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Background: Hepatocellular carcinoma (HCC), a prevalent primary malignant tumor, is notorious for its high mortality rate. Despite advancements in HCC treatment, patient outcomes remain suboptimal. This study endeavors to assess the potential prognostic significance of POLH-AS1 in HCC. Methods: In this research, we gathered RNA-Seq information from individuals with HCC in The Cancer Genome Atlas (TCGA). We analyzed the levels of POLH-AS1 expression in both HCC cells and tissues using statistical tests. Additionally, we examined various prognostic factors in HCC using advanced methodologies. Furthermore, we employed Spearman's rank correlation analysis to examine the association between POLH-AS1 expression and the tumor's immune microenvironment. Finally, the functional roles of POLH-AS1 in HCC were validated in two HCC cell lines (HEP3B and HEPG2). Results: Our analysis revealed elevated POLH-AS1 expression across various cancers, including HCC, with heightened expression correlating with HCC progression. Notably, POLH-AS1 expression emerged as a potential biomarker for HCC patient survival and prognosis. Mechanistically, we identified the involvement of POLH-AS1 in tumorigenesis pathways such as herpes simplex virus 1 infection, interactions with neuroactive receptors, and the cAMP signaling pathway. Lastly, inhibition of POLH-AS1 was discovered to hinder the proliferation, invasion and migration of HEP3B and HEPG2 HCC cells. Conclusions: POLH-AS1 emerges as a promising prognostic biomarker and therapeutic target for HCC, offering potential avenues for enhanced patient management and treatment strategies. [ABSTRACT FROM AUTHOR]

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework's adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into the network, enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques. Additionally, attention and boundary detection modules are included to augment the network's capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains. Furthermore, a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network's boundary maintenance capability through additional loss. The method was evaluated using breast data from 207 patients at Ruijin Hospital, resulting in a 6.64% increase in Dice similarity coefficient compared to the benchmark U-Net. Experimental results demonstrate the superiority of the method over other segmentation techniques, with fewer model parameters. [ABSTRACT FROM AUTHOR]

Management of diabetic wounds presents a global health challenge due to elevated levels of ROS in the wound microenvironment, persistent dysregulation of inflammation modulation, and limitations in commercially available dressings. Addressing this issue, we have developed a pH-responsive and glucose-sensitive multifunctional hydrogel dressing that dynamically responds to the wound microenvironment and enables on-demand drug release. The dressing incorporates a matrix material based on aminophenylboronic acid-functionalized alginate and a polyhydroxy polymer, alongside an enhancer phase consisting of self-assembled metal–phenol coordination nanospheres formed by tannic acid and iron ions. Using the dynamic borate ester bonds and catechol-metal ion coordination bonds, the dressing exhibits remarkable shape adaptability, self-healing capability, tissue adhesiveness, antioxidant activity, and photothermal responsiveness, without additional curatives or crosslinking agents. As a wound dressing, it elicits macrophage polarization towards an anti-inflammatory phenotype while maintaining long-lasting antimicrobial effects. In a diabetic mouse model of full-thickness wound infections, it effectively mitigated inflammation and vascular damage, significantly expediting the wound healing process with a commendable 97.7% wound closure rate. This work provides a new direction for developing multifunctional smart hydrogel dressings that can accelerate diabetic wound healing for human health. [ABSTRACT FROM AUTHOR]

In this paper, we consider the existence of positive solutions for a system of fractional q -difference equations with generalized p -Laplacian operators. By using Guo-Krasnosel'skii fixed point theorem, we obtain some existence results of positive solutions for this system with two parameters under some different combinations of superlinearity and sublinearity of the nonlinear terms. In the end, we give two examples to illustrate our main results. [ABSTRACT FROM AUTHOR]

Perovskite quantum dots (QDs), emerging with excellent bright-green photoluminescence (PL) and a large absorption coefficient, are of great potential for the fabrication of light sources in underwater optical wireless communication systems. However, the instability caused by low formation energy and abundant surface traps is still a major concern for perovskite-based light sources in underwater conditions. Herein, we propose ultra-stable zero dimensional–two dimensional (0D–2D) CsPbBr3 QD/1,4-bis(4-methylstyryl)benzene (p-MSB) nanoplate (NP) heterostructures synthesized via a facile approach at room temperature in air. CsPbBr3 QDs can naturally nucleate on the p-MSB NP toluene solution, and the radiative combination is drastically intensified owing to the electron transfer within the typical type-II heterostructures, leading to a sharply increased PLQY of the heterostructure thin films up to 200% compared with the pristine sample. The passivation of defects within CsPbBr3 QDs can be effectively realized with the existence of p-MSB NPs, and thus the obviously improved PL is steadily witnessed in an ambient atmosphere and thermal environment. Meanwhile, the enhanced humidity stability and a peak EQE of 9.67% suggests a synergetic strategy for concurrently addressing the knotty problems on unsatisfied luminous efficiency and stability of perovskites for high-performance green-emitting optoelectronic devices in underwater applications. [ABSTRACT FROM AUTHOR]