Expression of GLOD4 in the Testis of the Qianbei Ma Goat and Its Effect on Leydig Cells.

The expression pattern of GLOD4 in the testis and its regulatory effect on testicular cells was explored in goats to enhance our understanding of spermatogenesis and improve reproduction in breeding rams. In this study, we demonstrated the localization of GLOD4 in testicular cells using immunohistochemistry and subcellular localization analyses. Subsequently, we analyzed the GLOD4 expression pattern in four age-based groups (0, 6, 12, and 18 months old) using real-time quantitative polymerase chain reaction (qRT-PCR) and protein blotting. Finally, we performed GLOD4 silencing and overexpression studies in Leydig cells (LCs) and explored the effects on cell proliferation, the cell cycle, steroid hormone secretion and the expression of candidate testosterone hormone-regulated genes. GLOD4 was mainly expressed in Leydig cells, and the subcellular localization results showed that the GLOD4 protein was mainly localized in the cytoplasm and nucleus. Silencing of GLOD4 significantly suppressed the mRNA expression levels of the testosterone secretion-related genes CYP11A1, 3ß-HSD, and CYP17A1 and the mRNA expression levels of cell cycle-related genes CDK6, PCNA, and Cyclin E. Moreover, the cell cycle was blocked at the G2/M phase after GLOD4 silencing, which significantly suppressed testosterone secretion. In contrast, GLOD4 overexpression significantly increased the mRNA expression levels of the testosterone secretion-related genes CYP11A1, 3ß-HSD, and CYP17A1 and increased the expression of the cell cycle-related genes CDK6, PCNA, and Cyclin E. Moreover, GLOD4 overexpression promoted the cell cycle from G0/G1 phases to enter the S phase and G2/M phases, promoted the secretion of testosterone. Taken together, our experimental results indicate that GLOD4 may affect the development of cells in Qianbei Ma goats of different ages by influencing the cell cycle, cell proliferation, and testosterone hormone synthesis. These findings enhance our understanding of the functions of GLOD4 in goats.

Modulation of High-Frequency rTMS on Reward Circuitry in Individuals with Nicotine Dependence: A Preliminary fMRI Study.

Although previous studies have shown that repetitive transcranial magnetic stimulation (rTMS) can ameliorate addictive behaviors and cravings, the underlying neural mechanisms remain unclear. This study aimed to investigate the effect of high-frequency rTMS with the left dorsolateral prefrontal cortex (L-DLPFC) as a target region on smoking addiction in nicotine-dependent individuals by detecting the change of spontaneous brain activity in the reward circuitry. We recruited 17 nicotine-dependence participants, who completed 10 sessions of 10 Hz rTMS over a 2-week period and underwent evaluation of several dependence-related scales, and resting-state fMRI scan before and after the treatment. Functional connectivity (FC) analysis was conducted with reward-related brain regions as seeds, including ventral tegmental area, bilateral nucleus accumbens (NAc), bilateral DLPFC, and bilateral amygdala. We found that, after the treatment, individuals showed reduced nicotine dependence, alleviated tobacco withdrawal symptoms, and diminished smoking cravings. The right NAc showed increased FC with right fusiform gyrus, inferior temporal gyrus (ITG), calcarine fissure and surrounding cortex, superior occipital gyrus (SOG), lingual gyrus, and bilateral cuneus. No significant FC changes were observed in other seed regions. Moreover, the changes in FC between the right NAc and the right ITG as well as SOG before and after rTMS were negatively correlated with changes in smoking scale scores. Our findings suggest that high-frequency L-DLPFC-rTMS reduces nicotine dependence and improves tobacco withdrawal symptoms, and the dysfunctional connectivity in reward circuitry may be the underlying neural mechanism for nicotine addiction and its therapeutic target.

A Time-Dependent Damage Constitutive Model for Brittle Rock Materials Based on Subcritical Damage Theory.

In the domain of geotechnical engineering, a profound understanding of the long-term mechanical deformation characteristics of rocks is indispensable for the design and construction of structures, dams, tunnels, and various engineering projects. The deformation behavior of rocks under long-term loads directly impacts the stability and safety of engineering structures. This study employs micromechanical methods to investigate the subcritical extension of microcracks under stress corrosion. By examining the accumulated damage resulting from this phenomenon, the research explores the patterns of aging damage development and establishes a constitutive model for aging that incorporates cumulative damage over the stress history. The accuracy of the proposed model is evaluated through a comprehensive comparison of numerical results with experimental data. The experimental data set encompasses traditional triaxial compression tests, single-stage creep, multistage creep, and single-stage relaxation tests conducted under varying confining pressures. The predicted results exhibit strong consistency with the entire data set. Furthermore, this paper employs crack damage stress as an indicator characterizing the long-term strength of rock. Through frictional damage coupling analysis and derivation, an analytical expression for the long-term strength of rock materials containing microcracks is provided, serving as a theoretical basis for investigating the long-term mechanical performance of brittle rock materials and ensuring the long-term stability of large-scale rock engineering projects.

Controlling false discovery rate for mediator selection in high-dimensional data.

The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

Model Selection for Exposure-Mediator Interaction.

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators M and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

A robot-assisted tracheal intubation system based on a soft actuator?

PURPOSE: Tracheal intubation is the gold standard of airway protection and constitutes a pivotal life-saving technique frequently employed in emergency medical interventions. Hence, in this paper, a system is designed to execute tracheal intubation tasks automatically, offering a safer and more efficient solution, thereby alleviating the burden on physicians. METHODS: The system comprises a tracheal tube with a bendable front end, a drive system, and a tip endoscope. The soft actuator provides two degrees of freedom for precise orientation. It is fabricated with varying-hardness silicone and reinforced with fibers and spiral steel wire for flexibility and safety. The hydraulic actuation system and tube feeding mechanism enable controlled bending and delivery. Object detection of key anatomical features guides the robotic arm and soft actuator. The control strategy involves visual servo control for coordinated robotic arm and soft actuator movements, ensuring accurate and safe tracheal intubation. RESULTS: The kinematics of the soft actuator were established using a constant curvature model, allowing simulation of its workspace. Through experiments, the actuator is capable of 90° bending as well as 20° deflection on the left and right sides. The maximum insertion force of the tube is 2 N. Autonomous tracheal intubation experiments on a training manikin were successful in all 10 trials, with an average insertion time of 45.6 s. CONCLUSION: Experimental validation on the manikin demonstrated that the robot tracheal intubation system based on a soft actuator was able to perform safe, stable, and automated tracheal intubation. In summary, this paper proposed a safe and automated robot-assisted tracheal intubation system based on a soft actuator, showing considerable potential for clinical applications.

A systematic review of the role of TREM2 in Alzheimer's disease.

BACKGROUND: Given the established genetic linkage between triggering receptors expressed on myeloid cells 2 (TREM2) and Alzheimer's disease (AD), an expanding research body has delved into the intricate role of TREM2 within the AD context. However, a conflicting landscape of outcomes has emerged from both in vivo and in vitro investigations. This study aimed to elucidate the multifaceted nuances and gain a clearer comprehension of the role of TREM2. METHODS: PubMed database was searched spanning from its inception to January 2022. The search criteria took the form of ("Alzheimer's disease" OR "AD") AND ("transgenic mice model" OR "transgenic mouse model") AND ("Triggering receptor expressed on myeloid cells" OR "TREM2"). Inclusion criteria consisted of the following: (1) publication of original studies in English; (2) utilization of transgenic mouse models for AD research; and (3) reports addressing the subject of TREM2. RESULTS: A total of 43 eligible articles were identified. Our analysis addresses four pivotal queries concerning the interrelation of TREM2 with microglial function, Aß accumulation, tau pathology, and inflammatory processes. However, the diverse inquiries posed yielded inconsistent responses. Nevertheless, the inconsistent roles of TREM2 within these AD mouse models potentially hinge upon factors such as age, sex, brain region, model type, and detection methodologies. CONCLUSIONS: This review substantiates the evolving understanding of TREM2's disease progression-dependent impacts. Furthermore, it reviews the interplay between TREM2 and its effects across diverse tissues and temporal stages.

Enzymatic Synthesis of Novel Terpenoid Glycoside Derivatives Decorated with N-Acetylglucosamine Catalyzed by UGT74AC1.

The addition of the O-linked N-acetylglucosamine (O-GlcNAc) is a significant modification for active molecules, such as proteins, carbohydrates, and natural products. However, the synthesis of terpenoid glycoside derivatives decorated with GlcNAc remains a challenging task due to the absence of glycosyltransferases, key enzymes for catalyzing the transfer of GlcNAc to terpenoids. In this study, we demonstrated that the enzyme mutant UGT74AC1T79Y/L48M/R28H/L109I/S15A/M76L/H47R efficiently transferred GlcNAc from uridine diphosphate (UDP)-GlcNAc to a variety of terpenoids. This powerful enzyme was employed to synthesize GlcNAc-decorated derivatives of terpenoids, including mogrol, steviol, andrographolide, protopanaxadiol, glycyrrhetinic acid, ursolic acid, and betulinic acid for the first time. To unravel the mechanism of UDP-GlcNAc recognition, we determined the X-ray crystal structure of the inactivated mutant UGT74AC1His18A/Asp111A in complex with UDP-GlcNAc at a resolution of 1.66 Å. Through molecular dynamic simulation and activity analysis, we revealed the molecular mechanism and catalytically important amino acids directly involved in the recognition of UDP-GlcNAc. Overall, this study not only provided a potent biocatalyst capable of glycodiversifying natural products but also elucidated the structural basis for UDP-GlcNAc recognition by glycosyltransferases.

Enhanced Thermal Boundary Conductance across GaN/SiC Interfaces with AlN Transition Layers.

Heat dissipation plays a crucial role in the performance and reliability of high-power GaN-based electronics. While AlN transition layers are commonly employed in the heteroepitaxial growth of GaN-on-SiC substrates, concerns have been raised about their impact on thermal transport across GaN/SiC interfaces. In this study, we present experimental measurements of the thermal boundary conductance (TBC) across GaN/SiC interfaces with varying thicknesses of the AlN transition layer (ranging from 0 to 73 nm) at different temperatures. Our findings reveal that the addition of an AlN transition layer leads to a notable increase in the TBC of the GaN/SiC interface, particularly at elevated temperatures. Structural characterization techniques are employed to understand the influence of the AlN transition layer on the crystalline quality of the GaN layer and its potential effects on interfacial thermal transport. To gain further insights into the trend of TBC, we conduct molecular dynamics simulations using high-fidelity deep learning-based interatomic potentials, which reproduce the experimentally observed enhancement in TBC even for atomically perfect interfaces. These results suggest that the enhanced TBC facilitated by the AlN intermediate layer could result from a combination of improved crystalline quality at the interface and the "phonon bridge" effect provided by AlN that enhances the overlap between the vibrational spectra of GaN and SiC.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Comparing the impact of three-dimensional digital visualization technology versus traditional microscopy on microsurgeons in microsurgery: a prospective self-controlled study.

BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons' visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery. METHODS AND MATERIAL: Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool. RESULTS: Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D ( P =0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) ( P =0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms ( P =0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy ( P >0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: -1.17 to 1.08) points; P =0.95]. Subjective symptoms were quite mild after using both techniques. CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

Viewpoint-dependent highlight depiction with microdisparity for autostereoscopic displays.

The rendering of specular highlights is a critical aspect of 3D rendering on autostereoscopic displays. However, the conventional highlight rendering techniques on autostereoscopic displays result in depth conflicts between highlights and diffuse surfaces. To address this issue, we propose a viewpoint-dependent highlight depiction method with head tracking, which incorporates microdisparity of highlights in binocular parallax and preserves the motion parallax of highlights. Our method was found to outperform physical highlight depiction and highlight depiction with microdisparity in terms of depth perception and realism, as demonstrated by experimental results. The proposed approach offers a promising alternative to traditional physical highlights on autostereoscopic displays, particularly in applications that require accurate depth perception.

Integrative Metabolomics and Whole Transcriptome Sequencing Reveal Role for TREM2 in Metabolism Homeostasis in Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Dysregulation of various metabolism pathways may mediate the development of AD pathology and cognitive dysfunction. Variants of triggering receptor expressed on myeloid cells-2 (TREM2) are known to increase the risk of developing AD. TREM2 plays a role in AD development by maintaining cellular energy and biosynthesis, but the precise mechanism through which it accomplishes this is unknown. Metabolomic analysis of hippocampal tissue from APP/PS1 and APP/PS1-TREM2 knockout (KO) mice found that TREM2 KO was associated with abnormalities in several metabolism pathways, and the effect was particularly pronounced in lipid metabolism and glucose metabolism pathways. Consistently, transcriptomic analysis of these mice determined that most differentially expressed genes were involved in energy metabolism pathways. We screened seven differentially expressed genes in APP/PS1-TREM2 KO mice that may influence AD development by altering energy metabolism. Integrative analysis of the metabolomic and transcriptomic profiles showed that TREM2 may regulate lipid metabolism and sphingolipid metabolism by affecting lipoprotein lipase (LPL) expression, thereby influencing AD progression. Our results prompt further studies of the interactions among TREM2, LPL, glucolipid metabolism, and sphingolipid metabolism in AD to identify new diagnostic and treatment strategies.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.

Importance: Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning systems (DLSs) based on fundus images with a 45° field of view have been extensively applied in population screening, while the feasibility of using ultra-widefield (UWF) fundus image-based DLSs to detect retinal lesions in patients in rural areas warrants exploration. Objective: To explore the performance of a DLS for multiple retinal lesion screening using UWF fundus images from patients in rural areas. Design, Setting, and Participants: In this diagnostic study, a previously developed DLS based on UWF fundus images was used to screen for 5 retinal lesions (retinal exudates or drusen, glaucomatous optic neuropathy, retinal hemorrhage, lattice degeneration or retinal breaks, and retinal detachment) in 24 villages of Yangxi County, China, between November 17, 2020, and March 30, 2021. Interventions: The captured images were analyzed by the DLS and ophthalmologists. Main Outcomes and Measures: The performance of the DLS in rural screening was compared with that of the internal validation in the previous model development stage. The image quality, lesion proportion, and complexity of lesion composition were compared between the model development stage and the rural screening stage. Results: A total of 6222 eyes in 3149 participants (1685 women [53.5%]; mean [SD] age, 70.9 [9.1] years) were screened. The DLS achieved a mean (SD) area under the receiver operating characteristic curve (AUC) of 0.918 (0.021) (95% CI, 0.892-0.944) for detecting 5 retinal lesions in the entire data set when applied for patients in rural areas, which was lower than that reported at the model development stage (AUC, 0.998 [0.002] [95% CI, 0.995-1.000]; P < .001). Compared with the fundus images in the model development stage, the fundus images in this rural screening study had an increased frequency of poor quality (13.8% [860 of 6222] vs 0%), increased variation in lesion proportions (0.1% [6 of 6222]-36.5% [2271 of 6222] vs 14.0% [2793 of 19 891]-21.3% [3433 of 16 138]), and an increased complexity of lesion composition. Conclusions and Relevance: This diagnostic study suggests that the DLS exhibited excellent performance using UWF fundus images as a screening tool for 5 retinal lesions in patients in a rural setting. However, poor image quality, diverse lesion proportions, and a complex set of lesions may have reduced the performance of the DLS; these factors in targeted screening scenarios should be taken into consideration in the model development stage to ensure good performance.

Tunable Tail Swing of Nanomillipedes.

The physical properties of graphene nanoribbons (GNRs) are closely related to their morphology; meanwhile GNRs can easily slide on surfaces (e.g., superlubricity), which may largely affect the configuration and hence the properties. However, the morphological evolution of GNRs during sliding remain elusive. We explore the intriguing tail swing behavior of GNRs under various sliding configurations on Au substrate. Two distinct modes of tail swing emerge, characterized by regular and irregular swings, depending on the GNR width and initial position relative to the substrate. The mechanism can be explained by the moiré effect, presenting both symmetric and asymmetric patterns, resembling a mesmerizing nanomillipede. We reveal a compelling correlation between the tail swing mode and the edge wrinkle patterns of GNRs induced by the moiré effect. These findings provide fundamental understanding of how edge effects influence the tribomorphological responses of GNRs, offering valuable insights for precise manipulation and operation of GNRs.

Crosstalk between peripheral immunity and central nervous system in Alzheimer's disease.

The significance of peripheral immunity in the pathogenesis and progression of Alzheimer's diseases (AD) has been recognized. Brain-infiltrated peripheral immune components transporting across the blood-brain barrier (BBB) may reshape the central immune environment. However, mechanisms of how these components open the BBB for AD occurrence and development and correlations between peripheral and central immunity have not been fully explored. Herein, we formulate a hypothesis whereby peripheral immunity as a critical factor allows AD to progress. Peripheral central immune cell crosstalk is associated with early AD pathology and related risk factors. The damaged BBB permits peripheral immune cells to enter the central immune system to deprive its immune privilege promoting the progression toward developing AD. This review summarizes the influences of risk factors on peripheral immunity, alongside their functions, highlighting the concept of peripheral and central immunity as an integrated system in AD pathogenesis, which has received scant attention before.

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text.

Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.

EPT-Net: Edge Perception Transformer for 3D Medical Image Segmentation.

The convolutional neural network has achieved remarkable results in most medical image seg- mentation applications. However, the intrinsic locality of convolution operation has limitations in modeling the long-range dependency. Although the Transformer designed for sequence-to-sequence global prediction was born to solve this problem, it may lead to limited positioning capability due to insufficient low-level detail features. Moreover, low-level features have rich fine-grained information, which greatly impacts edge segmentation decisions of different organs. However, a simple CNN module is difficult to capture the edge information in fine-grained features, and the computational power and memory consumed in processing high-resolution 3D features are costly. This paper proposes an encoder-decoder network that effectively combines edge perception and Transformer structure to segment medical images accurately, called EPT-Net. Under this framework, this paper proposes a Dual Position Transformer to enhance the 3D spatial positioning ability effectively. In addition, as low-level features contain detailed information, we conduct an Edge Weight Guidance module to extract edge information by minimizing the edge information function without adding network parameters. Furthermore, we verified the effectiveness of the proposed method on three datasets, including SegTHOR 2019, Multi-Atlas Labeling Beyond the Cranial Vault and the re-labeled KiTS19 dataset called KiTS19-M by us. The experimental results show that EPT-Net has significantly improved compared with the state-of-the-art medical image segmentation method.