<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2563</id>
    <completedYear>2025</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName>Springer Science and Business Media LLC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data</title>
    <abstract language="eng">Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA&#13;
 $$_A$$&#13;
  receptor antagonist known to induce hypersynchrony, demonstrated the workflow’s ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline’s effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="doi">https://doi.org/10.1038/s41598-025-99479-7</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">ok</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="HAB_Review">ja</enrichment>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Manuel Ciba</author>
    <author>Marc Petzold</author>
    <author>Caroline L. Alves</author>
    <author>Francisco A. Rodrigues</author>
    <author>Yasuhiko Jimbo</author>
    <author>Christiane Thielemann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Biosensor</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mikroelektrode</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>1535</id>
    <completedYear>2016</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>46</pageFirst>
    <pageLast>50</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>46</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-01-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cell-based sensor chip for neurotoxicity measurements in drinking water</title>
    <abstract language="eng">Our drinking water contains residues of pharmaceuticals. A sub-group of these contaminants are neuro-active&#13;
substances, the antiepileptic carbamazepine being one of the most relevant. For assessment of the neurotoxicity of this&#13;
drug at a sub-therapeutic level, a cell-based sensor chip platform has been realized and characterized. For this&#13;
purpose, a microelectrode array chip was designed and processed in a clean room and optimized in terms of low&#13;
processing costs and good recording properties. For characterization of the system neuronal cells were plated on&#13;
microelectrode array chips and electrical activity was measured as a function of applied carbamazepine concentration.&#13;
We found that the relative spike rate decreased with increasing drug concentration resulted in IC50 values of around 36 μM. This value is five orders of magnitude higher than the maximal dose found in drinking water. IC50 values for&#13;
burst rate, burst duration and synchrony were slightly higher, suggesting spike rate being a more sensitive parameter to&#13;
carbamazepine.</abstract>
    <parentTitle language="eng">Lékař a technika - Clinician and Technology</parentTitle>
    <enrichment key="copyright">0</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Dennis Flachs</author>
    <author>Manuel Ciba</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microelectrode array</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Carbamazepine</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neurotoxicity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cell-based biosensor</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mikroelektrode</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Array</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Biosensor</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neurotoxizität</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
  </doc>
</export-example>
