Search

Chromatin organization, which is under the control of histone deacetylases (HDACs), is frequently deregulated in cancer cells. Amongst HDACs, HDAC8 plays an oncogenic role in different neoplasias by acting on both histone and non-histone substrates. Promising anti-cancer strategies have exploited dual-targeting drugs that inhibit both HDAC8 and tubulin. These drugs have shown the potential to enhance the outcome of anti-cancer treatments by simultaneously targeting multiple pathways critical to disease onset and progression. In this study, a structure-based virtual screening (SBVS) of 96403 natural compounds was performed towards the four Class I HDAC isoforms and tubulin. Using molecular docking and molecular dynamics simulations (MDs), we identified two molecules that could selectively interact with HDAC8 and tubulin. CNP0112925 (arundinin), bearing a polyphenolic structure, was confirmed to inhibit HDAC8 activity and tubulin organization, affecting breast cancer cell viability and triggering mitochondrial superoxide production and apoptosis. [ABSTRACT FROM AUTHOR]

The escalating severity of Bombyx mori nuclear polyhedrosis virus (BmNPV) infections poses significant challenges to the silkworm industry, especially when massive production shifts occur from the eastern regions to western regions with lower labor costs. Education and experience levels are different and disease control is badly needed. To solve the problems, we have developed an innovative CRISPR/Cas9 system specifically targeting BmNPV to enhance viral resistance. For the system, we selected BmNPV genes linked to virus replication and proliferation as targets, designing 2 sites for each gene. Mutating the target sequence renders the system incapable of efficiently cleaving the virus genome, hence decreasing cleavage efficiency. We conducted a search for “NGG” or “CCN” target sequences in the BmNPV genome, excluding non‐recurring and potential targets in the B. mori genome. We successfully identified 2 distinct target sequences in the BmNPV genome—one being repeated 12 times and the other three times. These sequences lead to fragmentation of virus genome into multiple large segments that are difficult to repair. Transgenic silkworms demonstrate robust resistance to viruses, significantly boosting their survival rates compared with wild‐type silkworms under various virus infection concentrations. Our system efficiently targets dozens of viral genomes with just 2 sequences, minimizing transposable elements while ensuring cutting effectiveness. This marks a pioneering advancement by using repetitive elements within the virus genome for targeted CRISPR cleavage, aiming for antiviral effects through genome fragmentation rather than disrupting essential viral genes. Our research introduces innovative concepts to CRISPR antiviral investigations and shows promise for the practical application of gene editing in industrial silkworm strains. [ABSTRACT FROM AUTHOR]

Background and Objective: Emerging as a hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been developed using X-ray-excitable nanoparticles. In contrast to conventional bio-optical imaging techniques like bioluminescence tomography (BLT) and fluorescence molecular tomography (FMT), CB-XLCT offers the advantage of greater imaging depth while significantly reducing interference from autofluorescence and background fluorescence, owing to its utilization of X-ray-excited nanoparticles. However, due to the intricate excitation process and extensive light scattering within biological tissues, the inverse problem of CB-XLCT is fundamentally ill-conditioned. Methods: An end-to-end three-dimensional deep encoder-decoder network, termed DeepCB-XLCT, is introduced to improve the quality of CB-XLCT reconstructions. This network directly establishes a nonlinear mapping between the distribution of internal X-ray-excitable nanoparticles and the corresponding boundary fluorescent signals. To improve the fidelity of target shape restoration, the structural similarity loss (SSIM) was incorporated into the objective function of the DeepCB-XLCT network. Additionally, a loss term specifically for target regions was introduced to improve the network's emphasis on the areas of interest. As a result, the inaccuracies in reconstruction caused by the simplified linear model used in conventional methods can be effectively minimized by the proposed DeepCB-XLCT method. Results and Conclusions: Numerical simulations, phantom experiments, and in vivo experiments with two targets were performed, revealing that the DeepCB-XLCT network enhances reconstruction accuracy regarding contrast-to-noise ratio and shape similarity when compared to traditional methods. In addition, the findings from the XLCT tomographic images involving three targets demonstrate its potential for multi-target CB-XLCT imaging. [ABSTRACT FROM AUTHOR]

Chromatin organization, which is under the control of histone deacetylases (HDACs), is frequently deregulated in cancer cells. Amongst HDACs, HDAC8 plays an oncogenic role in different neoplasias by acting on both histone and non-histone substrates. Promising anti-cancer strategies have exploited dual-targeting drugs that inhibit both HDAC8 and tubulin. These drugs have shown the potential to enhance the outcome of anti-cancer treatments by simultaneously targeting multiple pathways critical to disease onset and progression. In this study, a structure-based virtual screening (SBVS) of 96403 natural compounds was performed towards the four Class I HDAC isoforms and tubulin. Using molecular docking and molecular dynamics simulations (MDs), we identified two molecules that could selectively interact with HDAC8 and tubulin. CNP0112925 (arundinin), bearing a polyphenolic structure, was confirmed to inhibit HDAC8 activity and tubulin organization, affecting breast cancer cell viability and triggering mitochondrial superoxide production and apoptosis.

Influenza virus continually challenges both human and animal health. Moreover, influenza viruses are easy to mutate. In a certain degree, vaccines may not catch up with rapid mutant paces of viruses. Anti‐influenza drugs NIs (neuraminidase inhibitors) are one of the best choices. Therefore, based on ADMET properties, eight optimal natural multi‐targets NIs glycosides compounds (IC50 = 0.094–97.275 μM) are found from radix glycyrrhizae, flos sophorae, caulis spatholobi, radix astragali, radix glycyrrhizae, semen astragali complanati, and common fenugreek seed through network pharmacology, molecular docking, dynamics simulation, quantum chemistry, and in vitro experiment. Moreover, mechanism research illustrates these natural compounds treat influenza A virus through key targets TLR4, TNF, and IL6 (high fever, acute respiratory distress syndrome), MAPK1, and MAPK3 (MAPK signaling pathway, viral RNP export, and viral protein expression), IL1B (NOD‐like receptor signaling pathway, suppressed maturation of pro‐IL‐1β and pro‐IL‐18), CASP3 (apoptosis), AKT1 (inhibited premature apoptosis), and EP300 (viral myocarditis, chemoattraction of monocytes and macrophages, T‐cell activation antibody response). [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complicated disease for which there are still no ideal one-target drugs, while multi-target drugs are closer to ideal drugs and will provide new solutions for the clinical treatment of AD. DL0410 is a promising multi-target drug candidate for AD treatment that is not only a significant inhibitor against both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) but also an antagonist of histamine H3 receptor (H3R), and its therapeutic efficacy in treating cognitive dysfunction has been validated in a series of AD-related animal models, including scopolamine-induced mice, D-galactose-induced and Aβ-induced mice, and APP/PS1 and SAMP8 mice. Although the structure of DL0410 has been analyzed using various detection techniques, such as MS and NMR, its three-dimensional crystal structure still requires further confirmation. In this study, the crystal of DL0410 was grown in aqueous solution, and its structure was detected using the X-ray diffraction method. The crystal data, atomic coordinates, bond lengths, angles, and hydrogen bonds of DL0410 were obtained. Its stability was proven by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Based on this study, the molecular docking of DL0410 with AChE, BuChE, and H3R was performed to uncover their interaction mechanisms and explain their bioactivities. This study provides important information for new multi-target drug design and the research and development of new drugs for AD treatment. [ABSTRACT FROM AUTHOR]

As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging technique. However, in the actual ISAR imaging process, ship targets in a formation often lead to complicated motion conditions. Due to the close distance between the ship targets, the rough imaging results of the targets cannot be completely separated in the image domain, and the small differences in motion parameters lead to overlapping phenomena in the Doppler history. Therefore, for situations in which ship formation targets with little difference in motion parameters are included in the same radar beam, this paper proposes a multi-domain joint ISAR separation imaging method for multi-ship targets. First, the method performs echo separation using the Hough transform (HT) with the minimum entropy autofocus method in the image domain. Secondly, the time–frequency curve is extracted in the time–frequency domain using the short-time Fourier transform (STFT) for time–frequency analysis, which solves the problem of the ship formation targets being aliased on both echo and Doppler history after range compression and achieves the purpose of separating the echo signals of the sub-ship targets with high accuracy. Eventually, better-focused images of each target are obtained via further motion compensation and precise imaging. Finally, the effectiveness of the proposed method is verified using a simulation and measured data. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common chronic neurodegenerative disease worldwide. It causes cognitive dysfunction, such as aphasia and agnosia, and mental symptoms, such as behavioral abnormalities; all of which place a significant psychological and economic burden on the patients' families. No specific drugs are currently available for the treatment of AD, and the current drugs for AD only delay disease onset and progression. The pathophysiological basis of AD involves abnormal deposition of beta-amyloid protein (Ap), abnormal tau protein phosphorylation, decreased activity of acetylcholine content, glutamate toxicity, autophagy, inflammatory reactions, mitochondria-targeting, and multi-targets. The US Food and Drug Administration (FDA) has approved five drugs for clinical use: tacrine, donepezil, carbalatine, galantamine, memantine, and lecanemab. We have focused on the newer drugs that have undergone clinical trials, most of which have not been successful as a result of excessive clinical side effects or poor efficacy. Although aducanumab received rapid approval from the FDA on 7 June 2021, its long-term safety and tolerability require further monitoring and confirmation. In this literature review, we aimed to explore the possible pathophysiological mechanisms underlying the occurrence and development of AD. We focused on anti-Ap and anti-tau drugs, mitochondria-targeting and multi-targets, commercially available drugs, bottlenecks encountered in drug development, and the possible targets and therapeutic strategies for future drug development. We hope to present new concepts and methods for future drug therapies for AD. [ABSTRACT FROM AUTHOR]

Mixed infections caused by drug-resistant bacteria and fungi pose a severe threat to human health, and multi-target drugs may provide an effective approach to combat drug-resistant pathogens. Therefore, this study aimed to investigate the efficacies of some oleanolic acid (OA) derivatives against multidrug-resistant (MDR) bacteria and fungi using in vitro and in vivo experiments. Novel amphiphilic OA derivatives were designed and optimised, in which compounds G1 and J1 exhibited effective antimicrobial activity (MICs = 1–2 μg/mL), high selectivity against MDR strains, rapid bactericidal activity, and good predictive pharmacokinetics. Mechanistically, both compounds prevented drug resistance by disrupting the bacterial cell membrane, inserting into the DNA, and binding to DNA gyrase. Additionally, J1 reduced microbial count in a mouse MRSA skin infection model and accelerated wound healing much better than vancomycin. Conclusively, this study presents a new class of potential drugs for resistant bacteria and fungi. [Display omitted] • OA derivatives against MRSA, VRE and C. albicans simultaneously. • Killed bacteria quickly and low drug resistance. • G1 and J1 exert dual targets to avoid drug resistance. • G1 and J1 damage cell membrane and bind to DNA. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a complicated disease for which there are still no ideal one-target drugs, while multi-target drugs are closer to ideal drugs and will provide new solutions for the clinical treatment of AD. DL0410 is a promising multi-target drug candidate for AD treatment that is not only a significant inhibitor against both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) but also an antagonist of histamine H3 receptor (H3R), and its therapeutic efficacy in treating cognitive dysfunction has been validated in a series of AD-related animal models, including scopolamine-induced mice, D-galactose-induced and Aβ-induced mice, and APP/PS1 and SAMP8 mice. Although the structure of DL0410 has been analyzed using various detection techniques, such as MS and NMR, its three-dimensional crystal structure still requires further confirmation. In this study, the crystal of DL0410 was grown in aqueous solution, and its structure was detected using the X-ray diffraction method. The crystal data, atomic coordinates, bond lengths, angles, and hydrogen bonds of DL0410 were obtained. Its stability was proven by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Based on this study, the molecular docking of DL0410 with AChE, BuChE, and H3R was performed to uncover their interaction mechanisms and explain their bioactivities. This study provides important information for new multi-target drug design and the research and development of new drugs for AD treatment.

Consistently emerging variants and the life-threatening consequences of SARS-CoV-2 have prompted worldwide concern about human health, necessitating rapid and accurate point-of-care diagnostics to limit the spread of COVID-19. Still, However, the availability of such diagnostics for COVID-19 remains a major rate-limiting factor in containing the outbreaks. Apart from the conventional reverse transcription polymerase chain reaction, loop-mediated isothermal amplification-based (LAMP) assays have emerged as rapid and efficient systems to detect COVID-19. The present study aims to develop RT-LAMP-based assay system for detecting multiple targets in N, ORF1ab, E, and S genes of the SARS-CoV-2 genome, where the end-products were quantified using spectrophotometry, paper-based lateral-flow devices, and electrochemical sensors. The spectrophotometric method shows a LOD of 10 agµL−1 for N, ORF1ab, E genes and 100 agµL−1 for S gene in SARS-CoV-2. The developed lateral-flow devices showed an LOD of 10 agµL−1 for all four gene targets in SARS-CoV-2. An electrochemical sensor developed for N-gene showed an LOD and E-strip sensitivity of log 1.79 ± 0.427 pgµL−1 and log 0.067 µA/pg µL−1/mm2, respectively. The developed assay systems were validated with the clinical samples from COVID-19 outbreaks in 2020 and 2021. This multigene target approach can effectively detect emerging COVID-19 variants using combination of various analytical techniques at testing facilities and in point-of-care settings. [ABSTRACT FROM AUTHOR]

The important factor for the supply chain management of fresh products is partner selection. Environment protection is also an important factor, however the factor is not taken into account by the traditional supplier selection. Therefore, the supplier selection standard includes the green standard in this paper. Our work is to investigate an optimal mathematical modeling for green partner selection. The four targets of the proposed model are cost, product quality, green appraisal score and time. The proposed genetic algorithm with multi-targets are to search the set of optimum solutions using by weighted sum method. The proposed model introduces a supply chain network structure to analysis average number Pareto-optimal solutions with genetic algorithm for the four problems. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and is associated with high morbidity and mortality. Pogostemon cablin (Blanco) Benth/Huo Xiang (HX) is a perennial herb with unique anti-oxidant and anti-inflammatory properties, and thus, can positively affect liver function. In this study, we used network pharmacology to predict the potential mechanism of HX on NAFLD. Pharmacological experiments were used to verify the effect of HX on the functions of NAFLD. Network pharmacology identified nine components that interacted with 82 NAFLD-related targets, revealing four target genes: TNF, IL6, TP53, and AKT1. HX prevents the development and progression of NAFLD through different pathways and targets with quercetin-regulated lipid metabolism, anti-inflammatory, and anti-oxidant pathways playing an essential role in the treatment of NAFLD. Compared with feeding HFD, HX significantly attenuated lipid accumulation in vivo with mice and also in vitro with mouse liver cells. A high dose of HX decreased hepatocyte lipid accumulation and the abundance of SREBF1 and FASN. Validation experiments revealed that HX inhibited the activation of NF-κB/IκB signaling and decreased the release and levels of pro-inflammatory factors (TNF-α and IL-6). These data suggest that HX can attenuate abnormal lipid metabolic responses and enhance antioxidant mechanisms. Thus, the pharmacological effects from plants used in traditional Chinese medicine are achievde through a multi-level response.

Existing RFID localization techniques mainly focus on the location of a single target, which do not fully utilize the multi-targets identification merit of UHF RFID systems. In this paper, a multi-targets localization algorithm called merged re-shaken particle swarm optimization (MRPSO) is proposed. The ranging information and the angle information, which are obtained by using multi-frequency phase information with the help of frequency hopping technique, are merged together during the iteration process of MRPSO. In addition, based on the traditional PSO algorithm, a low weight particle filtering method was proposed, and a re-shaken scheme was introduced to improve localization accuracy. By allocating particles to each target, MRPSO can obtain each target location simultaneously after PSO process, which avoids time consuming. The experimental results show that MRPSO can achieve single-station multi-target localization with a median accuracy of 9.64 cm. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction Acne vulgaris is a common skin problem caused by inflammation of the sebaceous glands and hair follicles as a result of hormonal fluctuations, bacteria, and overproduction of oil. The plant Peltophorum pterocarpum ( P. pterocarpum ) has been investigated for possible medical uses. Its anti-inflammatory, antibacterial, and antioxidant qualities are well recognised, and they may be applied to treat several diseases. This study investigates the plant's phytochemicals for their effectiveness in treating acne. Methods The Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database was utilised to extract the phytochemicals from P. pterocarpum . The absorption, distribution, metabolism, and excretion (ADME) analysis was conducted using the online tool SwissADME. The SwissTargetPrediction tool determines the potential targets of these phytochemicals. Targets for acne have been identified using the Open Targets Platform database. The common targets of P. pterocarpum and acne were identified using the Venn diagram drawing tool Venny 2.1.0, and a protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Following that, hub genes were identified by Cytoscape 3.10.2. The web tool ShinyGO 0.80 has enabled simpler evaluation of enrichment analysis for these hub genes. Results Five genes were shown to be key targets because they are directly engaged in the relaxin signalling pathway by pathway analysis: epidermal growth factor receptor (EGFR) and various matrix metalloproteinases (MMPs) (MMP1, MMP2, MMP9, and MMP13). The phytochemicals found in P. pterocarpum , including quercetin, rhamnetin, hirsutidin, and (+)-leucocyanidin, target these key genes. Conclusion This study highlights the potential of P. pterocarpum as a multi-target therapeutic agent for acne vulgaris. By targeting key genes in the relaxin signalling pathway, the phytochemicals from P. pterocarpum present a promising approach for acne management., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Biju et al.)

Considerable pharmacological studies have demonstrated that the extracts and ingredients from different parts (seeds, peels, pulps, and flowers) of Litchi exhibited anticancer effects by affecting the proliferation, apoptosis, autophagy, metastasis, chemotherapy and radiotherapy sensitivity, stemness, metabolism, angiogenesis, and immunity via multiple targeting. However, there is no systematical analysis on the interaction network of “multiple ingredients-multiple targets-multiple pathways” anticancer effects of Litchi. In this study, we summarized the confirmed anticancer ingredients and molecular targets of Litchi based on published articles and applied network pharmacology approach to explore the complex mechanisms underlying these effects from a perspective of system biology. The top ingredients, top targets, and top pathways of each anticancer function were identified using network pharmacology approach. Further intersecting analyses showed that Epigallocatechin gallate (EGCG), Gallic acid, Kaempferol, Luteolin, and Betulinic acid were the top ingredients which might be the key ingredients exerting anticancer function of Litchi, while BAX, BCL2, CASP3, and AKT1 were the top targets which might be the main targets underling the anticancer mechanisms of these top ingredients. These results provided references for further understanding and exploration of Litchi as therapeutics in cancer as well as the application of “Component Formula” based on Litchi’s effective ingredients.

Considerable pharmacological studies have demonstrated that the extracts and ingredients from different parts (seeds, peels, pulps, and flowers) of Litchi exhibited anticancer effects by affecting the proliferation, apoptosis, autophagy, metastasis, chemotherapy and radiotherapy sensitivity, stemness, metabolism, angiogenesis, and immunity via multiple targeting. However, there is no systematical analysis on the interaction network of "multiple ingredients-multiple targets-multiple pathways" anticancer effects of Litchi. In this study, we summarized the confirmed anticancer ingredients and molecular targets of Litchi based on published articles and applied network pharmacology approach to explore the complex mechanisms underlying these effects from a perspective of system biology. The top ingredients, top targets, and top pathways of each anticancer function were identified using network pharmacology approach. Further intersecting analyses showed that Epigallocatechin gallate (EGCG), Gallic acid, Kaempferol, Luteolin, and Betulinic acid were the top ingredients which might be the key ingredients exerting anticancer function of Litchi, while BAX, BCL2, CASP3, and AKT1 were the top targets which might be the main targets underling the anticancer mechanisms of these top ingredients. These results provided references for further understanding and exploration of Litchi as therapeutics in cancer as well as the application of "Component Formula" based on Litchi's effective ingredients. [ABSTRACT FROM AUTHOR]

In practical device-free localization (DFL) applications, for enlarging the monitoring area and improving localization accuracy, too many nodes need to be deployed, which results in a large volume of DFL data with high dimensions. This arises a key problem of seeking an accurate and efficient approach for DFL. In order to address this problem, this paper regards DFL as a problem of sparse-representation-based classification; builds a sparse model; and then proposes two sparse-coding-based algorithms. The first algorithm, sparse coding via the iterative shrinkage-thresholding algorithm (SC-ISTA), is efficient for handling high-dimensional data. And then, subspace techniques are further utilized, followed by performing sparse coding in the low-dimensional signal subspace, which leads to the second algorithm termed subspace-based SC-ISTA (SSC-ISTA). Experiments with the real-world data set are conducted for single-target and multi-target localization, and three typical machine learning algorithms, deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, are compared. Experimental results show that both SC-ISTA and SSC-ISTA can achieve high localization accuracies of 100% and are robust to noisy data when SNR is greater than 10 dB, and the time costs for sparse coding of SC-ISTA and SSC-ISTA are 2.1 × 10-3 s and 2.1 × 10-4 s respectively, which indicates that the proposed algorithms outperform the other three ones.

In order to improve a machine moving beam’s comprehensive performances,which includes static and dynamic characteristics,it must have a reasonable beam ribs layout and structural properties. The crossbeam was simplified to get the mechanical vibration model,the maximum coupling deformation was solved by the theoretical calculation method. Using 3D software to establish the three-dimensional modeling of beam and ANSYS was used for static,modal analysis. Taking beam’s quality and the maximum coupling deformation,the maximum coupling stress,the first-order frequency as objective functions to judge the merits and drawbacks of crossbeam’s optimization,taking the crossbeam’s rib structure,the rib thickness,the angle of support stiffened plate as design variables for multi-objective optimization design. Orthogonal experimental method was used to design the orthogonal experiment with three levels and four factors. Using overall balance method to chose the optimal level,and then getting the best test program. The results show that: Theoretical analysis and simulation results are very similar. the beam with "井"type rib structure,rib thickness of 25 mm,the support stiffened plate tilted 55 ° design is the optimal solution.Compared with the original design,the beam deformation reduced by 7. 455% and the first-order frequency increased by 2. 956%,while the quality reduced by 473 kg. It shows that using the orthogonal experimental method to design reasonable levels and factors can achieve optimum results with fewer tests,and the orthogonal experimental method is valuable for engineering application.

Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.

With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city. [ABSTRACT FROM AUTHOR]

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.

Abstract Current target-oriented paradigm for novel antidepressant discovery has been difficult to succeed and the failures always bring huge economic losses. Although abundant ledge of disease related genes and drug action targets has been accumulated, the successful application of the knowledge for new drug discovery is limited. Here, we predicted and validated potential antidepressants and molecular targets from DrugBank recorded drugs using a novel network-based drug repositioning approach. This approach predicted relationships between drug and targets through network-based integration of drug chemical similarity, therapeutic similarity and protein–protein interactions. We predicted genome-wide relations of drugs and targets, and then screened drugs that connect to depression-related targets of known antidepressants. Six drugs were predicted and experimentally validated to have antidepressant-like effects in the tail suspension test (TST) and forced swimming test (FST) in mice. Alverine, which is a gastrointestinal antispasmodic drug, was further validated to display antidepressant-like effects in the learned helplessness and chronic unpredictable stress models of depression. Four targets, including serotonin transporter, norepinephrine transporter, serotonin 1A receptor and serotonin 2A receptor, were included in the predictable system and confirmed as primary sites of action for alverine. The results suggest that alverine may be an effective antidepressant drug and the network-based drug repositioning may be a promising drug discovery paradigm for complex multi-genetic diseases such as depression. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

As one of the major active components of Qizhiweitong (QZWT) prescription, corydalis alkaloid (CA) has been reported to have noticeable effect of anti-inflammation in pharmacological studies. However, the anti-inflammatory function and potential mechanism of CA in anti-inflammation remain to be clarified. Hence, the aim of this study was to investigate the anti-inflammation efficacy and potential mechanism of CA in anti-inflammation by metabonomic approach coupled with related gene analyzes in vivo and in vitro. Mice acute inflammation was induced by subcutaneous injection of formalin in hind paws, and evaluated the anti-inflammatory effect of CA via detecting the index of paw edema. The results indicated that CA has an anti-inflammatory effect on mice through alleviating the paw edema significantly. Moreover, metabolic profiling was performed by high performance liquid chromatography quadrupole-time-of-flight mass spectrometer (HPLC-QTOF-MS) combined with multivariate data analysis, such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Thirteen metabolic targets (including lysophosphatidylcholine (LysoPC), choline, arachidonic acid, phosphodimethylethanolamine) and related pathways of the cholinergic anti-inflammatory and arachidonic acid (AA) metabolism were identified, Meanwhile, the relevant target genes, like iNOS, NF-κB, TNF-α, were detected and verified in vitro. The results indicated that CA has a good pharmacological effect of anti-inflammation through regulating multiple disordered targets and metabolic networks, which provides a theoretical basis for further research on CA. Furthermore, there is a great potential for the development of CA to be a new anti-inflammatory drug with high effective activities, little side effects and weak drug resistance. [ABSTRACT FROM AUTHOR]

Recent years have seen an unpretending increase in research using dietary phytochemicals for targeting cancer and cancer stem cells (CSCs) due to the limited efficacy of conventional chemotherapy and radiotherapy and numerous associated side effects. A large number of dietary phytochemicals using traditional recommendation and experimental approaches have been demonstrated to have anti-proliferative, anti-metastatic, reactive oxygen species (ROS) inducing, anti-angiogenic, pro-apoptotic effects and efficacy in targeting cellular molecules and pathways implicated in malignancy. Researchers have shown the knack of phytochemicals in interfering with the CSCs self-renewal process. Thus, dietary phytochemicals can play a significant role in the cancer therapy owing to the plethora of targets without toxicity. In this review, we have discussed about the basic knowledge of CSCs, their identification, characterization, mechanism of self-renewal pathways (Wnt/β-catenin, Hedgehog, and Notch), features that help in the survival of CSCs and use of phytochemicals to replace chemotherapy. Applications of phytochemicals including curcumin, epigallocatechin-3-gallate (EGCG), resveratrol, lycopene, and sulforaphane for their effect on targeting cancer and in particular CSCs along with their molecular mechanisms responsible for pharmacological action are also discussed. [ABSTRACT FROM PUBLISHER]

A key focus in the field of drug discovery has been motivated by the neuroprotection of natural compounds. Cerebral ischemia is a multifaceted pathological process with a series of mechanisms, and a perspective for the development of neuroprotectants from traditional herbal medicine or natural products is a promising treatment for this disease. Natural compounds with the effects of anti-oxidation, anti-inflammation, anti-apoptosis, and neurofunctional regulation exhibit therapeutic effects on experimental ischemic brain injury. Conferring to the pharmacological mechanisms underlying neuroprotection, a study found that androgapholide, a diterpene lactone compound, exhibits varying degrees of neuroprotective activities in both in vitro and in vivo experimental models of stroke. The neuroprotective mechanisms of andrographolide are suggested as: (I) increasing nuclear factor E2-related factor 2-heme oxygenase (Nrf2-HO-1) expression through p38-mitogen activated protein kinase (MAPK) regulation, (II) inducing cerebral endothelial cells (CEC) apoptosis and caspase-3 activation, (III) down regulating Bax, inducible nitric oxide synthase (iNOS), and (IV) inhibiting hydroxyl radical (OH-) formation, and activating transcription factor NF-κB signaling pathways. Recently, several researchers have also been trying to unveil the principal mechanisms involved in the neuroprotective effects of andrographolide. Therefore, this review aims to summarize an overview on the neuroprotective effects of andrographolide and exemplifies the essential mechanisms involved. This paper can provide information that andrographolide drug discovery may be a promising strategy for the development of a novel class of neuroprotective drug [ABSTRACT FROM AUTHOR]

Purpose To prove that frequency modulated continuous wave (FMCW) radar can be applied for continuous and timely monitoring of vital signs for multi-human targets. Methods The chest-wall periodic vibration information resulting from respiration and heartbeat is modulated in the reflected electromagnetic wave of radar from the surface of human bodies or tissue boundaries. The range and vibration information of multi-targets is acquired by a Fourier transform and demodulation processing. To further extract the respiration and heartbeat signals from the mixed vibration phase signal when the respiration signal has several significant harmonics and variant frequencies, a novel recovery and separation method based on a double parameter least mean square (LMS) filter is developed. Results The FMCW wide-band radar is capable of multiple vital signal monitoring and subject localization under a low signal to noise ratio (SNR). Compared with the bandpass filtering (BPF) method and wavelet transform (WT) method, the proposed method based on a double parameter LMS filter can obtain more accurate respiration and heartbeat signals. Conclusions The FMCW wide-band radar is a reliable, robust, and harmless tool for continuous and timely monitoring of cardiac and respiratory rates for multi-human targets, which has a potential to be applied in wards or home healthcare. [ABSTRACT FROM AUTHOR]

Bioactive natural products (BNPs) are the marrow of medicinal plants, which are the secondary metabolites of organisms and have been the most famous drug discovery database. Bioactive natural products are famous for their enormous number and great safety in medical applications. However, BNPs are troubled by their poor druggability compared with synthesis drugs and are challenged as medicine (only a few BNPs are applied in clinical settings). In order to find a reasonable solution to improving the druggability of BNPs, this review summarizes their bioactive nature based on the enormous pharmacological research and tries to explain the reasons for the poor druggability of BNPs. And then focused on the boosting research on BNPs loaded drug delivery systems, this review further concludes the advantages of drug delivery systems on the druggability improvement of BNPs from the perspective of their bioactive nature, discusses why BNPs need drug delivery systems, and predicts the next direction. • The universal bioactivity characteristics of BNPs are firstly summarized. • The druggability enhancement pathways of BNPs by DDSs is discussed. • The current integration between BNPs and DDSs are comprehensively discussed. [ABSTRACT FROM AUTHOR]

Transient receptor potential vanilloid type 1 (TRPV1), a heat-sensitive calcium channel protein, contributes to inflammation as well as to acute and persistent pain. Since TRPV1 occupies a central position in pathways of neuronal inflammatory signaling, it represents a highly attractive potential therapeutic target for neuroinflammation. In the present work, we have in silico identified a series of diarylurea analogues for hTRPV1, of which 11 compounds showed activity in the nanomolar to micromolar range as validated by in vitro biological assays. Then, we utilized molecular docking to explore the detailed interactions between TRPV1 and the compounds to understand the contributions of the different substituent groups. Tyr511, Leu518, Leu547, Thr550, Asn551, Arg557, and Leu670 were important for the recognition of the small molecules by TRPV1. A hydrophobic group in R2 or a polar/hydrophilic group in R1 contributed significantly to the activities of the antagonists at TRPV1. In addition, the subtle different binding pose of meta-chloro in place of para-fluoro in the R2 group converted antagonism into partial agonism, as was predicted by our short-term molecular dynamics (MD) simulation and validated by bioassay. Importantly, compound 15, one of our best TRPV1 inhibitors, also showed potential binding affinity (1.39 μM) at cannabinoid receptor 2 (CB2), which is another attractive target for immuno-inflammation diseases. Furthermore, compound 1 and its diarylurea analogues were predicted to target the C-X-C chemokine receptor 2 (CXCR2), although bioassay validation of CXCR2 with these compounds still needs to be performed. This prediction from the modeling is of interest, since CXCR2 is also a potential therapeutic target for chronic inflammatory diseases. Our findings provide novel strategies to develop a small molecule inhibitor to simultaneously target two or more inflammation-related proteins for the treatment of a wide range of inflammatory disorders including neuroinflammation and neurodegenerative diseases with potential synergistic effect. [ABSTRACT FROM AUTHOR]

This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs' test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé's method and the Chow test were applied to determine abrupt changes. Finally, Sen's slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic that has devastated the lives of millions. Researchers around the world are relentlessly working in hopes of finding a cure. Even though the virus shares similarities with reported SARS-CoV and MERS-CoV at the genomic and proteomic level, efforts to repurpose already known drugs against SARS-CoV-2 have resulted ineffective. In this succinct review, we discuss the different potential targets in SARS-CoV-2 at both the genomic and proteomic levels. In addition, we analyze the compounds inhibiting individual target protein as well as multiple targets of SARS-CoV-2. ACE-2 receptor in humans has also been considered a target, keeping the role of the receptor in mind. The mechanism of action of these compounds has also been highlighted along with their clinical manifestation. Towards the end of the review, a brief note on the drugs currently in clinical trials and the current status of the vaccines are also examined. In conclusion, compounds targeting multiple targets of the virus hold the key in putting an end to the coronavirus malady.Communicated by Ramaswamy H. Sarma.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Hyperuricemia is closely related to the occurrence of gout, hypertension, diabetes, hyperlipidemia, cardiovascular disease, kidney disease, metabolic syndrome, etc. However, xanthine oxidase inhibitors (XOIs) can fundamentally solve the problem of excessive uric acid. Compared to single-target drugs, multi-target drugs are not prone to adverse reactions and exert a synergistic effect. Therefore, the discovery of new multi-target XOIs and their mechanism of therapeutic hyperuricemia are important to overcome adverse effects and resistance to currently available drugs., Objective: The purpose of this paper is to obtain novel diazine derivatives as promising multi-target XOIs and discover the interaction mechanism for the better treatment of hyperuricemia., Methods: Novel multi-target XOIs diazine derivatives, and their interaction mechanism have been obtained through QSAR, molecular docking, dynamics simulation, and network pharmacology. In addition, ADMET properties and synthetic accessibility of novel XOIs have been considered using ADMETLAB 2.0 and SwissADME., Results: 24 novel diazine derivatives as potential multi-target XOIs lead compounds have been found through virtual screening of the PubChem database. Moreover, the most notable top five compounds are worthy of further developing as multi-target XOIs drugs. XDH, TBK1, DGAT1, MYC, CDKN1A, PPARD, PDE6C, and EIF4E are recommended as relevant targets of therapeutic hyperuricemia., Conclusion: Through the combination of different methods, we have discovered five novel promising diazine derivatives as potential multi-target XOIs drugs. Meanwhile, eight targets have been found to be helpful in the research on therapeutic hyperuricemia. We expect this investigation will offer clear insights into the production of efficient XOIs drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Some fibroblast growth factors (FGFs) play a critical role in tumorigenesis and progression. Among them, FGF2 was highly expressed in some tumors, and antagonists binding to FGF2 can suppress the growth of tumor cells. Therefore, FGF2 has been considered as an important target in cancer therapy. In this study, we identified a novel FGF2-binding short peptide (P8, PLLQATAGGGS-NH) using phage display technology and alanine scanning. The P8 peptide suppressed FGF2-induced proliferation with no cytotoxic effect on cells, arrested the cycle at the G0/G1 phase in B16-F10 cells, and downregulated the activation of fibroblast growth factor receptor substrate 2α (FRS2α)/ERK cascade in B16-F10, NIH-H460, and SGC-7901 cells. Besides, P8 peptide can also inhibit the phosphorylation of FRS2α stimulated by FGF1 and KGF2. These implied that P8 peptide may develop as a multi-target antagonist peptide contributing to tumor treatment. [ABSTRACT FROM AUTHOR]

Abstract: Lithium ion conducting thin film electrolytes, Li2O-B2O3-P2O5 glass systems, were prepared with a wide range of chemical composition by co-sputtering method with multi-targets. The maximum ionic conductivity at room temperature was 1.22×10−6 S/cm and the activation energy of this specimen was 0.54eV when enhanced by the mixed former effect and the high network modifier content. The increased ion conductivity of the films appeared to be associated with the formation of stable tetrahedral borate at high lithium concentration and this structural change facilitated faster ion migrations. The relationship between the glass structure and electrical performance of the mixed former glasses was investigated by the infrared spectra analysis of borate networks. The conductivity of the mixed former electrolytes increased gradually with increasing the RF power on Li2O target, and the maximum conductivity was obtained at 100W. The structural role of the network modifier in xLi2O-(1-x)(B2O3-P2O5) glasses was analyzed by the O1s spectra of X-ray Photoelectron Spectroscopy. A quantitative deconvolution of O1s spectra suggests that the increased conductivity with increasing lithium content is due to the formation of non-bridging oxygen. [Copyright &y& Elsevier]

Through introducing the probability of success, the paper proposed the single-target merger timing under double uncertainty (both the size and the realization of the synergy are uncertain). Furthermore, the paper extended the model to multi-targets merging situation, figuring out the merger tinting under both independent merger strategy and sequential merger strategy. The paper also pointed out the optimal merger strategy of multi-targets merging through comparing and contrasting the threshold and acquirer's firm value of two merging strategies. Because of the extra information value of sequential merger, the threshold of sequential merger strategy is smaller than that of independent merger strategy, and there exists a sequential merger interval where sequential merger strategy is superior to independent merger strategy. Finally, through numerical analyzing the paper investigated the impact of correlation coefficient, success probability, and the acquirer's bargaining power on the threshold and the sequential merger interval. [ABSTRACT FROM AUTHOR]