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    <timestamp>20250915064018000</timestamp>
    <depositor>
      <depositor_name>Editor</depositor_name>
      <email_address>editor.jddt@gmail.com</email_address>
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    <registrant>Universal Journal of Pharmaceutical Research</registrant>
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      <journal_metadata>
        <full_title>Universal Journal of Pharmaceutical Research</full_title>
        <abbrev_title>Univ J Pharm Res</abbrev_title>
        <issn media_type="electronic">2456-8058</issn>
        <issn media_type="print">2831-5235</issn>
      </journal_metadata>
      <journal_issue>
        <publication_date media_type="online">
          <month>09</month>
          <day>15</day>
          <year>2025</year>
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          <title>A NON-PARAMETRIC FRAMEWORK FOR ANALYZING SPATIAL HETEROGENEITY AND CONTAMINATION PATHWAYS IN HEALTHCARE ENVIRONMENTS</title>
        </titles>
        <contributors>
          <person_name contributor_role="author" sequence="first">
            <surname>Mostafa Essam Eissa</surname>
          </person_name>
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        <jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1">
          <jats:p>Background: The systematic management of microbial bioburden in Class C healthcare cleanrooms is a critical factor in patient safety. Standard environmental monitoring often overlooks the complex spatial and statistical relationships of contamination. This study applies a rigorous statistical framework to a comprehensive environmental monitoring dataset to accurately map contamination risk.
Methods: A cross-sectional analysis was performed on 318 microbial surface samples from 28 distinct operational locations in a Class C facility. Colony Forming Unit (CFU) data were analyzed using non-parametric statistics due to non-normal distribution, confirmed by Shapiro-Wilk tests on all locations with sufficient sample size (n=12). The Kruskal-Wallis test with Dunn's post-hoc analysis was used for group comparisons. Spearman's correlation was used to assess inter-location relationships.
Results: Significant spatial heterogeneity in microbial contamination was confirmed (p&lt;0.0001). Dunn's test identified CP C 11 W as the location with the highest contamination burden (mean CFU=12.17). The most statistically robust contrasts were observed when comparing high-burden sites against the cleanest location, CP C 32 WNme (mean CFU=0.67), which serves as a control benchmark. Multiple high-burden locations, including CP C 11 W and CP C 30 NCu, were found to be significantly more contaminated than this benchmark. No Spearman correlations survived the strict Bonferroni correction; however, the relationship between CP C 11 W and CP C 45 Wif (r=0.882, p&lt;0.05) approached the significance threshold, suggesting a potential pathway requiring further investigation.
Conclusions: Microbial contamination within the facility is spatially patterned, not random. The analysis provides a definitive hierarchy of risk, highlighting CP C 11 W as the primary target for enhanced sanitation. While correlational pathways could not be statistically confirmed, near-significant results provide a clear direction for future, more targeted sampling to validate operational links between zones.
                  
Peer Review History: 
Received 5 June 2025;   Reviewed 12 July 2025; Accepted 24 August; Available online 15 September 2025
Academic Editor: Dr. Asia Selman Abdullah, Pharmacy institute, University of Basrah, Iraq, asia_abdullah65@yahoo.com
Reviewers:
Dr. Alfonso Alexander Aguileral, University of Veracruz,  Mexico, aalexander_2000@yahoo.com
Dr. Ali Abdullah A. Al-Mehdar, University of Basrah, Iraq, asia_abdullah65@yahoo.com
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          <month>09</month>
          <day>15</day>
          <year>2025</year>
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