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"Lin, Yuqing"
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Single-atom Ni-N4 provides a robust cellular NO sensor
Nitric oxide (NO) has been implicated in a variety of physiological and pathological processes. Monitoring cellular levels of NO requires a sensor to feature adequate sensitivity, transient recording ability and biocompatibility. Herein we report a single-atom catalysts (SACs)-based electrochemical sensor for the detection of NO in live cellular environment. The system employs nickel single atoms anchored on N-doped hollow carbon spheres (Ni SACs/N-C) that act as an excellent catalyst for electrochemical oxidation of NO. Notably, Ni SACs/N-C shows superior electrocatalytic performance to the commonly used Ni based nanomaterials, attributing from the greatly reduced Gibbs free energy that are required for Ni SACs/N-C in activating NO oxidation. Moreover, Ni SACs-based flexible and stretchable sensor shows high biocompatibility and low nanomolar sensitivity, enabling the real-time monitoring of NO release from cells upon drug and stretch stimulation. Our results demonstrate a promising means of using SACs for electrochemical sensing applications.
The monitoring of nitric oxide is important to a number of disease states and biomedical applications. Here, the authors report on a single nickel atom catalyst based sensor for detecting nitric oxide production from cells.
Journal Article
A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection
Efforts have been dedicated by researchers in the field of natural language processing (NLP) to detecting and combating fake news using an assortment of machine learning (ML) and deep learning (DL) techniques. In this paper, a review of the existing studies is conducted to understand and curtail the dissemination of fake news. Specifically, we conducted a benchmark study using a wide range of (1) classical ML algorithms such as logistic regression (LR), support vector machines (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), XGBoost (XGB) and an ensemble learning method of such algorithms, (2) advanced ML algorithms such as convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, CNN-BiGRU and a hybrid approach of such techniques and (3) DL transformer-based models such as BERTbase and RoBERTabase. The experiments are carried out using different pretrained word embedding methods across four well-known real-world fake news datasets—LIAR, PolitiFact, GossipCop and COVID-19—to examine the performance of different techniques across various datasets. Furthermore, a comparison is made between context-independent embedding methods (e.g., GloVe) and the effectiveness of BERTbase—contextualised representations in detecting fake news. Compared with the state of the art’s results across the used datasets, we achieve better results by solely relying on news text. We hope this study can provide useful insights for researchers working on fake news detection.
Journal Article
Gallic Acid Alleviates Gouty Arthritis by Inhibiting NLRP3 Inflammasome Activation and Pyroptosis Through Enhancing Nrf2 Signaling
Gallic acid is an active phenolic acid widely distributed in plants, and there is compelling evidence to prove its anti-inflammatory effects. NLRP3 inflammasome dysregulation is closely linked to many inflammatory diseases. However, how gallic acid affects the NLRP3 inflammasome remains unclear. Therefore, in the present study, we investigated the mechanisms underlying the effects of gallic acid on the NLRP3 inflammasome and pyroptosis, as well as its effect on gouty arthritis in mice. The results showed that gallic acid inhibited lactate dehydrogenase (LDH) release and pyroptosis in lipopolysaccharide (LPS)-primed and ATP-, nigericin-, or monosodium urate (MSU) crystal-stimulated macrophages. Additionally, gallic acid blocked NLRP3 inflammasome activation and inhibited the subsequent activation of caspase-1 and secretion of IL-1β. Gallic acid exerted its inhibitory effect by blocking NLRP3-NEK7 interaction and ASC oligomerization, thereby limiting inflammasome assembly. Moreover, gallic acid promoted the expression of nuclear factor E2-related factor 2 (Nrf2) and reduced the production of mitochondrial ROS (mtROS). Importantly, the inhibitory effect of gallic acid could be reversed by treatment with the Nrf2 inhibitor ML385. NRF2 siRNA also abolished the inhibitory effect of gallic acid on IL-1β secretion. The results further showed that gallic acid could mitigate MSU-induced joint swelling and inhibit IL-1β and caspase 1 (p20) production in mice. Moreover, gallic acid could moderate MSU-induced macrophages and neutrophils migration into joint synovitis. In summary, we found that gallic acid suppresses ROS generation, thereby limiting NLRP3 inflammasome activation and pyroptosis dependent on Nrf2 signaling, suggesting that gallic acid possesses therapeutic potential for the treatment of gouty arthritis.
Journal Article
Corilagin Restrains NLRP3 Inflammasome Activation and Pyroptosis through the ROS/TXNIP/NLRP3 Pathway to Prevent Inflammation
Corilagin, a gallotannin, shows excellent antioxidant and anti-inflammatory effects. The NLRP3 inflammasome dysfunction has been implicated in a variety of inflammation diseases. However, it remains unclear how corilagin regulates the NLRP3 inflammasome to relieve gouty arthritis. In this study, bone marrow-derived macrophages (BMDMs) were pretreated with lipopolysaccharide (LPS) and then incubated with NLRP3 inflammasome agonists, such as adenine nucleoside triphosphate (ATP), nigericin, and monosodium urate (MSU) crystals. The MSU crystals were intra-articular injected to induce acute gouty arthritis. Here we showed that corilagin reduced lactate dehydrogenase (LDH) secretion and the proportion of propidium iodide- (PI-)stained cells. Corilagin suppressed the expression of N-terminal of the pyroptosis executive protein gasdermin D (GSDMD-NT). Corilagin restricted caspase-1 p20 and interleukin (IL)-1β release. Meanwhile, corilagin attenuated ASC oligomerization and speck formation. Our findings confirmed that corilagin diminished NLRP3 inflammasome activation and macrophage pyroptosis. We further discovered that corilagin limited the mitochondrial reactive oxygen species (ROS) production and prevented the interaction between TXNIP and NLRP3, but ROS activator imiquimod could antagonize the inhibitory function of corilagin on NLRP3 inflammasome and macrophage pyroptosis. Additionally, corilagin ameliorated MSU crystals induced joint swelling, inhibited IL-1β production, and abated macrophage and neutrophil migration into the joint capsule. Collectively, these results demonstrated that corilagin suppressed the ROS/TXNIP/NLRP3 pathway to repress inflammasome activation and pyroptosis and suggest its potential antioxidative role in alleviating NLRP3-dependent gouty arthritis.
Journal Article
Nanoscaffolds in promoting regeneration of the peripheral nervous system
The ability to surgically repair peripheral nerve injuries is urgently needed. However, traditional tissue engineering techniques, such as autologous nerve transplantation, have some limitations. Therefore, tissue engineered autologous nerve grafts have become a suitable choice for nerve repair. Novel tissue engineering techniques derived from nanostructured conduits have been shown to be superior to other successful functional neurological structures with different scaffolds in terms of providing the required structures and properties. Additionally, different biomaterials and growth factors have been added to nerve scaffolds to produce unique biological effects that promote nerve regeneration and functional recovery. This review summarizes the application of different nanoscaffolds in peripheral nerve repair and further analyzes how the nanoscaffolds promote peripheral nerve regeneration.
Journal Article
Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media
The prevalence of fake news on social media has led to major sociopolitical issues. Thus, the need for automated fake news detection is more important than ever. In this work, we investigated the interplay between news content and users’ posting behavior clues in detecting fake news by using state-of-the-art deep learning approaches, such as the convolutional neural network (CNN), which involves a series of filters of different sizes and shapes (combining the original sentence matrix to create further low-dimensional matrices), and the bidirectional gated recurrent unit (BiGRU), which is a type of bidirectional recurrent neural network with only the input and forget gates, coupled with a self-attention mechanism. The proposed architectures introduced a novel approach to learning rich, semantical, and contextual representations of a given news text using natural language understanding of transfer learning coupled with context-based features. Experiments were conducted on the FakeNewsNet dataset. The experimental results show that incorporating information about users’ posting behaviors (when available) improves the performance compared to models that rely solely on textual news data.
Journal Article
Burden and characteristics of inherited retinal diseases in China
Hereditary eye diseases, particularly inherited retinal diseases (IRDs), are major causes of visual impairment and blindness. However, IRDs real-world impact in China remains limited. Our study aimed to investigate the socio-demographic characteristics, clinical burden, and perceived quality of life among individuals affected by inherited retinal diseases, with retinitis pigmentosa (RP) as the most common subtype. A cross-sectional national survey was conducted using both online and paper-based questionnaires distributed through hospitals and patient organizations. The questionnaire included sections on demographics, clinical history, genetic testing, symptoms, and the social and economic impact of the disease. A total of 1219 valid responses were collected. RP accounted for 49.08% of diagnoses. Nearly half of the patients (48.81%) reported onset before school age. Night blindness (59.15%) and visual field constriction (58.08%) were the most common symptoms. Cataracts were present in 18.21% of respondents. Psychosocial burden scores across seven domains (e.g., daily life, work, family) exceeded a mean of 6/10, indicating moderate-to-high stress levels. Additionally, 57.51% of households reported annual income below 100,000 RMB (~$14,000 USD), highlighting financial barriers to IRDs patient care and treatment. This survey reveals a substantial burden associated with hereditary eye diseases in China, impacting patients’ functional ability, mental well-being, and socioeconomic status. Our findings underscore the urgent need for public awareness, policy support, and affordable therapeutic options to address the needs of this underserved population.
Journal Article
The Power of Context: A Novel Hybrid Context-Aware Fake News Detection Approach
The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorporating auxiliary information. Our approach is based on the utilisation of Bidirectional Encoder Representations from Transformers (BERT) to capture contextualised semantic knowledge. Additionally, we employ a multichannel Convolutional Neural Network (mCNN) integrated with stacked Bidirectional Gated Recurrent Units (sBiGRU) to jointly learn multi-aspect language representations. This enables our model to effectively identify valuable clues from news content while simultaneously incorporating content- and context-based cues, such as user posting behaviour, to enhance the detection of fake news. Through extensive experimentation on four widely used real-world datasets, our proposed framework demonstrates superior performance (↑3.59% (PolitiFact), ↑6.8% (GossipCop), ↑2.96% (FA-KES), and ↑12.51% (LIAR), considering both content-based features and additional auxiliary information) compared to existing state-of-the-art approaches, establishing its effectiveness in the challenging task of fake news detection.
Journal Article
A Case Study on Recycling Industrial Wastewater with Nanofiltration Membrane Separation Technology
As pressure on water resources intensifies and stringent regulations for groundwater and surface water are enacted, wastewater recycling has emerged as a key research objective for many enterprises. In this study, based on the actual wastewater discharged from Eternal Electronic (Suzhou, China) Co., Ltd., the performance differences in different membrane materials in treating this wastewater were analyzed and compared. The NF90 membrane was ultimately selected as the most suitable choice for treating this wastewater with optimal operating conditions of 150 psi, 25 °C, and 1 L/min, respectively. All the indices of wastewater treatment can satisfy the standard of circulating cooling water. In addition, this work is closely combined with the practical applications by using an HCl solution (pH = 3–4) to clean the fouled membranes, thus effectively solving the membrane fouling in the treatment process. This approach not only satisfies the environmental management requirements of enterprises but also provides valuable insights for similar wastewater treatment projects.
Journal Article