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10.1021/jasms.0c00318 http://scihub22266oqcxt.onion/10.1021/jasms.0c00318 33395529!ä!33395529 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33395529&cmd=llinks): Failed to open stream: HTTP request failed! 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Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools |pdf=|usr=}}{{33395529}} gab.com Text Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools JO: J Am Soc Mass Spectrom http://www.kidney.de/pget.php?&tw=1&pmid=33395529 #FOAvip Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects. Twit Text FOAVip Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools ????J Am Soc Mass Spectrom http://www.kidney.de/pget.php?&tw=1&pmid=33395529 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33395529 #FOAvip English Wikipedia <ref>{{Cite pmid|33395529}}</ref> Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools #MMPMID33395529 Favela KA; Hartnett MJ; Janssen JA; Vickers DW; Schaub AJ; Spidle HA; Pickens KS J Am Soc Mass Spectrom 2021[Apr]; 32 (4): 860-871 PMID33395529show ga Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects. |*Masks[MESH] |Automation, Laboratory[MESH] |COVID-19/prevention & control[MESH] |Chromatography, Gas/*methods[MESH] |Humans[MESH] |Inhalation Exposure/prevention & control[MESH] |Manufactured Materials/*analysis[MESH] |Mass Spectrometry/*methods[MESH] |Models, Chemical[MESH] |Organic Chemicals/analysis[MESH] |Polymers/analysis[MESH] |Safety[MESH]Nontargeted Analysis of Face Masks: Comparison of Manual Curation to A(epmc).pdf DeepDyve Pubget Overpricing