<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>44(3)</volume><submitter>Klijn ME</submitter><funding>Karlsruher Institut für Technologie (KIT)</funding><pubmed_abstract>The protein cloud-point temperature (T&lt;sub>Cloud&lt;/sub>) is a known representative of protein-protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume T&lt;sub>Cloud&lt;/sub> detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent T&lt;sub>Cloud&lt;/sub> (T&lt;sub>Cloud,app&lt;/sub>). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on T&lt;sub>Cloud,app&lt;/sub> detection of lysozyme. Different image analysis s</pubmed_abstract><journal>Bioprocess and biosystems engineering</journal><pagination>525-536</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7889528</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination.</pubmed_title><pmcid>PMC7889528</pmcid><pubmed_authors>Klijn ME</pubmed_authors><pubmed_authors>Hubbuch J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination.</name><description>The protein cloud-point temperature (T&lt;sub>Cloud&lt;/sub>) is a known representative of protein-protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume T&lt;sub>Cloud&lt;/sub> detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent T&lt;sub>Cloud&lt;/sub> (T&lt;sub>Cloud,app&lt;/sub>). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on T&lt;sub>Cloud,app&lt;/sub> detection of lysozyme. Different image analysis s</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Mar</publication><modification>2025-04-04T13:38:24.817Z</modification><creation>2021-03-06T08:17:49Z</creation></dates><accession>S-EPMC7889528</accession><cross_references><pubmed>33237399</pubmed><doi>10.1007/s00449-020-02465-8</doi></cross_references></HashMap>