Antimicrobial activity and Quality Control Parameters of Talicati Vatakam - A Classical Siddha Drug

Tāḷicāti vaṭakam (TSV), a polyherbal siddha drug was chosen, it was screened for antimicrobial study and also subjected to standardization parameters. The ingredients were procured, authenticated and prepared the drug as per standard operating procedure. The ethanolic extract of TSV was screened for nine bacteria and two fungi. Then the drug was investigated for the phytochemical profile, physicochemical parameters, thin layer chromatographic  photo documentation (TLC), high-performance thin-layer chromatography (HPTLC) finger print profile. Antimicrobial assay revealed  inhibitory activity against all test pathogens. TLC under UV  showed 8 bands at short wavelength, 13 bands at long wavelength; 8 bands showed post derivatization with vanillin Sulphuric acid reagent. The present investigation concluded that the siddha herbal preparation of TSV have great potential on antimicrobial against pathogens. This siddha formulation can be used to prevent the bacterial and fungal infections and the standards could be used for quality control of the drug

Prognostic value of ankle-brachial index and dobutamine stress echocardiography for cardiovascular morbidity and all-cause mortality in patients with peripheral arterial disease

BackgroundPeripheral arterial disease (PAD) is associated with an excessive risk for cardiovascular events and mortality. To determine measures prognostic of adverse events, ankle-brachial index (ABI) was compared with dobutamine stress echocardiography (DSE) in patients referred to our vascular center for the evaluation of PAD.MethodsThe medical records of consecutive patients referred for the concurrent evaluation of PAD and coronary artery disease (CAD) between 1992 and 1995 were reviewed for subsequent cardiovascular events and death.ResultsAmong 395 patients (mean age, 69.7 ± 9.6 years; 40% women), 341 had abnormal ABI and 268 had abnormal DSE (95 fixed and 173 stress-induced wall motion abnormalities). During a mean follow-up of 4.7 years, 27.3% of patients experienced a cardiovascular event, and 39.4% died. By multivariate analysis, ABI provided the strongest prediction of all-cause mortality (hazard ratio [HR], 2.34; 95% confidence interval [CI], 1.36 to 4.05; P = .002). Conversely, DSE with inducible or fixed wall motion abnormalities showed no association with cardiovascular events or increased mortality in multivariate analysis. The only DSE variable independently predictive of mortality was decreased left ventricular ejection fraction (<50%) at peak stress (HR, 1.70; 95% CI, 1.22 to 2.36; P = .002). Statin and aspirin therapy, but not β-blockers, were protective. There was no relation between ABI and wall motion index score at rest or after stress.ConclusionsIn high-risk patients referred to our vascular center for the evaluation of PAD, the assessment of ABI provided a strong independent prediction of all-cause mortality. Therefore, proper interpretation of this simple, affordable, and reproducible measure extends beyond the assessment of PAD severity. Although a poor left ventricular response to dobutamine was also predictive, other echo variables were not

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

International audienceThere are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling