Structural Similarity Research Articles

Thematic Content, Not Number Matching, Drives Syntactic Bootstrapping

ABSTRACT Children use correlations between the syntax of a clause and the meaning of its predicate to draw inferences about word meanings. On one proposal, these inferences are underwritten by a structural similarity between syntactic and semantic representations: learners expect that the number of clause arguments exactly matches the number of participant roles in the event concept under which its referent is viewed. We argue against this proposal, and in favor of a theory rooted in syntactic and semantic contents – in mappings from syntactic positions to thematic relations. We (i) provide evidence that infants view certain scenes under a concept with three participant relations (a girl taking a truck from a boy), and (ii) show that toddlers do not expect these representations to align numerically with clauses used to describe those scenes: they readily accept two-argument descriptions (“she pimmed the truck!”). This argues against syntactic bootstrapping theories underwritten by mappings between structural features of syntactic and semantic representations. Instead, our findings support bootstrapping based on grammatical and thematic content. Children’s earliest inferences may rely on the assumption that the syntactic asymmetry between subject and object correlates with a difference in how their referents relate to the event described by the sentence.

Benzimidazole scaffold as a potent anticancer agent with different mechanisms of action (2016-2023).

Benzimidazole scaffolds have potent anticancer activity due to their structure similarity to nucleoside. In addition, benzimidazoles could function as hydrogen donors or acceptors and bind to different drug targets that participate in cancer progression. The literature had many anticancer agents containing benzimidazole cores that gained much interest. Provoked by our endless interest in benzimidazoles as anticancer agents, we summarized the successful trials of the benzimidazole scaffolds in this concern. Moreover, we discuss the substantial opportunities in cancer treatment using benzimidazole-based drugs that may direct medicinal chemists for a compelling future design of more active chemotherapeutic agents with potential clinical applications. The uniqueness of this work lies in the highlighted benzimidazole scaffold hybridization with different molecules and benzimidazole-metal complexes, detailed mechanisms of action, and the IC50 of the developed compounds determined by different laboratories after 2015.

Bacterial Protein Signatures Identified in Porphyromonas gingivalis Containing-Autophagic Vacuoles Reveal Co-Evolution Between Oral Red/Orange Complex Bacteria and Gut Bacteria.

Modern oral bacterial species present as a concoction of commensal and opportunistic pathogens originating from their evolution in humans. Due to the intricate colonization mechanisms shared amongst oral and gut bacteria, these bacteria have likely evolved together to establish and adapt in the human oro-digestive tract, resulting in the transfer of genetic information. Our liquid chromatography-with-tandem-mass-spectrometry (LC-MS-MS) analyses have revealed protein signatures, Elongation Factor Tu, RagB/SusD nutrient uptake outer membrane protein and DnaK, specifically from Porphyromonas gingivalis -containing autophagic vacuoles isolated from the infected human primary gingival epithelial cells. Interestingly, our Mass-Spectrometry analysis reported similar proteins from closely related oral bacteria, Tannerella forsythia and Prevotella intermedia . In our phylogenetic study of these key protein signatures, we have established that pathogenic oral bacteria share extensive relatedness to each other and gut resident bacteria. We show that in the virulence factors identified from gut bacteria, Elongation Factor Tu and DnaK, there are several structural similarities and conservations with proteins from oral pathogenic bacteria. There are also major similarities in the RagB/SusD proteins of oral bacteria to prominent gut bacteria. These findings not only highlight the shared virulence mechanisms amongst oral bacterial pathogens/pathobionts but also gut bacteria and elucidate their co-evolutions in the human host.

RAN: Infrared and Visible Image Fusion Network Based on Residual Attention Decomposition

Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.

A random mutagenesis screen enriched for missense mutations in bacterial effector proteins.

To remodel their hosts and escape immune defenses, many pathogens rely on large arsenals of proteins (effectors) that are delivered to the host cell using dedicated translocation machinery. Effectors hold significant insight into the biology of both the pathogens that encode them and the host pathways that they manipulate. One of the most powerful systems biology tools for studying effectors is the model organism, Saccharomyces cerevisiae. For many pathogens, the heterologous expression of effectors in yeast is growth inhibitory at a frequency much higher than housekeeping genes, an observation ascribed to targeting conserved eukaryotic proteins. Abrogation of yeast growth inhibition has been used to identify bacterial suppressors of effector activity, host targets, and functional residues and domains within effector proteins. We present here a yeast-based method for enriching for informative, in-frame, missense mutations in a pool of random effector mutants. We benchmark this approach against three effectors from Legionella pneumophila, an intracellular bacterial pathogen that injects a staggering >330 effectors into the host cell. For each protein, we show how in silico protein modeling (AlphaFold2) and missense-directed mutagenesis can be combined to reveal important structural features within effectors. We identify known active site residues within the metalloprotease RavK, the putative active site in SdbB, and previously unidentified functional motifs within the C-terminal domain of SdbA. We show that this domain has structural similarity with glycosyltransferases and exhibits in vitro activity consistent with this predicted function.

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods. The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.

Pan-serotype dengue virus inhibitor JNJ-A07 targets NS4A-2K-NS4B interaction with NS2B/NS3 and blocks replication organelle formation

Dengue fever represents a significant medical and socio-economic burden in (sub)tropical regions, yet antivirals for treatment or prophylaxis are lacking. JNJ-A07 was described as highly active against the different genotypes within each serotype of the disease-causing dengue virus (DENV). Based on clustering of resistance mutations it has been assumed to target DENV non-structural protein 4B (NS4B). Using a photoaffinity labeling compound with high structural similarity to JNJ-A07, here we demonstrate binding to NS4B and its precursor NS4A-2K-NS4B. Consistently, we report recruitment of the compound to intracellular sites enriched for these proteins. We further specify the mechanism-of-action of JNJ-A07, which has virtually no effect on viral polyprotein cleavage, but targets the interaction between the NS2B/NS3 protease/helicase complex and the NS4A-2K-NS4B cleavage intermediate. This interaction is functionally linked to de novo formation of vesicle packets (VPs), the sites of DENV RNA replication. JNJ-A07 blocks VPs biogenesis with little effect on established ones. A similar mechanism-of-action was found for another NS4B inhibitor, NITD-688. In summary, we unravel the antiviral mechanism of these NS4B-targeting molecules and show how DENV employs a short-lived cleavage intermediate to carry out an early step of the viral life cycle.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis

Objective. Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons. Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network. Results. The proposed model achieves a mean absolute error (MAE) of 18.76±5.167 in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of 0.95±0.09 and a Frechet inception distance (FID) of 145.60±8.38 . The model yields a MAE of 26.83±8.27 to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of 0.73±0.06 and a FID distance equal to 122.58±7.55 . The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of 27.89±2.22 and 26.08±2.95 to synthesize MRI from CT input. Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

Design, Synthesis, and Xanthine Oxidase Inhibitory Activity of 4-(5-Aminosubstituted-4-cyanooxazol-2-yl)benzoic Acids.

Xanthine oxidase is a known therapeutic target for the treatment of hyperuricemia and related diseases. Despite the availability of current drugs such as allopurinol and febuxostat, the search for new compounds to effectively inhibit this enzyme remains relevant. In our study, 75 virtual structures of 4-(5-aminosubstituted-4-cyanooxazol-2-yl)benzoic acids with structural similarity to febuxostat were designed for evaluation of their potency against xanthine oxidase. After molecular docking simulations, eight compounds were selected for synthesis and in vitro testing. The synthesized compounds were found to exhibit in vitro xanthine oxidase inhibitory activity in the nanomolar concentration range. The most effective inhibitors with 4-benzylpiperidin-1-yl or 1,2,3,4-tetrahydroisoquinolin-2-yl substituents at position 5 of the oxazole ring had IC50 values close to that of febuxostat. The kinetic data suggest a mixed-type inhibition when the inhibitor binds preferentially to the free enzyme rather than to the enzyme-substrate complex. Molecular docking and molecular dynamic simulations were carried out to get insight into the key interactions of the inhibitors bound to the xanthine oxidase active site.

A multi-task generative model for simultaneous post-contrast MR image synthesis and brainstem glioma segmentation

ObjectivesThis study aims to generate post-contrast MR images reducing the exposure of gadolinium-based contrast agents (GBCAs) for brainstem glioma (BSG) detection, simultaneously delineating the BSG lesion, and providing high-resolution contrast information. MethodsA retrospective cohort of 30 patients diagnosed with brainstem glioma was included. Multi-contrast images, including pre-contrast T1 weighted (pre-T1w), T2 weighted (T2w), arterial spin labeling (ASL) and post-contrast T1w images, were collected. A multi-task generative model was developed to synthesize post-contrast T1w images and simultaneously segment BSG masks from the multi-contrast inputs. Performance evaluation was conducted using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) metrics. A perceptual study was also undertaken to assess diagnostic quality. ResultsThe proposed model achieved SSIM of 0.86 ± 0.04, PSNR of 26.33 ± 0.05 and MAE of 57.20 ± 20.50 for post-contrast T1w image synthesis. Automated delineation of the BSG lesions achieved Dice similarity coefficient (DSC) score of 0.88 ± 0.27. ConclusionsThe proposed model can synthesize high-quality post-contrast T1w images and accurately segment the BSG region, yielding satisfactory DSC scores. Clinical relevance statementThe synthesized post-contrast MR image presented in this study has the potential to reduce the usage of gadolinium-based contrast agents, which may pose risks to patients. Moreover, the automated segmentation method proposed in this paper aids radiologists in accurately identifying the brainstem glioma lesion, facilitating the diagnostic process.

Spatial-aware contrastive learning for cross-domain medical image registration.

With the rapid advancement of medical imaging technologies, precise image analysis and diagnosis play a crucial role in enhancing treatment outcomes and patient care. Computed tomography (CT) and magnetic resonance imaging (MRI), as pivotal technologies in medical imaging, exhibit unique advantages in bone imaging and soft tissue contrast, respectively. However, cross-domain medical image registration confronts significant challenges due to the substantial differences in contrast, texture, and noise levels between different imaging modalities. The purpose of this study is to address the major challenges encountered in the field of cross-domain medical image registration by proposing a spatial-aware contrastive learning approach that effectively integrates shared information from CT and MRI images. Our objective is to optimize the feature space representation by employing advanced reconstruction and contrastive loss functions, overcoming the limitations of traditional registration methods when dealing with different imaging modalities. Through this approach, we aim to enhance the model's ability to learn structural similarities across domain images, improve registration accuracy, and provide more precise imaging analysis tools for clinical diagnosis and treatmentplanning. With prior knowledge that different domains of images (CT and MRI) share same content-style information, we extract equivalent feature spaces from both images, enabling accurate cross-domain point matching. We employ a structure resembling that of an autoencoder, augmented with designed reconstruction and contrastive losses to fulfill our objectives. We also propose region mask to solve the conflict between spatial correlation and distinctiveness, to obtain a better representation space. Our research results demonstrate the significant superiority of the proposed spatial-aware contrastive learning approach in the domain of cross-domain medical image registration. Quantitatively, our method achieved an average Dice similarity coefficient (DSC) of 85.68%, target registration error (TRE) of 1.92 mm, and mean Hausdorff distance (MHD) of 1.26 mm, surpassing current state-of-the-art methods. Additionally, the registration processing time was significantly reduced to 2.67 s on a GPU, highlighting the efficiency of our approach. The experimental outcomes not only validate the effectiveness of our method in improving the accuracy of cross-domain image registration but also prove its adaptability across different medical image analysis scenarios, offering robust support for enhancing diagnostic precision and patient treatmentoutcomes. The spatial-aware contrastive learning approach proposed in this paper introduces a new perspective and solution to the domain of cross-domain medical image registration. By effectively optimizing the feature space representation through carefully designed reconstruction and contrastive loss functions, our method significantly improves the accuracy and stability of registration between CT and MRI images. The experimental results demonstrate the clear advantages of our approach in enhancing the accuracy of cross-domain image registration, offering significant application value in promoting precise diagnosis and personalized treatment planning. In the future, we look forward to further exploring the application of this method in a broader range of medical imaging datasets and its potential integration with other advanced technologies, contributing more innovations to the field of medical image analysis andprocessing.

The Allergen Profile of Two Edible Insect Species-Acheta domesticus and Hermetia illucens.

Edible insect proteins are increasingly introduced as an alternative sustainable food source to address the world's need to feed the growing population. Tropomyosin is the main insect allergen; however, additional potential allergens are not well characterized and the impact of extraction procedures on immunological reactivity is unknown. Proteins from different commercial food products derived from cricket (Acheta domesticus) and black soldier fly (BSF) (Hermetia illucens) are extracted using five different extraction buffers. The proteins are analyzed by SDS-PAGE and immunoblotting using allergen-specific antibodies and crustacean allergic patient sera. IgE binding bands are analyzed by mass spectrometry as well as the complete allergen profile of all 30 extracts. Urea-based buffers are most efficient in extracting insect allergens. Shrimp-specific antibody cross-reactivity to tropomyosin from cricket and BSF indicates high sequence and structural similarity between shrimp and insects. Additional unique allergens are identified in both species, including hemocyanin, vitellogenin, HSP20, apolipophorin-III, and chitin-binding protein. Identifying potential allergenic proteins and their isoforms in cricket and BSF requires specific extraction approaches using urea-based methods. While tropomyosin is the most abundant and immunoreactive allergen, seven unique allergens are identified, highlighting the need for insect species-specific allergen detection in food products.

Drug Repurposing for Amyotrophic Lateral Sclerosis Based on Gene Expression Similarity and Structural Similarity: A Cheminformatics, Genomic and Network-Based Analysis

Drug Repurposing for Amyotrophic Lateral Sclerosis Based on Gene Expression Similarity and Structural Similarity: A Cheminformatics, Genomic and Network-Based Analysis

MEvo-GAN: A Multi-Scale Evolutionary Generative Adversarial Network for Underwater Image Enhancement

Inunderwater imaging, achieving high-quality imagery is essential but challenging due to factors such as wavelength-dependent absorption and complex lighting dynamics. This paper introduces MEvo-GAN, a novel methodology designed to address these challenges by combining generative adversarial networks with genetic algorithms. The key innovation lies in the integration of genetic algorithm principles with multi-scale generator and discriminator structures in Generative Adversarial Networks (GANs). This approach enhances image details and structural integrity while significantly improving training stability. This combination enables more effective exploration and optimization of the solution space, leading to reduced oscillation, mitigated mode collapse, and smoother convergence to high-quality generative outcomes. By analyzing various public datasets in a quantitative and qualitative manner, the results confirm the effectiveness of MEvo-GAN in improving the clarity, color fidelity, and detail accuracy of underwater images. The results of the experiments on the UIEB dataset are remarkable, with MEvo-GAN attaining a Peak Signal-to-Noise Ratio (PSNR) of 21.2758, Structural Similarity Index (SSIM) of 0.8662, and Underwater Color Image Quality Evaluation (UCIQE) of 0.6597.

Conformational analysis of a new peptide derived from feline immunodeficiency virus gp36 in SDS micelles: An NMR-MD based investigation.

Feline immunodeficiency virus (FIV) shares structural similarities with human immunodeficiency virus (HIV): the surface glycoprotein gp36 corresponds to the HIV gp41, which drives virus-host cell interactions and is targeted by the peptide entry inhibitor enfuvirtide. Following a similar drug design strategy for the development of an anti-FIV therapy, the present study investigates 627-646gp36 NHR, a peptide sequence derived from a region of gp36 that was previously found to interfere with the antiviral activity of the peptide C8, which instead derives from the gp36 MPER. CD, NMR, and MD simulations were employed to probe the conformational characteristics of 627-646gp36 NHR in the membrane-mimicking environment of SDS micelles. Our data show that 627-646gp36 NHR is characterized by three dynamic helix structures. MD simulations involving 627-646gp36 NHR, C8, and a larger protein, including the CHR and MPER regions, suggest that the interaction of C8 with the MPER region, the origin of the antiviral activity of C8, is disfavored in the presence of 627-646gp36 NHR in the simulation. This evidence can be useful for interpreting the molecular mechanism that leads to interference with the activity of C8, providing information on the folding/unfolding mechanism of the viral glycoprotein to design new strategies to inhibit viral entry.

Investigating layer-by-layer films of carbon nanotubes and nickel phthalocyanine towards diquat detection

The indiscriminate use of pesticides makes us susceptible to the toxicity of these chemical compounds, which may be present in high quantities in our food. It is crucial to develop inexpensive and rapid methods for determining these pesticides for government control or even for the general population. In this study, we investigated the fabrication of self-assembled LbL films using multi-walled carbon nanotubes (MWCNT) and nickel tetrasulphonated phthalocyanine (NiTsPc) as an electrochemical sensor for the herbicide Diquat (DQ). The Layer-by-Layer (LbL) assembly of the (MWCNT/NiTsPc) film was examined, along with its structural and morphological characteristics. The effect of the number of layers in DQ detection was evaluated by cyclic voltammetry, followed by the detection through differential pulse voltammetry. The achieved limit of detection was 9.62 × 10−7 mol L−1. A ~ 30% decrease in sensitivity was observed in the presence of Paraquat, a banned herbicide and electrochemical interferent due to the structural similarities, which is regularly neglected in the most published studies. The sensor was tested in real samples, demonstrating a recovery of 98.5% in organic apples.

Real-Time Registration of Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images Using an Acousto-Optic Tunable Filter Spectrometer

Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning.Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans.The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT.SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

A Comprehensive Review of The Molecular Dynamic Study Of Chalcones, Coumarins and Chromones as Selective MAO‐B Inhibitors [2015‐Till Date

AbstractMolecular dynamics (MD) simulation is an in silico method used in the biomolecular level of research to study how the protein interacts with the target with time. It provides a detailed information of the protein dynamics and ligand structure with the crucial amino acid interactions. Monoamine oxidase B (MAO−B) is a crucial isoenzyme responsible for the catalyses of oxidative deamination of various biogenic amines in the brain and peripheral tissues. The selective inhibitors of MAO−B are considered as the management of symptoms of neurodegenerative disorders like Alzheimer's disease(AD) and Parkinson disease(PD). Recently the structural scaffolds containing chalcones, coumarins and chromones derived candidates shown potent, selective, competitive and reversible type of MAO−B inhibitors. The structural similarities between the above scaffolds can produce almost similar type of interactions in the inhibitor binding cavity of MAO−B. Numerous molecular simulation reports were supported by the above mentioned fact. The current review focus on the last ten year molecular dynamics report of chalcones, coumarins and chromones towards MAO−B inhibition. The review also focuses on the software details used in the MD dynamics simulation and the structural requirement from each class of compound for the recognition of MAO−B inhibitory activity.

Design, synthesis and evaluation of new methyl piperazine derivatives as anticancer agents

BackgroundTo overcome the problem of side effects and toxicity, development of new anticancer agents is needed. Recently, piperidine salicylanilide derivatives with nanomolar epidermal growth factor receptor (EGFR) inhibitory and cytotoxicity activity have been reported. In the present study effect of replacing piperidine in reported piperidine salicylanilide with N-methyl piperazine and changing substituent’s of phenyl ring at other end on anticancer activity have been explored. A series of sixteen methyl piperazine incorporated phenyl benzamide and phenyl methanone derivatives have been synthesized and tested in a panel of three cancer cell lines (adenocarcinomic human alveolar basal epithelial cells (A-549), human colon carcinoma (HCT-116) and human pancreatic carcinoma (MIAPaCa-2)), using gefitinib as standard. Further, to study the probable mechanism, due to their structural similarity with EGFR inhibitors, docking interactions with EGFR active site were observed using Schrodinger suite.ResultThe results indicated that most of the compounds showed promising activity; out of which, compound A-11 was most active having cytotoxicity much better than that of gefitinib. It showed IC50 value of 5.71 µM against A-549 cell line, 4.26 µM against HCT-116 colon cancer line and 31.36 µM against MIAPaCa-2 cell line.ConclusionIt was found that these compounds fit well in the active site and may be exhibiting anticancer activity via EGFR inhibition.Graphical