ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files

Additional file 2. Recreated 3D geometry optimized structures of 29 molecules as visualized in the original program (Gauss View)

ChemTextMiner: An open source tool kit for mining medical literature abstracts

Text mining involves recognizing patterns from a wealth of information hidden latent in unstructured text and deducing explicit relationships among data entities by using data mining tools. Text mining of Biomedical literature is essential for building biological network connecting genes, proteins, drugs, therapeutic categories, side effects etc. related to diseases of interest. We present an approach for textmining biomedical literature mostly in terms of not so obvious hidden relationships and build biological network applied for the textmining of important human diseases like MTB, Malaria, Alzheimer and Diabetes. The methods, tools and data used for building biological networks using a distributed computing environment previously used for ChemXtreme[1] and ChemStar[2] applications are also described

Applications of Support Vector Machines as a Robust tool in High Throughput Virtual Screening

Chemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules

ASSOCIATION AND CORRELATION OF MEAN PLATELET VOLUME AND PLATELET COUNT IN ACUTE ISCHEMIC STROKE

Objective: Role of platelets in the pathogenesis of the atherothrombosis and ischemic stroke has been documented. Mean platelet volume (MPV) and platelet count (PC) could be important predictors of acute ischemic stroke (AIS), its severity; therefore we investigated the correlation of MPV & PC in AIS patients. Methods: We studied MPV and PC of 52 AIS patients consecutively admitted in Neurology department at Geetanjali Medical University, India. Platelet variables were measured and compared with control of similar age, sex and without vascular events. Results: Out of 52 patients, 30 (57.69%) had Thirty (57.69%) patients had significantly higher MPV in AIS group (12.45fL compared with normal range of 6–11 fL in control,p<0.001). No significant differences were found between male and females, but the total mean was elevated. The mean of PC was 1.76×105 cells/cumm (normal range) and there was no correlation between the change in PC and AIS in both sexes. Repeated measurements of MPV and PC were also recorded on follow-up which showed no significant changes from the acute phase; however, MPV remained elevated. The comparison of MPV in patients with mRS score 2 versus 4, 2 versus 5, 3 versus 4 and 5, and 4 versus 5 were found to be statistically significant (p<0.05). Conclusion: Increased MPV has an independent association with AIS and its severity and it could not change after acute treatment. It is possible that these changes precede the vascular event, and further studies are warranted to unravel the underlying mechanism

City profile : Ahmedabad

This research investigates potential pathways through which urban planning and governance mechanisms become drivers of deprivations, conflicts and violence. While also developing a background understanding of Ahmedabad, it discusses contexts of demography and economic transformations since liberalization; their impacts for urban poverty and inequality; the historical growth of the city and resulting spatial segmentation; the current status of housing amongst urban poor and low-income groups; and the urban development paradigm in terms of planning, housing, basic services, street vending and public transport. How can urban planning and governance interventions help reduce urban tensions, inequalities, conflicts and violence in Indian cities

Synthesis, Biological Evaluation and Molecular Modeling Studies of Novel Chromone/Aza-Chromone Fused α-Aminophosphonates as Src Kinase Inhibitors

A series of novel chromone/aza-chromone fused α-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 µM. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors

Computational analysis of next generation sequencing data and its applications in clinical oncology

Next generation sequencing (NGS) has made great strides in sequencing technology as it enables sequencing of genes in a high throughput manner with low cost. Various NGS platforms such as Illumina, Roche, ABI/SOLiD are used for wet-lab analysis of NGS data and computational tools such as BWA, Bowtie, Galaxy, SanGeniX are used for dry-lab analysis of NGS data. One of the important aspects of NGS data is its usage in early disease diagnosis especially in cancer which was earlier not possible with conventional sequencing technologies such as Sanger sequencing, NGS can identify all those mutations which cannot be identified using conventional sequencing technologies as researchers can now sequence the whole genome, exome or transcriptome. Exome sequencing is preferred, as a higher number of mutations are found to exist in the exome part of genes. The present comprehensive review encompasses the complete NGS data analysis workflow that includes alignment of NGS reads, identification and annotation of mutations and visualization, discussion of software tools for variant identification and annotation, evaluation of structural variation in NGS data, and study of different DNA sequencing technologies. In the field of clinical oncology, NGS has already proven its usefulness, and the mortality rate has been reduced, as now doctors can suggest a proper treatment to their patients by checking the complete genomic profile. However, data storage and the complexity in interpreting enormous amounts of data obtained with NGS still remain a computational challenge to researchers, as for each sample, the number of different and very large analysis files are generated directly from the raw sequence read file to the final result file. NGS resultant data is very complex, and its interpretation requires expert bioinformatics assistance, as a large number of mutations are identified from samples, but to differentiate clinically significant mutations among them with appropriate use of validation methods is a challenging task. This review is intended to provide researchers with a complete overview of NGS along with knowledge of how the tools will be employed, and insight into identification and interpretation of cancer mutations for clinical diagnostics. Keywords: Next generation sequencing, Mutations, Cancer, Sanger sequencing, Variant identification and annotation, Data analysi

A clinical study on the role of Ksara Vasti and Triphala Guggulu in Raktarsha (Bleeding piles)

Shonitarsha is a common affliction which has been described and treated since the beginning of human civilization. Hemorrhoidal cushions are a part of normal anatomy but become pathological when swollen or inflamed. Treatment of piles in modern medicine is hemorrhoidectomy which results in repeated recurrences. Ayurveda provides a cure and prevents recurrences. Present study was carried out using a combination of Apamarga Kshara Basti and Triphalaguggulu. The results of the clinical assessment of the indigenous formulation on 129 patients with bleeding piles are reported in this paper; 55 patients of a total of 129 showed marked relief

MOESM5 of ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files

Additional file 5. Instruction for compilation of chemengine source code available online and operation manual