Drug Analysis by TLC–DESI MS

2015 ◽  
pp. 199-204
Author(s):  
Anna -Kułakowska ◽  
Przemysław Mielczarek ◽  
Piotr Suder ◽  
Jerzy Silberring
Keyword(s):  
Drug Analysis ◽  
Desi Ms

Crude Drug Analysis, Antimicrobial and Larvicidal Screening of the Methanolic Leaf Extract of Cestrum nocturnum L. (Fam. Solanaceae)

Planta Medica ◽  
2013 ◽  
Vol 79 (05) ◽  
Author(s):  
PE Balbas ◽  
JE Briones ◽  
JA Camposano ◽  
DI Carrido ◽  
KM Convento ◽  
...  
Keyword(s):  
Leaf Extract ◽  
Drug Analysis ◽  
Crude Drug

Intraoperative assessment of isocitrate dehydrogenase mutation status in human gliomas using desorption electrospray ionization–mass spectrometry

2020 ◽  
Vol 132 (1) ◽  
pp. 180-187 ◽  
Author(s):  
Clint M. Alfaro ◽  
Valentina Pirro ◽  
Michael F. Keating ◽  
Eyas M. Hattab ◽  
R. Graham Cooks ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Isocitrate Dehydrogenase ◽  
Tumor Resection ◽  
Desorption Electrospray Ionization ◽  
Extent Of Resection ◽  
Ionization Mass ◽  
Idh Mutation ◽  
Mutation Status ◽  
Desi Ms

OBJECTIVEThe authors describe a rapid intraoperative ambient ionization mass spectrometry (MS) method for determining isocitrate dehydrogenase (IDH) mutation status from glioma tissue biopsies. This method offers new glioma management options and may impact extent of resection goals. Assessment of the IDH mutation is key for accurate glioma diagnosis, particularly for differentiating diffuse glioma from other neoplastic and reactive inflammatory conditions, a challenge for the standard intraoperative diagnostic consultation that relies solely on morphology.METHODSBanked glioma specimens (n = 37) were analyzed by desorption electrospray ionization–MS (DESI-MS) to develop a diagnostic method to detect the known altered oncometabolite in IDH-mutant gliomas, 2-hydroxyglutarate (2HG). The method was used intraoperatively to analyze tissue smears obtained from glioma patients undergoing resection and to rapidly diagnose IDH mutation status (< 5 minutes). Fifty-one tumor core biopsies from 25 patients (14 wild type [WT] and 11 mutant) were examined and data were analyzed using analysis of variance and receiver operating characteristic curve analysis.RESULTSThe optimized DESI-MS method discriminated between IDH-WT and IDH-mutant gliomas, with an average sensitivity and specificity of 100%. The average normalized DESI-MS 2HG signal was an order of magnitude higher in IDH-mutant glioma than in IDH-WT glioma. The DESI 2HG signal intensities correlated with independently measured 2HG concentrations (R2 = 0.98). In 1 case, an IDH1 R132H–mutant glioma was misdiagnosed as a demyelinating condition by frozen section histology during the intraoperative consultation, and no resection was performed pending the final pathology report. A second craniotomy and tumor resection was performed after the final pathology provided a diagnosis most consistent with an IDH-mutant glioblastoma. During the second craniotomy, high levels of 2HG in the tumor core biopsies were detected.CONCLUSIONSThis study demonstrates the capability to differentiate rapidly between IDH-mutant gliomas and IDH-WT conditions by DESI-MS during tumor resection. DESI-MS analysis of tissue smears is simple and can be easily integrated into the standard intraoperative pathology consultation. This approach may aid in solving differential diagnosis problems associated with low-grade gliomas and could influence intraoperative decisions regarding extent of resection, ultimately improving patient outcome. Research is ongoing to expand the patient cohort, systematically validate the DESI-MS method, and investigate the relationships between 2HG and tumor heterogeneity.

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

Recent applications of nanomaterials based on electrochemical drug analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Huseyin Senturk ◽  
Hakan Karadeniz ◽  
Arzum Erdem
Keyword(s):  
Chemical Properties ◽  
Drug Analysis ◽  
Physical And Chemical Properties ◽  
Physical And Chemical ◽  
General Perspective ◽  
And Chemical Properties

: Regarding to the development of nanomaterials, they could be widely used in electrochemical drug analysis due to their unique physical and chemical properties. Herein, we presented a general perspective to different nanomaterials based electrochemical approaches developed for drug analysis, that were performed in the last decade while summarizing their advantages with further applications.

Pharmaceutico- Analytical Study of Abhra Sindoora

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Dr. Jyothi B. ◽  
Dr. M. V. Sobagin ◽  
Dr. M.C. Patil
Keyword(s):  
Analytical Study ◽  
Current Trend ◽  
Drug Analysis ◽  
Sem Analysis ◽  
Secondary Amines ◽  
Particle Analysis ◽  
Hexagonal Crystal ◽  
Xrd Study ◽  
Edxrf Analysis ◽  
Working Standards

Background: Abhra Sindoora[1] (ABS) is a unique Rasa Yoga with having more potent and indication in Tridoshahara, Swasa, Kasa etc. It is one of the important classical Kupipakva Rasayana containing Hingulotha Parada (purified mercury), Shuddha Gandhaka (purified sulfur) and Dhanyabhraka in 1:1:1 proportion. Aim: Pharmaceutico-Analytical study of Abhra Sindoora. Materials and Methods: Hingulotha Parada (purified mercury), Shuddha Gandhaka (purified sulfur) and Dhanyabhraka are used to prepare Kajjali and lavigated with Vatankura (leaf buds of Ficus bengalensis), Swarasa (juice) and Arka (Calotrapis procera) ksheera (milk). This Kajjali is processed by Kupipakva method. Results and Conclusion: The current trend in applied instrumental medical research encourages good medical practice, clinical and research based drug analysis. The main aim of analytical study is to find out working standards for the formulations and safe use of therapeutics. Abhra Sindoora was prepared in 48 hours with 28% yield. It was also characterized by using modern instrumental analysis like XRD, SEMEDX, EDXRF, FTIR and PARTICLE ANALYSIS. The SEM analysis evaluated that prepared Abhra Sindoora has particles in nanometers, least being 14.87nm. SEMEDX study confirmed the presence of C, O, Si, S, K, and HgM. XRD study confirmed the presence of Hg3.00S3.00 in hexagonal crystal system. The EDXRF analysis evaluated the presence of K, Ca, Ti, Mn, Fe, S, Br and Hg. FTIR analysis shows organic compounds with functional groups like secondary amines, Nitro, Carboxylic acids, Bromine, Esters, Alkines, and Iodides etc.

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