Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of lesions. To address this challenge, we propose a general multi-scale in multi-scale subtraction network (M2SNet) to finish diverse segmentation from medical image. Specifically, we first design a basic subtraction unit (SU) to produce the difference features between adjacent levels in encoder. Next, we expand the single-scale SU to the intra-layer multi-scale SU, which can provide the decoder with both pixel-level and structure-level difference information. Then, we pyramidally equip the multi-scale SUs at different levels with varying receptive fields, thereby achieving the inter-layer multi-scale feature aggregation and obtaining rich multi-scale difference information. In addition, we build a training-free network “LossNet” to comprehensively supervise the task-aware features from bottom layer to top layer, which drives our multi-scale subtraction network to capture the detailed and structural cues simultaneously. Without bells and whistles, our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks of diverse image modalities, including color colonoscopy imaging, ultrasound imaging, computed tomography (CT), and optical coherence tomography (OCT). The source code can be available at https://github.com/Xiaoqi-Zhao-DLUT/MSNet .
Background and Aims: Given the lack of efficient biomarkers for hepatocellular carcinoma (HCC) diagnosis, this study aimed to develop an HCC diagnostic strategy based on serum protein glycosylation signatures. We characterized differential N-glycosylation patterns of serum IgG to differentiate HCC from healthy controls and liver cirrhosis, and elucidated the molecular mechanisms driving aberrant Neu5Gc elevation in HCC to provide a theoretical basis for clinical application and differential diagnosis of HCC. Methods: LIP-ELISA was applied to quantify serum Neu5Gc in 6,768 healthy individuals for baseline establishment. IgG was purified and subsequently analyzed by RPLC-MS/MS for glycosylation profiling in HCC and healthy samples. Bioinformatic analysis of CMAH and related gene clusters modulating Neu5Gc synthesis was conducted. Results: In a cohort of 1,114 participants, the LIP-ELISA platform achieved 80.21% sensitivity, 96.01% specificity, and 92.46% accuracy for primary HCC diagnosis. Serum IgG from HCC patients displayed multibranched N-glycans modified with core fucose and Neu5Gc. Key molecules involved in glycan modification were identified, enabling the development of multiplexed gene detection for HCC, LC, and chronic hepatitis B. In vitro assays confirmed hypoxia-induced sialic acid accumulation in HCC cells. Meanwhile, CMAH-knockout mouse experiments verified that an exogenous high-sialic-acid diet compensates for endogenous Neu5Gc synthesis deficiency, revealing a dietary-mediated compensatory mechanism for Neu5Gc elevation. Conclusions: This study established an LIP-ELISA-based clinical diagnostic platform combining AFP and Neu5Gc, defined sialic acid-modified glycan structures, and preliminarily identified regulators of Neu5Gc biosynthesis, providing novel insights for HCC diagnosis and mechanism research.
ABSTRACT The design of a wearable bioelectronic device for electrotherapeutic wound healing and real‐time monitoring is critical for smart healthcare. However, developing multifunctional materials remains challenging due to energy supply or sensing interface issues. Herein, a simple strategy for integrating wound dressings of battery‐free electrotherapy and wound sensors via Dopamine (DA)‐modified MXene‐silver nanowire (Ag NWs)‐bacterial cellulose (BC) (PMAB) cross‐linked interpenetrating networks has been presented. Specifically, DA and BC significantly enhanced the antioxidant and mechanical properties of MXene, while Ag NWs improved the electrical and antimicrobial activities of PMAB. The solid‐state supercapacitor fabricated upon PMAB displayed excellent energy storage properties (2.5 F cm−2), replacing conventional power for delivering electrical stimulation (ES) to accelerate wound healing. NIH 3T3 fibrolast showed rapid migration and higher proliferation rate (over 70%) under ES (1 V). Meanwhile, wound dressing of cross‐linking interpenetrating structure of MXene and BC performs superior mechanosensing properties, with internal resistance change only 1.5 times of initial resistance over 60 days, which enables monitoring physical signal stabilization for wound assessment and management. This work would provide novel ideas of smartsensors for designing battery‐free wearable wound dressings.
Image encryption is a reliable means to safeguard digital image security, in which chaotic systems are extensively applied due to their unique properties. However, the existing image encryption algorithms cannot strike an appropriate balance between efficiency and security. To address these limitations, a 6-dimensional (6D) discrete hyperchaotic system is constructed. Phase and bifurcation diagrams are employed to assess the dynamic characteristics, and the pseudo-randomness of the designed system is also analyzed. We devise a secure image encryption method building upon this system, in the process of scrambling, to disturb the pixel positions, we design a scrambling method utilizing the concept of Rubik's Cube to make the scrambling result more sufficient. Then, the dynamic DNA encoding is applied for diffusion processing to produce the ciphertext. Simulation experiments and evaluation analysis results demonstrate that the devised method has greater security and running performance than existing schemes.
Dual-source heat pump systems combining photovoltaic-thermal (PVT) and air-source technologies have attracted considerable research interest due to their energy complementarity. Based on the climatic characteristics of the Dalian region, this study conducted field measurements and data analysis on a developed dual-source heat pump system incorporating three adaptive operational modes: (1) PVT mode, (2) PVT/air dual-source mode, and (3) photovoltaic (PV)/air-source mode. Compared to Mode (3), Mode (1) achieves a 5.76% higher heating capacity and an 11.56% greater electrical efficiency. Meanwhile, Mode (2) demonstrates a 12.23% increase in heating capacity, and a 9.14% improvement in electrical efficiency relative to Mode (3). A data-driven methodology is provided to quantify the system's evaporation temperature, the thermal efficiency of PVT mode, and the coefficient of performance (COP) of the PVT heat pump. The economic assessment demonstrates that the proposed dual-source heat pump system achieves a heating cost as low as RMB 0.1125/kWh and a payback period of 6.4 years, indicating favorable economic benefits. This study provides fundamental data and computational methods for the optimized operation of the PVT/air dual-source heat pump.
