A PILOT STUDY ON THE EFFICACY OF SIDDHA MEDICINE SEENTHIL SARKARAI IN THE MANAGEMENT OF MOOTHIRAKIRICHARAM (URINARY TRACT INFECTION)

Objective: The present pilot study was designed to evaluate the efficacy of Seenthil sarkarai, a Classical Siddha medicine in Moothirakiricharam (Urinary Tract Infection) patients.Method: This study was approved by IEC and registered in Clinical Trial Registry of India and was conducted at the National Institute of Siddha, Tambaram, Chennai, India. The investigator recruited 20 patients of both male and female with an inclusion and exclusion criteria. All the patients were treated with Seenthil Sarkarai at the dose level of 2 grams two times a day with warm water for 21 days. Dietary regimen was advised for the patients. Study outcome was studied with negative Urine culture and also based on the improvement in the reduction of Burning micturition, Frequency of micturition, Dysuria with fever, Foul smelling urine.Results: The symptoms such as burning micturition, persistent urge to urinate, foul smelling urine, and presence of bacteriuria reduced markedly within 21 days of duration. Out of 20 patients, 12 patients were completely relieved from the compliance of UTI with negative Urine culture. The symptoms of UTI were markedly reduced in remaining 8 patients. There were no adverse effect reported during the study and no recurrence of UTI was noticed in 12 patients who were completely cured.Conclusion: Results suggest that the trial drug Seenthil Sarkarai is effective and safe for the management of Moothirakiricharam (UTI) and in alleviating recurrences

EVALUATION OF ANTIOXIDANT ACTIVITY OF SEERAGA CHOORANAM IN- VITRO ASSAY (A SIDDHA POLYHERBAL PREPARATION)

Siddha Medicine (Tamil Citta- or Tami?-maruttuvam) is a system of traditional medicine originating in Tamil Nadu in South India. The drugs used by the Siddhars could be classified into three groups: Thaavaram, Thaathu and Jangamam seeraga chooranam is a drug under the category of Thaavaram. It is a poly herbal formulation which is mentioned in the text Pullipanivaithyam 500 which has been therapeutically used for Pithanoigal, Kirukirupu, Vaanthi, Mayakkam, Ajeeranam, Kaangai, Veppam.Siddha system emphasis mainly on healthy long life by preventing ageing and degenerative disease with medicines having antioxidant activity. Even though this drug has been used based on traditional knowledge no scientific work has been done to evaluate the antioxidant properties of Seeraga choornam based on various in vitro assays. The study result confirmed that the drug Seeraga choornam has promising therapeutic antioxidant activity when compared with the standard drug. This research work can help for medical practitioners to use this polyherbal compound for the treatment of cancer

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

Background: Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. Methodology: A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.' More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method's performance. Results: Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI's On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI's Genes & Disease, NCI's Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer. Conclusions: We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids

Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data

Background: Most 'transcriptomic' data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, 'transcriptomic' data may help build better classification models. However, most proposed methods for integrative analysis of 'transcriptomic' data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that "an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources". Results: To evaluate the feasibility of the TRL-FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and other traditional methods, using 21 microarray datasets generated from three studies on brain cancer, prostate cancer, and lung disease, respectively. The results show that TRL-FM statistically significantly outperforms TRL as well as traditional models based on single source data. In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging. Conclusions: The capability of utilizing transferred abstract knowledge derived from source data using feature mapping enables the TRL-FM framework to mimic the human process of learning and adaptation when performing related tasks. The novel TRL-FM methodology for integrative modeling for multiple 'transcriptomic' datasets is able to intelligently incorporate domain knowledge that traditional methods might disregard, to boost predictive power and generalization performance. In this study, TRL-FM's abstraction of knowledge is achieved in the form of functional modules, but the overall framework is generalizable in that different approaches of acquiring abstract knowledge can be integrated into this framework

A Minimum of Three Motifs Is Essential for Optimal Binding of Pseudomurein Cell Wall-Binding Domain of Methanothermobacter thermautotrophicus

We have biochemically and functionally characterized the pseudomurein cell wall-binding (PMB) domain that is present at the C-terminus of the Surface (S)-layer protein MTH719 from Methanothermobacter thermautotrophicus. Chemical denaturation of the protein with guanidinium hydrochloride occurred at 3.8 M. A PMB-GFP fusion protein not only binds to intact pseudomurein of methanogenic archaea, but also to spheroplasts of lysozyme-treated bacterial cells. This binding is pH dependent. At least two of the three motifs that are present in the domain are necessary for binding. Limited proteolysis revealed a possible cleavage site in the spacing sequence between motifs 1 and 2 of the PMB domain, indicating that the motif region itself is protected from proteases

On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue

Background: Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50% of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. Methods: Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. Results: All Bayesian models achieved high classification performance (AUC>0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93%) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method. Conclusions: The results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients