levels at 16-18 Weeks of Gestation with the Risk of Gestational Diabetes Mellitus.

OBJECTIVE: To evaluate the associations of serum uric acid (UA), urea nitrogen (UN), and urine specific gravity (USG) levels in the first trimester of pregnancy with the risk of gestational diabetes mellitus (GDM). PATIENTS AND METHODS: A retrospective cohort study was conducted in 1,769 pregnant women aged 31.55 ± 3.91 years. UA, UN, and USG levels were measured during the 16-18th week of gestation. GDM was diagnosed by an oral 75 g glucose tolerance test during the 24-28th week of gestation. RESULTS: A multivariate adjusted logistic regression analysis showed that UA levels in the highest quartile increased the risk of GDM by 55.7% (odds ratio [OR]: 1.557, 95% confidence interval [CI]: 1.055-2.298; p = 0.026) compared to those in the lowest quartile. USG levels in the second, third, and fourth quartiles increased the risk of GDM by 67.6% (95% CI: 1.090-2.421), 112.4% (95% CI: 1.446-3.119), and 94.5% (95% CI: 1.314-2.880), respectively, compared to those in the first quartile (p trend = 0.001). No significant association between UN levels and the GDM risk was observed. When the extreme composite biomarker score quartiles were compared, the adjusted OR (95% CI) for GDM was 1.909 (95% CI: 1.332-2.736). Age-stratified analyses revealed similar results in women aged ≤35 years only, but not in those aged >35 years. CONCLUSION: Higher levels of UA and USG and a higher composite kidney function biomarker score during the 16-18th week of gestation were positively and independently associated with an increased risk of GDM.

Pharmacokinetics and Tissue Distribution of Loratadine, Desloratadine and Their Active Metabolites in Rat based on a Newly Developed LC-MS/MS Analytical Method.

Loratadine (LOR) and its major metabolite, desloratadine (DL) are new-generation antihistamines. The hydroxylated metabolites of them, 6-OH-DL, 5-OH-DL and 3-OH-DL are also active because of their ability to inhibit binding of pyrilamine to brain H1 receptors and a tendency for distributing to specific immune-regulatory tissues. In this study, a new validated LC-MS/MS method to simultaneously quantify LOR, DL, 6-OH-DL, 5-OH-DL and 3-OH-DL in plasma and tissues was established and applied to an investigation of their pharmacokinetics and target-tissue distribution tendency for the first time. Pharmacokinetics parameters in rat were measured and the results suggest that the body's exposure to active metabolites were much higher than to the prodrug with LOR, but much lower with DL. The tissue distribution study shows that LOR, DL and their active metabolites were widely distributed in the liver, spleen, thymus, heart, adrenal glands and pituitary gland. For immune-regulatory tissues, the concentrations of LOR, DL and their active metabolites in the spleen were much higher than in the thymus, which is related to the spleen, one of the sites where immune responses occur. LOR and its metabolites might inhibit immune-mediated allergic inflammation through the hypothalamic-pituitary-adrenal (HPA) axis. It was also found that the concentration of LOR in the heart was highest after liver and adrenal glands while those of DL, 6-OH-DL and 5-OH-DL in the liver, adrenal glands and spleen were all higher than those in the heart, which suggests that LOR may have a greater tendency to distribute in the heart than its metabolites.

Effect of graphene quantum dot size on plant growth.

We found a straightforward dependence of plant growth on the sizes of graphene quantum dots. Enormous GQDs, such as graphene with dimensions of micrometers, neither promoted nor inhibited the growth. In contrast, synthesized GQDs with dimensions of about 10 nm best promoted the plant growth. Moreover GQDs synthesized using an "intelligent" chemistry robot yielded even better growth results than did GQDs synthesized conventionally by humans. In addition, a theoretical model was derived for the mechanism of the promotion of plant growth by GQDs.

Evaluation of plasma cytokine protein array profile: the highlighted PDGF-BB in rheumatoid arthritis.

OBJECTIVES: The cytokines play critical roles in the complex pathogenesis of rheumatoid arthritis (RA), but the specific cytokines are still in need of being discovered. This multi-stage study was performed to identify novel RA cytokines in plasma and further understand the pathological mechanism of the identified cytokines. METHOD: The plasma cytokine protein profile was evaluated by using Human Cytokine Antibody Array 440 in 18 subjects (RA: healthy control = 9:9). Then, enzyme-linked immunosorbent assay (ELISA) was used to validate the highlighted cytokines in 80 subjects (RA: healthy control = 40:40). Further functional experiments on fibroblast-like synoviocytes were performed to identify the pathological mechanisms of the highlighted cytokines for RA. RESULTS: A total of seven significant cytokines have differential expressions between RA patients and controls (fold change (FC) > 2, P value < 0.05). The difference in plasma for the highlighted platelet-derived growth factor (PDGF)-BB was validated in an independent validation sample (P = 0.005). Further, the PDGF-BB obviously promotes cell proliferation of MH7A cell, probably by inhibiting cell apoptosis and accelerating the cell cycle. The PDGF-BB can also promote MH7A cell migration. CONCLUSIONS: This study evaluated the plasma cytokine protein array profile associated with RA and highlighted the importance of PDGF-BB. PDGF-BB has an important role in RA-FLS proliferation and migration. These results suggest that PDGF-BB might contribute to occurrence and development of RA. Key Points • The levels of plasma cytokines were systemically tested using Human Cytokine Antibody Arrays. • The expression difference of PDGF-BB was validated in an independent sample. • PDGF-BB obviously promotes cell proliferation and migration in RA-FLS.

Toward "On-Demand" Materials Synthesis and Scientific Discovery through Intelligent Robots.

A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self-optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On-Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future.

Theoretical investigations of a new two-dimensional semiconducting boron-carbon-nitrogen structure.

A new two-dimensional boron-carbon-nitrogen (BCN) structure is predicted and is theoretically investigated based on density functional theory. The BCN structure belongs to the space group C222, and is composed of twelve B, twelve C and twelve N atoms per orthorhombic cell (named oC-B12C12N12). It consists of small hollow spheres with two hexagons per sphere. The dynamical, thermal and mechanical stabilities of oC-B12C12N12 are respectively evaluated by phonon spectroscopy, ab initio molecular dynamics calculations and elastic constant measurements. The simulated in-plane stiffness and Poisson ratio display anisotropic features. The band structure shows that oC-B12C12N12 is a direct semiconductor with a gap of 2.72 eV (GW). oC-B12C12N12 has an absorption range from the visible light spectrum to the ultraviolet. Therefore, due to its small direct band gap and optical absorption, oC-B12C12N12 may be a good candidate for electronic and optical applications.

Lithium Clustering during the Lithiation/Delithiation Process in LiFePO4 Olivine-Structured Materials.

Olivine-structured LiFePO4 is one of the most popular cathode materials in lithium-ion batteries (LIBs) for sustainable applications. Significant attention has been paid to investigating the dynamics of the lithiation/delithiation process in Li x FePO4 (0 ≤ x ≤ 1), which is crucial for the development of high-performance LiFePO4 material. Various macroscopic models based on experimental evidence have been proposed to explain the mechanism of phase transition from LiFePO4 to FePO4, such as the shrinking core (i.e., core-shell) model, Laffont's (i.e., new core-shell) model, domino-cascade model, phase transformation wave, solid solution model, many-particle models, etc. However, these models, unfortunately, contradict each other and their validity is still under debate. An atomistic model is urgently required to depict the lithiation/delithiation process in Li x FePO4. In this article, we reveal the lithiation/delithiation process in LiFePO4 simulated by a computational model using the generalized gradient approximation (GGA + U) method. We find that the clustered configuration is the most energetically favorable, leading to co-operative Jahn-Teller distortion among the inter-polyhedrons that can be observed clearly from the bond patterns. This atomistic model not only offers answers to experimental results obtained at moderate or high rates but also gives the direction to further improve the rate capability of LiFePO4 cathode material for high-power LIBs.

A magnification-continuous calibration method for SEM-based nanorobotic manipulation systems.

Calibration for scanning electron microscope (SEM) based nanorobotic manipulation systems is important and difficult. Most current calibration methods are cumbersome because they require customized high precision calibration boards and repeated calibration procedures in different magnifications. This paper presents a convenient magnification-continuous calibration method with high precision for SEM-based nanorobotic manipulation systems. The projection matrix containing a continuous magnification factor is obtained by modifying the affine camera model. This facilitates the simplification of the parameter computing process. Movement features are used to align the moving axes of micropositioning stages and calibrate the system, which benefits for the realization of efficient automatic calibration. Three experiments are carried out, and the results demonstrate that the proposed method is effective and practical for calibrating SEM-based nanorobotic manipulation systems under a wide range of continuous magnifications. Experiments also confirm that high precision measurements can be conducted in different magnifications with only once calibration and the relative error is within 1%.

Using path analysis to investigate the relationships between depression, anxiety, and health-related quality of life among patients with coronary artery disease.

PURPOSE: Demographic and clinical indicators usually influence depression, anxiety, and health-related quality of life (HRQoL) in patients with coronary artery disease (CAD). The aim of this study was to assess the direct and indirect association that existed among the identified variables, psychosocial status, and HRQoL in CAD patients. METHODS: CAD patients with at least one of the main coronary artery and/or branch stenosis over 50% were eligible for inclusion. HRQoL, depression, and anxiety were tested by questionnaires within 3 days after angiography. Mono-factor and multiple linear regression models were used to examine the independent associations of depression, anxiety, and HRQoL. A path analysis was conducted to examine the association among demographic/clinical indicators, depression, anxiety, and HRQoL. RESULTS: The sample consisted of 414 subject, patients with depression accounted for 40.82%, and patients with anxiety accounted for 25.12%. The direct effects of SAS scores on HRQoL (B = - 0.26, ß = - 0.16), of SDS scores on HRQoL (B = - 0.70, ß = - 0.47), of gender on HRQoL (B = 4.05, ß = 0.17), and of NYHA classification on HRQoL (B = - 3.46, ß = - 0.18) were significant (p < 0.001). The indirect effects of gender on HRQoL (B = 2.16, ß = 0.09) and of Gensini scores on HRQoL (B = - 0.06, ß = - 0.08) were also statistically significant (p < 0.001). CONCLUSIONS: Depression and anxiety were common CAD patients and played an important role in HRQoL. Gender differences were found in determinants of HRQoL and the state of depression and anxiety directly, and women's anxiety, depression, and quality of life were worse than men's. NYHA classification and Gensini scores also played direct and indirect role in HRQoL, respectively.