scholarly journals Physiochemical Properties of Sago Starch Modified by Acid Treatment in Alcohol

2008 ◽  
Vol 5 (4) ◽  
pp. 307-311 ◽  
Author(s):  
Yiu
Keyword(s):  
Acid Treatment ◽  
Sago Starch ◽  
Physiochemical Properties

scholarly journals A Review on Indian Sago Starch And Its Pharmacuetical Applications

2013 ◽  
Vol 2 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Ranu Yadav ◽  
Garima Garg
Keyword(s):  
Pharmaceutical Industry ◽  
Life Sciences ◽  
Starting Material ◽  
Present Review ◽  
Sago Starch ◽  
Chemical Modifications ◽  
Physiochemical Properties ◽  
Native Starch ◽  
Pharmaceutical Applications ◽  
Commercial Applications

Indian sago starch extracted from Tapioca roots finds its application not only as a food but also numerous commercial applications. In the present review we are discussing concisely the extraction, physiochemical properties, chemical modifications and pharmaceutical applications of Indian sago starch. The sago starch is a cheap, easily available, biodegradable and a versatile polymer. Starch has always been an important excipient in the pharmaceutical industry. It is conventionally used as a binder, disintegrant, diluent, granulating agent. It is also a starting material for many other chemicals like ethanol, glucose and cyclodextrin. Several modifications were attempted on native starch to improve and modulate its physiochemical properties. DOI: http://dx.doi.org/10.3329/ijpls.v2i3.15456 International Journal of Pharmaceutical and Life Sciences Vol.2(3) 2013: 99-106

Fasciaton of Pectate Elementary Fibrils

1973 ◽  
Vol 31 ◽  
pp. 60-61
Author(s):  
George F. Leeper
Keyword(s):  
Acid Treatment ◽  
Carbon Film ◽  
Final Concentration ◽  
Cellulose Microfibrils ◽  
Pectic Acid ◽  
Elementary Fibrils

Polysaccharide elementary fibrils are usually fasciated into microfibrils of from one hundred to a few hundred Angstroms wide. Cellulose microfibrils when subjected to acid treatment dissociate into component elementary fibrils. For pectic acid it was observed that variations in pH could cause a change in the fasciation of the elementary fibrils.Solutions of purified pectic acid and sodium phosphotungtate were adjusted to various pH levels with NaOH or HCl and diluted to give a final concentration of 0.5 and 1% for the polysaccharide and negative stains respectively. Micrographs were made of the samples after drying on a carbon film covered grid. The average number of elementary fibrils was determined by counting the number of elementary fibrils in each fascicle intersected by lines drawn across the micrograph.

Does anti-acid treatment influence disease progression in SSc-ILD? Data form the German SSc-network

2020 ◽  
Author(s):  
M Kreuter ◽  
F Bonella ◽  
N Blank ◽  
E Siegert ◽  
J Henes ◽  
...  
Keyword(s):  
Disease Progression ◽  
Acid Treatment ◽  
Data Form

Effect of valproic acid treatment on body composition, leptin and soluble leptin receptor in epileptic children

2006 ◽  
Vol 37 (06) ◽  
Author(s):  
M Rauchenzauner ◽  
E Haberlandt ◽  
S Scholl-Bürgi ◽  
D Karall ◽  
E Schönherr ◽  
...  
Keyword(s):  
Valproic Acid ◽  
Body Composition ◽  
Acid Treatment ◽  
Leptin Receptor ◽  
Soluble Leptin Receptor ◽  
Epileptic Children

SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

2018 ◽  
Author(s):  
Caitlin C. Bannan ◽  
David Mobley ◽  
A. Geoff Skillman
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Molecular Graph ◽  
Gaussian Process Regression ◽  
Ionization State ◽  
Training Set ◽  
Physiochemical Properties ◽  
Quantile Plots ◽  
Physical And Chemical ◽  
Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

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