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Conductive hydrogels, particularly those incorporating poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), have revolutionized wearable health monitoring by merging tissue-like softness with robust electronic functionality. This review systematically explores design strategies for PEDOT:PSS-based hydrogels, focusing on advanced gelation methods, including polymer crosslinking, ionic interactions, and light-induced polymerization, to engineer hierarchical networks that balance conductivity and mechanical adaptability. Cutting-edge fabrication techniques such as electrochemical patterning, additive manufacturing, and laser-assisted processing further enable precise microstructural control, enhancing interfacial compatibility with biological systems. The applications of these hydrogels in wearable sensors are highlighted through their capabilities in real-time mechanical deformation tracking, dynamic tissue microenvironment analysis, and high-resolution electrophysiological signal acquisition. Environmental stability and long-term durability are critical for ensuring reliable operation under physiological conditions and mitigating performance degradation caused by fatigue, oxidation, or biofouling. By addressing critical challenges in environmental stability and long-term durability, PEDOT:PSS hydrogels demonstrate transformative potential for personalized healthcare, where their unique combination of softness, biocompatibility, and tunable electro-mechanical properties enables seamless integration with human tissues for continuous, patient-specific physiological monitoring. These systems offer scalable solutions for multi-modal diagnostics, empowering tailored therapeutic interventions and chronic disease management. The review concludes with insights into future directions, emphasizing the integration of intelligent responsiveness and energy autonomy to advance next-generation bioelectronic interfaces.
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http://dx.doi.org/10.3390/polym17091192 | DOI Listing |
J Environ Pathol Toxicol Oncol
January 2025
Department of Pharmacy, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China.
Despite advancements in systemic therapy, the mortality rate for patients with metastatic melanoma remains around 70%, underscoring the imperative for alternative treatment strategies. Through the establishment of a chemoresistant melanoma model and a subsequent drug investigation, we have identified pacritinib, a medication designed for treating myelofibrosis and severe thrombocytopenia, as a potential candidate to overcome resistance to melanoma therapy. Our research reveals that pacritinib, administered at clinically achievable concentrations, effectively targets dacarbazine-resistant melanoma cells by suppressing IRAK1 rather than JAK2.
View Article and Find Full Text PDFCrit Rev Ther Drug Carrier Syst
January 2025
Department of Pharmacology, PSG College of Pharmacy, Coimbatore 641004, Tamil Nadu, India.
Treating neurological disorders is challenging due to the blood-brain barrier (BBB), which limits therapeutic agents, including proteins and peptides, from entering the central nervous system. Despite their potential, the BBB's selective permeability is a significant obstacle. This review explores recent advancements in protein therapeutics for BBB-targeted delivery and highlights computational tools.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Mathematics and Information Science, Guangxi University, Nanning, 530004, China. Electronic address:
This study presents a novel variable gain intermittent boundary control (VGIBC) approach for stabilizing delayed stochastic reaction-diffusion Cohen-Grossberg neural networks (SRDCGNN). In contrast to traditional constant gain intermittent boundary control (CGIBC) methods, the proposed VGIBC framework dynamically adjusts the control gain based on the operational duration within each control cycle, thereby improving adaptability to variations in work interval lengths. The time-varying control gain is designed using a piecewise interpolation method across work intervals, defined by a finite set of static gain matrices.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Electronic Science and Engineering, Nanjing University, China. Electronic address:
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.
View Article and Find Full Text PDFNeural Netw
September 2025
organization=Chongqing Key Laboratory of Computer Network and Communication Technology, School of Computer Science and Technology (National Exemplary Software School), Chongqing University of Posts and Telecommunications, city=Chongqing, postcode=400065, country=China. Electronic address: tianh519@1
Image deblurring and compression-artifact removal are both ill-posed inverse problems in low-level vision tasks. So far, although numerous image deblurring and compression-artifact removal methods have been proposed respectively, the research for explicit handling blur and compression-artifact coexisting degradation image (BCDI) is rare. In the BCDI, image contents will be damaged more seriously, especially for edges and texture details.
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