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Background Plasma cell-free DNA (cfDNA) methylation is emerging as a non-invasive marker for various cancers. We aimed to identify specific methylation markers for diagnosis, differentiation from benign tumors, and prognosis of breast cancer (BC), which are essential for clinical decision-making yet seldom examined together. Methods BC-specific methylation markers were identified using an in-house 850K dataset combined with large-scale publicly available 450 or 850K datasets. Multiplex digital droplet PCR (mddPCR) assays were developed to detect methylation in cfDNA from 201 BC patients, 83 healthy donors, and 71 individuals harboring benign tumors. Diagnostic and prognostic performance were evaluated using logistic and Cox regression models, respectively. The basic mechanism of a selected gene was explored through in vitro experiments. Results We identified 21 BC-specific methylated CpG sites that distinguished BC from tumor-adjacent tissues with high diagnostic accuracy. In the cfDNA cohort, three mddPCR assays targeting eight markers achieved an area under the curve (AUC) of 0.856 (95% CI = 0.814–0.898) for distinguishing BC from healthy controls, and 0.742 (95% CI = 0.684–0.801) for differentiating BC from benign tumors. Notably, combining these methylation markers with mammography and ultrasound improved diagnostic performance, resulting in an AUC of 0.898 (95% CI = 0.858–0.938) for differentiating BC from benign tumors. In the TCGA-BC dataset, prognostic model based on six sites was associated with poor overall survival prognosis (hazard ratio = 2.826, 95%CI: 1.841–4.338, p  < 0.0001). In vitro experiments elucidated that FAM126A overexpression regulates BC cells malignant phenotypes. Conclusions Our study demonstrated potential values of methylation-based markers in the detection and prognosis of BC.

In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields.

The \"gut-brain axis\" provides a theoretical foundation for the connection between intestinal and neurological diseases, but whether this reflects a shared genetic etiology or causal relationships exist remains unclear. We used genome-wide association study summary data from FinnGen and UK Biobank to investigate the genetic correlations and causal relationships between three intestinal diseases and six neurological diseases. We observed positive global genetic correlations between irritable bowel syndrome and epilepsy (r = 0.429, p = 1.53 × 10 ), and stroke (r = 0.368, p = 2.56×10 ). Upon dividing the whole genome into 1703 independent regions, local genetic correlations were identified in a region between ulcerative colitis and multiple sclerosis (Chr6: 31571218-32682664). We also identified 12 novel pleiotropic SNPs shared between intestinal and neurological diseases, as well as a functional gene shared between ulcerative colitis and multiple sclerosis. SNP heritability enrichment analysis indicated that ulcerative colitis and multiple sclerosis have enrichment in several immune cells. Two-sample Mendelian randomization indicated the causal effect of Crohn's disease on Parkinson's disease (FDR = 1.34 × 10 , OR = 1.092). The methylome Mendelian randomization analysis also showed causal relationships between several intestinal and neurological diseases. Through comprehensive and systematic statistical analysis, we identified the global and local genetic correlations and causal relationships between several intestinal and neurological diseases and discovered shared pleiotropic loci and genes between them. Furthermore, the consistent SNP heritability enrichment observed in immune cells also indicated the crucial role of the immune system in the \"gut-brain axis.\"

The intratumoral microbiota is a vital part of the tumor microenvironment, yet its interplay with host gene expression and immune regulation remains unclear. Based on a machine learning framework for the interaction analysis of intratumoral microbiota and host genes, as well as the construction of the Immune and Prognosis-Related Microbial Score, our findings suggest that intratumoral microbiota may influence gene expression by affecting host pathways, especially immune-related pathways. Moreover, immune-related intratumoral microbiota are significantly associated with patient survival and TME immunity and may influence prognosis by affecting immune cells, pathways, or gene expression, offering new perspectives and potential biomarkers for predicting personalized patient prognosis in the future.

In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields.

Gestational diabetes mellitus (GDM) has a high heritability and frequently co-occurs with type 2 diabetes (T2D), indicating shared genetic mechanisms. Firstly, we employed RHOGE to ascertain the genetic correlation and putative causal directions between GDM and T2D. Subsequently, the Genotype-Tissue Expression Project v8 eQTls files and the FinnGen R11 dataset were employed to conduct cross-tissue transcriptome association studies, Functional Summary-based Imputation in single tissues, and Gene Analysis combined with Multimarker Analysis of Genomic Annotation for GDM and T2D, respectively. A total of 5 genes were identified as GDM susceptibility and 97 genes linked to T2D susceptibility. Of these, four genes ( COBLL1 , NRBP1 , IFT172 and TRIM54 ) were identified as being shared. Mendelian randomization and colocalization analyses revealed the causal associations of them with GDM and T2D in distinct tissues. Subsequent analyses indicated COBLL1 may influence GDM and T2D risk by regulation of actin filament polymerization and interactions with chemical responses. NRBP1 may confer protective effects against diabetes through regulation of insulin secretion, while IFT172 and TRIM54 may play a role in energy balance signaling and metabolism of skeletal muscle, respectively. Our study provides insight into the shared genetic mechanism between GDM and T2D and identifies potential targets for pharmacological intervention. This study identifies shared genetic mechanisms between gestational diabetes mellitus (GDM) and type 2 diabetes (T2D), highlighting 5 genes linked to GDM and 97 genes associated with T2D. Four genes (COBLL1, NRBP1, IFT172, TRIM54) are shared, offering potential targets for treatment.

Advancement in fluorescence imaging techniques enables the study of protein dynamics and localization with unprecedented spatiotemporal resolution. However, current imaging tools are unable to elucidate dynamic protein interactomes underlying imaging observations. Conversely, proteomics tools such as proximity labeling enable the analysis of protein interactomes at a single time point but lack information about protein dynamics. We herein develop Silicon-rhodamine-enabled Identification (SeeID) for near-infrared light controlled proximity labeling that could bridge the gap between imaging and proximity labeling. SeeID is benchmarked through characterization of various organelle-specific proteomes and the KRAS protein interactome. The fluorogenic nature of SiR allows for intracellular proximity labeling with high subcellular specificity. Leveraging SiR as both a fluorophore and a photocatalyst, we develop a protocol that allows the study of dynamic protein interactomes of Parkin during mitophagy. We discover the association of the proteasome complex with Parkin at early time points, indicating the involvement of the ubiquitin-proteasome system for protein degradation in the early phase of mitophagy. Additionally, by virtue of the deep tissue penetration of near-infrared light, we achieve spatiotemporally controlled proximity labeling in vivo across the mouse brain cortex with a labeling depth of ~2 mm using an off-the-shelf 660 nm LED light set-up. Live-cell imaging and proximity labeling face limits in tracking protein dynamics. Here, the authors present SeeID, a NIR-activated method using SiR as a photocatalyst, enabling precise intracellular labeling, organelle- and disease-relevant interactome mapping, and deep-tissue studies in vivo.

Background Previous researches have not distinguished the between-person effects from the within-person effects when exploring the relationship between self-esteem, coping styles, and anxiety symptoms among adolescents. To address this gap, this study investigated reciprocal associations among self-esteem, coping styles, and anxiety symptoms in a three-wave longitudinal panel survey, using an analytical strategy that disaggregates the within-person and the between-person variance. Methods Data was drawn from the Longitudinal Study of Adolescents’ Mental and Behavioral Well-being Research study conducted in 10 public schools in the Guangdong province of China. All participants had a baseline visit (N = 1957, mean age 13.6, grades 7 and 10) and follow-up interviews at 1-year intervals for 3 years. A random intercept cross-lagged panel model combined with mediation analysis was performed. Results At the within-person level, the following results were observed. (1) Low self-esteem and anxiety symptoms bidirectionally predicted each other. (2) Low self-esteem and negative coping style bidirectionally predicted each other. (3) Anxiety symptoms predicted subsequent negative coping style but not vice versa. At the between-person level, we obtained the following main results. (1) Significant predictive effects on the random intercept were found among all three study constructs. (2) There were sex differences regarding the association between self-esteem and anxiety symptoms and the correlation of females was stronger than that of males. (3) Self-esteem mediated the reciprocal relations between coping styles and anxiety symptoms. Conclusions Overall, findings revealed a reciprocal relationship between low self-esteem and anxiety symptoms for both females and males. Besides, anxiety symptoms predict subsequent negative coping style but not vice versa. We also highlighted the mediating role of self-esteem in the reciprocal relations between coping styles and anxiety symptoms. Thus, interventions targeted at promoting self-esteem and cultivating positive coping style may help reduce adolescent anxiety.

Background While the association between screen time (ST) and psychosocial well-being has been extensively examined, limited studies have investigated the dynamic patterns of ST, and their impact on subsequent psychosocial well-being among adolescents. Therefore, this longitudinal study aimed to examine the association between ST trajectories and the subsequent psychosocial well-being among Chinese adolescents. Methods Data were drawn from the Longitudinal Study of Adolescents’ Mental and Behavioral Well-being Research (Registration No. ChiCTR1900022032). The final analysis included 1480 participants who completed baseline and two follow-up surveys. Standardized measures were employed to assess ST and multiple psychosocial well-being, including depressive symptoms, anxiety, externalizing problems, and coping style. Group-based trajectory modeling and generalized linear mixed models were performed. Results Over the two-year follow-up period, two distinct ST trajectories emerged: continued high (298 [20.1%]) and continued low (1182 [79.9%]). Compared with those in the continued low ST group, adolescents in the continued high group exhibited a higher likelihood of presenting depressive symptoms ( β  = 0.97, 95% CI = 0.43 ~ 1.50), anxiety symptoms ( β  = 0.29, 95% CI = 0.05 ~ 0.53), and emotional problems ( β  = 0.35, 95% CI = 0.22 ~ 0.48), and were less likely to demonstrate prosocial behavior or employ positive coping style. The stratified analysis demonstrated that the aforementioned associations only existed among female adolescents. Conclusions Persistent high exposure to ST was associated with an increased odds of emotional problems and a decreased probability of engaging in prosocial behavior and positive coping style, with particularly noteworthy effects observed among female adolescents. These findings underscore the importance of reducing ST exposure to improve the psychological well-being of adolescents.

Current understanding of the load-transfer mechanism in the skeletal contact state of asphalt mixtures and its influence on macroscopic mechanical properties remains insufficient. This knowledge gap leads to difficulties in accurately predicting the performance of designed mixtures, thereby restricting the service life of asphalt pavements and the sustainable development of road engineering. This study investigated the skeletal contact characteristics, coarse aggregate movement, and crack propagation of three asphalt mixture types—Stone Mastic Asphalt (SMA), Asphalt Concrete (AC), and Open-Graded Friction Course (OGFC)—under loading. The methodology incorporated Computed Tomography (CT) technology, a Voronoi diagram-based skeletal contact evaluation method, and discrete element numerical simulation. The research aimed to elucidate the influence mechanisms of different skeletal structures on macroscopic performance and to validate the efficacy of the skeletal contact evaluation method. The findings revealed that under splitting load, the tensile stress contact force chains within the asphalt mixture’s skeleton were predominantly distributed along both sides of the specimen’s central axis. For all three gradations, compressive stress contact force chains (points) accounted for over 65% of the total, indicating that the asphalt mixture skeleton primarily bore and transmitted compressive stresses. The interlocking structure formed by coarse aggregates significantly enhanced the stability of the asphalt mixture skeleton, reduced its displacement under load, and improved the mixture’s resistance to cracking. In the three gradations, shear stress-induced cracks outnumbered those caused by tensile stress, with shear stress cracks accounting for over 55% of the total cracks. This suggests that under splitting load, cracks resulting from shear failure were more prevalent than those from tensile failure. SMA-20 demonstrated the best crack resistance, followed by AC-20, while OGFC-20 performed the poorest. These conclusions are consistent with the results of the Voronoi diagram-based skeletal contact evaluation, confirming the correlation between the contact conditions of the asphalt mixture skeleton and its mechanical performance. Specifically, inadequate skeletal contact leads to a significant deterioration in mechanical properties. The research results elucidate the influence of skeletal contact characteristics with different gradations on both mesoscopic features and macroscopic mechanical behavior, providing a crucial basis for optimizing asphalt mixture design.