Difference between revisions of "Main Page/Featured article of the week/2024"

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<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 08–14:</h2>
<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 15–21:</h2>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Jadhav IntJofMolSci23 24-9.png|240px]]</div>
'''"[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|A metabolomics and big data approach to cannabis authenticity (authentomics)]]"'''
 
With the increasing accessibility of [[cannabis]] ([[Cannabis sativa|''Cannabis sativa'' L.]], also known as marijuana and [[hemp]]), its products are being developed as [[Cannabis concentrate|extracts]] for both recreational and [[Cannabis (drug)|therapeutic]] use. This has led to increased scrutiny by [[Regulatory compliance|regulatory bodies]], who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive [[Metabolomics|metabolites]] and potentially harmful [[Contamination|contaminants]], such as [[heavy metals]] and other impurities. However, the complexity of cannabis' metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy ... ('''[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|Full article...]]''')<br />
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 08–14:</h2>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:GA Ishii SciTechAdvMatMeth2023 3-1.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:GA Ishii SciTechAdvMatMeth2023 3-1.jpg|240px]]</div>
'''"[[Journal:Integration of X-ray absorption fine structure databases for data-driven materials science|Integration of X-ray absorption fine structure databases for data-driven materials science]]"'''  
'''"[[Journal:Integration of X-ray absorption fine structure databases for data-driven materials science|Integration of X-ray absorption fine structure databases for data-driven materials science]]"'''  

Revision as of 16:57, 22 January 2024

Featured article of the week archive - 2024

Welcome to the LIMSwiki 2024 archive for the Featured Article of the Week.

Featured article of the week: January 15–21:

Fig2 Jadhav IntJofMolSci23 24-9.png

"A metabolomics and big data approach to cannabis authenticity (authentomics)"

With the increasing accessibility of cannabis (Cannabis sativa L., also known as marijuana and hemp), its products are being developed as extracts for both recreational and therapeutic use. This has led to increased scrutiny by regulatory bodies, who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive metabolites and potentially harmful contaminants, such as heavy metals and other impurities. However, the complexity of cannabis' metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy ... (Full article...)


Featured article of the week: January 08–14:

GA Ishii SciTechAdvMatMeth2023 3-1.jpg

"Integration of X-ray absorption fine structure databases for data-driven materials science"

With the aim of introducing data-driven science and establishing an infrastructure for making X-ray absorption fine structure (XAFS) spectra findable and reusable, we have integrated XAFS databases in Japan. This integrated database (MDR XAFS DB) enables cross searching of spectra from more than 2,000 samples and more than 700 unique materials with machine-readable metadata. The introduction of a materials dictionary with approximately 6,000 synonyms has improved the search performance, and links with large external databases have been established. In order to compare spectra in the database, the energy calibration policies of each institution were compiled, and the energy calibration methods across institutions were shown ... (Full article...)


Featured article of the week: January 01–07:

Fig1 Heavey ForSciIntSyn2023 7.jpg

"Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand"

The investigation of quality issues detected within the forensic process is a critical feature in robust quality management systems (QMSs) to provide assurance of the validity of reported laboratory results and inform strategies for continuous improvement and innovation. A survey was conducted to gain insight into the current state of practice in the management and handling of quality issues amongst the government service provider agencies of Australia and New Zealand. The results demonstrate the value of standardized quality system structures for the recording and management of quality issues, but also areas where inconsistent reporting increases the risk of overlooking important data to inform continuous improvement ... (Full article...)