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Ribosomal proteins (RPs), in conjunction with rRNA, are major components of ribosomes involved in the cellular process of protein biosynthesis, known as “translation”. The viruses, as the small infectious pathogens with limited genomes, must recruit a variety of host factors to survive and propagate, including RPs. At present, more and more information is available on the functional relationship between RPs and virus infection. This review focuses on advancements in my own understanding of critical roles of RPs in the life cycle of viruses. Various RPs interact with viral mRNA and proteins to participate in viral protein biosynthesis and regulate the replication and infection of virus in host cells. Most interactions are essential for viral translation and replication, which promote viral infection and accumulation, whereas the minority represents the defense signaling of host cells by activating immune pathway against virus. RPs provide a new platform for antiviral therapy development, however, at present, antiviral therapeutics with RPs involving in virus infection as targets is limited, and exploring antiviral strategy based on RPs will be the guides for further study.

Neuroinflammation refers to a highly complicated reaction of the central nervous system (CNS) to certain stimuli such as trauma, infection, and neurodegenerative diseases. This is a cellular immune response whereby glial cells are activated, inflammatory mediators are liberated and reactive oxygen and nitrogen species are synthesized. Neuroinflammation is a key process that helps protect the brain from pathogens, but inappropriate, or protracted inflammation yields pathological states such as Parkinson’s disease, Alzheimer’s, Multiple Sclerosis, and other neurodegenerative disorders that showcase various pathways of neurodegeneration distributed in various parts of the CNS. This review reveals the major neuroinflammatory signaling pathways associated with neurodegeneration. Additionally, it explores promising therapeutic avenues, such as stem cell therapy, genetic intervention, and nanoparticles, aiming to regulate neuroinflammation and potentially impede or decelerate the advancement of these conditions. A comprehensive understanding of the intricate connection between neuroinflammation and these diseases is pivotal for the development of future treatment strategies that can alleviate the burden imposed by these devastating disorders.

The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.

Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Protein tyrosine kinases (PTKs) are a class of proteins with tyrosine kinase activity that phosphorylate tyrosine residues of critical molecules in signaling pathways. Their basal function is essential for maintaining normal cell growth and differentiation. However, aberrant activation of PTKs caused by various factors can deviate cell function from the expected trajectory to an abnormal growth state, leading to carcinogenesis. Inhibiting the aberrant PTK function could inhibit tumor growth. Therefore, tyrosine kinase inhibitors (TKIs), target-specific inhibitors of PTKs, have been used in treating malignant tumors and play a significant role in targeted therapy of cancer. Currently, drug resistance is the main reason for limiting TKIs efficacy of cancer. The increasing studies indicated that tumor microenvironment, cell death resistance, tumor metabolism, epigenetic modification and abnormal metabolism of TKIs were deeply involved in tumor development and TKI resistance, besides the abnormal activation of PTK-related signaling pathways involved in gene mutations. Accordingly, it is of great significance to study the underlying mechanisms of TKIs resistance and find solutions to reverse TKIs resistance for improving TKIs efficacy of cancer. Herein, we reviewed the drug resistance mechanisms of TKIs and the potential approaches to overcome TKI resistance, aiming to provide a theoretical basis for improving the efficacy of TKIs.

Collagen is the major component of the tumor microenvironment and participates in cancer fibrosis. Collagen biosynthesis can be regulated by cancer cells through mutated genes, transcription factors, signaling pathways and receptors; furthermore, collagen can influence tumor cell behavior through integrins, discoidin domain receptors, tyrosine kinase receptors, and some signaling pathways. Exosomes and microRNAs are closely associated with collagen in cancer. Hypoxia, which is common in collagen-rich conditions, intensifies cancer progression, and other substances in the extracellular matrix, such as fibronectin, hyaluronic acid, laminin, and matrix metalloproteinases, interact with collagen to influence cancer cell activity. Macrophages, lymphocytes, and fibroblasts play a role with collagen in cancer immunity and progression. Microscopic changes in collagen content within cancer cells and matrix cells and in other molecules ultimately contribute to the mutual feedback loop that influences prognosis, recurrence, and resistance in cancer. Nanoparticles, nanoplatforms, and nanoenzymes exhibit the expected gratifying properties. The pathophysiological functions of collagen in diverse cancers illustrate the dual roles of collagen and provide promising therapeutic options that can be readily translated from bench to bedside. The emerging understanding of the structural properties and functions of collagen in cancer will guide the development of new strategies for anticancer therapy.

Carbon supported PtCo intermetallic alloys are known to be one of the most promising candidates as low-platinum oxygen reduction reaction electrocatalysts for proton-exchange-membrane fuel cells. Nevertheless, the intrinsic trade-off between particle size and ordering degree of PtCo makes it challenging to simultaneously achieve a high specific activity and a large active surface area. Here, by machine-learning-accelerated screenings from the immense configuration space, we are able to statistically quantify the impact of chemical ordering on thermodynamic stability. We find that introducing of Cu/Ni into PtCo can provide additional stabilization energy by inducing Co-Cu/Ni disorder, thus facilitating the ordering process and achieveing an improved tradeoff between specific activity and active surface area. Guided by the theoretical prediction, the small sized and highly ordered ternary Pt 2 CoCu and Pt 2 CoNi catalysts are experimentally prepared, showing a large electrochemically active surface area of ~90 m 2 g Pt ‒1 and a high specific activity of ~3.5 mA cm ‒2 . Platinum-based intermetallic alloys are promising candidates as low-platinum oxygen reduction reaction catalysts for proton exchange membrane fuel cells. Here, the authors develop small sized and highly ordered Pt 2 CoCu and Pt 2 CoNi catalysts for fuel cells by machine-learning accelerated computational screening.

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses.

The long-term impact of human exploitation and environmental changes has led to a decline in grassland productivity and soil fertility, which eventually results in grassland degradation. The application of organic fertilizer is an effective improvement measure; however, it is still not fully understood how the addition of organic fertilizer influences grassland soil fertility and plant composition. A set of experiments were conducted in Inner Mongolia in China to reveal the tradeoff between steppe plants and soil microorganisms and the eco-physiological mechanisms involved, and how the addition of vermicompost and mushroom residues affect microbial diversity, enzyme activities, and the chemical properties of soil in degraded Leymus chinensis grassland. Organic fertilizer improved the soil nutrient status and shaped distinct bacterial communities. Compared with the control the available phosphorus (AP) and available potassium (AK) contents were highest under treatments a3 and b3, and the aboveground biomass was highest under the b3 treatment. Soil sucrase activities increased by 7.88% under the b3 treatment. Moreover, the richness index significantly increased by 7.07% and 7.23% under the a1 and b2 treatments, respectively. The most abundant Actinobacteria and Proteobacteria were detected in the organic fertilizer treatment. A linear discriminant analysis effect size (LEfSe) indicated that the bacterial community was significantly increased under the b3 treatment. A canonical correspondence analysis (RDA) and spearman correlation heatmap confirmed that total P (TP) and urease were the key driving factors for shaping bacterial communities in the soil. Our results indicated that the application of large amounts of vermicompost and mushroom residues increased the availability of nutrients and also enhanced the biodiversity of soil bacterial communities in L. chinensis grasslands, which will contribute to the sustainable development of agro-ecosystems.

Rhei Radix et Rhizoma, also known as rhubarb or Da Huang , has been widely used as a spice and as traditional herbal medicine for centuries, and is currently marketed in China as the principal herbs in various prescriptions, such as Da-Huang-Zhe-Chong pills and Da-Huang-Qing-Wei pills. Emodin, a major bioactive anthraquinone derivative extracted from rhubarb, represents multiple health benefits in the treatment of a host of diseases, such as immune-inflammatory abnormality, tumor progression, bacterial or viral infections, and metabolic syndrome. Emerging evidence has made great strides in clarifying the multi-targeting therapeutic mechanisms underlying the efficacious therapeutic potential of emodin, including anti-inflammatory, immunomodulatory, anti-fibrosis, anti-tumor, anti-viral, anti-bacterial, and anti-diabetic properties. This comprehensive review aims to provide an updated summary of recent developments on these pharmacological efficacies and molecular mechanisms of emodin, with a focus on the underlying molecular targets and signaling networks. We also reviewed recent attempts to improve the pharmacokinetic properties and biological activities of emodin by structural modification and novel material-based targeted delivery. In conclusion, emodin still has great potential to become promising therapeutic options to immune and inflammation abnormality, organ fibrosis, common malignancy, pathogenic bacteria or virus infections, and endocrine disease or disorder. Scientifically addressing concerns regarding the poor bioavailability and vague molecular targets would significantly contribute to the widespread acceptance of rhubarb not only as a dietary supplement in food flavorings and colorings but also as a health-promoting TCM in the coming years.