Rapid growth in the variety and quantity of apps makes it difficult for users to protect their privacy, although existing regulations have been introduced and the Android ecosystem is constantly being improved, there are still violations as privacy policies may not fully comply with regulations, and app behavior may not be fully consistent with privacy policies. To solve such issues, this paper proposes an automated method called VioDroid-Finder aiming at the evaluation of compliance and consistency for Android apps. We first study existing common regulations and conclude the privacy policy content into 7 aspects (i.e., privacy categories), for privacy policies, different compliance rules are required to be complied with in each privacy category. Secondly, we present a policy structure parser model based on the structure extraction/rebuilding method (which can convert the unstructured text to an XML tree) and subtitle similarity calculation algorithm. Thirdly, we propose a violation analyzer using the BERT model to classify each sentence in the privacy policy, we collect existing issues and combine them with manual observations to define 6 types of violations and detect them based on classification results. Then, we propose an inconsistency analyzer that converts permissions, APIs, and GUI into a set of personal information based on static analysis, inconsistencies are detected by comparing that set with personal information declared in the privacy policy. Finally, we evaluate 600 Chinese apps using the proposed method, from which we detect many violations and inconsistencies reflecting the current widespread privacy violation issues.

BACKGROUND AND OBJECTIVE:Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. METHODS:The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. RESULTS:There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. CONCLUSION:The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

Objective:To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information.Methods:We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase. Additionally, a visual attention mechanism based on vertical and horizontal spatial dimensions embedded in the feature extraction module to enhance the capability of the object detection algorithm to represent key information related to NCP lesions.Results:Experimental results on the NCP dataset showed that the detection method based on the detail upsampling algorithm improved the recall rate by 1.07% compared with the baseline model, with the AP50 reaching 85.14%. After embedding the attention mechanism in the feature extraction module, 86.13% AP50, 73.92% recall, and 90.37% accuracy were achieved, which were better than those of the popular object detection models.Conclusion:The feature information mining of CT images based on deep learning can further improve the lesion detection ability. The proposed approach helps radiologists rapidly identify NCP lesions on CT images and provides an important clinical basis for early intervention and high-intensity monitoring of NCP patients.

As the primary means of daily communication, SMS and instant messaging have the advantage of being inexpensive, but as a result, they have received the attention of practitioners in the underground industry. By using morph to bypass the regulation of platforms that mainly rely on rules, they disseminate information that draws traffic for industries such as pornography and gambling. To alleviate the above problems, existing researchers try to use the Chinese spelling error correction model, but the effect could be better. To this end, we manually analyze 11,358 deformed texts in real scenarios and identify four prevalent morph types in the field. Further, we propose a machine translation model-based algorithm for morph resolution, which achieves more than 96.2% BLEU and F1 values in the validation set. We also utilize the alignment and enhancement algorithms to improve the model performance by nearly 2.3% to 98.6%. The above results show that our proposed method can effectively identify the connection between the lexeme and the target word, thus resolving morphs and providing more efficient data support for traceability and information mining in the underground industry.

Migraine as a common neurological disorder still lacks effective therapies. Tetramethylpyrazine (TMP) is the main bioactive component from Ligusticum chuanxiong hort., a traditional edible-medicinal herb. This study aimed to investigate the action of TMP on migraine by metabolomics with mass spectrometry imaging (MSI) analysis and molecular exploring, including random forest model analysis, KEGG enrichment analysis and metabolite-metabolite interaction network analysis. The results indicated that 26 key representative metabolic biomarkers were identified, especially γ-glu-cys, which were highly related to glutathione (GSH) metabolism. MSI found the abundance of eleven endogenous metabolites were modulated by TMP, particularly glucose, the most important energy metabolism molecule, and GSH were increased that maintains intracellular redox balance, which was consistent with activation of Nrf2 signals by TMP. These findings provide insights into the effectiveness of metabolomics integrated with MSI in explaining the metabolic mechanisms of TMP, and afford valuable information for healthy development of TMP in migraine.

BACKGROUND:Neuroinflammation with associated oxidative stress aggravates the pathogenesis and progression of migraine. Ligustrazine (LGZ) is a key component from traditional edible-medicinal herb Ligusticum chuanxiong Hort., and has the effects of anti-platelet aggregation, expanding small arteries, improving microcirculation and promoting blood circulation and removing blood stasis in clinic. HYPOTHESIS/PURPOSE:This study aims to investigate the pharmacological effect and mechanism of LGZ in migraine. STUDY DESIGN/METHODS:A mouse model of migraine was induced by nitroglycerin (NTG), and LPS/IFN-γ stimulated microglial cell model was conducted to investigate neuroinflammation, the paracrine interactions between microglia and neurons were determined by the co-culture system, and the effect of LGZ on stability of SIRT1 protein was measured by cellular thermal shift assay (CETSA). Whilst, the SIRT1 inhibitor EX527 was used alone or co-treatment with LGZ in vitro or in vivo. RESULTS:LGZ significantly attenuated migraine-like behaviors in NTG-induced mice, and ameliorated neuroinflammation and related oxidative damage in brain tissue, but co-treatment with SIRT1 inhibitor EX527 abolished the protective effects of LGZ. Mechanistically, LGZ mitigated neuroinflammation by upregulating SIRT1 expression and subsequently inhibiting the activation of NF-κB pathway in microglia. CETSA indicated that LGZ significantly maintained the stability of SIRT1 protein in microglia. While, in the co-culture system, culture medium from LPS/IFN-γ-treated microglia exacerbated neuronal damage and oxidative stress, which was suppressed by treating LPS/IFN-γ-induced microglia with LGZ, this effect might be related to the activation of Nrf2 signals in neurons. Notably, SIRT1 inhibitor EX527 abrogated the effects of LGZ both in vitro and in vivo. CONCLUSION:Consequently, SIRT1 might be an important pharmacological target of LGZ, which attenuates migraine associated neuroinflammation and oxidative stress by interfering the crosstalk between microglia and neurons, thereby relieving migraine.

Accurate automatic segmentation of medical images has long faced challenges such as significant lesion scale variations and blurred boundaries. This study proposes a Boundary-Adaptive Transformer (BAformer) specifically designed for 2D medical image segmentation. BAformer employs a hierarchical architecture that focuses on boundary-related features across varying resolutions. Additionally, we introduce a Dense Feed-forward Network (DenseFFN) to reuse features, enabling each layer to accumulate information from previous layers. Finally, we extend the benefits of the dense network to the decoder, balancing parameter efficiency and performance. Extensive experiments demonstrate that BAformer achieves state-of-the-art performance on several challenging medical segmentation tasks. Furthermore, BAformer can seamlessly integrate with existing segmentation networks, demonstrating its versatility and effectiveness.

As cyber attacks are growing, Cyber Threat Intelligence (CTI) enhances the ability of security systems to resist novel cyber threats. However, since most CTI is unstructured data written in natural language, it needs to be understood and summarized by security experts to be effectively utilized. To address the problem, we adopt the ATT&CK matrix as the taxonomy to propose a method for automated mapping of unstructured threat intelligence to tactics and techniques. The proposed method contains a pre-processor for text denoising, a label extractor for classifying which tactics and techniques category the text belongs to, and a post-processor for correcting the classification results. The label extractor consists of two multi-label classifiers based on DistilBERT for tactics and techniques classification respectively. The post-processor corrects the classification results based on the relations between tactics, techniques, and sub-techniques in the matrix, eliminating errors caused by the independence between categories. In the evaluation, we collect the text data from the ATT&CK knowledge base and real cyber threat reports to build an experiment dataset, which contains 26,602 sentence samples. We apply the proposed method to the dataset to verify its effectiveness. The results show that the proposed method can accurately retrieve tactics and techniques with F0.5 score of 85.50% and 75.17% separately, which outperforms the baseline method by about 10%.

Models that utilize self-attention mechanisms, including but not limited to Vision Transformers (ViTs), have shown promising performance in visual tasks like semantic segmentation. This is attributed to their capacity to capture global features of images, enabling them to learn more comprehensive representations. However, transformer-based models typically demand a considerable amount of training data to achieve satisfactory performance, while being deficient in the ability to efficiently extract local image features. As a result, these models may not be as effective in some computer vision tasks that involve small-scale datasets, like medical image segmentation. To address these issues, this paper proposes a dual-stream encoding-based transformer dubbed as Dual-stream Transformer (DS-Former). The dual-stream module in DS-Former can simultaneously acquire local and global features in the image and construct relation between the two kinds of features via self-attention. Compared with the simple splicing or serial connection, the dual-stream module can extract more comprehensive and hierarchical feature information from the fusion interaction of the two features. Our method is evaluated on the UK Biobank (UKBB) cardiac magnetic resonance imaging (CMR) dataset and The Beyond the Cranial Vault (BTCV) abdominal challenge dataset. The experimental results indicate that our DS-Former outperforms other state-of-the-art approaches on both datasets, indicating its potential for medical images semantic segmentation.

Neuroinflammation, characterized by the secretion of abundant inflammatory mediators, pro inflammatory polarization of microglia, and the recruitment of infiltrating myeloid cells to foci of inflammation, drives or exacerbates the pathological processes of central nervous system disorders, especially in neurodegenerative diseases. Autophagy plays an essential role in neuroinflammatory processes, and the underlaying physiological mechanisms are closely correlated with neuroinflammation-related signals. Inhibition of mTOR and activation of AMPK and FOXO1 enhance autophagy and thereby suppress NLRP3 inflammasome activity and apoptosis, leading to the relief of neuroinflammatory response. And autophagy mitigates neuroinflammation mainly manifested by promoting the polarization of microglia from a pro-inflammatory to an anti-inflammatory state, reducing the production of pro-inflammatory mediators, and up-regulating the levels of anti-inflammatory factors. Notably, epigenetic modifications are intimately associated with autophagy and the onset and progression of various brain diseases. Non-coding RNAs, including microRNAs, circular RNAs and long noncoding RNAs, and histone acetylation have been reported to adjust autophagy-related gene and protein expression to alleviate inflammation in neurological diseases. The present review primarily focuses on the role and mechanisms of autophagy in neuroinflammatory responses, as well as epigenetic modifications of autophagy in neuroinflammation to reveal potential therapeutic targets in central nervous system diseases.

The survival prognosis of human papillomavirus (HPV)-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) is largely different, and little is known about the anti-tumor mechanism of tumor-infiltrated exhausted CD8 + T cells (Tex) in HNSCC. We performed cell-level multi-omics sequencing on human HNSCC samples to decipher the multi-dimensional characteristics of Tex cells. A proliferative exhausted CD8 + T cell cluster (P-Tex) which was beneficial to survival outcomes of patients with HPV-positive HNSCC was identified. Interestingly, P-Tex cells expressed CDK4 genes as high as cancer cells, which could be simultaneously inhibited by CDK4 inhibitors and might be a potential reason for the ineffectiveness of CDK4 inhibitors in treating HPV-positive HNSCC. P-Tex cells could aggregate in the antigen-presenting cell niches and activate certain signaling pathways. Together, our findings suggest a promising role for P-Tex cells in the prognosis of patients with HPV-positive HNSCC by providing modest but persistent anti-tumor effects.

Obesity is the basis of numerous metabolic diseases and has become a major public health issue due to its rapidly increasing prevalence. Nevertheless, current obesity therapeutic strategies are not sufficiently effective, so there is an urgent need to develop novel anti-obesity agents. Naturally occurring saponins with outstanding bio-activities have been considered promising drug leads and templates for human diseases. Cyclosiversioside F (CSF) is a paramount multi-functional saponin separated from the roots of the food-medicinal herb Astragali Radix, which possesses a broad spectrum of bioactivities, including lowering blood lipid and glucose, alleviating insulin resistance, relieving adipocytes inflammation, and anti-apoptosis. Recently, the therapeutic potential of CSF in obesity and relevant disorders has been gradually explored and has become a hot research topic. This review highlights the role of CSF in treating obesity and obesity-induced complications, such as diabetes mellitus, diabetic nephropathy, cardiovascular and cerebrovascular diseases, and non-alcoholic fatty liver disease. Remarkably, the underlying molecular mechanisms associated with CSF in disease therapy have been partially elucidated, especially PI3K/Akt, NF-κB, MAPK, apoptotic pathway, TGF-β, NLRP3, Nrf-2, and AMPK, with the aim of promoting the development of CSF as a functional food and providing references for its clinical application in obesity-related disorders therapy.

Galangin is an important flavonoid with natural activity, that is abundant in galangal and propolis. Currently, various biological activities of galangin have been disclosed, including anti-inflammation, antibacterial effect, anti-oxidative stress and aging, anti-fibrosis, and antihypertensive effect. Based on the above bioactivities, more and more attention has been paid to the role of galangin in neurodegenerative diseases, rheumatoid arthritis, osteoarthritis, osteoporosis, skin diseases, and cancer. In this paper, the natural sources, pharmacokinetics, bioactivities, and therapeutic potential of galangin against various diseases were systematically reviewed by collecting and summarizing relevant literature. In addition, the molecular mechanism and new preparation of galangin in the treatment of related diseases are also discussed, to broaden the application prospect and provide reference for its clinical application. Furthermore, it should be noted that current toxicity and clinical studies of galangin are insufficient, and more evidence is needed to support its possibility as a functional food.

Heart failure (HF) is the most common complication following myocardial infarction, closely associated with ventricular remodeling. Aconitum carmichaelii Debx., a traditional Chinese herb, possesses therapeutic effects on HF and related cardiac diseases. However, its effects and mechanisms on HF-associated cardiac diseases are still unclear. In the present study, a water extraction of toasted Aconitum carmichaelii Debx. (WETA) was verified using UPLC-Q/TOF-MS. The heart function of HF rats was assessed by echocardiography and strain analysis, and myocardial injury was measured by serum levels of CK-MB, cTnT, and cTnI. The pathological changes of cardiac tissues were evaluated by 2,3,5-triphenyltetrazolium chloride (TTC) staining, hematoxylin and eosin (H&E) staining, and Masson’s trichrome staining. Additionally, the levels of inflammation-related genes and proteins and components related to vascular remodeling were detected by RT-qPCR, Western blot, and immunofluorescence. WETA significantly inhibited the changes in echocardiographic parameters and the increase in heart weight, cardiac infarction size, the myonecrosis, edema, and infiltration of inflammatory cells, collagen deposition in heart tissues, and also mitigated the elevated serum levels of CK-MB, cTnT, and cTnI in ISO-induced rats. Additionally, WETA suppressed the expressions of inflammatory genes, including IL-1β, IL-6, and TNF-α and vascular injury-related genes, such as VCAM1, ICAM1, ANP, BNP, and MHC in heart tissues of ISO-induced HF rats, which were further confirmed by Western blotting and immunofluorescence. In summary, the myocardial protective effect of WETA was conferred through inhibiting inflammatory responses and abnormal vascular remodeling in ISO-treated rats.

Naturally occurring flavonoids have long been utilized as essential templates for the development of novel drugs and as critical ingredients for functional foods. Astragalin (AG) is a natural flavonoid that can be isolated from a variety of familiar edible plants, such as the seeds of green tea, Morus alba L., and Cuscuta chinensis. It is noteworthy that AG has a wide range of pharmacological activities and possesses therapeutic effects against a variety of diseases, covering cancers, osteoarthritis, osteoporosis, ulcerative colitis, mastitis, obesity, diabetes mellitus, diabetic complications, ischemia/reperfusion injury, neuropathy, respiratory diseases, and reproductive system diseases. This article reviewed the natural source and pharmacokinetics of AG and systematically summarized the pharmacological activities and potential mechanisms of AG in treating diverse diseases in order to promote the development of AG as a functional food, in doing so providing references for its clinical application in disease therapy.

Chronic inflammation participates in the progression of multiple chronic diseases, including obesity, diabetes mellitus (DM), and DM related complications. Diabetic ulcer, characterized by chronic wounds that are recalcitrant to healing, is a serious complication of DM tremendously affecting the quality of life of patients and imposing a costly medical burden on society. Matrix metalloproteases (MMPs) are a family of zinc endopeptidases with the capacity of degrading all the components of the extracellular matrix, which play a pivotal part in healing process under various conditions including DM. During diabetic wound healing, the dynamic changes of MMPs in the serum, skin tissues, and wound fluid of patients are in connection with the degree of wound recovery, suggesting that MMPs can function as essential biomarkers for the diagnosis of diabetic ulcer. MMPs participate in various biological processes relevant to diabetic ulcer, such as ECM secretion, granulation tissue configuration, angiogenesis, collagen growth, re-epithelization, inflammatory response, as well as oxidative stress, thus, seeking and developing agents targeting MMPs has emerged as a potential way to treat diabetic ulcer. Natural products especially flavonoids, polysaccharides, alkaloids, polypeptides, and estrogens extracted from herbs, vegetables, as well as animals that have been extensively illustrated to treat diabetic ulcer through targeting MMPs-mediated signaling pathways, are discussed in this review and may contribute to the development of functional foods or drug candidates for diabetic ulcer therapy. This review highlights the regulation of MMPs in diabetic wound healing, and the potential therapeutic ability of natural products for diabetic wound healing by targeting MMPs.

Background:Neuroinflammation has been reported as a potential contributing factor to brain diseases, and is characterized by activated microglia with release of multiple inflammatory mediators. 2,3,5,6-Tetramethylpyrazine (TMP) is an active alkaloid in Ligusticum chuanxiong Hort. and has various biological activities, including anti-inflammatory and neuroprotection properties. However, the anti-neuroinflammatory activity of TMP has been less studied and its potential molecular mechanisms in this field remain unclear. This study aimed to investigate the effects of TMP and its underlying mechanisms in neuroinflammation.Methods:In vitro, lipopolysaccharide (LPS)-stimulated BV2 microglia were used to assess the effects of TMP on inflammatory cytokines as well as the components of the SIRT1/NF-κB signaling pathway, which were measured by using ELISA, western blotting, qRT-qPCR and immunofluorescence. Moreover, LPS-induced acute neuroinflammation model in mice was performed to detect whether TMP could exert anti-neuroinflammatory effects in vivo, and the EX527, a SIRT1 inhibitor, were given intraperitoneally every two days prior to TMP treatment. Serums and spinal trigeminal nucleus (Sp5) tissues were collected for ELISA assay, and the Sp5 tissues were used for HE staining, Nissl staining, immunofluorescence, qRT-PCR and western blotting.Results:In vitro, TMP treatment significantly reduced the secretion of pro-inflammatory cytokines, including TNF-α and IL-6, promoted SIRT1 protein expression and inactivated NF-κB signaling pathway in LPS-induced neuroinflammation. Interestingly, pretreatment with EX527 blocked the therapeutic effects of TMP on neuroinflammation in vitro. Furthermore, TMP reduced the levels of pro-inflammatory cytokines and chemokines, and prevented microglia from polarizing towards a pro-inflammatory state through activating SIRT1 and inhibiting NF-κB activation in LPS-induced neuroinflammation in mice. And EX527 reversed the beneficial effects of TMP against LPS exposure in mice.Conclusion:In summary, this study unravels that TMP could mitigate LPS-induced neuroinflammation via SIRT1/NF-κB signaling pathway.

Natural edible pigments play a paramount part in the food industry. Procyanidin B2 (PB2), one of the most representative naturally occurring edible pigments, is usually isolated from the seeds, fruits, and leaves of lots of common plants, such as grapes, Hawthorn, black soybean, as well as blueberry, and functions as a food additive in daily life. Notably, PB2 has numerous bioactivities and possesses the potential to treat/prevent a wide range of human diseases, such as diabetes mellitus, diabetic complications, atherosclerosis, and non-alcoholic fatty liver disease, and the underlying mechanisms were partially elucidated, including mediating signaling pathways like NF-κB, MAPK, PI3K/Akt, apoptotic axis, and Nrf-2/HO-1. This paper presents a review of the natural sources, bioactivities, and the therapeutic/preventive potential of PB2 and the possible mechanisms, with the aim of promoting the development of PB2 as a functional food and providing references for its clinical application in the treatment of diseases.