Paracetamol is a non-steroidal, anti-inflammatory drug widely used in pharmaceutical applications for its sturdy, antipyretic and analgesic action. However, an overdose of paracetamol can cause fulminant hepatic necro...Paracetamol is a non-steroidal, anti-inflammatory drug widely used in pharmaceutical applications for its sturdy, antipyretic and analgesic action. However, an overdose of paracetamol can cause fulminant hepatic necrosis and other toxic effects. Thus, the development of advantageous analytical tools to detect and determine paracetamol is required. Due to simplicity, higher sensitivity and selectivity as well as costefficiency, electrochemical sensors were fully investigated in last decades. This review describes the advancements made in the development of electrochemical sensors for the paracetamol detection and quantification in pharmaceutical and biological samples. The progress made in electrochemical sensors for the selective detection of paracetamol in the last 10 years was examined, with a special focus on highly innovative features introduced by nanotechnology. As the literature is rather extensive, we tried to simplify this work by summarizing and grouping electrochemical sensors according to the by which manner their substrates were chemically modified and the analytical performances obtained.展开更多
In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our...In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.展开更多
文摘Paracetamol is a non-steroidal, anti-inflammatory drug widely used in pharmaceutical applications for its sturdy, antipyretic and analgesic action. However, an overdose of paracetamol can cause fulminant hepatic necrosis and other toxic effects. Thus, the development of advantageous analytical tools to detect and determine paracetamol is required. Due to simplicity, higher sensitivity and selectivity as well as costefficiency, electrochemical sensors were fully investigated in last decades. This review describes the advancements made in the development of electrochemical sensors for the paracetamol detection and quantification in pharmaceutical and biological samples. The progress made in electrochemical sensors for the selective detection of paracetamol in the last 10 years was examined, with a special focus on highly innovative features introduced by nanotechnology. As the literature is rather extensive, we tried to simplify this work by summarizing and grouping electrochemical sensors according to the by which manner their substrates were chemically modified and the analytical performances obtained.
文摘In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost impor-tance.Despite this,to the best of our knowledge,no tool exists for such prediction.Therefore,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical structures.The current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas aeruginosa.The deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,respectively.In addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.
