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Abstract Metastasis is the leading cause of high mortality in colorectal cancer (CRC), which is not only driven by changes occurring within the tumor cells, but is also influenced by the dynamic interaction between cancer cells and components in the tumor microenvironment (TME). Currently, the exploration of TME remodeling and its impact on CRC metastasis has attracted increasing attention owing to its potential to uncover novel therapeutic avenues. Noteworthy, emerging studies suggested that tumor-associated macrophages (TAMs) within the TME played important roles in CRC metastasis by secreting a variety of cytokines, chemokines, growth factors and proteases. Moreover, TAMs are often associated with poor prognosis and drug resistance, making them promising targets for CRC therapy. Given the prognostic and clinical value of TAMs, this review provides an updated overview on the origin, polarization and function of TAMs, and discusses the mechanisms by which TAMs promote the metastatic cascade of CRC. Potential TAM-targeting techniques for personalized theranostics of metastatic CRC are emphasized. Finally, future perspectives and challenges for translational applications of TAMs in CRC development and metastasis are proposed to help develop novel TAM-based strategies for CRC precision medicine and holistic healthcare.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.

Although wind power ramp events (WPREs) are relatively scarce, they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity market. In this paper, an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesian network (BN) theory. The method uses the maximum weight spanning tree (MWST) and greedy search (GS) to build a BN that has the highest fitting degree with the observed data. Meanwhile, an extended imprecise Dirichlet model (IDM) is developed to estimate the parameters of the BN, which quantificationally reflect the ambiguous dependencies among the random ramp event and various meteorological variables. The BN is then applied to predict the interval probability of each possible ramp state under the given meteorological conditions, which is expected to cover the target probability at a specified confidence level. The proposed method can quantify the uncertainty of the probabilistic ramp event estimation. Meanwhile, by using the extracted dependencies and Bayesian rules, the method can simplify the conditional probability estimation and perform reliable prediction even with scarce samples. Test results on a real wind farm with three-year operation data illustrate the effectiveness of the proposed method.

High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC.

A large number of literatures have investigated the selective photocatalytic reaction of 4-aminothiophenol (PATP) to p,p'-dimercaptoazobenzene (DMAB). Most of them mainly study the contribution of substrate, excitation wavelength, exposure time, pH and added cations to plasmon-assisted surface catalytic reactions. However, we mainly study focuses on the effects of solvents on the dimerization of PATP to DMAB under the action of Ag nanoparticles (NPs). In experiments, a variety of diols was selected as solvents for the probe molecule PATP, and power-dependent SERS spectra were obtained at an excitation wavelength of 532 nm. From the laser-dependent SERS spectrum, we found that the characteristic peak enhancement effect of the product DMAB in different solvents is significantly different. That is, different solvents could regulate the rate at which DMAB is produced from PATP. Based on the experimental results, we further explored how different diol solvents regulate the response of PATP to DMAB. Our conclusion is that the solvent in the system can quickly capture the hot electrons generated by the decay of the plasmon, so that the remaining holes can oxidize PATP to form DMAB. The ability to trap hot electrons is different due to the difference in the position of the functional groups in the solvent, so that the photocatalytic reaction rate of the hole-oxidized PATP is different. The ability to capture electrons varies depending on the position of the functional groups in the solvent, so the oxidation rate of the photocatalytic reaction is also different. This work not only deepens our understanding of the mechanism of hole-driven surface catalysis oxidation reaction, but also provides a convenient method for regulating the rate of catalytic oxidation.

A novel approach based on Coulomb explosion has been developed to separate single-walled carbon nanotubes (SWNTs) from their bundle. With this technique, we can readily separate a bundle of SWNTs into smaller bundles with uniform diameter as well as some individual SWNTs. The separated SWNTs have a typical length of several microns and form a nanotree at one end of the original bundle. More importantly, this separating procedure involves no surfactant and includes only one-step physical process. The separation method offers great conveniences for the subsequent individual SWNT or multiterminal SWNTs device fabrication and their physical properties studies. [ABSTRACT FROM AUTHOR]

We have developed a low-cost and effective method to align single-walled carbon nanotubes (SWNTs) using a series of diamond wire drawing dies. The obtained SWNTs are highly dense and perfectly aligned. X-ray diffraction (XRD) indicates that the highly dense and perfectly aligned SWNTs (HDPA-SWNTs) form a two-dimensional triangular lattice with a lattice constant of 19.62 Å. We observe a sharp (002) reflection in the XRD pattern, which should be ascribed to an intertube spacing 3.39 Å of adjacent SWNTs. Raman spectra reveal that the radical breath mode (RBM) of SWNTs with larger diameter in the HDPA-SWNTs is suppressed compared with that of as-grown SWNTs. The HDPA-SWNTs have a large density, ∼1.09 g/cm 3, and a low resistivity, ∼2 mΩ cm, at room temperature, as well as a large response to light illumination. [ABSTRACT FROM AUTHOR]

The photocurrent generated in single-walled carbon nanotube bundles upon camera flash illumination has been studied under different ambient pressures and light intensities. The results show that the intensity of photocurrent depends closely on the ambient pressure and light intensity. With the ambient pressure reduced, the photocurrent exhibits a logarithmic growth behaviour. Meanwhile, the photocurrent increases with the increase in light intensity. In this work, a dynamic model is employed to unveil the origins of the observed photocurrent. A much smaller lifetime of photocarriers ([?]10 ms) is observed than that needed for gas molecular desorption or photodesorption (seconds or longer). Our results are consistent with the model of Schottky barriers being responsible for photocurrent generation. [ABSTRACT FROM AUTHOR]