Molecular designing and in silico evaluation of darunavir derivatives as anticancer agents

Darunavir is a synthetic nonpeptidic protease inhibitor which has been tested for anticancer properties. To deduce and enhance the anticancer activity of the Darunavir, we have modified its reactive moiety in an effective way. We designed 9 analogues in ChemBioOffice 2010 and minimized using the LigPrep tool of Schrödinger 2011. These analogues can obstruct the activity of other signalling pathways which are implicated in many tumors. Results of the QikProp showed that all the analogues lied in the specified range of all the pharmacokinetic (ADMET) properties required to become the successful drug. Docking study was performed to test its anticancer activity against the biomarkers of the five main types of cancers i.e. bone, brain, breast, colon and skin cancer. Grid was generated for each oncoproteins by specifying the active site amino acids. The binding model of best scoring analogue with each protein was assessed from their G-scores and disclosed by docking analysis using the XP visualizer tool. An analysis of the receptor-ligand interaction studies revealed that these nine Darunavir analogues are active against all cancer biomarkers and have the features to prove themselves as anticancer drugs, further to be synthesized and tested against the cell lines.     

Methyl Gallate from Galla rhois Successfully Controls Clinical Isolates of Salmonella Infection in Both In Vitro and In Vivo Systems

Galla rhois is a commonly used traditional medicine for the treatment of pathogenic bacteria in Korea as well as in other parts of Asia. Methyl gallate (MG), a major component of Galla Rhois, exhibits strong antibacterial activity, but its mechanism of action against Salmonella spp. is unclear. In the present study, we investigated the antibacterial actions of MG against Salmonella. The antibacterial activity determined by broth dilution method indicated that the antibacterial activity of MG against Salmonella strains ranged from 3.9 to 125 µg/ml. In vitro bacterial viability test indicated that MG significantly decreased the viability of Salmonella over 40% when combined with ATPase inhibitors. The time-kill curves showed that a combined MG and ATPase inhibitors (DCCD and NaN3) treatment reduced the bacterial counts dramatically after 24 h. Oral administration of MG showed a strong anti-bacterial activity against WS-5 infected BALB/c mice. In contrast to the untreated Salmonella infected control animals, MG treated groups showed no clinical symptoms of the disease, such as lethargy and liver damage. It was observed that MG treatment significantly increased the survival of animals from Salmonella infection, while in untreated groups all animal succumbed to disease by the sixth day post infection. Thus, the present study demonstrates the therapeutic ability of MG against Salmonella infections.     

Genetic variation at the growth hormone (GH1) and growth hormone receptor (GHR) loci as a risk factor for hypertension and stroke

An increased prevalence of both hypertension and cerebrovascular stroke is apparent in growth hormone (GH) deficiency whilst hypertension is a frequent complication in acromegaly. This has suggested a possible link between GH, stature and arterial function. Since the risk of both hypertension and stroke also appears to be inversely correlated with adult height, we have instigated an exploratory study to assess whether inter-individual variation in the genes encoding human growth hormone (GH1) and the GH receptor (GHR) might be associated with an increased risk of hypertension and stroke. GH1 promoter haplotypes were found to differ significantly not only between hypertensive patients (n=111) and controls (n=121) but also between stroke patients (n=155) and controls (n=158). Intriguingly, the association between GH1 promoter haplotype and risk of hypertension was much greater in females than in males. An inverse correlation between height and central systolic blood pressure was apparent in both hypertensive patients and normal controls but was much stronger in individuals carrying at least one GH1 promoter risk haplotype. The GH1 genotype therefore constitutes a risk factor for hypertension that interacts with stature. A strong association was found between the presence of at least one GH1 risk haplotype and a family history of stroke at an early age (odds ratio: 9.07, 95% confidence interval: 1.14–72.22). Three novel GH variants (Arg16His, Phe176Cys, Cys189Arg) were identified during the course of this study. Although two exhibited markedly reduced biological activity in vitro, their clinical significance remains unclear. No association was found between GHR genotype and either hypertension or stroke, nor was any interaction noted between GHR and GH1 genotypes in terms of a disease association. However, an association between GHRd3 genotype and hypertension was observed among stroke patients, particularly females. Elevated HDL was found to be a risk factor for hypertension in individuals lacking a copy of the GHRd3 allele. Weak associations with GHR genotype were also noted for peripheral systolic and diastolic blood pressure in hypertensive patients. Although the underlying mechanisms are still unclear, our findings are consistent with a complex relationship between height, hypertension, GH1 promoter haplotype, GHR polymorphism and the risk of stroke.     

Diagnosis of influenza viruses with special reference to novel H1N1 2009 influenza virus

On 15 April and 17 April 2009, novel swineorigin influenza A (H1N1) virus was identifi ed in specimens obtained from two epidemiologically unlinked patients in the United States. The ongoing outbreak of novel H1N1 2009 influenza (swine influenza) has caused more than 3,99,232 laboratory confi rmed cases of pandemic influenza H1N1 and over 4735 deaths globally. This novel 2009 influenza virus designated as H1N1 A/swine/California/04/2009 virus is not zoonotic swine flu and is transmitted from person to person and has higher transmissibility then that of seasonal influenza viruses. In India the novel H1N1 virus infection has been reported from all over the country. A total of 68,919 samples from clinically suspected persons have been tested for influenza A H1N1 across the country and 13,330 (18.9%) of them have been found positive with 427 deaths. At the All India Institute of Medical Sciences, New Delhi India, we tested 1096 clinical samples for the presence of novel H1N1 influenza virus and seasonal influenza viruses. Of these 1096 samples, 194 samples (17.7%) were positive for novel H1N1 influenza virus and 197 samples (18%) were positive for seasonal influenza viruses. During outbreaks of emerging infectious diseases accurate and rapid diagnosis is critical for minimizing further spread through timely implementation of appropriate vaccines and antiviral treatment. Since the symptoms of novel H1N1 influenza infection are not specifi c, laboratory confi rmation of suspected cases is of prime importance.     

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.