mRNA Sonotransfection of Tumors with Polymeric Microbubbles: Co-Formulation versus Co-Administration.

Despite its high potential, non-viral gene therapy of cancer remains challenging due to inefficient nucleic acid delivery. Ultrasound (US) with microbubbles (MB) can open biological barriers and thus improve DNA and mRNA passage. Polymeric MB are an interesting alternative to clinically used lipid-coated MB because of their high stability, narrow size distribution, and easy functionalization. However, besides choosing the ideal MB, it remains unclear whether nanocarrier-encapsulated mRNA should be administered separately (co-administration) or conjugated to MB (co-formulation). Therefore, the impact of poly(n-butyl cyanoacrylate) MB co-administration with mRNA-DOTAP/DOPE lipoplexes or their co-formulation on the transfection of cancer cells in vitro and in vivo is analyzed. Sonotransfection improved mRNA delivery into 4T1 breast cancer cells in vitro with co-administration being more efficient than co-formulation. In vivo, the co-administration sonotransfection approach also resulted in higher transfection efficiency and reached deeper into the tumor tissue. On the contrary, co-formulation mainly promoted transfection of endothelial and perivascular cells. Furthermore, the co-formulation approach is much more dependent on the US trigger, resulting in significantly lower off-site transfection. Thus, the findings indicate that the choice of co-administration or co-formulation in sonotransfection should depend on the targeted cell population, tolerable off-site transfection, and the therapeutic purpose.

Latest advances: Improving the anti-inflammatory and immunomodulatory properties of PEEK materials.

Excellent biocompatibility, mechanical properties, chemical stability, and elastic modulus close to bone tissue make polyetheretherketone (PEEK) a promising orthopedic implant material. However, biological inertness has hindered the clinical applications of PEEK. The immune responses and inflammatory reactions after implantation would interfere with the osteogenic process. Eventually, the proliferation of fibrous tissue and the formation of fibrous capsules would result in a loose connection between PEEK and bone, leading to implantation failure. Previous studies focused on improving the osteogenic properties and antibacterial ability of PEEK with various modification techniques. However, few studies have been conducted on the immunomodulatory capacity of PEEK. New clinical applications and advances in processing technology, research, and reports on the immunomodulatory capacity of PEEK have received increasing attention in recent years. Researchers have designed numerous modification techniques, including drug delivery systems, surface chemical modifications, and surface porous treatments, to modulate the post-implantation immune response to address the regulatory factors of the mechanism. These studies provide essential ideas and technical preconditions for the development and research of the next generation of PEEK biological implant materials. This paper summarizes the mechanism by which the immune response after PEEK implantation leads to fibrous capsule formation; it also focuses on modification techniques to improve the anti-inflammatory and immunomodulatory abilities of PEEK. We also discuss the limitations of the existing modification techniques and present the corresponding future perspectives.

Unraveling the mechanisms of intervertebral disc degeneration: an exploration of the p38 MAPK signaling pathway.

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD.

Unsupervised Visual-Textual Correlation Learning With Fine-Grained Semantic Alignment.

With the rapid growth of multimedia data on the Internet, there has been a rapid rise in the demand for visual-textual cross-media retrieval between images and sentences. However, the heterogeneous property of visual and textual data brings huge challenges to measure the cross-media similarity for retrieval. Although existing methods have achieved great progress with the strong learning ability of the deep neural network, they rely heavily on the scale of training data with manual annotation, that is, either pairwise image-sentence annotation or category annotation as supervised information for visual-textual correlation learning, which are extremely labor and time consuming to collect. Without any pairwise or category annotation, it is highly challenging to construct a correlation between images and sentences due to their inconsistent distributions and representations. But people can naturally understand the correlation between visual and textual data in high-level semantic, and those images and sentences containing the same group of semantic concepts can be easily matched in human brain. Inspired by the above human cognitive process, this article proposes an unsupervised visual-textual correlation learning (UVCL) approach to construct correlations without any manual annotation. The contributions are summarized as follows: 1) unsupervised semantic-guided cross-media correlation mining is proposed to bridge the heterogeneous gap between visual and textual data. We measure the semantic matching degree between images and sentences, and generate descriptive sentences according to the concepts extracted from images to further augment the training data in an unsupervised manner. Therefore, the approach can exploit the semantic knowledge within both visual and textual data to reduce the gap between them for further correlation learning and 2) unsupervised visual-textual fine-grained semantic alignment is proposed to learn cross-media correlation by aligning the fine-grained visual local patches and textual keywords with fine-grained soft attention as well as semantic-guided hard attention, and the results can effectively highlight the fine-grained semantic information within both images and sentences to boost visual-textual alignment. Extensive experiments are conducted to perform visual-textual cross-media retrieval in unsupervised setting without any manual annotation on two widely used datasets, namely, Flickr-30K and MS-COCO, which verify the effectiveness of our proposed UVCL approach.

Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits.

Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite's content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits.

MAVA: Multi-level Adaptive Visual-textual Alignment by Cross-media Bi-attention Mechanism.

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform crossmedia retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning crossmedia correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach.

Overexpression of GAS5 inhibits abnormal activation of Wnt/ß-catenin signaling pathway in myocardial tissues of rats with coronary artery disease.

OBJECTIVE: The aim of this study is to investigate the clinical value of long noncoding RNA growth arrest-specific transcript 5 (LncRNA GAS5) in the diagnosis of coronary artery disease (CAD) and its protective effect on myocardial injury in rats with CAD. METHODS: Patients with CAD and healthy controls were selected to measure the expression of GAS5, and further to perform the correlation analysis and ROC curve. In addition, the rat models of CAD were also established to observe the effect of GAS5 on hyperlipidemia, myocardial injury, cardiomyocyte apoptosis, oxidative stress, and inflammatory injury of rats with CAD, and the effect of the Wnt/ß-catenin signaling pathway was also determined. RESULTS: Overexpression of GAS5 in CAD rats determines improvement of hyperlipidemia, attenuation of myocardial injury, inhibition of cardiomyocyte apoptosis, oxidative stress, inflammatory injury, and abnormal activation of the Wnt/ß-catenin signaling pathway in myocardial tissues. CONCLUSION: Our study demonstrates that downregulation of GAS5 is found in CAD, and overexpression of GAS5 inhibits abnormal activation of the Wnt/ß-catenin signaling pathway in myocardial tissues of CAD rats.

Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network.

Nowadays, cross-modal retrieval plays an important role to flexibly find useful information across different modalities of data. Effectively measuring the similarity between different modalities of data is the key of cross-modal retrieval. Different modalities such as image and text have imbalanced and complementary relationship, and they contain unequal amount of information when describing the same semantics. For example, images often contain more details that cannot be demonstrated by textual descriptions and vice versa. Existing works based on Deep Neural Network (DNN) mostly construct one common space for different modalities, to find the latent alignments between them, which lose their exclusive modality-specific characteristics. Therefore, we propose modality-specific cross-modal similarity measurement (MCSM) approach by constructing the independent semantic space for each modality, which adopts an endto- end framework to directly generate modality-specific crossmodal similarity without explicit common representation. For each semantic space, modality-specific characteristics within one modality are fully exploited by recurrent attention network, while the data of another modality is projected into this space with attention based joint embedding, which utilizes the learned attention weights for guiding the fine-grained cross-modal correlation learning, and captures the imbalanced and complementary relationship between different modalities. Finally, the complementarity between the semantic spaces for different modalities is explored by adaptive fusion of the modality-specific cross-modal similarities to perform cross-modal retrieval. Experiments on the widely-used Wikipedia, Pascal Sentence, MS-COCO datasets as well as our constructed large-scale XMediaNet dataset verify the effectiveness of our proposed approach, outperforming 9 stateof- the-art methods.