Significance of knowledge regarding Koshtha in Panchakarma

The Koshtha is very important matter in study of Ayurveda especially in Panchakarma. Koshtha is of three types, Mridu, Madhya and Krura on the bases of dominant Dosha in person and effect of some specific Ahara and Vihara after consuming it. After Koshtha examination, physician gets knowledge about predominant Dosha and makes judgement for line of treatment, advice regarding do and don’ts. Appropriate diet can be decided as diet is supposed to be one of the lines of treatment. Since 5 cleaning procedures are inevitable in case of vitiation of Dosha and Snehana is inevitable preoperative procedure before performing cleaning ones. For Snehana which unctuous material should be used is decided by Koshtha examination. Also dose and drug of choice of emetics and purgatives can be decided by it

A comprehensive study on Niragni Sweda

Swedana Karma is one of the Poorvakarma for Panchakarma procedures which relieves Shoola, Stambha, Gaurava and Shaitya as well as which induces sweating and softness in the body. Several types of classification of Sweda are mentioned in our classics and according to Agni Bheda, there are two types of Sweda, 1) Sagni Sweda 2) Niragni Sweda. Vyayama, Ushnasadan, Guru Pravarana, Kshudha, Bahupan, Bhaya, Krodha, Upanah, Aahav, Aatapa, Adhva and Bharharan are the types of Niragni Sweda which are useful in the diseases of Kapha Avrita Vata and Meda Avrita Vata. It is also important for (OPD level) those patients who can’t hospitalised and can be performed by themselves without any precaution or need of instruments. Niragni Sweda is very useful in the diseases which are in the list of contraindicated for Sagni Sweda, like Prameha, Sthaulya, Urustambha etc. Among these ten types, Vyayam, Upanaha, Guru Pravarana, Ushnasadan and Kshudha are main types of non thermal sudation

Analysis of the Molecular Networks in Androgen Dependent and Independent Prostate Cancer Revealed Fragile and Robust Subsystems

Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK pathways. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen-independent prostate cancers

Spatial normalization of reverse phase protein array data.

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src

Perturbation biology: inferring signaling networks in cellular systems.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology

Combinatorial Targeted Therapies For The Treatment Of Glioblastoma Tumorsphere Lines

The depth of our knowledge about the molecular genetics of glioblastoma (GBM) stands in stark contrast with our ability to treat it successfully. This work reports research aimed at developing combination therapies to effectively inhibit cell growth in GBM tumorspheres. In our first study, our goal was to identify synergistic pairs of drugs across three tumorsphere lines bearing genomic alterations representative of the established signaling subclasses of GBM tumors – the NF1 deleted (represented by the tumorsphere line TS565), PGDFRA amplified (represented by the tumorsphere line TS543) and EGFR activated (represented by the tumorsphere line TS676) types. Using 12 targeted drug pairs, we identified drug combinations whose effects were cell-line specific and reduced cell viability com-pared to the single drugs. We quantified synergy using two measures, the well known Combination Index, as well as a measure we defined as the Efficacy Index that is able to detect synergies in instances where the Combination Index defined at 50% is unable to capture synergy. Predominant among the synergistic drug combinations we report are the combination of MEK and AKT1/2 inhibition in the line TS543, that of the drugs gefitinib (EGFRi) and AG538 (IGFRi) in line TS565 and of gefitinib and stattic (STAT3i) in line TS676. In a second study, we sought to extend our findings from combination therapies to a clinically distinct, frequently observed subset of treatment resistant EGFR-driven GBM tumors. To improve the efficacy of EGFR inhibition, we rationally selected drugs that that may synergize with lapatinib based on the action of their respective targets on key oncogenic pathways, and explored the optimal sequence and timing of administration. In TS676 tumorspheres, which have an EGFR amplification, express the EGFRvIII mutantation and have low PTEN ex-pression, the combination of lapatinib and obatoclax was synergistic when obatoclax was applied before lapatinib. The observed synergy correlated positively with time delays from 3h to 24h. We then studied this combination in two other tumorsphere lines TS600, with an EGFR gain, and GBM39, which is EGFR amplified with the vIII mutation but PTEN intact. Sequential administration was only mildly beneficial in TS600 and not beneficial in GBM39. A time-course protein array experiment designed to illuminate the network aspects of the effects of lapatinib and obatoclax in TS676 and TS600 revealed that the most effective sequential combination in TS676 was obatoclax preceding lapatinib by 12h. This was associated with higher cleaved caspase-3 activation than the less effective co-treatment with lapatinib and obatoclax. We applied network based modeling methodologies to help test hypotheses that may explain the increased vulnerability of TS676 to lapatinib upon pretreatment with obatoclax. This study presents encouraging results demonstrating the role of drug combination timing and order on the observed effect and synergy of therapies aimed at treating EGFR driven GBM tumorspheres. We show that BCL2 inhibition by obatoclax offers a potent and promising means of increasing cellular sensitivity to lapatinib. However, further work in other EGFR driven GBM models that have lost PTEN expression is a desirable next step towards revealing the relationship of sequential synergy to this well studied co-occurrence of genetic alterations