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Recent observational studies have reported associations between serum mineral nutrient levels, gut microbiota composition, and neurological, psychiatric, and metabolic diseases. However, the causal effects of mineral nutrients on gut microbiota and their causal associations with diseases remain unclear and require further investigation. This study aimed to identify the associations between serum mineral nutrients, gut microbiota, and risk of neurological, psychiatric, and metabolic diseases using Mendelian randomization (MR). We conducted an MR study using the large-scale genome-wide association study (GWAS) summary statistics of 5 serum mineral nutrients, 196 gut microbes at the phylum, order, family, and genus levels, and a variety of common neurological, psychiatric, and metabolic diseases. Initially, the independent causal associations of mineral nutrients and gut microbiota with diseases were examined by MR. Subsequently, the causal effect of mineral nutrients on gut microbiota was estimated to investigate whether specific gut microbes mediated the association between mineral nutrients and diseases. Finally, we performed sensitivity analyses to assess the robustness of the study results. After correcting for multiple testing, we identified a total of 33 causal relationships among mineral nutrients, gut microbiota, and diseases. Specifically, we found 4 causal relationships between 3 mineral nutrition traits and 3 disease traits, 15 causal associations between 14 gut microbiota traits and 6 disease traits, and 14 causal associations involving 4 mineral nutrition traits and 15 gut microbiota traits. Meanwhile, 118 suggestive associations were identified. The current study reveals multiple causal associations between serum mineral nutrients, gut microbiota, risk of neurological, psychiatric, and metabolic diseases, and potentially provides valuable insights for subsequent nutritional therapies. [ABSTRACT FROM AUTHOR]

Adenosine triphosphate (ATP) is a multifunctional small molecule, necessary for all modern Earth life, which must be a component of the last universal common ancestor (LUCA). However, the relatively complex structure of ATP causes doubts about its accessibility on prebiotic Earth. In this paper, based on previous studies on the synthesis of ATP components, a plausible prebiotic pathway yielding this key molecule is constructed, which relies on terrestrial volcanism to provide the required materials and suitable conditions. [ABSTRACT FROM AUTHOR]

Fluorescent chemosensors are powerful imaging tools used in a broad range of biomedical fields. However, the application of fluorescent dyes in bioimaging still remains challenging, with small Stokes shifts, interfering signals, background noise, and self-quenching on current microscope configurations. In this work, we reported a supramolecular cage (CA) by coordination-driven self-assembly of benzothiadiazole derivatives and Eu(OTf)3. The CA exhibited high fluorescence with a quantum yield (QY) of 38.57%, good photoluminescence (PL) stability, and a large Stokes shift (153 nm). Furthermore, the CCK-8 assay against U87 glioblastoma cells verified the low cytotoxicity of CA. We revealed that the designed probes could be used as U87 cells targeting bioimaging. [ABSTRACT FROM AUTHOR]

An improved hybrid firefly algorithm with probability attraction model (IHFAPA) is proposed to solve the problems of low computational efficiency and low computational accuracy in solving complex optimization problems. First, the method of square-root sequence was used to generate the initial population, so that the initial population had better population diversity. Second, an adaptive probabilistic attraction model is proposed to attract fireflies according to the brightness level of fireflies, which can minimize the brightness comparison times of the algorithm and moderate the attraction times of the algorithm. Thirdly, a new location update method is proposed, which not only overcomes the deficiency in that the relative attraction of two fireflies is close to 0 when the distance is long but also overcomes the deficiency that the relative attraction of two fireflies is close to infinity when the distance is small. In addition, a combinatorial variational operator based on selection probability is proposed to improve the exploration and exploitation ability of the firefly algorithm (FA). Later, a similarity removal operation is added to maintain the diversity of the population. Finally, experiments using CEC 2017 constrained optimization problems and four practical problems in engineering show that IHFAPA can effectively improve the quality of solutions. [ABSTRACT FROM AUTHOR]

In recent years, incredible endeavors have been devoted to the design and self-assembly of discrete metal–organic cages (MOCs) with expanding intricacy and functionality. The controlled synthesis of metal–organic supramolecular cages with large branched chains remains an interesting and challenging work in supramolecular chemistry. Herein, a tetrahedral metal–organic supramolecular cage (ZnII4L4) containing 12 dendritic carbazole arms is unprecedentedly constructed through coordination-driven subcomponent self-assembly and characterized in different ways. Interestingly, tetrahedral supramolecular Cage-1 exhibited the potential for aggregation-induced emission (AIE) performance and stimulus-responsive luminescence features, and it achieved color-tunable photoluminescence due to the introduction of dendritic carbazole arms. Crucially, owing to the great photophysical properties of Cage-1 in solution, Cage-1 was enabled to act as a fluorescent ink for the vapor-responsive recording and wiping of information. [ABSTRACT FROM AUTHOR]

Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) widely exists in real-life practical applications; it is a topic that is under investigation and presents unsolved challenges. The existing solutions still have some challenges in convergence speed, iteration time, and avoiding local optimization. In this work, a new method is introduced, called the discrete carnivorous plant algorithm (DCPA) with similarity elimination to tackle the TSP. In this approach, we use a combination of six steps: first, the algorithm redefines subtraction, multiplication, and addition operations, which aims to ensure that it can switch from continuous space to discrete space without losing information; second, a simple sorting grouping method is proposed to reduce the chance of being trapped in a local optimum; third, the similarity-eliminating operation is added, which helps to maintain population diversity; fourth, an adaptive attraction probability is proposed to balance exploration and the exploitation ability; fifth, an iterative local search (ILS) strategy is employed, which is beneficial to increase the searching precision; finally, to evaluate its performance, DCPA is compared with nine algorithms. The results demonstrate that DCPA is significantly better in terms of accuracy, average optimal solution error, and iteration time. [ABSTRACT FROM AUTHOR]

Space exploration has brought many challenges to human physiology. In order to evaluate and reduce possible pathological reactions triggered by space environments, we conducted bioinformatics analyses on the methylation data of the Mars 520 mission and human transcriptome data in the experiment simulating gravity changes. The results suggest that gene expression levels and DNA methylation levels were changed under the conditions of isolation and gravity changes, and multiple viral infection-related pathways were found in the enrichment analysis results of changed genes including Epstein Barr virus (EBV) infection, Hepatitis B virus (HBV) infection, Herpes simplex virus (HSV) infection and Kaposi's sarcoma-associated herpesvirus (KHSV) infection. In this study, we found that Epigallocatechin-3-gallate (EGCG) and vitamin D are helpful in reducing viral infection risk. In addition, the causal associations between nutrients and viral infections were calculated using Two sample Mendelian Randomization (2SMR) method, the results indicated that vitamin D can reduce EBV infection and HBV infection risk. In summary, our study suggests that space environments increase the risk of human viral infection, which may be reduced by supplementing EGCG and vitamin D. These results can be used to formulate medical plans for astronauts, which have practical application value for future space exploration. [ABSTRACT FROM AUTHOR]

Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer's disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong's test, p  <  0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction. [ABSTRACT FROM AUTHOR]

We report that the pincer nickel complexes display prostate cancer antitumor properties through inhibition of cell proliferation. Notably, they display better antitumor properties than cisplatin. Mechanistic studies reveal that these pincer nickel complexes trigger cell apoptosis, most likely due to cell cycle arrest. Interestingly, these complexes also inhibit androgen receptor (AR) and prostate-specific antigen (PSA) signaling, which are critical for prostate cancer survival and progression. Our study reveals a novel function of pincer nickel complexes as potential therapeutic drugs in prostate cancer. [ABSTRACT FROM AUTHOR]

Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life's energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer. [ABSTRACT FROM AUTHOR]

Aging is one of the hottest topics in biomedicine. Previous research suggested that ω-3 fatty acids have preventive effects on aging. However, most of previous studies on the anti-aging effects of ω-3 fatty acids are focused on clinical observations, and the anti-aging mechanisms of ω-3 fatty acids have not been fully elucidated. This stimulated our interest to use multi-omics data related to ω-3 fatty acids in order to interpret the anti-aging mechanisms of ω-3 fatty acids. First, we found that ω-3 fatty acids can affect methylation levels and expression levels of genes associated with age-related diseases or pathways in humans. Then, a Mendelian randomization analysis was conducted to determine whether there is a causal relationship between the effect of ω-3 fatty acids on blood lipid levels and variation in the gut microbiome. Our results indicate that the impact of ω-3 fatty acids on aging is partially mediated by the gut microbiome (including Actinobacteria, Bifidobacteria and Streptococcus). In conclusion, this study provides deeper insights into the anti-aging mechanisms of ω-3 fatty acids and supports the dietary supplementation of ω-3 fatty acids in aging prevention. [ABSTRACT FROM AUTHOR]

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs. [ABSTRACT FROM AUTHOR]

The debate on the temperature of the environment where life originated is still inconclusive. Metabolic reactions constitute the basis of life, and may be a window to the world where early life was born. Temperature is an important parameter of reaction thermodynamics, which determines whether metabolic reactions can proceed. In this study, the scale of the prebiotic metabolic network at different temperatures was examined by a thermodynamically constrained network expansion simulation. It was found that temperature has limited influence on the scale of the simulated metabolic networks, implying that early life may have occurred in a relatively wide temperature range. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer (BC) is a typical global cancer and the second leading cause of cancer-related deaths among women worldwide. BC is a heterogeneous disease with several subtypes, and it is a challenge to use multi-omic data effectively to find suitable drugs for patients. In this paper, we used the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drug combinations that could be used to treat different BC subtypes. Our results showed that this precision drug discovery strategy identified important disease-related genes in individuals and specific groups, supporting its efficiency, high reliability, and practical application value in drug discovery. Breast cancer (BC) is a common disease and one of the main causes of death in females worldwide. In the omics era, researchers have used various high-throughput sequencing technologies to accumulate massive amounts of biomedical data and reveal an increasing number of disease-related mutations/genes. It is a major challenge to use these data effectively to find drugs that may protect human health. In this study, we combined the GeneRank algorithm and gene dependency network to propose a precision drug discovery strategy that can recommend drugs for individuals and screen existing drugs that could be used to treat different BC subtypes. We used this strategy to screen four BC subtype-specific drug combinations and verified the potential activity of combining gefitinib and irinotecan in triple-negative breast cancer (TNBC) through in vivo and in vitro experiments. The results of cell and animal experiments demonstrated that the combination of gefitinib and irinotecan can significantly inhibit the growth of TNBC tumour cells. The results also demonstrated that this systems pharmacology-based precision drug discovery strategy effectively identified important disease-related genes in individuals and special groups, which supports its efficiency, high reliability, and practical application value in drug discovery. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified tens of genetic variants associated with Parkinson's disease (PD). Nevertheless, the genes or DNA elements that affect traits through these genetic variations are usually undiscovered. This study was the first to combine meta-analysis GWAS data and expression data to identify PD risk genes. Four known genes, CRHR1, KANSL1, NSF and LRRC37A, and two new risk genes, STX4 and BST1, were identified. Among them, CRHR1 is a known drug target, indicating that hydrocortisone may become a potential drug for the treatment of PD. Furthermore, the potential pathogenesis of CRHR1 and LRRC37A was explored by applying DNA methylation (DNAm) data, indicating a pathogenesis whereby the effect of a genetic variant on PD is mediated by genetic regulation of transcription through DNAm. Overall, this research identified the risk genes and pathogenesis that affect PD through genetic variants, which has significance for the diagnosis and treatment of PD. [ABSTRACT FROM AUTHOR]

Docosahexaenoic acid (DHA) is effective in the prevention and treatment of cancer, congenital disorders, and various chronic diseases. According to the omnigenic hypothesis, these complex diseases are caused by disordered gene regulatory networks comprising dozens to hundreds of core genes and a mass of peripheral genes. However, conventional research on the disease intervention mechanism of DHA only focused on specific types of genes or pathways instead of examining genes at the network level, resulting in conflicting conclusions. In this study, we used HotNet2, a heat diffusion kernel algorithm, to calculate the gene regulatory networks of connectivity map (cMap)-derived agents (including DHA) based on gene expression profiles, aiming to interpret the disease intervention mechanism of DHA at the network level. As a result, significant gene regulatory networks for DHA and 676 cMap-derived agents were identified respectively. The biological functions of the DHA-regulated gene network provide preliminary insights into the mechanism by which DHA intervenes in disease. In addition, we compared the gene regulatory networks of DHA with those of cMap-derived agents, which allowed us to predict the pharmacological effects and disease intervention mechanism of DHA by analogy with similar agents with clear indications and mechanisms. Some of our analysis results were supported by experimental observations. Therefore, this study makes a significant contribution to research on the disease intervention mechanism of DHA at the regulatory network level, demonstrating the potential application value of this methodology in clarifying the mechanisms about nutrients influencing health. [ABSTRACT FROM AUTHOR]

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms. [ABSTRACT FROM AUTHOR]

Phosphates are essential for modern metabolisms. A recent study reported a phosphate-free metabolic network and suggested that thioesters, rather than phosphates, could alleviate thermodynamic bottlenecks of network expansion. As a result, it was considered that a phosphorus-independent metabolism could exist before the phosphate-based genetic coding system. To explore the origin of phosphorus-dependent metabolism, the present study constructs a protometabolic network that contains phosphates prebiotically available using computational systems biology approaches. It is found that some primitive phosphorylated intermediates could greatly alleviate thermodynamic bottlenecks of network expansion. Moreover, the phosphorus-dependent metabolic network exhibits several ancient features. Taken together, it is concluded that phosphates played a role as important as that of thioesters during the origin and evolution of metabolism. Both phosphorus and sulfur are speculated to be critical to the origin of life. [ABSTRACT FROM AUTHOR]

Breast cancer is a high-risk disease worldwide. For such complex diseases that are induced by multiple pathogenic genes, determining how to establish an effective drug discovery strategy is a challenge. In recent years, a large amount of genetic data has accumulated, particularly in the genome-wide identification of disorder genes. However, understanding how to use these data efficiently for pathogenesis elucidation and drug discovery is still a problem because the gene–disease links that are identified by high-throughput techniques such as phenome-wide association studies (PheWASs) are usually too weak to have biological significance. Systems genetics is a thriving area of study that aims to understand genetic interactions on a genome-wide scale. In this study, we aimed to establish two effective strategies for identifying breast cancer genes based on the systems genetics algorithm. As a result, we found that the GeneRank-based strategy, which combines the prognostic phenotype-based gene-dependent network with the phenotypic-related PheWAS data, can promote the identification of breast cancer genes and the discovery of anti-breast cancer drugs. [ABSTRACT FROM AUTHOR]

We applied Mendelian randomization analyses to investigate the potential causality between blood minerals (calcium, magnesium, iron, copper, and zinc) and osteoporosis (OP), gout, rheumatoid arthritis (RA), type 2 diabetes (T2D), Alzheimer's disease (AD), bipolar disorder (BD), schizophrenia, Parkinson's disease and major depressive disorder. Single nucleotide polymorphisms (SNPs) that are independent (r2 < 0.01) and are strongly related to minerals (p < 5 × 10−8) are selected as instrumental variables. Each standard deviation increase in magnesium (0.16 mmol/L) is associated with an 8.94-fold increase in the risk of RA (p = 0.044) and an 8.78-fold increase in BD (p = 0.040) but a 0.10 g/cm2 increase in bone density related to OP (p = 0.014). Each per-unit increase in copper is associated with a 0.87-fold increase in the risk of AD (p = 0.050) and BD (p = 0.010). In addition, there is suggestive evidence that calcium is positively correlated (OR = 1.36, p = 0.030) and iron is negatively correlated with T2D risk (OR = 0.89, p = 0.010); both magnesium (OR = 0.26, p = 0.013) and iron (OR = 0.71, p = 0.047) are negatively correlated with gout risk. In the sensitivity analysis, causal estimation is not affected by pleiotropy. This study supports the long-standing hypothesis that magnesium supplementation can increase RA and BD risks and decrease OP risk and that copper intake can reduce AD and BD risks. This study will be helpful to address some controversial debates on the relationships between minerals and chronic diseases. [ABSTRACT FROM AUTHOR]

Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection. [ABSTRACT FROM AUTHOR]

Gastric cancer results in malignant tumors with high morbidity and mortality, and seriously affects the health and life quality of patients. Early detection and appropriate treatment for early-stage gastric cancer patients are very helpful to reducing the recurrence rate and improving survival rates. Hence, the selection of a suitable surgical treatment is an important part. At present, surgical treatment selection has been researched in numerous studies, but there is no study integrating fuzzy decision-making theory with quantitative analysis, considering the patient’s conditions with other relative conditions, and which can handle multisource heterogeneous information at the same time. Hence, this paper proposes a novel selection model of surgical treatments for early gastric cancer based on heterogeneous multiple-criteria group decision-making (MCGDM), which is helpful to selecting the most appropriate surgery in the case of asymmetric information between doctors and patients. Subjective and objective criteria are comprehensively taken into account in the index system of the selection model for early gastric cancer, which combines fuzzy theory with quantitative data analysis. Moreover, the evaluation information obtained from the patient’s conditions, the surgery, and the hospital’s medical status, etc., including crisp numbers, interval numbers, neutrosophic numbers, and probabilistic linguistic labels, is more complete and real, so the surgical treatment selection is accurate and reliable. Furthermore, the technique for order of preference by similarity to ideal solution (TOPSIS) method is employed to solve the prioritization of early gastric cancer surgical treatments. Finally, an empirical study of surgical treatment selection for early gastric cancer surgery is conducted, and the results of sensitivity analysis and comparative analysis suggest that the proposed selection model of surgical treatments for early gastric cancer patients is reliable and effective. [ABSTRACT FROM AUTHOR]

This study introduces simplified neutrosophic linguistic numbers (SNLNs) to describe online consumer reviews in an appropriate manner. Considering the defects of studies on SNLNs in handling linguistic information, the cloud model is used to convert linguistic terms in SNLNs to three numerical characteristics. Then, a novel simplified neutrosophic cloud (SNC) concept is presented, and its operations and distance are defined. Next, a series of simplified neutrosophic cloud aggregation operators are investigated, including the simplified neutrosophic clouds Maclaurin symmetric mean (SNCMSM) operator, weighted SNCMSM operator, and generalized weighted SNCMSM operator. Subsequently, a multi-criteria decision-making (MCDM) model is constructed based on the proposed aggregation operators. Finally, a hotel selection problem is presented to verify the effectiveness and validity of our developed approach. [ABSTRACT FROM AUTHOR]

Asthma is a chronic disease that is caused by airway inflammation. The main features of asthma are airway hyperresponsiveness (AHR) and reversible airway obstruction. The disease is mainly managed using drug therapy. The current asthma drug treatments are divided into two categories, namely, anti-inflammatory drugs and bronchodilators. However, disease control in asthma patients is not very efficient because the pathogenesis of asthma is complicated, inducing factors that are varied, such as the differences between individual patients. In this paper, we delineate the genetic mechanisms of asthma, and present asthma-susceptible genes and genetic pharmacology in an attempt to find a diagnosis, early prevention, and treatment methods for asthma. Finally, we reposition some clinical drugs for asthma therapy, based on asthma genetics. [ABSTRACT FROM AUTHOR]

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved. [ABSTRACT FROM AUTHOR]

Over the course of human history, billions of people worldwide have been infected by various viruses. Despite rapid progress in the development of biomedical techniques, it is still a significant challenge to find promising new antiviral targets and drugs. In the past, antiviral drugs mainly targeted viral proteins when they were used as part of treatment strategies. Since the virus mutation rate is much faster than that of the host, such drugs feature drug resistance and narrow-spectrum antiviral problems. Therefore, the targeting of host molecules has gradually become an important area of research for the development of antiviral drugs. In recent years, rapid advances in high-throughput sequencing techniques have enabled numerous genetic studies (such as genome-wide association studies (GWAS), clustered regularly interspersed short palindromic repeats (CRISPR) screening, etc.) for human diseases, providing valuable genetic and evolutionary resources. Furthermore, it has been revealed that successful drug targets exhibit similar genetic and evolutionary features, which are of great value in identifying promising drug targets and discovering new drugs. Considering these developments, in this article the authors propose a host-targeted antiviral drug discovery strategy based on knowledge of genetics and evolution. We first comprehensively summarized the genetic, subcellular location, and evolutionary features of the human genes that have been successfully used as antiviral targets. Next, the summarized features were used to screen novel druggable antiviral targets and to find potential antiviral drugs, in an attempt to promote the discovery of new antiviral drugs.

Psoriasis is a genetically-regulated, T lymphocyte-mediated autoimmune skin disease that causes systemic damage, seriously affecting patient quality of life and survival. Psoriasis treatments, which aim to control the disease's development, are greatly limited because its etiology and pathogenesis have not yet been fully elucidated. A large number of studies have demonstrated that immunogenetic elements are the most important factors responsible for psoriasis susceptibility. This paper delineates the immunogenetic mechanisms of psoriasis and provides useful information with regards to performing drug repositioning for the treatment of psoriasis., Competing Interests: The authors declare no conflict of interest.

The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information., Competing Interests: The authors declare no conflict of interests.

Oxidative damage can lead to a wide range of diseases. Nrf2 is an important transcription factor that regulates many of the cytoprotective enzymes involved in the oxidative stress response. Therefore, targeting the regulation of Nrf2 activation is one logical and effective strategy to prevent or lower the risk of oxidative stress-related diseases. Until now, most research has focused on electrophilic indirect Nrf2 activators, but the risk of 'off-target' effects may be associated with these activators. To find novel small non-electrophilic modulators of Nrf2, we started from chemical agents derived from a connectivity map (cMap) and identified 22 non-electrophilic potential Nrf2-activating drugs through a drug repositioning tactic. By determining the expression changes of antioxidant genes in MCF7 cells that were treated with the potential Nrf2 activators using quantitative real-time polymerase chain reaction RT-PCR (real-time polymerase chain reaction) (qRT-PCR), astemizole was found to have a greater scale of upregulating antioxidant genes NQO1 , HO-1 , and GCLM than the positive control d,l-sulforaphane, although the testing concentration was lower than that of the control. Astemizole is a good potential redox regulator and deserves more pharmacodynamic experimentation to test and verify its feasibility for use as an Nrf2 activator., Competing Interests: The authors declare no conflict of interest.

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb). Due to the extensive use of anti-tuberculosis drugs and the development of mutations, the emergence and spread of multidrug-resistant tuberculosis is recognized as one of the most dangerous threats to global tuberculosis control. Some single mutations have been identified to be significantly linked with drug resistance. However, the prior research did not take gene-gene interactions into account, and the emergence of transmissible drug resistance is connected with multiple genetic mutations. In this study we use the bioinformatics software GBOOST (The Hong Kong University, Clear Water Bay, Kowloon, Hong Kong, China) to calculate the interactions of Single Nucleotide Polymorphism (SNP) pairs and identify gene pairs associated with drug resistance. A large part of the non-synonymous mutations in the drug target genes that were included in the screened gene pairs were confirmed by previous reports, which lent sound solid credits to the effectiveness of our method. Notably, most of the identified gene pairs containing drug targets also comprise Pro-Pro-Glu (PPE) family proteins, suggesting that PPE family proteins play important roles in the drug resistance of Mtb. Therefore, this study provides deeper insights into the mechanisms underlying anti-tuberculosis drug resistance, and the present method is useful for exploring the drug resistance mechanisms for other microorganisms., Competing Interests: The authors declare no conflict of interest.

Pressure ulcer is a complex and significant health problem in long-term bedridden patients, and there is currently no effective treatment or efficient prevention method. Furthermore, the molecular mechanisms and pathogenesis contributing to the deep injury of pressure ulcers are unclear. The aim of the study was to explore the role of endoplasmic reticulum (ER) stress and Akt/GSK3β signaling in pressure ulcers. A model of pressure-induced deep tissue injury in adult Sprague-Dawley rats was established. Rats were treated with 2-h compression and subsequent 0.5-h release for various cycles. After recovery, the tissue in the compressed regions was collected for further analysis. The compressed muscle tissues showed clear cellular degenerative features. First, the expression levels of ER stress proteins GRP78, CHOP, and caspase-12 were generally increased compared to those in the control. Phosphorylated Akt and phosphorylated GSK3β were upregulated in the beginning of muscle compression, and immediately significantly decreased at the initiation of ischemia-reperfusion injury in compressed muscles tissue. These data show that ER stress may be involved in the underlying mechanisms of cell degeneration after pressure ulcers and that the Akt/GSK3β signal pathway may play an important role in deep tissue injury induced by pressure and ischemia/reperfusion.

Polyprenols of Ginkgo biloba L. leaves (GBP) are a new type of lipid with 14-24 isoprenyl units, which in humans have strong bioactivity like the dolichols. A large amount of work showed that GBP had good antibacterial activity and powerful protective effects against acute hepatic injury induced by carbon tetrachloride and alcohol, as well as antitumor activity, but the safety of GBP was not considered. The current study was designed to evaluate the toxicity of these polyprenols. Acute toxicity in mice was observed for 14 days after GBP oral dosing with 5, 7.5, 10, 15 and 21.5 g/kg body weight (b. wt.) Further, an Ames toxicity assessment was carried out by plate incorporation assay on spontaneous revertant colonies of TA97, TA98, TA100 and TA102, with GBP doses designed as 8, 40, 200, 1000 and 5000 μg/dish, and subchronic toxicity was evaluated in rats for 91 days at GBP doses of 500, 1000 and 2000 mg/kg b. wt./day. The weight, food intake, hematological and biochemical indexes, the ratio of viscera/body weight, and histopathological examinations of tissue slices of organs were all investigated. The results showed that no animal behavior and appearance changes and mortality were seen during the observation period with 21.5 g/kg GBP dose in the acute toxicity test. Also, no mutagenicity effects were produced by GBP (mutation rate < 2) on the four standard Salmonella strains (p > 0.05) in the Ames toxicity test. Furthermore, the no observed adverse effect level (NOAEL) of GBP was 2000 mg/kg for 91 days feeding of rats in the subchronic toxicity tests. Results also showed the hematological and biochemical indexes as well as histopathological examination changed within a small range, and all clinical observation indexes were normal. No other distinct impacts on cumulative growth of body weight, food intake and food utilization rate were discovered with GBP. No significant difference was discovered for the rats' organ weight and the ratio of viscera to body weight (p > 0.05). Reversible pathological changes in the histopathological examinations of tissue slices of organs were not observed. GBP could therefore be considered as a safe material with minor side effects.

Unlabelled: In order to improve the bioavailability levels of polyprenols (derived from ginkgo leaves (GBP)) in the human body, a GBP nanoemulsion was prepared, and its antiviral activity was evaluated against influenza A H3N2 and hepatitis B virus in vitro., Methods: A GBP nanoemulsion was prepared by inversed-phase emulsification (IPE). Next, we investigated the antiviral activity of the GBP nanoemulsion on influenza A H3N2 and hepatitis B virus in vitro by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenlytetrezolium bromide) method. ELISA and the fluorescent quantitative PCR method were used to measure the content of HBsAg, HBeAg and DNA virus in human samples., Results: The GBP nanoemulsion exhibited uniformity at an average particle size 97 nm with a hydrophilic-lipophilic balance (HLB) of 9.5. GBP is non-toxic to normal cells, hepatitis B virus DNA, hepatitis B virus antigen and HepG2215. Furthermore, GBP could reach a 70% virucidal activity and a 74.9% protection rate (*** p < 0.001) on MDCK cells infected with H3N2 virus at a high concentration of 100 μg/mL. GBP had a good inhibition rate on HBsAg (52.11%, ** p < 0.01) at 50 μg/mL and Day 9 of incubation, and a 67.32% inhibition effect on HBeAg at a high concentration of 100 μg/mL and Day 9. GBP had good inhibition on HBV DNA with CT 18.6 and lower copies (** p < 0.01) at a middle concentration of 12.5 to 25 μg/mL., Conclusions: The GBP nanoemulsion was very stable and non-toxic and had very strong antiviral activity against influenza A H3N2 and hepatitis B virus in vitro. The inhibitory effects and reactive mechanisms were similar to the drug, 3TC; by lengthening the incubation time and increasing the drug concentration, GBP has promising potential as an antiviral drug.

In order to find bamboo leaves with high contents of bioactive polysaccharides, 32 samples were chosen to analyze their polysaccharide content by GC and sulfuric acid-anthrone colorimetric assays. Purified polysaccharides (BLPS) were separated from the four varieties P. nigra (Lodd.) Munro (PN), P. vivax McClure (PV), Chimonobambusa quadrangularis (Fenzi) Makino (CQ), and P. bambussoides cv. Tanakae (PB) by ultrasound extraction, solution precipitation, ion exchange resin, DEAE-52 and Sephadex G-100 chromatography. BLPS structural characterization was accomplished by HPLC-GPC, Fourier transform infra-red spectroscopy (FTIR) and NaIO4-HIO4 oxidation reactions. The results showed that the total polysaccharides of the bamboo leaves in samples 1-32 ranged between 1.4% and 5.4%, Samples No. 29-No. 32 (PN, PV, CQ, and PB) contained 2-3 fold more polysaccharides than No. 1~No. 28 among the 32 different species, particularly the content of galactose was in a range of 21.5%-34.1% for these four typical bamboo species leaves, which was also more than 2-3 fold higher than in No. 1-No. 28. Sugar analysis indicated that PN-PBLPS-1, PV-PBLPS-1, CQ-PBLPS-1 and PB-PBLPS-1 from the four varieties were homogeneous polysaccharides with molecular weights of 2.04 × 104, 1.15 × 104, 8.75 × 104 and 1.48 × 104 Da, respectively. PB-PBLPS-1 was a mixture of α-galactopyranose and β-d-glucopyranose linkages with α-(1→6) or β-(1→6)glycosidic bonds, while PN-PBLPS-1, PV-PBLPS-1, and CQ-PBLPS-1 had α galactopyranose linkages with α-(1→6) glycosidic bonds.

Natural medicines have attracted wide attention in recent years. It is of great significance to clarify the pharmacological mechanisms of natural medicines. In prior studies, we established a method for elucidating pharmacological mechanisms of natural products contained in connectivity map (cMap), in terms of module profiles of gene expression in chemical treatments. In this study, we explore whether this methodology is applicable to dissecting the pharmacological mechanisms of natural medicines beyond the agents contained in cMap. First, the gene expression profiles of curcumin (a typical isolated natural medicine) and Si-Wu-Tang (a classic traditional Chinese medicine formula) treatments were merged with those of cMap-derived 1309 agents, respectively. Then, a biclustering analysis was performed using FABIA method to identify gene modules. The biological functions of gene modules provide preliminary insights into pharmacological mechanisms of both natural medicines. The module profile can be characterized by a binary vector, which allowed us to compare the expression profiles of natural medicines with those of cMap-derived agents. Accordingly, we predicted a series of pharmacological effects for curcumin and Si-Wu-Tang by the indications of cMap-covered drugs. Most predictions were supported by experimental observations, suggesting the potential use of this method in natural medicine dissection.

Due to the diverse medicinal effects, polyphenols are among the most intensively studied natural products. However, it is a great challenge to elucidate the polypharmacological mechanisms of polyphenols. To address this challenge, we establish a method for identifying multiple targets of chemical agents through analyzing the module profiles of gene expression upon chemical treatments. By using FABIA algorithm, we have performed a biclustering analysis of gene expression profiles derived from Connectivity Map (cMap), and clustered the profiles into 49 gene modules. This allowed us to define a 49 dimensional binary vector to characterize the gene module profiles, by which we can compare the expression profiles for each pair of chemical agents with Tanimoto coefficient. For the agent pairs with similar gene expression profiles, we can predict the target of one agent from the other. Drug target enrichment analysis indicated that this method is efficient to predict the multiple targets of chemical agents. By using this method, we identify 148 targets for 20 polyphenols derived from cMap. A large part of the targets are validated by experimental observations. The results show that the medicinal effects of polyphenols are far beyond their well-known antioxidant activities. This method is also applicable to dissect the polypharmacology of other natural products.

Primitive proteins are proposed to have utilized organic cofactors more frequently than transition metals in redox reactions. Thus, an experimental validation on whether a protein constituted solely by early amino acids and an organic cofactor can perform electron transfer activity is an urgent challenge. In this paper, by substituting "late amino acids (C, F, M, T, W, and Y)" with "early amino acids (A, L, and V)" in a flavodoxin, we constructed a flavodoxin mutant and evaluated its characteristic properties. The major results showed that: (1) The flavodoxin mutant has structural characteristics similar to wild-type protein; (2) Although the semiquinone and hydroquinone flavodoxin mutants possess lower stability than the corresponding form of wild-type flavodoxin, the redox potential of double electron reduction Em,7 (fld) reached -360 mV, indicating that the flavodoxin mutant constituted solely by early amino acids can exert effective electron transfer activity.

Epidemiological analyses in a large Chinese population have revealed that consumption of vegetables and legumes is inversely associated with the risk of type 2 diabetes (T2D). However, the health benefits of these plants have not been fully explained, which stimulated our interest to identify antidiabetic components from vegetables and legumes through searching medicinal databases, especially those containing traditional Chinese medicines. The results not only provide meaningful clues to understanding the antidiabetic potentials of these plants but also display the possibility of pinpointing food component functions by searching medicinal databases.

Accumulating evidence indicates that consumption of tea, especially green tea, is good for preventing cancer. To elucidate the cancer preventive mechanisms of green tea, much effort has been devoted to investigating the anticancer effects of (-)-epigallocatechin-3-gallate (EGCG), the major component of green tea. It has been revealed that EGCG restrained carcinogenesis in a variety of tissues through inhibition of mitogen-activated protein kinases (MAPK), growth factor-related cell signaling, activation of activator protein 1 (AP-1) and nuclear factor-B (NF-kappaB), topoisomerase I, matrix metalloproteinases and other potential targets. Therefore, EGCG is a multipotent anticancer agent, which not only provides solid evidence to support the anticancer potential of green tea, but also offers new clues for discovering multiple-targeted anticancer drugs.

By examination of the 8659 drugs recorded in the Comprehensive Medicinal Chemistry (CMC) database, 78 catecholics (including five pyrogallolics) were identified, of which 17 are currently prescribed by FDA. Through analyzing the substituent patterns, ClogPs and O-H bond dissociation enthalpies (BDEs) of the catecholic drugs, some molecular features that may benefit circumventing the toxicity of catecholics were revealed: i) strong electron-donating substituents are excluded; ii) ClogP 3; iii) an energy penalty exists for quinone formation. Besides, the present analyses also suggest that the clinical usage and dosage of currently prescribed catecholic drugs are of importance in designing or screening catecholic antioxidants.