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A novel catalyst, consisting of hollow silica-coated porous carbon with embedded iron oxide particles (FeO x @C/SiO 2 ), was synthesized by the extended Stöber method. Iron ions were incorporated in a resorcinol-formaldehyde resin in the presence of citric acid to form a template, which was then coated with a silica layer. The iron oxide-embedded porous carbon and hollow silica were simultaneously formed during calcination under N 2 atmosphere. Through this process, silica endowed the iron oxide with low crystallinity and small size, resulting in a higher catalytic activity in the heterogeneous Fenton system for the decolorization of a methylene blue (MB) solution within 25 min. Moreover, the sample maintained 78.71% of its catalytic activity after three cycles., Competing Interests: The authors declare that they have no conflict of interest., (This journal is © The Royal Society of Chemistry.)

In traditional graph neural networks (GNNs), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learning. In reality, network topology and node content each provide unique and important information, and they are not always consistent because of noise, irrelevance, or missing links between nodes. A pure topology-driven feature aggregation approach between unaligned neighborhoods may deteriorate learning from nodes with poor structure-content consistency, due to the propagation of incorrect messages over the whole network. Alternatively, in this brief, we advocate a co-alignment graph convolutional learning (CoGL) paradigm, by aligning topology and content networks to maximize consistency. Our theme is to enforce the learning from the topology network to be consistent with the content network while simultaneously optimizing the content network to comply with the topology for optimized representation learning. Given a network, CoGL first reconstructs a content network from node features then co-aligns the content network and the original network through a unified optimization goal with: 1) minimized content loss; 2) minimized classification loss; and 3) minimized adversarial loss. Experiments on six benchmarks demonstrate that CoGL achieves comparable and even better performance compared with existing state-of-the-art GNN models.

Two-dimensional magnetic van der Waals materials provide a fertile platform for the design and control of topological spin textures such as skyrmions. However, despite studies reporting skyrmions in many 2D magnetic systems, those of the hosting van der Waals materials remain limited. Here, via first-principles calculations and Monte Carlo simulations, we propose BiCrX 3 as a new family of materials for hosting skyrmions. Due to the large SOC of the X atom and intrinsic inversion asymmetry, an inherent large DMI occurs in all systems, enabling intriguing Néel-type skyrmions. Furthermore, upon applying a moderate magnetic field, isolated skyrmions and skyrmion lattices emerge in our systems, and are robust within a relatively wide temperature range.

Chemoresistance of cancer cells is a major problem in treating cancer. Knowledge of how cancer cells may die or resist cancer drugs is critical to providing certain strategies to overcome tumour resistance to treatment. Paclitaxel is known as a chemotherapy drug that can suppress the proliferation of cancer cells by inducing cell cycle arrest and induction of mitotic catastrophe. However, today, it is well known that paclitaxel can induce multiple kinds of cell death in cancers. Besides the induction of mitotic catastrophe that occurs during mitosis, paclitaxel has been shown to induce the expression of several pro-apoptosis mediators. It also can modulate the activity of anti-apoptosis mediators. However, certain cell-killing mechanisms such as senescence and autophagy can increase resistance to paclitaxel. This review focuses on the mechanisms of cell death, including apoptosis, mitotic catastrophe, senescence, autophagic cell death, pyroptosis, etc., following paclitaxel treatment. In addition, mechanisms of resistance to cell death due to exposure to paclitaxel and the use of combinations to overcome drug resistance will be discussed., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent graph convolutional networks (GCNs) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. Noisy node content, combined with sparse features, provides essential challenges for existing methods to be used in real-world noisy networks. In this article, we propose feature-based attention GCN (FA-GCN), a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each node feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes to learn and vary feature importance, with respect to their connections. By using a spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than the state-of-the-art methods in both noise-free and noisy network environments.

In recent years, low-dose computed tomography (LDCT) has played an increasingly important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation on patients while maintaining the same diagnostic image quality. Current deep learning-based denoising methods applied to LDCT imaging only use normal dose CT (NDCT) images as positive examples to guide the denoising process. Recent studies on contrastive learning have proved that the original images as negative examples can also be helpful for network learning. Therefore, this paper proposes a novel content-noise complementary network with contrastive learning for an LDCT denoising task. First, to better train our proposed network, a contrastive learning loss, taking the NDCT image as a positive example and the original LDCT image as a negative example to guide the network learning is added. Furthermore, we also design a network structure that combines content-noise complementary learning strategy, attention mechanism, and deformable convolution for better network performance. In an evaluation study, we compare the performance of our designed network with some of the state-of-the-art methods in the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset. The quantitative and qualitative evaluation results demonstrate the feasibility and effectiveness of applying our proposed CCN-CL network model as a new deep learning-based LDCT denoising method., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Psoriasis vulgaris is an inflammatory skin disease. Observational studies have shown associations between circulating cytokine levels and psoriasis vulgaris. But the causal relationship between circulating cytokine and psoriasis vulgaris remains elusive. Methods: To assess the causal effects of cytokine levels on the risk of psoriasis vulgaris and vice versa, we performed a two-sample Mendelian randomization (MR) study by using the inverse-variance weighted (IVW), weighted median, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) in genome-wide association summary statistics of 41 circulating cytokines in up to 8,293 individuals and psoriasis vulgaris in 399,883 individuals. Results: We identified that increasing RANTES level induced an elevated risk of psoriasis vulgaris in IVW ( β = 0.33, S.E. = 0.12, p = 0.006). This causal effect showed consistency across the weighted median ( β = 0.35, S.E. = 0.15, p = 0.022) and MR-PRESSO method ( β = 0.33, S.E. = 0.11, p = 0.028). Conclusions: Our results suggest a potential causal effect of elevated RANTES concentration on the increased risk of psoriasis vulgaris., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhao, Zhang, Liu, Tang, Wang, Wang, Wu, Yang, Li and Li.)

Porcine decellularized small intestine submucosa (SIS) is a collagen membrane, which offers great potential as an organic substrate template in mineralization processes due to its good biodegradability and biocompatibility. However, a long period of mineralization and low efficiency are apparent, and the mechanism of collagen fiber mineralization has often been neglected in the previous literature. Thus, in this paper, we present a novel model of biomimetic collagen mineralization which uses dopamine (DA) molecules with the activating and retouching function of SIS collagen membranes and regulating collagen mineralization to construct the structure of mineralized collagen hard tissues. The crystal biomimetic mineralization growth of calcium phosphate on membranes is studied in different solid-liquid interfaces with a double ion self-assembled diffusion system under the simulated physiological microenvironment. In the system, pDA@SIS membranes are used to control the concentration of Ca 2+ and PO 4 3- ionic diffusion to generate supersaturation reaction conditions in 1-14 days. The system can successfully obtain polycrystals with low crystallinity on the pDA-collagen complex template surface of collagen fibers and along the collagen fibers. It initiates a generalized bionic mineralization pathway which can reduce the nucleation interfacial energy to promote rapid hydroxyapatite (HAP) nucleation and crystallization and accelerate the rate of collagen fiber mineralization. The pDA@SIS mineralized collagen membrane shows good biocompatibility with 100% cellular activity in the CCK-8 test, which significantly improved the adhesion proliferation of MC3T3-E1 cells. The pDA-SIS collagen complex, as a new type of mineralization template, may propose a new collagen mineralization strategy to produce a mineralized pDA@SIS scaffold bone-like material for tissue engineering or can potentially be applied in bone repair and regeneration., Competing Interests: The authors declare no potential conflicts of interest with respect to the authorship and/or publication of this article., (This journal is © The Royal Society of Chemistry.)

Titanium implants have been widely applied in dentistry and orthopedics due to their biocompatibility and resistance to mechanical fatigue. TiO 2 nanotube arrays (TiO 2 NTAs) on titanium implant surfaces have exhibited excellent biocompatibility, bioactivity, and adjustability, which can significantly promote osseointegration and participate in its entire path. In this review, to give a comprehensive understanding of the osseointegration process, four stages have been divided according to pivotal biological processes, including protein adsorption, inflammatory cell adhesion/inflammatory response, additional relevant cell adhesion and angiogenesis/osteogenesis. The impact of TiO 2 NTAs on osseointegration is clarified in detail from the four stages. The nanotubular layer can manipulate the quantity, the species and the conformation of adsorbed protein. For inflammatory cells adhesion and inflammatory response, TiO 2 NTAs improve macrophage adhesion on the surface and induce M2-polarization. TiO 2 NTAs also facilitate the repairment-related cells adhesion and filopodia formation for additional relevant cells adhesion. In the angiogenesis and osteogenesis stage, TiO 2 NTAs show the ability to induce osteogenic differentiation and the potential for blood vessel formation. In the end, we propose the multi-dimensional regulation of TiO 2 NTAs on titanium implants to achieve highly efficient manipulation of osseointegration, which may provide views on the rational design and development of titanium implants., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Wu et al.)

According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called-Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)-for forecasting 3D geo-spatiotemporal sequences. Specifically, we propose (1) a novel 4D higher-order recurrent neural network with empirical orthogonal function analysis to capture the hidden uncorrelated patterns of each hierarchy, (2) a convolutional tensor-train decomposition to capture higher-order space-time correlations, and (3) a mechanism that incorporates prior physics from domain experts by informing the learning in latent space. The advantage of our proposed approach is clear: constrained by the law of physics, the prediction model simultaneously learns good representations for frame dependencies (both short-term and long-term high-level dependency) and inter-hierarchical relations within each time frame. Experiments on geo-spatiotemporal data collected from the GoM demonstrate that the PITT-ConvLSTM model can successfully forecast the volumetric velocity of the LC and its eddies for a period greater than 1 week., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Huang, Tang, Zhuang, VanZwieten and Cherubin.)

As the most successful therapy for missing teeth, dental implant has become increasingly prevalent around the world. A lot of papers have reported diverse local risk factors affecting the success and survival rate of dental implants, either for a short or a long period. However, there are also many types of systemic disorders or relatively administrated medicine that may jeopardize the security and success of dental implant treatment. Additionally, the coronavirus disease 2019 pandemic also poses a challenge to dental implant clinicians. Some of these risk factors are clinically common but to some extent unfamiliar to dentists, thus optimal measurements are often lacking when they occur in dental clinics. In this review, we analyze potential systemic risk factors that may affect the success rate of dental implants. Some of them may affect bone mineral density or enhance the likelihood of local infection, thus impeding osseointegration. Others may even systemically increase the risk of the surgery and threaten patients' life. In order to help novices receive high-risk patients who need to get dental implant treatment in a more reasonable way, we accordingly review recent research results and clinical experiments to discuss promising precautions, such as stopping drugs that impact bone mineral density or the operation, and addressing any perturbations on vital signs., (©2021 Society for Leukocyte Biology.)

The skeletal system and immune system seem to be two independent systems. However, there in fact are extensive and multiple crosstalk between them. The concept of osteoimmunology was created to describe those interdisciplinary events, but it has been constantly updated over time. In this review, we summarize the interactions between the skeletal and immune systems in the co-development of the two systems and the progress of certain typical bone abnormalities and bone regeneration on the cellular and molecular levels according to the mainstream novel study. At the end of the review, we also highlighted the possibility of extending the research scope of osteoimmunology to other systemic diseases. In conclusion, we propose that osteoimmunology is a promising perspective to uncover the mechanism of related diseases; meanwhile, a study from the point of view of osteoimmunology may also provide innovative ideas and resolutions to achieve the balance of internal homeostasis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhou, Wu, Yu, Tang, Liu, Jia, Yang and Xiang.)

With the rapid development of biomedical sciences, contradictory results on the relationships between biological responses and material properties emerge continuously, adding to the challenge of interpreting the incomprehensible interfacial process. In the present paper, we use cell proliferation on titanium dioxide nanotubes (TNTs) as a case study and apply machine learning methodologies to decipher contradictory results in the literature. The gradient boosting decision tree model demonstrates that cell density has a higher impact on cell proliferation than other obtainable experimental features in most publications. Together with the variation of other essential features, the controversy of cell proliferation trends on various TNTs is understandable. By traversing all combinational experimental features and the corresponding forecast using an exhausted grid search strategy, we find that adjusting cell density and sterilization methods can simultaneously induce opposite cell proliferation trends on various TNTs diameter, which is further validated by experiments. This case study reveals that machine learning is a burgeoning tool in deciphering controversial results in biomedical researches, opening up an avenue to explore the structure-property relationships of biomaterials., (© The Author(s) 2021. Published by Oxford University Press.)

MicroRNAs (miRNAs) are a class of small noncoding RNA molecules containing only 20-22 nucleotides. MiRNAs play a role in gene silencing and translation suppression by targeting and binding to mRNA. Proper control of miRNA expression is very important for maintaining a normal physiological environment because miRNAs can affect most cellular pathways, including cell cycle checkpoint, cell proliferation, and apoptosis pathways, and have a wide range of target genes. With these properties, miRNAs can modulate multiple signalling pathways involved in cancer development, such as cell proliferation, apoptosis, and migration pathways. MiRNAs that activate or inhibit the molecular pathway related to tumour angiogenesis are common topics of research. Angiogenesis promotes tumorigenesis and metastasis by providing oxygen and diffusible nutrients and releasing proangiogenic factors and is one of the hallmarks of tumour progression. CRC is one of the most common tumours, and metastasis has always been a difficult issue in its treatment. Although comprehensive treatments, such as surgery, radiotherapy, chemotherapy, and targeted therapy, have prolonged the survival of CRC patients, the overall response is not optimistic. Therefore, there is an urgent need to find new therapeutic targets to improve CRC treatment. In a series of recent reports, miRNAs have been shown to bidirectionally regulate angiogenesis in colorectal cancer. Many miRNAs can directly act on VEGF or inhibit angiogenesis through other pathways (HIF-1a, PI3K/AKT, etc.), while some miRNAs, specifically many exosomal miRNAs, are capable of promoting CRC angiogenesis. Understanding the mechanism of action of miRNAs in angiogenesis is of great significance for finding new targets for the treatment of tumour angiogenesis. Deciphering the exact role of specific miRNAs in angiogenesis is a challenge due to the high complexity of their actions. Here, we describe the latest advances in the understanding of miRNAs and their corresponding targets that play a role in CRC angiogenesis and discuss possible miRNA-based therapeutic strategies.

Drugs are loaded into PMMA bone cement to reduce the risk of infection in freshly implanted prostheses or to promote the differentiation and growth of osteoblasts. However, the same method of loading of drugs in the bone cement cannot simultaneously achieve an effective antibacterial response and long-term treatment outcomes for osteoporosis based on a patient's clinical needs. In the present study, gentamicin sulfate (GS)/alendronate (ALN)-dual-loaded gelatin modified PMMA bone cement (GAPBC) was fabricated to provide rapid and continuous antibiotic release and long-term anti-osteoporotic therapy. Specifically, the gelatin microspheres were loaded with the drugs using separate methodologies, namely, ALN was loaded during fabrication of the gelatin microspheres after which GS was absorbed onto the gelatin from solution. The results demonstrate that sequential release of the GS and ALN was achieved, GS release playing a major role over the first 24 hours and ALN release dominant after 3 weeks of immersion in PBS, resulting from the graded distribution within the gelatin microspheres, and the final drug release ratio of GS (73.6%) and ALN (68.5%) from the modified bone cement was significantly higher than from PMMA bone cement. Therefore, GAPBC represents a potential drug carrier for future clinical applications.

Bone defect repair at load-bearing sites is a challenging clinical problem for orthopedists. Defect reconstruction with implants is the most common treatment; however, it requires the implant to have good mechanical properties and the capacity to promote bone formation. In recent years, the piezoelectric effect, in which electrical activity can be generated due to mechanical deformation, of native bone, which promotes bone formation, has been increasingly valued. Therefore, implants with piezoelectric effects have also attracted great attention from orthopedists. In this study, we developed a bioactive composite scaffold consisting of BaTiO 3 , a piezoelectric ceramic material, coated on porous Ti6Al4V. This composite scaffold showed not only appropriate mechanical properties, sufficient bone and blood vessel ingrowth space, and a suitable material surface topography but also a reconstructed electromagnetic microenvironment. The osteoconductive and osteoinductive properties of the scaffold were reflected by the proliferation, migration, and osteogenic differentiation of mesenchymal stem cells. The ability of the scaffold to support vascularization was reflected by the proliferation and migration of human umbilical vein endothelial cells and their secretion of VEGF and PDGF-BB. A well-established sheep spinal fusion model was used to evaluate bony fusion in vivo. Sheep underwent implantation with different scaffolds, and X-ray, micro-computed tomography, van Gieson staining, and elemental energy-dispersive spectroscopy were used to analyze bone formation. Isolated cervical angiography and visualization analysis were used to assess angiogenesis at 4 and 8 months after transplantation. The results of cellular and animal studies showed that the piezoelectric effect could significantly reinforce osteogenesis and angiogenesis. Furthermore, we also discuss the molecular mechanism by which the piezoelectric effect promotes osteogenic differentiation and vascularization. In summary, Ti6Al4V scaffold coated with BaTiO 3 is a promising composite biomaterial for repairing bone defects, especially at load-bearing sites, that may have great clinical translation potential.

Network embedding aims to preserve topological structures of a network using low-dimensional vectors and has shown to be effective for driving a myriad of graph mining tasks (e.g., link prediction or classification) free of the stressful feature extraction procedure. Many methods have been proposed to integrate node content and/or label information, with nodes sharing similar content/labels being close to each other in the learned latent space. To date, existing methods either consider networked instances with a single label or consider a set of labels as a whole for node representation learning. Therefore, they cannot handle network of instances containing multiple labels (i.e. multi-labels), which are ubiquitous in describing complex concepts of instances. In this article, we formulate a new multi-label network embedding (MLNE) problem to learn feature representation for networked multi-label instances. We argue that the key to MLNE learning is to aggregate node topology structures, node content, and multi-label correlations. We propose a two-layer network embedding framework to couple information for effective learning. To capture higher order label correlations, we use labels to form a high-level label-label network over a low-level node-node network, in which the label network interacts with the node network through multi-labeling relations. The low-level node-node network can be enhanced by latent label-specific features from high-level label network with well-captured high-order correlations between labels. To enable the multi-label informed network embedding, we force both node and label representations being optimized under the same low-dimensional latent space by a unified training objective. Experiments on real-world data sets demonstrate that MLNE achieves better performance compared with methods with or without considering label information.

As promising photocatalysts, nano-sized Cu2O particles suffer from severe charge recombination and insufficient light absorption, resulting in their unsatisfactory photocatalytic performance. Herein, we have designed a core-shell structured Au nanorod@octahedron Cu2O with preferentially edge-loaded Au nanoparticles (Au(R)@Cu2O-Au(P)) for an efficient photocatalytic degradation reaction. A "push-pull" synergetic effect of Au(R) and Au(P) was found to improve the transfer and separation of charge carriers from the bulk to the surface of Cu2O. Furthermore, the light beyond Cu2O particles' absorption range can penetrate the shell (Cu2O) and for being utilized by the Au(R) core via its localized surface plasmon resonance effect (LSPR) to inject hot electrons into the conduction band of Cu2O for photocatalytic reactions. Moreover, the investigation of the size effect of Au(R)@Cu2O-Au(P) reveals that both the short charge transfer distance and the more efficient charge transfer have contributed to its enhanced photocatalytic activity.

Hollow carbon and MnO x composite particles (HC-Mn) were fabricated by using polyacrylic acid (PAA) and Mn ion co-assembled colloids as the soft template and resorcinol formaldehyde resin (RF) as the carbon source. The formation process was well studied and a plausible formation mechanism was proposed. The Mn ions played two key roles in the synthesis: first, they promoted the aggregation of the PAA molecules, thus forming the PAA-Mn colloids in solution with high water content, which were suitable for the subsequent RF coating. Secondly, considerable Mn ions were retained after template removal, which were transformed into MnO x particles simultaneously during carbonization. This approach was facile and effective and the as-prepared HC-Mn showed superior catalytic activity toward methylene blue (MB) degradation., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

Tiny particles with a negative charge in water can be removed effectively by inorganic positively charged nanofiber membranes due to their physical separation and electrostatic adsorption properties. However, the charged positive and isoelectric points (IEP) on the surface of the positively charged nanofiber membranes need to be further improved to meet the requirements of the Environmental Protection Agency for virus filters (excellent adsorption and separation properties in solutions with pH between 5 and 9). The positively charged SiO 2 @ZrO 2 nanofibrous membrane was fabricated by electrospinning combined with the impregnation method and calcined in an anoxic atmosphere. The effects of the impregnating solution concentration and centrifugal speeds on the morphology, pore size and nanofiber diameter of the composite nanofibrous membrane were investigated. The phase composition, the element valence state, the surface charge and the adsorption separation properties of the composite nanofibrous membranes were characterized. Flexible SiO 2 @ZrO 2 nanofibrous membranes with high specific surface area and water flux were obtained. The surface isoelectric point is 7.3 when calcined in a mixed atmosphere of N 2 (96%)/H 2 (4%), which is higher than that when calcined in air and vacuum. Some oxygen vacancies were present on the surface of the ZrO x (0 < x < 2) shell, resulting in the tetragonal zirconia still being available at room temperature. The interception rate of the positively charged SiO 2 @ZrO 2 nanofibrous membranes for a titan yellow solution with a concentration of 10 mg L -1 reaches 99.996%. Its maximum adsorption capacity can reach 63.27 mg cm -3 when filtering a 1 L titan yellow solution. The resulting nanofibrous membranes have potential application for the separation of bacteria and viruses from water., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)