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Abstract Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: “ https://github.com/KuangHaiYue/GLNNMDA.git ”.

Abstract It is a consensus in the international manned space field that factors such as microgravity during the space flight can cause anxiety, depression and other important brain function abnormalities in astronauts. However, the neural mechanism at the molecular level is still unclear. Due to the limitations of research conditions, studies of biological changes in the primate brain have been comparatively few. We took advantage of -6° head-down bed rest (HDBR), one of the most implemented space analogues on the ground, to investigate the effects of simulated weightlessness on non-human primate brain metabolites. The Rhesus Macaque monkeys in the experiment were divided into three groups: the control group, the 42-day simulated weightlessness group with HDBR, and the recovery group, which had 28 days of free activity in the home cage after the HDBR. Liquid chromatography-mass spectrometry (LC-MS) was used to perform metabolomics analysis on specific brain areas of the monkeys under three experimental conditions. Our results show that simulated weightlessness can cause neurotransmitter imbalances, the amino acid and energy metabolism disorders, and hormone disturbances. But these metabolomics changes are reversible after recovery. Our study suggests that long-term brain damage in space flight might be reversible at the metabolic level. This lays a technical foundation for ensuring brain health and enhancing the brain function in future space studies.

Abstract The concept of Yin-Yang, originating in ancient Chinese philosophy, symbolizes two opposing but complementary forces or principles found in all aspects of life. This concept can be quite fitting in the context of extracellular vehicles (EVs) and inflammatory diseases. Over the past decades, numerous studies have revealed that EVs can exhibit dual sides, acting as both pro- and anti-inflammatory agents, akin to the concept of Yin-Yang theory (i.e., two sides of a coin). This has enabled EVs to serve as potential indicators of pathogenesis or be manipulated for therapeutic purposes by influencing immune and inflammatory pathways. This review delves into the recent advances in understanding the Yin-Yang sides of EVs and their regulation in specific inflammatory diseases. We shed light on the current prospects of engineering EVs for treating inflammatory conditions. The Yin-Yang principle of EVs bestows upon them great potential as, therapeutic, and preventive agents for inflammatory diseases. Graphical Abstract

Abstract This study aims to assess the global prevalence of kinesiophobia and the potential influencing factors among patients with heart disease. A comprehensive search was conducted in PubMed, Embase, Web of Science, PsycINFO, and Scopus databases to identify studies reporting on the prevalence of kinesiophobia and its influencing factors in heart disease patients up to January 2024. A random-effects model was employed to aggregate prevalence rates. Heterogeneity sources were investigated through subgroup analysis, while differences in the prevalence of kinesiophobia across regions, types of heart disease, and gender were evaluated. Additionally, a qualitative analysis of the factors influencing kinesiophobia was performed. This research incorporated 15 studies from six countries, with 14 providing data on the prevalence of kinesiophobia and nine exploring its potential influencing factors. The findings indicated that the overall prevalence of kinesiophobia among heart disease patients was 61.0% (95% CI 49.4–72.6%). Subgroup analysis revealed that the prevalence in upper-middle-income countries was 71.8% (95% CI 66.2–77.4%), while it stands at 49.9% (95% CI 30.2–69.5%) in high-income countries. The prevalence rates among patients with coronary artery disease, heart failure, and atrial fibrillation were 63.2% (95% CI 45.2–81.3%), 69.2% (95% CI 57.6–80.8%), and 71.6% (95% CI 67.1–76.1%), respectively. Gender-wise, no significant difference was observed in the prevalence of kinesiophobia between men and women (52.2% vs. 51.8%). A total of 24 potential influencing factors of kinesiophobia were identified, with education level, monthly income, anxiety, and exercise self-efficacy being the most recognized. The prevalence of kinesiophobia in patients with heart disease is notably high and is influenced by a multitude of factors. Early implementation of targeted preventive measures is imperative to mitigate the incidence of kinesiophobia in this population.

Objective With increasing difficulty in phosphate ore prospecting, there are an increasing number of geological exploration reports. The manual recognition of geological information related to phosphate rock mineralization in massive documents is time-consuming and inefficient. It cannot meet the needs of knowledge sharing, dissemination and intelligent management of geological reports. Methods To quickly obtain the ore-forming geological knowledge hidden in the phosphate ore reports, this work intends to establish an automatic recognition method for ore-forming geological entities based on the extreme learning machine network(XLNET) model. First, BIO labelling of entities was carried out to establish a geological entity dictionary, and XLNET was used as the underlying preprocessing model to learn the bidirectional semantics of sentences. Then, the BILSTM-Attention-CRF(bidirectional long short term memory(BILSTM)-self attention layer(Attention)-conditional random field(CRF)) model was used to realize intelligent classification of multiple text labels. Finally, the ore-forming conditions and ore-forming model of phosphate ore in the reports were roughly predicted by locating the distribution position of phosphate ore entities in the report. Results Comparing this model with the other three models, these results show that the accuracy rate, recall rate and F1 value of this model are close to 90%, which are 2%, 5% and 6% higher than those of the previous three models, respectively. Conclusion This study provides a more efficient method for automatic geological entity recognition for geological researchers in the Kaiyang phosphate mine.

The covered karst collapse has the characteristics of concealment, suddenness, uncertainty, and mechanism complexity, which leads to its prevention and treatment more difficult. In the process of covered karst collapse, soil caves formed in the overlying soil, develop and expand until the surface collapse. Numerical simulation such as the finite element method is an effective method of soil cave collapse; however, the finite element method can not solve the problem of large deformation of rock mass such as particle migration and soil collapse. Based on the analysis of the geological conditions of karst collapse in Jiangshan city, Zhejiang province, this study used the particle flow code (PFC) to simulate the formation process of covered karst soil cave and then to reveal the displacement of special soil particles, the development trend of cracks, and the change of system imbalance force on a micro level. The multi-field variation characteristics of the formation of covered karst soil cave under external force conditions were reproduced. The results show that the development process of karst soil cave could be summarized into three stages including crack generation, connected surface formation, and particle peeling. As the karst soil cave began to develop, there was a good consistency among the displacement curve of soil particles, the penetration degree of cracks, and the unbalance force, and the vertical expansion speed was larger than the horizontal speed. The critical flow velocity of the initial soil cavity is 0.104 cm/s when the overburden soil is clay. This study can provide basic information for the treatment of karst collapse.

Abstract Background Cancer cells frequently evolve necroptotic resistance to overcome various survival stress during tumorigenesis. However, we have previously showed that necroptosis is widespread in head and neck squamous cell carcinoma (HNSCC) and contributes to tumor progression and poor survival via DAMPs-induced migration and invasiveness in peri-necroptotic tumor cells. This implicated an alternative strategy that cancers cope with necroptotic stress by reprogramming a pro-invasive necroptotic microenvironment (NME). Here, we aim to decipher how necroptotic cells shape the NME and affect HNSCC progression. Methods Both our pre-established cellular necroptotic model and newly established Dox-induce intratumoral necroptosis model were used to investigate how necroptosis affect HNSCC progression. Transcriptomic alterations in peri-necroptotic tumor cells were analyzed by RNA-seq and validated in the NME in mice and patients’ samples. The differential DAMPs compositon among apopotosis. Necrosis, and necroptosis were analyzed by label-free proteomic technique, and the necroptosis-specific DAMPs were then identified and validated. The potential receptor for ISG15 were simulated using molecular docking and further validated by in vitro assays. Then the ISG15-RAGE axis was blocked by either knockdown of necroptotic-ISG15 release and RAGE inhibitor FPS-ZM1, and the impact on tumor progression were tested. Last, we further tested our findings in a HNSCC-patients cohort. Results Necroptosis played a crucial role in driving tumor-cell invasiveness and lymphatic metastasis via tumor-type dependent DAMPs-releasing. Mechanistically, necroptotic DAMPs induced peri-necroptotic EMT via NF-κB and STAT3 signaling. Furthermore, intrinsic orchestration between necroptotic and cGAS-STING signaling resulted in producing a group of interferon stimulated genes (ISGs) as HNSCC-dependent necroptotic DAMPs. Among them, ISG15 played an essential role in reprogramming the NME. We then identified RAGE as a novel receptor for extracellular ISG15. Either blockage of ISG15 release or ISG15-RAGE interaction dramatically impeded necroptosis-driven EMT and lymphatic metastasis in HNSCC. Lastly, clinicopathological analysis showed high ISG15 expression in NME. Extensive necroptosis and high tumor-cell RAGE expression correlated with tumor progression and poor survival of HNSCC patients. Conclusions Our data revealed a previously unknown cGAS-ISG15-RAGE dependent reprogramming of the necroptotic microenvironment which converts the necroptotic stress into invasive force to foster HNSCC-cell dissemination. By demonstrating the programmatic production of ISG15 via necroptosis-cGAS orchestration and its downstream signaling through RAGE, we shed light on the unique role of ISG15 in HNSCC progression. Targeting such machineries may hold therapeutic potential for restoring intratumoral survival stress and preventing lymphatic metastasis in HNSCC. Graphical Abstract

Abstract Wind power prediction holds significant value for the stability of the electrical grid when wind power is connected to the grid. Using neural networks for wind power prediction may have some limitations, such as slow speed and low accuracy. This paper proposes to enhance the power prediction accuracy and speed by optimizing the neural network through health assessment wind turbines. Firstly, based on wind turbine actual operating data, a health assessment is conducted to obtain a health matrix of wind turbine. Then, by calculating the weights of the matrix, the power prediction strategy of the network is optimized. Following that, matrix approximation hyperparameters are utilized to expedite the optimization process. Finally, some tests are conducted on neural network power prediction, act as optimized back propagation (BP) neural network and whale swarm algorithm–support vector regression (WSA-SVR) neural networks are employed for wind power prediction. Results show noticeable optimization: after optimizing the BP network, power prediction accuracy increased by about 40%, and prediction speed rose by about 20%; after optimizing the WSA-SVR network, power prediction accuracy improved by 10%, and prediction speed surged by about 45%. Further analysis shows that this method can improve the accuracy and speed of most neural network wind power prediction algorithms.

A common malignant bone neoplasm in teenagers is Osteosarcoma. Chemotherapy, surgical therapy, and radiation therapy together comprise the usual clinical course of treatment for Osteosarcoma. While Osteosarcoma and other bone tumors are typically treated surgically, however, surgical resection frequently fails to completely eradicate tumors, and in turn becomes the primary reason for postoperative recurrence and metastasis, ultimately leading to a high rate of mortality. Patients still require radiation and/or chemotherapy after surgery to stop the spread of the tumor and its metastases, and both treatments have an adverse influence on the body’s organ systems. In the postoperative management of osteosarcoma, bone scaffolds can load cargos (growth factors or drugs) and function as drug delivery systems (DDSs). This review describes the different kinds of bone scaffolds that are currently available and highlights key studies that use scaffolds as DDSs for the treatment of osteosarcomas. The discussion also includes difficulties and perspectives regarding the use of scaffold-based DDSs. The study may serve as a source for outlining efficient and secure postoperative osteosarcoma treatment plans.

IntroductionAccumulating evidence shows that human health and disease are closely related to the microbes in the human body.MethodsIn this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe–disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe–disease relationships, disease similarity, and microbial similarity. Then, we adopted the improved GCN and the CSAE to extract neighbor relations in the adjacency matrix and novel feature representations in heterogeneous networks. After that, in order to estimate the likelihood of a potential microbe associated with a disease, we integrated these two types of representations to create unique eigenmatrices for diseases and microbes, respectively, and obtained predicted scores for potential microbe–disease associations by calculating the inner product of these two types of eigenmatrices.Results and discussionBased on the baseline databases such as the HMDAD and the Disbiome, intensive experiments were conducted to evaluate the prediction ability of GCANCAE, and the experimental results demonstrated that GCANCAE achieved better performance than state-of-the-art competitive methods under the frameworks of both 2-fold and 5-fold CV. Furthermore, case studies of three categories of common diseases, such as asthma, irritable bowel syndrome (IBS), and type 2 diabetes (T2D), confirmed the efficiency of GCANCAE.

Abstract Background In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations. Results In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs. Conclusions Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git

Abstract The prediction of potential microbe–drug associations is of great value for drug research and development, especially, methods, based on deep learning, have been achieved significant improvement in bio-medicine. In this manuscript, we proposed a novel computational model named NMGMDA based on the nuclear norm minimization and graph attention network to infer latent microbe–drug associations. Firstly, we created a heterogeneous microbe–drug network in NMGMDA by fusing the drug and microbe similarities with the established drug–microbe associations. After this, by using GAT and NNM to calculate the predict scores. Lastly, we created a fivefold cross validation framework to assess the new model NMGMDA's progressiveness. According to the simulation results, NMGMDA outperforms some of the most advanced methods, with a reliable AUC of 0.9946 on both MDAD and aBioflm databases. Furthermore, case studies on Ciprofloxacin, Moxifoxacin, HIV-1 and Mycobacterium tuberculosis were carried out in order to assess the effectiveness of NMGMDA even more. The experimental results demonstrated that, following the removal of known correlations from the database, 16 and 14 medications as well as 19 and 17 microbes in the top 20 predictions were validated by pertinent literature. This demonstrates the potential of our new model, NMGMDA, to reach acceptable prediction performance.

Abstract ABC (ATP-binding cassette) transporter proteins are one of the most extensive protein families known to date and are ubiquitously found in animals, plants, and microorganisms. ABCs have a variety of functions, such as plant tissue development regulation, hormone transport, and biotic and abiotic stress resistance. However, the gene characterization and function of the ABC gene family in almond (Prunus dulcis) have not been thoroughly studied. In this study, we identified 117 PdABC genes using the whole genome of ‘Wanfeng’ almond obtained by sequencing and explored their protein characterization. The PdABC family members were classified into eight subfamilies. The members of the same subfamily had conserved motifs but poorly conserved numbers of exons and introns and were unevenly distributed among the eight subfamilies and on the eight chromosomes. Expression patterns showed that PdABC family members were significantly differentially expressed during almond development, dormant freezing stress, and salt stress. We found that PdABC59 and PdABC77 had extremely high expression levels in pollen. PdABC63 and PdABC64 had high expression levels during almond petal development and multiple stages of flower development. PdABC98 was highly expressed in annual dormant branches after six temperature-freezing stress treatments. PdABC29, PdABC69, and PdABC98 were highly expressed under different concentrations of salt stress. This study preliminarily investigated the expression characteristics of ABC genes in different tissues of almond during flower development, freezing stress and salt stress, and the results will provide a reference for further in-depth research and breeding of almond in the future.

AbstractTendons are fibroblastic structures that link muscle and bone. There are two kinds of tendon injuries, including acute and chronic. Each form of injury or deterioration can result in significant pain and loss of tendon function. The recovery of tendon damage is a complex and time-consuming recovery process. Depending on the anatomical location of the tendon tissue, the clinical outcomes are not the same. The healing of the wound process is divided into three stages that overlap: inflammation, proliferation, and tissue remodeling. Furthermore, the curing tendon has a high re-tear rate. Faced with the challenges, tendon injury management is still a clinical issue that must be resolved as soon as possible. Several newer directions and breakthroughs in tendon recovery have emerged in recent years. This article describes tendon injury and summarizes recent advances in tendon recovery, along with stem cell therapy, gene therapy, Platelet-rich plasma remedy, growth factors, drug treatment, and tissue engineering. Despite the recent fast-growing research in tendon recovery treatment, still, none of them translated to the clinical setting. This review provides a detailed overview of tendon injuries and potential preclinical approaches for treating tendon injuries.

In this study, tea polyphenol oxidase (PPO) was purified via three-phase partitioning (TPP) using a deep eutectic solvent (DES) instead of t-butanol. First, the properties of 13 types of synthesized DESs were characterized, and DES-7 (thymol/dodecanoic acid) was selected as the best alternative solvent. The process parameters were optimized using response surface methodology. The experimental results revealed that when the (NH4)2SO4 concentration, DES to crude extract ratio, extraction time, and pH were 41%, 0.5:1, 75 min, and 5.6, respectively, the recovery and purification fold of tea PPO were 78.44% and 8.26, respectively. SDS-PAGE and native-PAGE were used to analyze the PPO before and after purification of the TTP system, and the molecular weight and purification effect of PPO were detected. Moreover, the DES could be recovered and recycled. The results indicate an environmentally friendly and stable DES, and provide a reference for the large-scale application of TPP to extract PPO.

IntroductionNumerous studies show that microbes in the human body are very closely linked to the human host and can affect the human host by modulating the efficacy and toxicity of drugs. However, discovering potential microbe-drug associations through traditional wet labs is expensive and time-consuming, hence, it is important and necessary to develop effective computational models to detect possible microbe-drug associations.MethodsIn this manuscript, we proposed a new prediction model named LCASPMDA by combining the learnable graph convolutional attention network and the self-paced iterative sampling ensemble strategy to infer latent microbe-drug associations. In LCASPMDA, we first constructed a heterogeneous network based on newly downloaded known microbe-drug associations. Then, we adopted the learnable graph convolutional attention network to learn the hidden features of nodes in the heterogeneous network. After that, we utilized the self-paced iterative sampling ensemble strategy to select the most informative negative samples to train the Multi-Layer Perceptron classifier and put the newly-extracted hidden features into the trained MLP classifier to infer possible microbe-drug associations.Results and discussionIntensive experimental results on two different public databases including the MDAD and the aBiofilm showed that LCASPMDA could achieve better performance than state-of-the-art baseline methods in microbe-drug association prediction.

Although early diagnosis and therapeutic advances have transformed the living quality and outcome of cancer patients, the poor prognosis for metastatic patients has not been significantly improved. Mechanisms underlying the complexity of metastasis cannot be simply determined by the straightforward ‘cause‐and‐effect relationships’. We have developed a ‘dry‐lab‐driven knowledge discovery and wet‐lab validation’ approach to embrace the complexity of cancer and metastasis. We have revealed for the first time that polymetastatic (POL) melanoma cells can utilize both the secretory protein pathway (S100A11–Sec23a) and the exosomal crosstalk (miR‐487a‐5p) to transfer their ‘polymetastatic competency’ to the oligometastatic (OL) melanoma cells, via synergistic co‐targeting of the tumor‐suppressor Nudt21. The downstream deregulated glycolysis was verified to regulate metastatic colonization efficiency. Further, two gene sets conferring independent prognosis in melanoma were identified, which have the potential for clinical translation and merit future clinical validation.

AbstractContext Qing Cuo Formula (QCF) is a traditional Chinese medicine for treating acne, but its active compounds and molecular mechanisms are unclear.Objective To investigate the material basis and molecular mechanism of QCF.Materials and methods In vivo experiments were conducted on 60 male golden hamsters with damp-heat acne, with a blank group, a spironolactone group and 3 QCF administration groups (given high, medium and low doses) over a 30-day period. Serum androgen and inflammatory cytokine levels were tested by ELISA. In vitro, chemical compositions of QCF were investigated by UPLC-LTQ-Orbitrap-MS. Network pharmacology approaches were used to analyse the protein–protein interaction (PPI) network and QCF active compounds-intersection targets-acne network. GO enrichment and KEGG pathway analysis was conducted subsequently.Results Low-dose QCF group (11.4 g/kg/day) showed significantly reduced levels of serum T (4.94 ± 0.36; 5.51 ± 0.36 ng/mL), DHT (6.67 ± 0.61; 8.09 ± 0.59 nmol/L), E2 (209.01 ± 20.92; 237.08 ± 13.94 pg/mL), IL-1α (36.84 ± 3.23; 44.07 ± 4.00 pg/mL) and FFA (128.32 ± 10.94; 148.00 ± 12.12 µmol/L) compared to the blank group (p

Dangerous rock disaster is prevalent geological hazard in southwest China. Under the influence of gravity, dangerous rock masses can easily trigger rockfalls, posing a significant threat to infrastructure such as roads, bridges, and railways. Therefore, it is crucial to effectively intercept rockfalls. Common interception facilities, including stone-blocking trenches, stone-blocking nets, and retaining walls, are often constrained by terrain and construction limitations, making it difficult to implement them on a large scale and resulting in reduced interception effectiveness. The southwestern region of China is rich in Cizhu bamboo, which exhibits rapid growth, strong reproductive capabilities, and resistance to bending, rendering it highly effective in intercepting falling rocks. This study employs a combination of model testing and numerical simulation to investigate the impact resistance of Cizhu bamboo and proposes a strategy for using Cizhu bamboo to intercept small-scale rockfalls. The research findings are as follows: (1) The energy requirements of Cizhu bamboo increase with diameter and wall thickness, while decreasing with age, length-to-weight ratio, and aspect ratio. (2) A cluster of Cizhu bamboo can consume a minimum of 3975.55 J and a maximum of 10890.88 J of energy during impact resistance. (3) Utilizing the CRockfall software, the interception effect of Cizhu bamboo on rockfalls is calculated, indicating that a slope with a height of 43 m and a dangerous rock of 0.5 m in diameter requires the planting of 2 rows, 3 lines, and 6 clusters of Cizhu bamboo.

Cordyceps militaris is considered to be of great medicinal potential due to its remarkable pharmacological effects, safety, and edible characteristics. With the completion of the genome sequence and the advancement of efficient gene-editing technologies, coupled with the identification of gene functions in Cordyceps militaris, this fungus is poised to emerge as an outstanding strain for medicinal engineering applications. This review focuses on the development and application of genomic editing techniques, including Agrobacterium tumefaciens-mediated transformation (ATMT), PEG-mediated protoplast transformation (PMT), and CRISPR/Cas9. Through the application of these techniques, researchers can engineer the biosynthetic pathways of valuable secondary metabolites to boost yields; such metabolites include cordycepin, polysaccharides, and ergothioneine. Furthermore, by identifying and modifying genes that influence the growth, disease resistance, and tolerance to environmental stress in Cordyceps militaris, it is possible to stimulate growth, enhance desirable traits, and increase resilience to unfavorable conditions. Finally, the green sustainable industrial development of C. militaris using agricultural waste to produce high-value-added products and the future research directions of C. militaris were discussed. This review will provide future directions for the large-scale production of bioactive ingredients, molecular breeding, and sustainable development of C. militaris.

Inner ear disorders have a variety of causes, and many factors can contribute to the exacerbation of cochlear and vestibular pathology. This systematic review aimed to analyze clinical data on the coexistence and potential causal interaction between allergic diseases and inner ear conditions. A search of PubMed and Web of Science identified 724 articles, of which 21 were selected for full-text analysis based on inclusion and exclusion criteria. The epidemiologic evidence found overwhelmingly supports an association between allergic disease and particular inner ear disorders represented by a high prevalence of allergic reactions in some patients with Ménière’s disease (MD), idiopathic sudden sensorineural hearing loss (ISSHL), and acute low-tone hearing loss (ALHL). In addition, patients with MD, ISSHL, and ALHL had higher levels of total serum IgE than healthy subjects. Finally, in some cases, changes in cochlear potential may have been induced by antigen exposure, while desensitization alleviated allergy and inner ear-related symptoms. The exact mechanism of interaction between the auditory/vestibular and immune systems is not fully understood, and further clinical and basic research is needed to understand the relationship between the two systems fully.

Tendon Sheath Giant Cell Tumor (TGCT) is a benign tumor that primarily grows within joints and bursae. However, it has a high postoperative recurrence rate, ranging from 15% to 45%. Although radiotherapy may reduce this recurrence rate, its applicability as a standard treatment is still controversial. Furthermore, the pathogenic mechanisms of TGCT are not clear, which limits the development of effective treatment methods. The unpredictable growth and high recurrence rate of TGCT adds to the challenges of disease management. Currently, our understanding of TGCT mainly depends on pathological slice analysis due to a lack of stable cell models. In this study, we first reviewed the medical records of two female TGCT patients who had undergone radiotherapy. Then, by combining bioinformatics and machine learning, we interpreted the pathogenesis of TGCT and its associations with other diseases from multiple perspectives. Based on a deep analysis of the case data, we provided empirical support for postoperative radiotherapy in TGCT patients. Additionally, our further analysis revealed the signaling pathways of differentially expressed genes in TGCT, as well as its potential associations with osteoarthritis and synovial sarcomas.

Abstract The biological barriers of the body, such as the blood–brain, placental, intestinal, skin, and air‐blood, protect against invading viruses and bacteria while providing necessary physical support. However, these barriers also hinder the delivery of drugs to target tissues, reducing their therapeutic efficacy. Extracellular vesicles (EVs), nanostructures with a diameter ranging from 30 nm to 10 μm secreted by cells, offer a potential solution to this challenge. These natural vesicles can effectively pass through various biological barriers, facilitating intercellular communication. As a result, artificially engineered EVs that mimic or are superior to the natural ones have emerged as a promising drug delivery vehicle, capable of delivering drugs to almost any body part to treat various diseases. This review first provides an overview of the formation and cross‐species uptake of natural EVs from different organisms, including animals, plants, and bacteria. Later, it explores the current clinical applications, perspectives, and challenges associated with using engineered EVs as a drug delivery platform. Finally, it aims to inspire further research to help bioengineered EVs effectively cross biological barriers to treat diseases.

Background: The wild dwarf almond (Prunus tenella) is one of the national key grade II-protected wild plants in China. It is a relic deciduous forest species from the middle Eocene of the ancient Mediterranean Sea and is also known as a “living fossil of plants.” It is distributed in Southeast Europe, West Asia, Central Asia, Siberia, and Xinjiang (Tacheng) and other areas of China. The plant grows on arid slopes, steppes, depressions, and valleys at an altitude of 1,200 m. The seeds of wild dwarf almonds are frost resistant and contain oil and bitter lentil glycosides, which possess medicinal value. Additionally, the seeds of wild dwarf almonds can be used as the original material for breeding new varieties of almonds and obtain ornamental flowers and trees.Results: The complete mitochondrial genome of P. tenella was sequenced and assembled using two sequencing platforms, namely, Illumina Novaseq6000 and Oxford Nanopore PromethION. The assembled genome was 452,158-bp long with a typical loop structure. The total number of A, T, C, and G bases in the genome was 122,066 (26.99%), 124,114 (27.45%), 103,285 (22.84%), and 102,693 (22.71%), respectively, with a GC content of 45.55%. A total of 63 unique genes, including 36 protein-coding genes, 24 tRNA genes, and 3 rRNA genes, were identified in the genome. Furthermore, codon usage, sequence duplication, RNA editing, and mitochondrial and chloroplast DNA fragment transfer events in the genome were analyzed. A phylogenetic tree was also constructed using 30 protein-coding genes that are common to the mitochondrial genomes of 24 species, which indicated that the genome of wild lentils is highly conserved with those of apples and pears belonging to Rosaceae.Conclusion: Assembly and annotation of the P. tenella mitochondrial genome provided comprehensive information about the mitochondrial genome of wild dwarf almonds, This study provides information on the mitochondrial genome of Prunus species and serves as a reference for further evolutionary studies on wild dwarf almonds.

AbstractThe assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively.

BackgroundPrevious research on the association between risk factors and gestational diabetes mellitus (GDM) primarily comprises observational studies with inconclusive results. The objective of this study is to investigate the causal relationship between 108 traits and GDM by employing a two-sample Mendelian randomization (MR) analysis to identify potential risk factors of GDM.MethodsWe conducted MR analyses to explore the relationships between traits and GDM. The genome-wide association studies (GWAS) for traits were primarily based on data from the UK Biobank (UKBB), while the GWAS for GDM utilized data from FinnGen. We employed a false discovery rate (FDR) of 5% to account for multiple comparisons.ResultsThe inverse-variance weighted (IVW) method indicated that the genetically predicted 24 risk factors were significantly associated with GDM, such as “Forced expiratory volume in 1-second (FEV1)” (OR=0.76; 95% CI: 0.63, 0.92), “Forced vital capacity (FVC)” (OR=0.74; 95% CI: 0.64, 0.87), “Usual walking pace” (OR=0.19; 95% CI: 0.09, 0.39), “Sex hormone-binding globulin (SHBG)” (OR=0.86; 95% CI: 0.78, 0.94). The sensitivity analyses with MR-Egger and weighted median methods indicated consistent results for most of the trats.ConclusionOur study has uncovered a significant causal relationship between 24 risk factors and GDM. These results offer a new theoretical foundation for preventing or mitigating the risks associated with GDM.

The geological and environmental background conditions of the Qingjiang River Basin are highly complex, particularly with frequent geological disasters along the Qingjiang reservoir bank. Previous susceptibility assessment for geological disasters was mostly focused on administrative areas and seldom specialized evaluations for the reservoir bank zone. Furthermore, there is still room for improvement in the evaluation index system, as well as in the pertinence and reliability of the evaluation method. To address these shortcomings, a more suitable susceptibility evaluation index system was constructed to obtain accurate and applicable susceptibility zoning results. The Yuxiakou to Ziqiu section of the Qingjiang River Basin was chosen as the research area, with the wading slope body on both sides of the river selected as the research object and the slope unit chosen as the evaluation unit. A susceptibility evaluation system composed of ten indicators, including slope, aspect, elevation range, slope type, NDVI, TWI, slope structure type, engineering geological rock formation, accumulation thickness, and valley evolution, was constructed.The logistic regression and random forest methods were used to construct the evaluation model based on the normalized certain factors, and different susceptibility zoning results were obtained. According to the evaluation results, the high-prone areas were mainly distributed in the middle to lower water wading areas of the left bank from the east of Yuxiakou to the east of Ziqiu, along the main stream of the Qingjiang River. The logistic regression model showed better applicability in the reservoir-bank section with complex topography and landforms. The research revealed that the accumulation thickness and valley evolution indicators were effective in representing the unique geological background conditions of the Qingjiang reservoir bank. The logistic regression model was able to learn the developmental law of disasters and has a reliable susceptibility prediction ability.

Abstract Background Increasing body fat or decreasing muscle and bone mass were associated with worse health outcomes in the adult population. The effects of nickel exposure on body composition are not known. The aim of the current study was to investigate the relationship between urinary nickel levels and body compositions. Materials and methods Two thousand seven hundred sixty-two participants were included in the analysis from the National Health and Nutrition Examination Surveys of 2017–2018 after excluding participants who have missing data on urinary nickel and those with missing all body mass component data. We used weighted generalized linear models to explore the relationship between urinary nickel and body mass components under interpolating missing covariable values. Simultaneously, sensitivity analyses and subgroup analysis were conducted to verify stability of analysis result. Curve fitting and saturation effect analysis were used to explore the possible nonlinear relationship between urine nickel and body compositions. Results Among the 2,762 participants, the average urinary nickel level was 1.58 ug/L. The weighted generalized linear models, the sensitivity analyses and subgroup analyses found no significant linear relationship between urinary nickel and body compositions. For body weight, BMI, TLM, ALM, TRF, TOF and BMC, the urine nickel saturation effect values were 0.76, 0.74, 0.5, 0.67, 0.64, 0.48, and 0.45 ug/L, respectively. For each 1 ug/L rise in urinary nickel levels at levels below the turning point, body weight increases (β = 9.06, 95% CI = 2.75, 15.36, p = 0.01), BMI increases (β = 3.20, 95% CI = 1.36, 5.05, p =

It is important to recover La3+ from metallurgical solutions or wastewater. However, the recovery rate of La3+ is usually less than 1% and the recovery methods are not environmentally friendly or user-friendly. Therefore, a straightforward, efficient, clean, and economically friendly method is needed. In this investigation, a modified adsorbent, COSAC-Na2EDTA-15, which was made from the Camellia oleifera shell (COS) and disodium ethylenediaminetetraacetic acid (Na2EDTA), was invented. In addition, characterization of the COSAC-Na2EDTA-15 adsorbent was conducted using SEM and XPS, and the principle of adsorption was revealed. The adsorption kinetics followed P-S-O KM, while the isotherm of COS-activated carbon (COSAC) aligned more closely with the Langmuir model. Compared to COSAC, the maximum La3+ adsorption capacity of COSAC-Na2EDTA-15 increased from 50 to 162.43 mg/g, and the content of O and N changed from 7.31% and 1.48% to 12.64% and 4.15%, respectively. The surface of the COSAC-Na2EDTA-15 exhibited abundant C, N, and O elements, and La3+ was detected on the sample surface after adsorption. The test and analysis results fully indicate that La3+ can be successfully adsorbed on the surface of COSAC-Na2EDTA-15. Because of its easy preparation, low cost, and superior performance, activated carbon made from COS finds extensive applications in the adsorption and recovery of rare earth elements.

The filamentous fungus Aspergillus oryzae (A. oryzae) has been extensively used for the biosynthesis of numerous secondary metabolites with significant applications in agriculture and food and medical industries, among others. However, the identification and functional prediction of metabolites through genome mining in A. oryzae are hindered by the complex regulatory mechanisms of secondary metabolite biosynthesis and the inactivity of most of the biosynthetic gene clusters involved. The global regulatory factors, pathway-specific regulatory factors, epigenetics, and environmental signals significantly impact the production of secondary metabolites, indicating that appropriate gene-level modulations are expected to promote the biosynthesis of secondary metabolites in A. oryzae. This review mainly focuses on illuminating the molecular regulatory mechanisms for the activation of potentially unexpressed pathways, possibly revealing the effects of transcriptional, epigenetic, and environmental signal regulation. By gaining a comprehensive understanding of the regulatory mechanisms of secondary metabolite biosynthesis, strategies can be developed to enhance the production and utilization of these metabolites, and potential functions can be fully exploited.

BackgroundThe existing literature on the link between sodium intake and cardiovascular disease (CVD) largely consists of observational studies that have yielded inconsistent conclusions. In this study, our objective is to assess the causal relationship between sodium intake and 50 CVDs using two-sample Mendelian randomization (MR) analysis.MethodsMR analyses were performed to investigate the associations between urinary sodium/creatinine ratio (UNa/UCr), an indicator of sodium intake, and 50 CVDs. The genome-wide association study (GWAS) for UNa/UCr was from the UK Biobank (UKBB), and the GWASs for CVDs were from FinnGen. A false discovery rate (FDR) threshold of 5% was applied for multiple comparison correction.ResultsThe inverse-variance weighted method indicated that the genetically predicted UNa/UCr was significantly associated with 7 of 50 CVDs, including “Coronary atherosclerosis” (OR = 2.01; 95% CI: 1.37, 2.95), “Diseases of arteries, arterioles and capillaries” (OR = 1.88; 95% CI: 1.20, 2.94), “Hard cardiovascular diseases” (OR = 1.71; 95% CI: 1.24, 2.35), “Ischemic heart diseases” (OR = 2.06; 95% CI: 1.46, 2.93), “Major coronary heart disease event” (OR = 1.99; 95% CI: 1.36, 2.91), “Myocardial infarction” (OR = 2.03; 95% CI: 1.29, 3.19), and “Peripheral artery disease” (OR = 2.50; 95% CI: 1.35, 4.63). Similar results were obtained with the MR-Egger and weighted median methods. No significant heterogeneity or horizontal pleiotropy was found in this analysis.ConclusionOur study has uncovered a significant positive causal relationship between UNa/UCr and various CVDs. These results offer a new theoretical foundation for advocating the restriction of sodium intake as a preventive measure against CVD.

Saccharomyces cerevisiae is one of the most extensively used biosynthetic systems for the production of diverse bioproducts, especially biotherapeutics and recombinant proteins. Because the expression and insertion of foreign genes are always impaired by the endogenous factors of Saccharomyces cerevisiae and nonproductive procedures, various technologies have been developed to enhance the strength and efficiency of transcription and facilitate gene editing procedures. Thus, the limitations that block heterologous protein secretion have been overcome. Highly efficient promoters responsible for the initiation of transcription and the accurate regulation of expression have been developed that can be precisely regulated with synthetic promoters and double promoter expression systems. Appropriate codon optimization and harmonization for adaption to the genomic codon abundance of S. cerevisiae are expected to further improve the transcription and translation efficiency. Efficient and accurate translocation can be achieved by fusing a specifically designed signal peptide to an upstream foreign gene to facilitate the secretion of newly synthesized proteins. In addition to the widely applied promoter engineering technology and the clear mechanism of the endoplasmic reticulum secretory pathway, the innovative genome editing technique CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated system) and its derivative tools allow for more precise and efficient gene disruption, site-directed mutation, and foreign gene insertion. This review focuses on sophisticated engineering techniques and emerging genetic technologies developed for the accurate metabolic regulation of the S. cerevisiae expression system.

Abstract Purpose The present study aimed to explore the binding ability of acyl-CoA binding protein 2 to fatty acid acyl-CoA esters and its effect on Monascus pigment production in M. ruber CICC41233. Methods The Mracbp2 gene from M. ruber CICC41233 was cloned with a total DNA and cDNA as the templates through the polymerase chain reaction. The cDNA of the Mracbp2 gene fragment was ligated to expression vector pGEX-6P-1 to construct pGEX-MrACBP2, which was expressed in Escherichia coli BL21 to obtain the fusion protein GST-MrACBP2 and then measure the binding ability of fatty acid acyl-CoA esters. Additionally, the DNA of the Mracbp2 gene fragment was ligated to expression vector pNeo0380 to construct pNeo0380-MrACBP2, which was homologously over-expressed in M. ruber CICC41233 to evaluate Monascus pigment production and fatty acid. Results The cloned Mracbp2 gene of the DNA and cDNA sequence was 1525 bp and 1329 bp in length, respectively. The microscale thermophoresis binding assay revealed that the purified GST-MrACBP2 had the highest affinity for palmitoyl-CoA (Kd =70.57 nM). Further, the Mracbp2 gene was homologously overexpressed in M. ruber CICC41233, and a positive transformant M. ruber ACBP-E was isolated. In the Monascus pigments fermentation, the expression level of the Mracbp2 gene was increased by 1.74-fold after 2 days and 2.38-fold after 6 days. The palmitic acid content and biomass in M. ruber ACBP2-E were significantly lower than that in M. ruber CICC41233 on 2 days and 6 days. However, compared with M. ruber CICC41233, the yields of total pigment, ethanol-soluble pigment, and water-soluble pigment in M. ruber ACBP2-E increased by 63.61%, 71.61%, and 29.70%, respectively. Conclusions The purified fusion protein GST-MrACBP2 exhibited the highest affinity for palmitoyl-CoA. The Mracbp2 gene was overexpressed in M. ruber CICC41233, which resulted in a decrease in palmitic acid and an increase in Monascus pigments. Overall, the effect of MrACBP2 on the synthesis of fatty acid and Monascus pigment was explored. This paper explored the effect of MrACBP2 on the fatty acid synthesis and the synthesis of Monascus pigment. The results indicated the regulation of fatty acid synthesis could affect Monascus pigment synthesis, providing a novel strategy for improving the yield of Monascus pigment.

Aspergillus oryzae, a biosafe strain widely utilized in bioproduction and fermentation technology, exhibits a robust hydrolytic enzyme secretion system. Therefore, it is frequently employed as a cell factory for industrial enzyme production. Moreover, A. oryzae has the ability to synthesize various secondary metabolites, such as kojic acid and L-malic acid. Nevertheless, the complex secretion system and protein expression regulation mechanism of A. oryzae pose challenges for expressing numerous heterologous products. By leveraging synthetic biology and novel genetic engineering techniques, A. oryzae has emerged as an ideal candidate for constructing cell factories. In this review, we provide an overview of the latest advancements in the application of A. oryzae-based cell factories in industrial production. These studies suggest that metabolic engineering and optimization of protein expression regulation are key elements in realizing the widespread industrial application of A. oryzae cell factories. It is anticipated that this review will pave the way for more effective approaches and research avenues in the future implementation of A. oryzae cell factories in industrial production.

To study the effects of the jacking stress level, height and strength ratio of the prestress tendons (λ) on the seismic performance of unbonded prestressed concrete (UPC) beams, six UPC beams and one reinforced concrete (RC) beam were tested under cyclic loads. The hysteretic characteristics, skeleton curves, ductility properties, energy dissipation capacity, strain distribution of reinforcement and self-centering capability of the specimens were studied and discussed. Numerical parameter analysis was also carried out by using OpenSees. The results indicate that three failure modes of UPC beams under cyclic loading were observed, namely the tension-failure mode involving a broken rebar, the compression-failure mode involving concrete crushing and the balanced failure. By considering the influence of the prestress position and magnitude, the modified reinforcing index ω was proposed to determine the failure mode. The ω is suggested to be less than 0.3 to ensure sufficient ductility. The effective stress level is linearly and positively related to the stiffness from cracking to yield Kcr and the ultimate bearing capacity of the UPC beam under cyclic loading. The stiffness of the UPC beam is slightly larger than that of the RC beam before yielding, and significantly greater than that of the RC beam after yielding. Due to the large strength reserve after yielding, the integrated seismic performance of the UPC beam is similar to that of the RC beam. When the λ was unchanged, the increase in the relative height of the prestressed tendons αh is beneficial for the overall performance factor F, ductility and crack control. The stiffness degradation performance depends on the λ but is independent of the αh. The total energy dissipation of the non-tensioned UPC specimen was 59% higher than that of the RC beam. The cumulative total energy dissipation of the tensioned UPC specimen was only 13% lower than that of the RC beam with the same number of cycles, indicating that the UPC specimen had a considerable energy dissipation capacity.

This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to optimize the hyperparameters of the LSTM network. First, the degradation mechanism of PEMFCs is analyzed. Second, the ACO algorithm is used to set the learning rate and dropout probability of the LSTM network combined with partial aging data, which can show the characteristics of the dataset. After that, the aging prediction model is built by using the LSTM and ACO (ACO-LSTM) method. Moreover, the convergence of the method is verified with previous studies. Finally, the fuel cell aging data provided by the Xiangyang Da’an Automotive Testing Center are used for verification. The results show that, compared with the traditional LSTM network, ACO-LSTM can predict the aging process of PEMFCs more accurately, and its prediction accuracy is improved by about 35%, especially when the training data are less. At the same time, the performance of the model trained by ACO-LSTM is also excellent under other operating conditions of the same fuel cell, and it has strong versatility.

Pulmonary arterial hypertension (PAH) is a severe progressive disease that may cause early right ventricular failure and eventual cardiac failure. The pathogenesis of PAH involves endothelial dysfunction, aberrant proliferation of pulmonary artery smooth muscle cells (PASMCs), and vascular fibrosis. Hypoxia has been shown to induce elevated secretion of vascular endothelial growth factor (VEGF), leading to the development of hypoxic PAH. However, the molecular mechanisms underlying hypoxic PAH remain incompletely understood. Programmed cell death (PCD) is a natural cell death and regulated by certain genes. Emerging evidence suggests that apoptotic resistance contributes to the development of PAH. Moreover, several novel types of PCD, such as autophagy, pyroptosis, and ferroptosis, have been reported to be involved in the development of PAH. Additionally, multiple diverse epigenetic mechanisms including RNA methylation, DNA methylation, histone modification, and the non-coding RNA molecule-mediated processes have been strongly linked to the development of PAH. These epigenetic modifications affect the expression of genes, which produce important changes in cellular biological processes, including PCD. Consequently, a better understanding of the PCD processes and epigenetic modification involved in PAH will provide novel, specific therapeutic strategies for diagnosis and treatment. In this review, we aim to discuss recent advances in epigenetic mechanisms and elucidate the role of epigenetic modifications in regulating PCD in hypoxia-induced PAH.

Nanotechnology has changed science in the last three decades. Recent applications of nanotechnology in the disciplines of medicine and biology have enhanced medical diagnostics, manufacturing, and drug delivery. The latest studies have demonstrated this modern technology’s potential for developing novel methods of disease detection and treatment, particularly in orthopedics. According to recent developments in bone tissue engineering, implantable substances, diagnostics and treatment, and surface adhesives, nanomedicine has revolutionized orthopedics. Numerous nanomaterials with distinctive chemical, physical, and biological properties have been engineered to generate innovative medication delivery methods for the local, sustained, and targeted delivery of drugs with enhanced therapeutic efficacy and minimal or no toxicity, indicating a very promising strategy for effectively controlling illnesses. Extensive study has been carried out on the applications of nanotechnology, particularly in orthopedics. Nanotechnology can revolutionize orthopedics cure, diagnosis, and research. Drug delivery precision employing nanotechnology using gold and liposome nanoparticles has shown especially encouraging results. Moreover, the delivery of drugs and biologics for osteosarcoma is actively investigated. Different kind of biosensors and nanoparticles has been used in the diagnosis of bone disorders, for example, renal osteodystrophy, Paget’s disease, and osteoporosis. The major hurdles to the commercialization of nanotechnology-based composite are eventually examined, thus helping in eliminating the limits in connection to some pre-existing biomaterials for orthopedics, important variables like implant life, quality, cure cost, and pain and relief from pain. The potential for nanotechnology in orthopedics is tremendous, and most of it looks to remain unexplored, but not without challenges. This review aims to highlight the up tp date developments in nanotechnology for boosting the treatment modalities for orthopedic ailments. Moreover, we also highlighted unmet requirements and present barriers to the practical adoption of biomimetic nanotechnology-based orthopedic treatments.

Background: Connexin 43 (Cx43) has been closely linked to the occurrence and progression of breast cancer. Distant metastasis of breast cancer is aided by the epithelial-mesenchymal transition of circulating tumor cells (CTCs). However, the impact of Cx43 expression on CTCs and the extent of its role in the disease remain unclear. Methods: We determined CTCs in 156 patients, who had breast cancer with a disease course of two or more years. We also measured the expression of Cx43 in the CTCs. The CTCs were detected in the blood of 139 of these patients. These 139 patients were divided into two groups: the Cx43 group and the non-Cx43 group based on their Cx43 expression. Results: Overall, Cx43 expression was found in 83 of the 139 patients (59.7%, 83/139 cases). The two groups significantly differed in terms of the number of mixed biphenotypic type CTCs and the total number of CTCs (P

Abstract Background Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe–drug associations have been proposed. Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe–drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe–drug pairs. Conclusion A novel computational model is proposed for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.

Abstract Melanoma, one of the most aggressive malignancies, its high mortality and low survival rates are associated with effective metastatic colonization. Melanoma metastasis hinges on the bidirectional cell-cell communication within the complex metastatic microenvironments (MME). Extracellular vesicles (EVs) are recognized as a new class of molecular mediator in MME programing. Published studies show that melanoma EVs can educate MME stromal cells to acquire the pro-metastatic phenotype to enhance metastatic colonization. Whether EVs can mediate the interactions between heterogenous cancer cells within the MME that alter the course of metastasis has not been investigated at the mechanistic level. In this study, melanoma parental cells (MPCs) and paired derivative cancer stem cell line melanoma stem cells (MSCs) that were derived from melanoma cell line M14 were used. We demonstrate that the EVs-mediated crosstalk between the MSCs and the MPCs is a novel mechanism for melanoma metastasis. We characterized miR-592, a relatively novel microRNA of prognostic potential, in mediation of such intercellular crosstalk. EVs can encapsulate and deliver miR-592 to target MPCs. Upon entering, miR-592 inhibits the expression of its gene target protein tyrosine phosphatase non-receptor type7 (PTPN7), a phosphatase targeting MAPKs. This leads to the relief of the inhibitory effect of PTPN7 on MAPK/ERK signaling and consequently the augmentation of metastatic colonization of MPCs. Thus, via the extracellular vesicle miR-592/PTPN7/MAPK axis, melanoma-CSCs can transfer their metastatic ability to the low-metastatic non-CSC melanoma cells.

Abstract Background Metastatic melanoma has a high mortality rate and poor survival. This is associated with efficient metastatic colonization, but the underlying mechanisms remain elusive. Communication between cancer stem cells (CSCs) and cancer cells plays an important role in metastatic dissemination. Whether cancer stem cells can alter the metastatic properties of non-CSC cells; and whether exosomal crosstalk can mediate such interaction, have not been demonstrated in melanoma prior to this report. Results The results revealed that exosomes secreted by highly metastatic melanoma CSCs (OL-SCs) promoted the invasiveness of the low metastatic melanoma cells (OL) and accelerated metastatic progression. miR-1268a was up-regulated in cells and exosomes of OL-SCs. Moreover, OL-SCs-derived exosomal miR-1268a, upon taking up by OL cells, promoted the metastatic colonization ability of OL cells in vitro and in vivo. In addition, the pro-metastatic activity of exosomal miR-1268a is achieved through inhibition of autophagy. Conclusion Our study demonstrates that OL cells can acquire the “metastatic ability” from OL-SCs cells. OL-SCs cells achieves this goal by utilizing its exosomes to deliver functional miRNAs, such as miR-1268a, to the targeted OL cells which in turn augments metastatic colonization by inactivating the autophagy pathway in OL cells.

Among the underlake tunnels in the urban areas, the Lianghu Tunnel in Wuhan is the longest one of China and the largest one in the world.The East Lake part of this tunnel passes through the karst area. Identifying its karst water system structure is of great significance to the safety of tunnel construction and operation.To reveal the structure of the karst water system and the characteristics of the water cycle in the area, a comprehensive method of geological-hydrogeological investigation and hydrogeochemical analysis was conducted to characterize the boundary of the karst aquifer system, summarize the karst development law, identify the source of cave sediments, analyze the hydraulic connection between karst water, pore water and surface water, as well as discuss the hydrological circulation model of the karst water system.The results show that the study area can be divided into two karst aquifer systems, the north and the south.The karst development degree is stronger in the northern karst aquifer system than that in the southern karst aquifer system but has a unified hydraulic connection through the fault zone. The cave sediments mainly derived from Quaternary residual layers and the weathering of the cave surrounding rock.The pore water in the Quaternary alluvial-pluvial aquifer has a weak hydraulic connection with karst water, while the pore water in the Quaternary residual aquifer has a close hydraulic connection with karst water.The karst water system structures of underlake tunnels were characterized by a variety of techniques and methods, which helps the risk assessment of water inrush and the safe construction of tunnel engineering.

Context: Physical exercise in men with prostate cancer (CaP) has shown benefits in improving cancer-related fatigue (CRF) and quality of life (QoL) during radiation therapy. However, types of exercises that are more effective are not well understood. Evidence acquisition: We searched PubMed, Web of Science, and ClinicalTrials.gov up to November 2021 to identify potentially relevant studies. Randomized controlled trials (RCTs) testing the effects of exercise training on CRF, QoL, and treatment-related toxicities in patients with CaP undergoing radiation therapy were included. The quality of individual studies was evaluated using the Tool for the assEssment of Study qualiTy and reporting in Exercise (TESTEX) scale. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation method. A meta-regression analysis was conducted to test the study-level covariates. A random-effect network meta-analysis was conducted based on a Bayesian model. Evidence synthesis: Eight RCTs with 466 participants were included. Exercise achieved significant improvements in CRF (standardized mean difference [SMD] = 1.24, 95% confidence interval or CI [0.43, 2.06], I2 = 93%) and QoL (SMD = 1.40, 95% CI [0.05, 2.75], I2 = 95%). Based on the meta-regression and Bayesian model, combined moderate-intensity continuous training aerobic exercise and resistance exercise (MICT/RES) showed the highest probability of ranking first in terms of CRF and QoL improvement, but the results of QoL were unstable. Exercise training also had a positive effect on urinary toxicities (SMD = –0.53, 95% CI [–0.79, –0.27], I2 = 0%). A subgroup analysis indicated that MICT/RES might be the most promising exercise modality for reducing intestinal toxicities (SMD = –1.76, 95% CI [–2.32, –1.20]). Conclusions: MICT/RES might be superior to any other types of exercise at reducing CRF. MICT/RES was more effective on significantly mitigating urinary and intestinal toxicities. Patient summary: In prostate cancer (CaP) survivors during radiation therapy, exercise training is an effective and safe intervention to reduce cancer-related fatigue (CRF) and improve quality of life (QoL), and should be prescribed as a rehabilitation option for clinical management. As for the types of exercises, moderate-intensity continuous training aerobic exercise and resistance exercise seem to be the most effective interventions to reduce CRF, improve QoL, and mitigate treatment-related symptoms.

Abstract Background Clinical skill training (CST) is indispensable for first-year surgical residents. It can usually be carried out through video-based flipped learning (FL) within a web-based learning environment. However, we found that residents lack the process of reflection, blindly imitating results in losing interest and passion for learning in the traditional teaching pattern. The teaching method of "spot the difference" (SDTM), which is based on the fundamentals of the popular game of "spot the difference," is designed to improve students' participation and reflective learning during skill training. This study aimed to evaluate this novel educational model's short-term and long-term effectiveness for surgical residents in China. Methods First-year residents who required a three-month rotation in the head and neck surgery department were recruited to participate in a series of CSTs. They were randomized into SDTM and traditional FL (control) groups. Clinical skill performance was assessed with validated clinical skill scoring criteria. Evaluations were conducted by comparing the scores that contain departmental rotation skill examinations and the first China medical licensing examination (CMLE) performance on practical skills. In addition, two-way subjective evaluations were also implemented as a reference for the training results. Training effects were assessed using t tests, Mann–Whitney–Wilcoxon tests, chi-square tests, and Cohen’ s effect size (d). The Cohen’ s d value was considered to be small (0.8). Results The SDTM group was significantly superior to the control group in terms of after-department skill examination (t=2.179, p0.8). There were no significant differences between the two groups on clinical competence (t=0.819, p=0.415, d=0.18), the first-time pass rate for CMLE (χ2 =1.663, p=0.197, d=0.29), and short-term operational skills improvement (t=1.747, p=0.084, d=0.39). Conclusions SDTM may be an effective method for enhancing residents' clinical skills, and the effect is significant both short- and long-term. The improvement effect seemed to be more significant in the peer-involved SDTM than training alone. However, despite positive objective results, SDTM still risks student learning burnout. Trial registration ISRCTN registry, ISRCTN10598469 , 02/04/2022，retrospectively registered.

Background: Cervical cancer is a common leading cause of cancer related to women death worldwide. Cylindromatosis (CYLD) is known as an important tumor suppressor in various human cancers, and a deubiquitination enzyme (DUB) as well. Previously, we identified Skp2 as an E3 ligase of Aurora B ubiquitination, but the DUB of Aurora B still remains unknown. Methods: Aurora B ubiquitination site is identified through in vivo ubiquitination assay. Activity of Aurora B and CENPA was detected by immunoblotting (IB) and immunofluorescence (IF) assay. Protein-to-protein interaction was investigated by immunoprecipitation (IP). Cell chromosome dynamics was monitored by live-cell time-lapse Imaging. Cancer cell proliferation, colony formation, apoptosis, and cell invasion and migration assays were also performed. Protein level was checked by immunohistochemical (IHC) staining in clinical cervical cancer samples. Results: We identified Lysine 115 (K115) as the main Aurora B ubiquitination site for Skp2. We could also detect an interaction of Aurora B with the DUB CYLD. We found that CYLD promoted deubiquitination of Aurora B, and regulated Aurora B activity and function as well. Compared with control, we found it took more time for the cells to finish cell mitosis with CYLD over-expression. Furthermore, we found that CYLD deficiency promoted cervical cancer cell proliferation, colony formation, cell migration and invasion, and inhibited apoptosis instead, whereas it is just opposite with CYLD over-expression. In clinical cervical cancer samples, we showed a negative correlation of CYLD expression with Aurora B activation and histological cancer cell invasion. Furthermore, there was less CYLD abundance and higher Aurora B activity in advanced cancer samples compared with early stage. Conclusions: Our findings uncover CYLD as a novel potential DUB of Aurora B, which inhibits Aurora B activation and its subsequent function in cell mitosis, and also provide more evidence for its tumor suppressor function in cervical cancer.

Pulmonary hypertension is characterized by pulmonary arterial remodeling that results in increased pulmonary vascular resistance, right ventricular failure, and premature death. It is a threat to public health globally. Autophagy, as a highly conserved self-digestion process, plays crucial roles with autophagy-related (ATG) proteins in various diseases. The components of autophagy in the cytoplasm have been studied for decades and multiple studies have provided evidence of the importance of autophagic dysfunction in pulmonary hypertension. The status of autophagy plays a dynamic suppressive or promotive role in different contexts and stages of pulmonary hypertension development. Although the components of autophagy have been well studied, the molecular basis for the epigenetic regulation of autophagy is less understood and has drawn increasing attention in recent years. Epigenetic mechanisms include histone modifications, chromatin modifications, DNA methylation, RNA alternative splicing, and non-coding RNAs, which control gene activity and the development of an organism. In this review, we summarize the current research progress on epigenetic modifications in the autophagic process, which have the potential to be crucial and powerful therapeutic targets against the autophagic process in pulmonary hypertension development.

Tendon wounds are a worldwide health issue affecting millions of people annually. Due to the characteristics of tendons, their natural restoration is a complicated and lengthy process. With the advancement of bioengineering, biomaterials, and cell biology, a new science, tissue engineering, has developed. In this field, numerous ways have been offered. As increasingly intricate and natural structures resembling tendons are produced, the results are encouraging. This study highlights the nature of the tendon and the standard cures that have thus far been utilized. Then, a comparison is made between the many tendon tissue engineering methodologies proposed to date, concentrating on the ingredients required to gain the structures that enable appropriate tendon renewal: cells, growth factors, scaffolds, and scaffold formation methods. The analysis of all these factors enables a global understanding of the impact of each component employed in tendon restoration, thereby shedding light on potential future approaches involving the creation of novel combinations of materials, cells, designs, and bioactive molecules for the restoration of a functional tendon.