Roles of Integrin in Cardiovascular Diseases: From Basic Research to Clinical Implications.

Cardiovascular diseases (CVDs) pose a significant global health threat due to their complex pathogenesis and high incidence, imposing a substantial burden on global healthcare systems. Integrins, a group of heterodimers consisting of α and ß subunits that are located on the cell membrane, have emerged as key players in mediating the occurrence and progression of CVDs by regulating the physiological activities of endothelial cells, vascular smooth muscle cells, platelets, fibroblasts, cardiomyocytes, and various immune cells. The crucial role of integrins in the progression of CVDs has valuable implications for targeted therapies. In this context, the development and application of various integrin antibodies and antagonists have been explored for antiplatelet therapy and anti-inflammatory-mediated tissue damage. Additionally, the rise of nanomedicine has enhanced the specificity and bioavailability of precision therapy targeting integrins. Nevertheless, the complexity of the pathogenesis of CVDs presents tremendous challenges for monoclonal targeted treatment. This paper reviews the mechanisms of integrins in the development of atherosclerosis, cardiac fibrosis, hypertension, and arrhythmias, which may pave the way for future innovations in the diagnosis and treatment of CVDs.

Higher-order neurodynamical equation for simplex prediction.

It is demonstrated that higher-order patterns beyond pairwise relations can significantly enhance the learning capability of existing graph-based models, and simplex is one of the primary form for graphically representing higher-order patterns. Predicting unknown (disappeared) simplices in real-world complex networks can provide us with deeper insights, thereby assisting us in making better decisions. Nevertheless, previous efforts to predict simplices suffer from two issues: (i) they mainly focus on 2- or 3-simplices, and there are few models available for predicting simplices of arbitrary orders, and (ii) they lack the ability to analyze and learn the features of simplices from the perspective of dynamics. In this paper, we present a Higher-order Neurodynamical Equation for Simplex Prediction of arbitrary order (HNESP), which is a framework that combines neural networks and neurodynamics. Specifically, HNESP simulates the dynamical coupling process of nodes in simplicial complexes through different relations (i.e., strong pairwise relation, weak pairwise relation, and simplex) to learn node-level representations, while explaining the learning mechanism of neural networks from neurodynamics. To enrich the higher-order information contained in simplices, we exploit the entropy and normalized multivariate mutual information of different sub-structures of simplices to acquire simplex-level representations. Furthermore, simplex-level representations and multi-layer perceptron are used to quantify the existence probability of simplices. The effectiveness of HNESP is demonstrated by extensive simulations on seven higher-order benchmarks. Experimental results show that HNESP improves the AUC values of the state-of-the-art baselines by an average of 8.32%. Our implementations will be publicly available at: https://github.com/jianruichen/HNESP.

HoRDA: Learning higher-order structure information for predicting RNA-disease associations.

CircRNA and miRNA are crucial non-coding RNAs, which are associated with biological diseases. Exploring the associations between RNAs and diseases often requires a significant time and financial investments, which has been greatly alleviated and improved with the application of deep learning methods in bioinformatics. However, existing methods often fail to achieve higher accuracy and cannot be universal between multiple RNAs. Moreover, complex RNA-disease associations hide important higher-order topology information. To address these issues, we learn higher-order structure information for predicting RNA-disease associations (HoRDA). Firstly, the correlations between RNAs and the correlations between diseases are fully explored by combining similarity and higher-order graph attention network. Then, a higher-order graph convolutional network is constructed to aggregate neighbor information, and further obtain the representations of RNAs and diseases. Meanwhile, due to the large number of complex and variable higher-order structures in biological networks, we design a higher-order negative sampling strategy to gain more desirable negative samples. Finally, the obtained embeddings of RNAs and diseases are feed into logistic regression model to acquire the probabilities of RNA-disease associations. Diverse simulation results demonstrate the superiority of the proposed method. In the end, the case study is conducted on breast neoplasms, colorectal neoplasms, and gastric neoplasms. We validate the proposed higher-order strategies through ablative and exploratory analyses and further demonstrate the practical applicability of HoRDA. HoRDA has a certain contribution in RNA-disease association prediction.

The role of KLF transcription factor in the regulation of cancer progression.

Kruppel-like factors (KLFs) are a family of zinc finger transcription factors that have been found to play an essential role in the development of various human tissues, including epithelial, teeth, and nerves. In addition to regulating normal physiological processes, KLFs have been implicated in promoting the onset of several cancers, such as gastric cancer, lung cancer, breast cancer, liver cancer, and colon cancer. To inhibit cancer progression, various existing medicines have been used to modulate the expression of KLFs, and anti-microRNA treatments have also emerged as a potential strategy for many cancers. Investigating the possibility of targeting KLFs in cancer therapy is urgently needed, as the roles of KLFs in cancer have not received enough attention in recent years. This review summarizes the factors that regulate KLF expression and function at both the transcriptional and posttranscriptional levels, which could aid in understanding the mechanisms of KLFs in cancer progression. We hope that this review will contribute to the development of more effective anti-cancer medicines targeting KLFs in the future.

The Interplay between Integrins and Immune Cells as a Regulator in Cancer Immunology.

Integrins are a group of heterodimers consisting of α and ß subunits that mediate a variety of physiological activities of immune cells, including cell migration, adhesion, proliferation, survival, and immunotolerance. Multiple types of integrins act differently on the same immune cells, while the same integrin may exert various effects on different immune cells. In the development of cancer, integrins are involved in the regulation of cancer cell proliferation, invasion, migration, and angiogenesis; conversely, integrins promote immune cell aggregation to mediate the elimination of tumors. The important roles of integrins in cancer progression have provided valuable clues for the diagnosis and targeted treatment of cancer. Furthermore, many integrin inhibitors have been investigated in clinical trials to explore effective regimens and reduce side effects. Due to the complexity of the mechanism of integrin-mediated cancer progression, challenges remain in the research and development of cancer immunotherapies (CITs). This review enumerates the effects of integrins on four types of immune cells and the potential mechanisms involved in the progression of cancer, which will provide ideas for more optimal CIT in the future.

Building a cobaloxime-based metal-organic framework for photocatalytic aerobic oxidation of arylboronic acids to phenols.

Herein, the design and synthesis of an unprecedented cobaloxime-based zirconium metal-organic framework (Zr-TCPCo) with an she net is reported. This heterogeneous material as a photocatalyst exhibits excellent catalytic activity for aerobic oxidation of arylboronic acids to phenols. Recycling experiments demonstrate the stability and reusability of Zr-TCPCo as a robust catalyst.

Apolipoproteins: New players in cancers.

Apolipoproteins (APOs), the primary protein moiety of lipoproteins, are known for their crucial role in lipid traffic and metabolism. Despite extensive exploration of APOs in cardiovascular diseases, their roles in cancers did not attract enough attention. Recently, research focusing on the roles of APOs in cancers has flourished. Multiple studies demonstrate the interaction of APOs with classical pathways of tumorigenesis. Besides, the dysregulation of APOs may indicate cancer occurrence and progression, thus serving as potential biomarkers for cancer patients. Herein, we summarize the mechanisms of APOs involved in the development of various cancers, their applications as cancer biomarkers and their genetic polymorphism associated with cancer risk. Additionally, we also discuss the potential anti-cancer therapies by virtue of APOs. The comprehensive review of APOs in cancers may advance the understanding of the roles of APOs in cancers and their potential mechanisms. We hope that it will provide novel clues and new therapeutic strategies for cancers.

Efficient Asynchronous Federated Learning for AUV Swarm.

The development of automatic underwater vehicles (AUVs) has brought about unprecedented profits and opportunities. In order to discover the hidden valuable data detected by an AUV swarm, it is necessary to aggregate the data detected by AUV swarm to generate a powerful machine learning model. Traditional centralized machine learning generates a large number of data exchanges and faces problems of enormous training data, large-scale models, and communication. In underwater environments, radio waves are strongly absorbed, and acoustic communication is the only feasible technology. Unlike electromagnetic wave communication on land, the bandwidth of underwater acoustic communication is extremely limited, with the transmission rate being only 1/105 of the electromagnetic wave. Therefore, traditional centralized machine learning cannot support underwater AUV swarm training. In recent years, federated learning could only interact with model parameters without interacting with data, which greatly reduced communication costs. Therefore, this paper introduces federated learning into the collaboration of an AUV swarm. In order to further reduce the constraints of underwater scarce communication resources on federated learning and alleviate the straggler effect, in this work, we designed an asynchronous federated learning method. Finally, we constructed the optimization problem of minimizing the weighted sum of delay and energy consumption, relying on jointly optimizing the AUV CPU frequency and signal transmission power. In order to solve this complex optimization problem of high-dimensional non-convex time series accumulation, we transformed the problem into a Markov decision process (MDP) and use the proximal policy optimization 2 (PPO2) algorithm to solve this problem. The simulation results demonstrate the effectiveness and superiority of our method.

Stable Tetradentate Gold(III)-TADF Emitters with Close to Unity Quantum Yield and Radiative Decay Rate Constant of up to 2 × 106 s-1 : High-Efficiency Green OLEDs with Operational Lifetime (LT90 ) Longer than 1800 h at 1000 cd m-2.

High maximum external quantum efficiency (EQEmax ), small efficiency roll-offs, and long operational lifetime at practical luminances are three crucial parameters for commercialization of organic light-emitting diodes (OLEDs). To simultaneously achieve these goals, it is desirable to have the radiative decay rate constant (kr ) as large as possible, which, for a thermally activated delayed fluorescent (TADF) emitter, requires both a large S1 →S0 radiative decay rate constant (kr S ) and a small singlet-triplet energy gap (ΔEST ). Here, the design of a class of tetradentate gold(III) TADF complexes for narrowing the ΔEST while keeping the kr S large is reported. The as-synthesized complexes display green emission with close to unity emission quantum yields, and kr approaching 2 × 106 s-1 in thin films. The vacuum-deposited green OLEDs based on 1 and 4 demonstrate maximum EQEs of up to 24 and 27% with efficiency roll-offs of 5.5 and 2.2% at 1000 cd m-2 , respectively; the EQEs maintain high at 10 000 cd m-2 (19% (1) and 24% (4)). A long LT90 device lifetime of 1820 h at 1000 cd m-2 for complex 1 is achieved, which is one of the longest device lifetimes of TADF-OLEDs reported in the literature.

Integrin-mediated cancer progression as a specific target in clinical therapy.

Integrins, a group of heterodimer receptors for cell-matrix and cell-cell adhesion, mediate various intracellular activities, including cell migration, polarity, survival, growth, and death. Multiple types of integrins are differentially expressed in various cancers during different stages of progression, which are involved in the regulation of cancer cell proliferation, invasion, migration, and angiogenesis. The crucial roles of integrins in tumor progression provide valuable clues for cancer diagnosis and targeted therapy. Numerous integrin inhibitors have been investigated in clinical trials to explore effective regimens and minimize side effects. Given the complexity of the integrin-mediated tumor-promoting effect, challenges and difficulties remain in the research and development of integrin inhibitors, which seriously restrict the efficacy and application of integrin-targeted therapy. Novel targeted therapy of integrins, however, is beneficial for patients as a potential avenue forward, which needs better pharmacological effect, valid experimental models, and in-depth understanding of integrins. This review provides the insight needed to elucidate the mechanisms underlying cancer progression and novel protocols for the clinical treatment of cancer.

Ligand Tailoring Strategy of a Metal-Organic Framework for Optimizing Methane Storage Working Capacities.

Methane, as the main component of natural gas, shale gas, and marsh gas, is regarded as an ideal clean energy to replace traditional fossil fuels and reduce carbon emissions. Porous materials with superior methane storage capacities are the key to the wide application of adsorbed natural gas technology in vehicle transportation. In this work, we applied a ligand tailoring strategy to a metal-organic framework (NOTT-101) to fine-tune its pore geometry, which was well characterized by gas and dye sorption measurements. High-pressure methane sorption isotherms revealed that the methane storage performance of the modified NOTT-101 can be effectively improved by decreasing the unusable uptake at 5 bar and increasing the total uptake under high pressures, achieving a substantially high volumetric methane storage working capacity of 190 cm3 (STP) cm-3 at 298 K and 5-80 bar.