Breast ultrasound image segmentation is vital in medical imaging, enabling precise delineation of tissues and lesions, which contributes to the diagnosis and treatment of breast diseases. This article reviews both traditional methods and recent advancements in deep learning techniques for breast ultrasound image segmentation. The discussion begins by highlighting the significance of image segmentation in breast disease diagnosis and its background within medical imaging. Traditional segmentation methods, such as thresholding, edge detection, and region growing, are examined, with an analysis of their applications and limitations in breast ultrasound segmentation. Subsequently, the focus shifts to deep learning approaches, including classic models like Convolutional Neural Networks, Fully Convolutional Networks, and U-Net, along with their improved algorithms. These methods learn hierarchical features directly from raw data, reducing reliance on manual preprocessing. U-Net, in particular, is highlighted as the benchmark for medical image segmentation due to its efficient data usage and ability to preserve fine-grained details. Comparative analysis demonstrates the advantages of deep learning in enhancing segmentation accuracy, reducing noise, and handling complex texture structures. The article concludes by summarizing current achievements and challenges in the field, while offering an outlook on the future developments aimed at advancing breast ultrasound image segmentation for improved diagnosis and treatment of breast diseases.

Objectives: To assess job satisfaction among anesthesia graduates working in various medical institutions across China. Methods: A cross-sectional survey was conducted, collecting demographic information, Minnesota Satisfaction Questionnaire scores, work pressure, and turnover intentions. Multiple linear regression analysis was  used to examine factors influencing job satisfaction. The electronic survey was distributed to Chinese anesthesia  graduates from December 2021 to January 2022. Results: A total of 595 questionnaires were distributed, with  318 valid responses, resulting in a response rate of 53.4%. The participants’ overall job satisfaction score on the  Minnesota Satisfaction Questionnaire was 75.85±12.57. Multiple linear regression analysis identified the following variables as significantly associated with job satisfaction: age, daily working hours, income, current position,  and work pressure. Conclusions: Anesthesia graduates in China reported slightly higher-than-average overall job  satisfaction. However, several issues remain. Attention should be given to the impact of factors such as youth, long  working hours, low income, current position, and high work pressure on job satisfaction. The government should  support anesthesiologists with improved training, job security, and benefits to enhance job satisfaction.

The deep integration of the metaverse and digital healthcare is fundamentally reshaping the nature of health data. Traditional electronic health records (EHRs), characterized as discrete, event-driven "solid records," are evolving into "digital life streams"—continuously generated, cross-platform flowing, and algorithmically reconfigured data processes. This ontological shift poses profound challenges to existing data governance frameworks. On one hand, the heightened fluidity of data renders traditional privacy mechanisms, which rely on clear data boundaries, increasingly ineffective. On the other hand, dominant platforms are "reterritorializing" this fluid data through technical standards, protocols, and hardware monopolies, thereby constructing novel power structures that extend from the software layer to the hardware layer. Employing "liquefaction" and "reterritorialization" as core analytical lenses, this paper systematically investigates the structural dilemmas of health data governance in the metaverse. It poses three central research questions: (1) How does the emergence of "digital life streams" alter the foundational premises of data governance; (2) How does the dialectic between liquefaction and reterritorialization shape the power dynamics of health data; (3) In the face of this dual movement, what form should a new governance paradigm take through a critical analysis of technological solutions (federated learning, zero-knowledge proofs, trusted execution environments), legal-regulatory models (GDPR, HIPAA), and market-based mechanisms—examined across five dimensions: power distribution, economic equity, value choices, accountability, and digital sovereignty—this paper reveals the inherent limitations of single-pronged governance approaches in addressing a highly dynamic data ecosystem. Building on this critique, the paper proposes a relational data governance paradigm and constructs a multi-level framework integrating micro-level technological architecture (privacy by default, interoperability standards, explainable algorithms), meso-level institutional innovations (data trusts, digital commons, participatory auditing), and macro-level legal reforms (digital personality rights, digital gatekeeper regulation, global minimum standards). This framework moves from "data ownership" to "data relational rights" and supplements "individual privacy protection" with "collective digital well-being," achieving a dual conceptual elevation. It further embeds operational mechanisms such as the "four elements" of data trusts (trustee composition, decision-making, benefit distribution, supervision) and Ostrom's eight principles for governing digital commons. Drawing on case studies including the Apple Health ecosystem, VR psychotherapy platforms, and the European Health Data Space (EHDS), the paper offers both theoretical integration and policy references for the governance of health data in the metaverse.

Key Words: metaverse; health data governance; digital life stream; liquefaction; reterritorialization; data trusts; digital personality rights.

Objective: The morphological complexity of dermatologic diseases poses considerable challenges to clinical diagnosis. Conventional manual interpretation of skin images is time-consuming and influenced by subjective variability, which limits diagnostic accuracy. Hence, developing advanced medical image segmentation techniques through multi-method fusion is of particular importance. Methods: A comprehensive dataset of dermatologic images was utilized and rigorously preprocessed to ensure reliability and consistency. Two representative deep learning models, SegNet and U-Net, were optimized to achieve precise delineation of lesion areas. Building upon their complementary strengths, a novel fusion-based image segmentation framework was proposed, integrating both models to enhance performance through synergistic learning. The effectiveness of the ensemble strategy was validated through extensive experiments using standard evaluation metrics, including the Dice coefficient and Intersection over Union. Results: Compared with each individual model, the Ensemble Model yielded consistent improvements across all evaluation indices, with a notable reduction in loss values. These enhancements indicate markedly better learning efficiency and generalization in dermatologic image segmentation tasks. Conclusion: By integrating multiple deep learning algorithms, this fusion technique solves the misclassification and omission issues observed in single-model segmentation. It significantly improves overall segmentation accuracy and demonstrates superior performance, particularly in edge detection of complex skin lesions.

Background: Colonoscopy is a key technique for the prevention and early detection of colorectal cancer. Water-assisted colonoscopy is increasingly adopted due to its potential to reduce patient discomfort. However, the temperature of the infused water plays a crucial role in both procedural quality and patient experience. This study aimed to optimize water-assisted colonoscopy by developing a constant-temperature water infusion system. Methods: A two-dimensional finite element model was established using COMSOL Multiphysics to simulate the heat transfer process between the heating base and the liquid container. The system consisted of a medical-grade 304 stainless steel container, a nichrome heating wire embedded in rubber, and an integrated piping network. Quadrilateral meshing was applied to short-range solid–liquid interfaces and triangular meshing elsewhere, resulting in detailed modeling for both natural heating (27,801 elements) and circulation heating (43,998 elements). Based on simulation results, a hardware platform was developed to deliver sterile water at a constant temperature of 37 °C for digestive endoscopic procedures. Results: Circulation heating demonstrated superior thermal efficiency and more uniform temperature distribution than natural heating. Under ambient conditions (25 °C ), the system reliably maintained water temperature at (37±1)°C . Partitioned meshing enhanced computational precision with a minimum element size of 0.1 mm. Solid-liquid coupling analysis confirmed stable heat conduction during dynamic infusion. The device allows for independent temperature presetting and stepless flow rate adjustment via a control panel. It is also compatible with standard endoscopic systems, thereby enhancing procedural efficiency and safety. Conclusion: The proposed constant-temperature water infusion system model offers a reliable and adaptable solution for water-assisted colonoscopy, improving both diagnostic performance and patient comfort through precise thermal regulation.