Evolution, persistence, and host adaption of a gonococcal AMR plasmid that emerged in the pre-antibiotic era.

Plasmids are diverse extrachromosomal elements significantly that contribute to interspecies dissemination of antimicrobial resistance (AMR) genes. However, within clinically important bacteria, plasmids can exhibit unexpected narrow host ranges, a phenomenon that has scarcely been examined. Here we show that pConj is largely restricted to the human-specific pathogen, Neisseria gonorrhoeae. pConj can confer tetracycline resistance and is central to the dissemination of other AMR plasmids. We tracked pConj evolution from the pre-antibiotic era 80 years ago to the modern day and demonstrate that, aside from limited gene acquisition and loss events, pConj is remarkably conserved. Notably, pConj has remained prevalent in gonococcal populations despite cessation of tetracycline use, thereby demonstrating pConj adaptation to its host. Equally, pConj imposes no measurable fitness costs and is stably inherited by the gonococcus. Its maintenance depends on the co-operative activity of plasmid-encoded Toxin:Antitoxin (TA) and partitioning systems rather than host factors. An orphan VapD toxin encoded on pConj forms a split TA with antitoxins expressed from an ancestral co-resident plasmid or a horizontally-acquired chromosomal island, potentially explaining pConj's limited distribution. Finally, ciprofloxacin can induce loss of this highly stable plasmid, reflecting epidemiological evidence of transient reduction in pConj prevalence when fluoroquinolones were introduced to treat gonorrhoea.

Collateral sensitivity increases the efficacy of a rationally designed bacteriophage combination to control Salmonella enterica.

The ability of virulent bacteriophages to lyse bacteria influences bacterial evolution, fitness, and population structure. Knowledge of both host susceptibility and resistance factors is crucial for the successful application of bacteriophages as biological control agents in clinical therapy, food processing, and agriculture. In this study, we isolated 12 bacteriophages termed SPLA phage which infect the foodborne pathogen Salmonella enterica. To determine phage host range, a diverse collection of Enterobacteriaceae and Salmonella enterica was used and genes involved in infection by six SPLA phages were identified using Salmonella Typhimurium strain ST4/74. Candidate host receptors included lipopolysaccharide (LPS), cellulose, and BtuB. Lipopolysaccharide was identified as a susceptibility factor for phage SPLA1a and mutations in LPS biosynthesis genes spontaneously emerged during culture with S. Typhimurium. Conversely, LPS was a resistance factor for phage SPLA5b which suggested that emergence of LPS mutations in culture with SPLA1a represented collateral sensitivity to SPLA5b. We show that bacteria-phage co-culture with SPLA1a and SPLA5b was more successful in limiting the emergence of phage resistance compared to single phage co-culture. Identification of host susceptibility and resistance genes and understanding infection dynamics are critical steps in the rationale design of phage cocktails against specific bacterial pathogens.IMPORTANCEAs antibiotic resistance continues to emerge in bacterial pathogens, bacterial viruses (phage) represent a potential alternative or adjunct to antibiotics. One challenge for their implementation is the predisposition of bacteria to rapidly acquire resistance to phages. We describe a functional genomics approach to identify mechanisms of susceptibility and resistance for newly isolated phages that infect and lyse Salmonella enterica and use this information to identify phage combinations that exploit collateral sensitivity, thus increasing efficacy. Collateral sensitivity is a phenomenon where resistance to one class of antibiotics increases sensitivity to a second class of antibiotics. We report a functional genomics approach to rationally design a phage combination with a collateral sensitivity dynamic which resulted in increased efficacy. Considering such evolutionary trade-offs has the potential to manipulate the outcome of phage therapy in favor of resolving infection without selecting for escape mutants and is applicable to other virus-host interactions.

Investigating the Inhibitory Potential of Flavonoids against Aldose Reductase: Insights from Molecular Docking, Dynamics Simulations, and gmx_MMPBSA Analysis.

Diabetes mellitus (DM) is a complex metabolic disorder characterized by chronic hyperglycemia, with aldose reductase playing a critical role in the pathophysiology of diabetic complications. This study aimed to investigate the efficacy of flavonoid compounds as potential aldose reductase inhibitors using a combination of molecular docking and molecular dynamics (MD) simulations. The three-dimensional structures of representative flavonoid compounds were obtained from PubChem, minimized, and docked against aldose reductase using Discovery Studio's CDocker module. The top 10 compounds Daidzein, Quercetin, Kaempferol, Butin, Genistein, Sterubin, Baicalein, Pulchellidin, Wogonin, and Biochanin_A were selected based on their lowest docking energy values for further analysis. Subsequent MD simulations over 100 ns revealed that Daidzein and Quercetin maintained the highest stability, forming multiple conventional hydrogen bonds and strong hydrophobic interactions, consistent with their favorable interaction energies and stable RMSD values. Comparative analysis of hydrogen bond interactions and RMSD profiles underscored the ligand stability. MMPBSA analysis further confirmed the significant binding affinities of Daidzein and Quercetin, highlighting their potential as aldose reductase inhibitors. This study highlights the potential of flavonoids as aldose reductase inhibitors, offering insights into their binding interactions and stability, which could contribute to developing novel therapeutics for DM complications.

An update on the prevalence of colistin and carbapenem-resistant Gram-negative bacteria in aquaculture: an emerging threat to public health.

Aquaculture has been recognized as a hotspot for the emergence and spread of antimicrobial resistance genes conferring resistance to clinically important antibiotics. This review gives insights into studies investigating the prevalence of colistin and carbapenem resistance (CCR) among Gram-negative bacilli in aquaculture. Overall, a high incidence of CCR has been reported in aquatic farms in several countries, with CCR being more prevalent among opportunistic human pathogens such as Acinetobacter nosocomialis, Shewanella algae, Photobacterium damselae, Vibrio spp., Aeromonas spp., as well as members of Enterobacteriaceae family. A high proportion of isolates in these studies exhibited wide-spectrum profiles of antimicrobial resistance, highlighting their multidrug-resistance properties (MDR). Several mobile colistin resistance genes (including, mcr-1, mcr-1.1, mcr-2, mcr-2.1, mcr-3, mcr-3.1, mcr-4.1, mcr-4.3, mcr-5.1, mcr-6.1, mcr-7.1, mcr-8.1, and mcr-10.1) and carbapenemase encoding genes (including, blaOXA-48, blaOXA-55, blaNDM, blaKPC, blaIMI, blaAIM, blaVIM, and blaIMP) have been detected in aquatic farms in different countries. The majority of these were carried on MDR Incompatibility (Inc) plasmids including IncA/C, and IncX4, which have been associated with a wide host range of different sources. Thus, there is a risk for the possible spread of resistance genes between fish, their environments, and humans. These findings highlight the need to monitor and regulate the usage of antimicrobials in aquaculture. A multisectoral and transdisciplinary (One Health) approach is urgently needed to reduce the spread of resistant bacteria and/or resistance genes originating in aquaculture and avoid their global reach.

Dimeric peptoids as antibacterial agents.

Building upon our previous study on peptoid-based antibacterials which showed good activity against Gram-positive bacteria only, herein we report the synthesis of 34 dimeric peptoid compounds and the investigation of their activity against Gram-positive and Gram-negative pathogens. The newly designed peptoids feature a di-hydrophobic moiety incorporating phenyl, bromo-phenyl, and naphthyl groups, combined with variable lengths of cationic units such as amino and guanidine groups. The study also underscores the pivotal interplay between hydrophobicity and cationicity in optimizing efficacy against specific bacteria. The bromophenyl dimeric guanidinium peptoid compound 10j showed excellent activity against S. aureus 38 and E. coli K12 with MIC of 0.8 µg mL-1 and 6.2 µg mL-1, respectively. Further investigation into the mechanism of action revealed that the antibacterial effect might be attributed to the disruption of bacterial cell membranes, as suggested by tethered bilayer lipid membranes (tBLMs) and cytoplasmic membrane permeability studies. Notably, these promising antibacterial agents exhibited negligible toxicity against mammalian red blood cells. Additionally, the study explored the potential of 12 active compounds to disrupt established biofilms of S. aureus 38. The most effective biofilm disruptors were ethyl and octyl-naphthyl guanidinium peptoids (10c and 10 k). These compounds 10c and 10 k disrupted the established biofilms of S. aureus 38 with 51 % at 4x MIC (MIC = 17.6 µg mL-1 and 11.2 µg mL-1) and 56 %-58 % at 8x MIC (MIC = 35.2 µg mL-1 and 22.4 µg mL-1) respectively. Overall, this research contributes insights into the design principles of cationic dimeric peptoids and their antibacterial activity, with implications for the development of new antibacterial compounds.

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation.

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework's GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib-an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation.

Genome-wide analysis of genes involved in efflux function and regulation within Escherichia coli and Salmonella enterica serovar Typhimurium.

The incidence of multidrug-resistant bacteria is increasing globally, with efflux pumps being a fundamental platform limiting drug access and synergizing with other mechanisms of resistance. Increased expression of efflux pumps is a key feature of most cells that are resistant to multiple antibiotics. Whilst expression of efflux genes can confer benefits, production of complex efflux systems is energetically costly and the expression of efflux is highly regulated, with cells balancing benefits against costs. This study used TraDIS-Xpress, a genome-wide transposon mutagenesis technology, to identify genes in Escherichia coli and Salmonella Typhimurium involved in drug efflux and its regulation. We exposed mutant libraries to the canonical efflux substrate acriflavine in the presence and absence of the efflux inhibitor phenylalanine-arginine ß-naphthylamide. Comparisons between conditions identified efflux-specific and drug-specific responses. Known efflux-associated genes were easily identified, including acrAB, tolC, marRA, ramRA and soxRS, confirming the specificity of the response. Further genes encoding cell envelope maintenance enzymes and products involved with stringent response activation, DNA housekeeping, respiration and glutathione biosynthesis were also identified as affecting efflux activity in both species. This demonstrates the deep relationship between efflux regulation and other cellular regulatory networks. We identified a conserved set of pathways crucial for efflux activity in these experimental conditions, which expands the list of genes known to impact on efflux efficacy. Responses in both species were similar and we propose that these common results represent a core set of genes likely to be relevant to efflux control across the Enterobacteriaceae.

TraDIS-Xpress: a high-resolution whole-genome assay identifies novel mechanisms of triclosan action and resistance.

Understanding the genetic basis for a phenotype is a central goal in biological research. Much has been learnt about bacterial genomes by creating large mutant libraries and looking for conditionally important genes. However, current genome-wide methods are largely unable to assay essential genes which are not amenable to disruption. To overcome this limitation, we developed a new version of "TraDIS" (transposon directed insertion-site sequencing) that we term "TraDIS-Xpress" that combines an inducible promoter into the transposon cassette. This allows controlled overexpression and repression of all genes owing to saturation of inserts adjacent to all open reading frames as well as conventional inactivation. We applied TraDIS-Xpress to identify responses to the biocide triclosan across a range of concentrations. Triclosan is endemic in modern life, but there is uncertainty about its mode of action with a concentration-dependent switch from bacteriostatic to bactericidal action unexplained. Our results show a concentration-dependent response to triclosan with different genes important in survival between static and cidal exposures. These genes include those previously reported to have a role in triclosan resistance as well as a new set of genes, including essential genes. Novel genes identified as being sensitive to triclosan exposure include those involved in barrier function, small molecule uptake, and integrity of transcription and translation. We anticipate the approach we show here, by allowing comparisons across multiple experimental conditions of TraDIS data, and including essential genes, will be a starting point for future work examining how different drug conditions impact bacterial survival mechanisms.

Application of TraDIS to define the core essential genome of Campylobacter jejuni and Campylobacter coli.

Campylobacter species are the major cause of bacterial gastroenteritis. As there is no effective vaccine, combined with the rapid increase in antimicrobial resistant strains, there is a need to identify new targets for intervention. Essential genes are those that are necessary for growth and/or survival, making these attractive targets. In this study, comprehensive transposon mutant libraries were created in six C. jejuni strains, four C. coli strains and one C. lari and C. hyointestinalis strain, allowing for those genes that cannot tolerate a transposon insertion being called as essential. Comparison of essential gene lists using core genome analysis can highlight those genes which are common across multiple strains and/or species. Comparison of C. jejuni and C. coli, the two species that cause the most disease, identified 316 essential genes. Genes of interest highlighted members of the purine pathway being essential for C. jejuni whilst also finding that a functional potassium uptake system is essential. Protein-protein interaction networks using these essential gene lists also highlighted proteins in the purine pathway being major 'hub' proteins which have a large number of interactors across the network. When adding in two more species (C. lari and C. hyointestinalis) the essential gene list reduces to 261. Within these 261 essential genes, there are many genes that have been found to be essential in other bacteria. These include htrB and PEB4, which have previously been found as core virulence genes across Campylobacter species in other studies. There were 21 genes which have no known function with eight of these being associated with the membrane. These surface-associated essential genes may provide attractive targets. The essential gene lists presented will help to prioritise targets for the development of novel therapeutic and preventative interventions.

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation.

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

Sustainable, economical and rapid treatment of multiple lung diseases using therapeutic potential of curcumin nanoparticles.

The study was designed to prepare pure curcumin nanoparticles in rapid and simple way for target specific drug delivery to kill bacteria lying deep down within the alveoli of lungs via inhaler. Three different methods including evaporation precipitation of nanosuspension (ENP), solid dispersion (SD) and anti-solvent precipitation (ASP) were selected to prepare nanocurcumin in pure form in very simple way. This was done to compare their efficiency in terms of particle size obtained and water solubility and bacterial toxicity of as prepared curcumin nanoparticles. In this comparative study, curcumin NPs obtained from three different methods having particles size 65.3 nm, 98.7 nm and 47.4 nm respectively. The NPs were characterized using various techniques like SEM, XRD, UV-Visible and FTIR for their particle size determination and solubility evaluation. These particles were screened off against five bacterial strains causing lung diseases. AB3 prepared by ASP method, being smallest sized nanostructures, showed maximum solubility in water. These nanoparticles can be used as drug directly via inhaler to the target area without using any support or nano-carrier. In this way minimum dose formulation is required to target bacteria.

EWS/FLI1 Characterization, Activation, Repression, Target Genes and Therapeutic Opportunities in Ewing Sarcoma.

Despite their clonal origins, tumors eventually develop into complex communities made up of phenotypically different cell subpopulations, according to mounting evidence. Tumor cell-intrinsic programming and signals from geographically and temporally changing microenvironments both contribute to this variability. Furthermore, the mutational load is typically lacking in childhood malignancies of adult cancers, and they still exhibit high cellular heterogeneity levels largely mediated by epigenetic mechanisms. Ewing sarcomas represent highly aggressive malignancies affecting both bone and soft tissue, primarily afflicting adolescents. Unfortunately, the outlook for patients facing relapsed or metastatic disease is grim. These tumors are primarily fueled by a distinctive fusion event involving an FET protein and an ETS family transcription factor, with the most prevalent fusion being EWS/FLI1. Despite originating from a common driver mutation, Ewing sarcoma cells display significant variations in transcriptional activity, both within and among tumors. Recent research has pinpointed distinct fusion protein activities as a principal source of this heterogeneity, resulting in markedly diverse cellular phenotypes. In this review, we aim to characterize the role of the EWS/FLI fusion protein in Ewing sarcoma by exploring its general mechanism of activation and elucidating its implications for tumor heterogeneity. Additionally, we delve into potential therapeutic opportunities to target this aberrant fusion protein in the context of Ewing sarcoma treatment.

Computational Exploration of the Effects of Mutations on GABA Aminotransferase in GABA Aminotransferase Deficiency.

Gamma-aminobutyric acid (GABA) transaminase-also called GABA aminotransferase (GABA-AT)-deficiency is a rare autosomal recessive disorder characterized by a severe neonatal-infantile epileptic encephalopathy with symptoms such as seizures, hypotonia, hyperreflexia, developmental delay, and growth acceleration. GABA transaminase deficiency is caused by mutations in GABA-AT, the enzyme responsible for the catabolism of GABA. Mutations in multiple locations on GABA-AT have been reported and their locations have been shown to influence the onset of the disease and the severity of symptoms. We examined how GABA-AT mutations influence the structural stability of the enzyme and GABA-binding affinity using computational methodologies such as molecular dynamics simulation and binding free energy calculation to understand the underlying mechanism through which GABA-AT mutations cause GABA-AT deficiency. GABA-AT 3D model depiction was carried out together with seven individual mutated models of GABA-AT. The structural stability of all the predicted models was analyzed using several tools and web servers. All models were evaluated based on their phytochemical values. Additionally, 100 ns MD simulation was carried out and the mutated models were evaluated using RMSD, RMSF, Rg, and SASA. gmxMMPBSA free energy calculation was carried out. Moreover, RMSD and free energy calculations were also compared with those obtained using online web servers. Our study demonstrates that P152S, Q296H, and R92Q play a more critical role in the structural instability of GABA-AT compared with the other mutated models: G465R, L211F, L478P, and R220K.

Discovery of GABA Aminotransferase Inhibitors via Molecular Docking, Molecular Dynamic Simulation, and Biological Evaluation.

γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.

Investigation of Flavonoid Scaffolds as DAX1 Inhibitors against Ewing Sarcoma through Pharmacoinformatic and Dynamic Simulation Studies.

Dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1 (DAX1) is an orphan nuclear receptor encoded by the NR0B1 gene. The functional study showed that DAX1 is a physiologically significant target for EWS/FLI1-mediated oncogenesis, particularly Ewing Sarcoma (ES). In this study, a three-dimensional DAX1 structure was modeled by employing a homology modeling approach. Furthermore, the network analysis of genes involved in Ewing Sarcoma was also carried out to evaluate the association of DAX1 and other genes with ES. Moreover, a molecular docking study was carried out to check the binding profile of screened flavonoid compounds against DAX1. Therefore, 132 flavonoids were docked in the predicted active binding pocket of DAX1. Moreover, the pharmacogenomics analysis was performed for the top ten docked compounds to evaluate the ES-related gene clusters. As a result, the five best flavonoid-docked complexes were selected and further evaluated by Molecular Dynamics (MD) simulation studies at 100 ns. The MD simulation trajectories were evaluated by generating RMSD, hydrogen bond plot analysis, and interaction energy graphs. Our results demonstrate that flavonoids showed interactive profiles in the active region of DAX1 and can be used as potential therapeutic agents against DAX1-mediated augmentation of ES after in-vitro and in-vivo evaluations.

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models.

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and ecosystems. Machine learning (ML) prediction models have been applied to predict ARGs in beach waters. However, the existing studies were conducted at a single location and had low prediction performance. Moreover, ML models are "black boxes" that do not reveal their predictions' internal nuances and mechanisms. This lack of transparency and trust can result in serious consequences when using these models in high-stakes decisions. In this study, we developed a gradient boosted regression tree based (GBRT) ML model and then described its behavior using six explainable artificial intelligence (XAI) model-agnostic explanation methods. We used hydro-meteorological and qPCR data from the beaches in South Korea and Pakistan and developed ML prediction models for aac (6'-lb-cr), sul1, and tetX with 10-fold time-blocked cross-validation performances of 4.9, 2.06 and 4.4 root mean squared logarithmic error, respectively. We then analyzed the local and global behavior of the developed ML model using four interpretation methods. The developed ML models showed that water temperature, precipitation and tide are the most important predictors for prediction of ARGs at recreational beaches. We show that the model-agnostic interpretation methods not only explain the behavior of the ML model but also provide insights into the behavior of the ML model under new unseen conditions. Moreover, these post-processing techniques can be a debugging tool for ML-based modeling.

Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam.

The length of global coastline is about 356 thousand kilometers with various dynamic natural and anthropogenic. Although the number of studies on coastal landscape categorization has been increasing, it is still difficult to distinguish precisely them because the used methods commonly are traditional qualitative ones. With the leverage of remote sensing data and GIS tools, it helps categorize and identify a variety of features on land and water based on multi-source data. The aim of study is using different natural - social profile data obtained from ALOS, NOAA, and multi-temporal Landsat satellite images as input data of the convolutional-neural-network (CvNet) models for coastal landscape classification. Studies used 900 cut-line samples which represent coastal landscapes in Vietnam for training and optimizing CvNet models. As a result, nine coastal landscapes were identified including: deltas, alluvial, mature and young sand dunes, cliff, lagoon, tectonic, karst, and transitional landscapes. Three CvNet models using three different optimizer types classified the landscapes of other 1150 cut-lines in Vietnam with the accuracies about 98% and low loss function value. Excepting dalmatian, karst and delta coastal landscapes, five others distribute heterogeneous along the coasts in Vietnam. Therefore, the evaluation of additional natural components is necessary and CvNet model have ability to update new landscape types in variety of tropical nation as a step toward coastal landscape classification at both national and global scales.

Computational Exploration of Licorice for Lead Compounds against Plasmodium vivax Duffy Binding Protein Utilizing Molecular Docking and Molecular Dynamic Simulation.

Plasmodium vivax (P. vivax) is one of the human's most common malaria parasites. P. vivax is exceedingly difficult to control and eliminate due to the existence of extravascular reservoirs and recurring infections from latent liver stages. Traditionally, licorice compounds have been widely investigated against viral and infectious diseases and exhibit some promising results to combat these diseases. In the present study, computational approaches are utilized to study the effect of licorice compounds against P. vivax Duffy binding protein (DBP) to inhibit the malarial invasion to human red blood cells (RBCs). The main focus is to block the DBP binding site to Duffy antigen receptor chemokines (DARC) of RBC to restrict the formation of the DBP-DARC complex. A molecular docking study was performed to analyze the interaction of licorice compounds with the DARC binding site of DBP. Furthermore, the triplicates of molecular dynamic simulation studies for 100 ns were carried out to study the stability of representative docked complexes. The leading compounds such as licochalcone A, echinatin, and licochalcone B manifest competitive results against DBP. The blockage of the active region of DBP resulting from these compounds was maintained throughout the triplicates of 100 ns molecular dynamic (MD) simulation, maintaining stable hydrogen bond formation with the active site residues of DBP. Therefore, the present study suggests that licorice compounds might be good candidates for novel agents against DBP-mediated RBC invasion of P. vivax.

Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations.

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.

Characteristics and comparisons of acute stroke in "recovered" to "active COVID-19 and "pre-pandemic" in Qatar database.

Understanding the relationship of COVID-19 to stroke is important. We compare characteristics of pre-pandemic stroke (PPS), cases in acute COVID infection (CS) and in patients who have recovered from COVID-19 infection (RCS). We interrogated the Qatar stroke database for all stroke admissions between Jan 2020 and Feb 2021 (PPS) to CS and RCS to determine how COVID-19 affected ischemic stroke sub-types, clinical course, and outcomes prior to, during and post-pandemic peak. There were 3264 cases admitted (pre-pandemic: 3111, stroke in COVID-19: 60 and recovered COVID-19 stroke: 93). Patients with CS were significantly younger, had more severe symptoms, fever on presentation, more ICU admissions and poor stroke recovery at discharge when compared to PPS and RCS. Large vessel disease and cardioembolic disease was significantly higher in CS compared to PPS or RCS. There was a significant decline in stroke mimics in CS. Stroke in RCS has characteristics similar to PPS with no evidence of lasting effects of the virus on the short-term. However, CS is a more serious disease and tends to be more severe and have a poor prognosis.