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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bhattacharya FrontInOnc2019 9.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mandrioli Molecules2019 24-11.png|240px]]</div>
'''"[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|AI meets exascale computing: Advancing cancer research with large-scale high-performance computing]]"'''
'''"[[Journal:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.]]"'''


The application of data science in [[cancer]] research has been boosted by major advances in three primary areas: (1) data: diversity, amount, and availability of biomedical data; (2) advances in [[artificial intelligence]] (AI) and machine learning (ML) algorithms that enable learning from complex, large-scale data; and (3) advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build ''in silico'' ML models that can provide transformative insights from data, including molecular dynamics simulations, [[Sequencing|next-generation sequencing]], omics, [[Molecular imaging|imaging]], and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next-generation high-performance computers; and (3) assessing robustness and reliability in the AI models. ('''[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|Full article...]]''')<br />
[[wikipedia:Cannabis|Cannabis]] has regained much attention as a result of updated legislation authorizing many different uses, and it can be classified on the basis of the content of [[wikipedia:Tetrahydrocannabinol|Δ9-tetrahydrocannabinol]] (Δ9-THC), a psychotropic substance for which there are legal limitations in many countries. For this purpose, accurate qualitative and quantitative determination is essential. The relationship between THC and [[wikipedia:Cannabidiol|cannabidiol]] (CBD) is also significant, as the latter substance is endowed with many specific and non-psychoactive proprieties. For these reasons, it becomes increasingly important and urgent to utilize fast, easy, validated, and harmonized procedures for determination of [[wikipedia:Cannabinoid|cannabinoids]]. The procedure described herein allows rapid determination of 10 cannabinoids from the [[wikipedia:Inflorescence|inflorescences]] of ''Cannabis sativa'' L. by extraction with organic solvents. Separation and subsequent detection are by [[wikipedia:Reversed-phase chromatography|reversed-phase]] [[high-performance liquid chromatography]] with ultraviolet detector (RP-HPLC-UV). ('''[[Journal:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|Full article...]]''')<br />
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Revision as of 16:43, 20 January 2020

Fig1 Mandrioli Molecules2019 24-11.png

"Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L."

Cannabis has regained much attention as a result of updated legislation authorizing many different uses, and it can be classified on the basis of the content of Δ9-tetrahydrocannabinol (Δ9-THC), a psychotropic substance for which there are legal limitations in many countries. For this purpose, accurate qualitative and quantitative determination is essential. The relationship between THC and cannabidiol (CBD) is also significant, as the latter substance is endowed with many specific and non-psychoactive proprieties. For these reasons, it becomes increasingly important and urgent to utilize fast, easy, validated, and harmonized procedures for determination of cannabinoids. The procedure described herein allows rapid determination of 10 cannabinoids from the inflorescences of Cannabis sativa L. by extraction with organic solvents. Separation and subsequent detection are by reversed-phase high-performance liquid chromatography with ultraviolet detector (RP-HPLC-UV). (Full article...)

Recently featured:

What is this sensor and does this app need access to it?
AI meets exascale computing: Advancing cancer research with large-scale high-performance computing
Building infrastructure for African human genomic data management