Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process

Evaluation of rapid immunochromatographic card test in comparison with IgM ELISA in diagnosis of dengue fever at a tertiary care hospital, South India

 Background: Dengue has emerged as a major public health concern throughout India because of the mortality and morbidity associated with it. It is the most common mosquito-borne viral disease of humans. Hence early and rapid laboratory diagnosis of dengue is crucial. This study aims to determine demographic, clinical and laboratory investigations of all the suspected cases of dengue fever and comparison of two commercial tests routinely useful in diagnosis of dengue fever. This study was conducted to determine seropositivity of dengue samples in patients suspected of dengue illness and to compare immunochromatographic card test (ICT) test and IgM ELISA test.Methods: A total of 702 serum samples from patients with suspected dengue infection were included and the study was undertaken at department of microbiology at a tertiary care hospital, Hyderabad from July to December 2021. All samples were subjected to rapid ICT and confirmed by dengue IgM-capture ELISA.Results: Out of 702 cases suspected of dengue, 85 (12%) samples were positive by IgM ELISA method. The most affected age group was 21-40 years with 55 cases (64.3%) were positive, followed by the age group 0f 0-20 years with 25% of the cases. Males were affected more than females with a percentage of 54% and 46% respectively. The highest number of suspected dengue patients admitted was in the month of September, i.e., 140 with 16 positive (14.81%) followed by August 122 samples (12.16%) and October 110 samples with 14 (11.03%) positive. The sensitivity and specificity of ICT was 95.5% and 100% when compared with IgM-ELISA.Conclusions: Dengue cases were more during August to November in the monsoon and post monsoon season which is useful to plan special preventive strategies. This study draws attention toward the male, young and adult age group. To conclude, in countries lacking infrastructure for the diagnostic labs especially in the rural and remote areas, the rapid dengue ICT tests can play a major role in diagnosis and in patient management of acute dengue infection. The rapid ICTs are very simple, easy to perform, and can be used as point of care tests. We suggest that the rapid ICT for dengue detection may be used in patients presenting with febrile illness

Synthesis, Characterization and Biological Properties of Tridentate NNO, NNS and NNN Donor Thiazole-Derived Furanyl, Thiophenyl and Pyrrolyl Schiff Bases and Their Co(II), Cu(II), Ni(II) and Zn(II) Metal Chelates

2-Aminothiazole undergoes condensation reactions with furane-, thiophene- and pyrrole-2-carboxylaldehyde to give tridentate NNO, NNS and NNN Schiff bases respectively. These tridentate Schiff bases formed complexes of the type [M (L)2]X2 where [M=Co(II), Cu(II), Ni(II) or Zn(II), L=N-(2-furanylmethylene)-2-aminothiazole (L1), N-(2-thiophenylmethylene)-2-aminothiazole (L2, N-(2-pyrrolylmethylene)-2-aminothiazole (L3) and X=Cl. The structures of these Schiff bases and of their complexes have been determined on the basis of their physical, analytical and spectral data. The screening results of these compounds indicated them to possess excellent antibacterial activity against tested pathogenic bacterial organisms e.g., Escherichia coli, Staphylococcus aureous and Pseudomonas aeruginosa. However, in comparison, their metal chelates have been shown to possess more antibacterial activity than the uncomplexed Schiff bases

THE ADVANTAGE OF GENETIC ALGORITHM IN ENERGY-EFFICIENT SCHEDULING FOR HETEROGENEOUS CLOUD COMPUTING

Nowadays Energy Consumption has been a heavy burden on the enterprise cloud computing infrastructure. This paper focuses on the hardware factors in energy consumption. Inspired by DVFS, it proposes a new energy-efficient (EE) model. This paper formulates the scheduling problem and genetic algorithm is applied to obtain higher efficiency value. Simulations are implemented to verify the advantage of genetic algorithm. In addition, the robustness of our strategy is validated by modifying the relevant parameters of the experimen

Factors Contributing To Absenteeism In Undergraduates Nursing Students.

Absenteeism is an act of being excessively away from educational institute which is a major indiscipline problem among students globally. Descriptive cross-sectional study design was used. Data was collected by using by self-administered questionnaire from nursing students of 13 nursing colleges offering 4-year Bachelor of Science in Nursing (BSN) program in Punjab province of Pakistan after taking care of all the ethical considerations. Systematic random sampling technique was used to select 130 participants from nursing institutes offering 4 year BSN program. Most influential factors for absenteeism identified were ‘not joined nursing studies as per choice’ (mean=3.77), ‘awaking up late for college’ (mean=3.62), ‘impending assignments’ (mean=3.),‘feel bored with certain subjects’ (mean=3.46),‘when teachers teaching skills are not up to mark’ (mean=3.67), ‘Lack of proper guidance in clinical area’ (mean=3.94), ‘shortage of staff in clinical area’ (mean=3.88), ‘Not want to be treated as workforce in clinical area’ (mean=3.82) very exhaustive and rigid or irregular timetable’ (mean=3.81). Nursing student’s absenteeism is interplay of multiple modifiable factors. Hence, necessary steps may be taken to overcome these modifiable factors to improve the quality of nursing education

PREVENTION OF FALL IN PLATELET COUNT BY CARICA PAPAYA LEAF JUICE IN CARBOPLATIN INDUCED THROMBOCYTOPAENIA IN MICE

ABSTRACT Background: There are no effective methods to treat thrombocytopenia once it occurs. Transfusions, growth factor injections and bone marrow transplant have their limitations. So there is increased need for research of drugs that could prevent and treat thrombocytopenia. The objective the study to determine the effect of different doses of male and female papaya leaf juice on prevention of carboplatin induced thrombocytopenia in mice. Methods: A total of 55 Swiss albino mice were randomly divided into five groups (C, M10, M5, F10 and F5). Thrombocytopaenia was induced in all groups by a single intraperitoneal injection of carboplatin. Male papaya leaf juice was given to prevent of thrombocytopaenia to groups M10 and M5 and female papaya leaf juice was given to F10 and F5. On days 0, 7, 14 and 21 blood samples were collected by cardiac puncture for platelet count. Significance of difference was calculated by one way ANOVA. Results: After carboplatin injection, platelet count decreased. Papaya leaf juice prevented fall in platelet count throughout the study period with p-value < 0.001. Difference between male and female papaya leaf juice was not significant while higher dose (10 ml/kg) produced significantly higher responses as compared to low dose (5 ml/kg). Conclusion: Papaya leaf juice prevents reversible thrombocytopaenia induced by carboplatin in a dose dependent manner. There is no difference between male and female plants in this respect

Effect of early tranexamic acid administration on mortality, hysterectomy, and other morbidities in women with post-partum haemorrhage (WOMAN): an international, randomised, double-blind, placebo-controlled trial

Background Post-partum haemorrhage is the leading cause of maternal death worldwide. Early administration of tranexamic acid reduces deaths due to bleeding in trauma patients. We aimed to assess the effects of early administration of tranexamic acid on death, hysterectomy, and other relevant outcomes in women with post-partum haemorrhage. Methods In this randomised, double-blind, placebo-controlled trial, we recruited women aged 16 years and older with a clinical diagnosis of post-partum haemorrhage after a vaginal birth or caesarean section from 193 hospitals in 21 countries. We randomly assigned women to receive either 1 g intravenous tranexamic acid or matching placebo in addition to usual care. If bleeding continued after 30 min, or stopped and restarted within 24 h of the first dose, a second dose of 1 g of tranexamic acid or placebo could be given. Patients were assigned by selection of a numbered treatment pack from a box containing eight numbered packs that were identical apart from the pack number. Participants, care givers, and those assessing outcomes were masked to allocation. We originally planned to enrol 15 000 women with a composite primary endpoint of death from all-causes or hysterectomy within 42 days of giving birth. However, during the trial it became apparent that the decision to conduct a hysterectomy was often made at the same time as randomisation. Although tranexamic acid could influence the risk of death in these cases, it could not affect the risk of hysterectomy. We therefore increased the sample size from 15 000 to 20 000 women in order to estimate the effect of tranexamic acid on the risk of death from post-partum haemorrhage. All analyses were done on an intention-to-treat basis. This trial is registered with ISRCTN76912190 (Dec 8, 2008); ClinicalTrials.gov, number NCT00872469; and PACTR201007000192283. Findings Between March, 2010, and April, 2016, 20 060 women were enrolled and randomly assigned to receive tranexamic acid (n=10 051) or placebo (n=10 009), of whom 10 036 and 9985, respectively, were included in the analysis. Death due to bleeding was significantly reduced in women given tranexamic acid (155 [1·5%] of 10 036 patients vs 191 [1·9%] of 9985 in the placebo group, risk ratio [RR] 0·81, 95% CI 0·65–1·00; p=0·045), especially in women given treatment within 3 h of giving birth (89 [1·2%] in the tranexamic acid group vs 127 [1·7%] in the placebo group, RR 0·69, 95% CI 0·52–0·91; p=0·008). All other causes of death did not differ significantly by group. Hysterectomy was not reduced with tranexamic acid (358 [3·6%] patients in the tranexamic acid group vs 351 [3·5%] in the placebo group, RR 1·02, 95% CI 0·88–1·07; p=0·84). The composite primary endpoint of death from all causes or hysterectomy was not reduced with tranexamic acid (534 [5·3%] deaths or hysterectomies in the tranexamic acid group vs 546 [5·5%] in the placebo group, RR 0·97, 95% CI 0·87-1·09; p=0·65). Adverse events (including thromboembolic events) did not differ significantly in the tranexamic acid versus placebo group. Interpretation Tranexamic acid reduces death due to bleeding in women with post-partum haemorrhage with no adverse effects. When used as a treatment for postpartum haemorrhage, tranexamic acid should be given as soon as possible after bleeding onset. Funding London School of Hygiene & Tropical Medicine, Pfizer, UK Department of Health, Wellcome Trust, and Bill & Melinda Gates Foundation

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling

The performance of quantitative structure–activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost

کشمیری مزاحمتی شاعری کے پچھتر سال

Resistance literature has a prominent place in Kashmiri language. It begins with Lalla ‘Arifa's composition which she presented as an organized effort against the Brahmanism in 14th Century. Her poetry motivates people to live for high values shunning mundane gains at the cost of human dignity. Iqbal, Fauq, Mahjūr, Āzad and Manto enriched Kashmiri language with resistance literature. Kashmir is still in the shackles of slavery despite the departure of the British in 1947 from the sub-continent. Today, Kashmir is badly bleeding by the tyrant India. Presently, Amīn Kamil, Dina Nāth Nadim, Ḥusain ‘Ali, Tanha Ansari etc. are raising their voices through their verses against the illegitimate occupation of their mother land.</p

Ameen Kamil: The Architect of Modern Kashmiri Literature

Kashmiri modern literature started from the 20th century whenbothMahjoor and Azad Kashmiri poets and writers started wrote aboutthe problems and misseries of their people. The writers and poetsof this Era were influenced by struggle freedom of kashmir. AminKamil is one of them who gave new directions to kashmiri poetryand prose. He was a trend setter in Kashmiri literature, who usednew symbols and ideas in his poems to motivate kashmiri poeplesto have a right path of liberty