<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>31249</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>19</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-08-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The role of AJB35136 and fdtA genes in biofilm formation by avian pathogenic Escherichia coli</title>
    <parentTitle language="eng">BMC Veterinary Research</parentTitle>
    <identifier type="issn">1746-6148</identifier>
    <identifier type="doi">10.1186/s12917-023-03672-7</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Artikelnummer">126</enrichment>
    <enrichment key="BTUfunderNamenotEU">Deutsche Forschungsgemeinschaft (DFG) / Open-Access-Publikationskosten (2023 -2025) / 512881332</enrichment>
    <enrichment key="BTUfunderNamenotEU">Bundesministerium für Bildung und Forschung (BMBF) / 03IPT611X, 03PSZZF1A</enrichment>
    <enrichment key="Fprofil">2 Gesundheit und Lifes Sciences / Health and Life Sciences</enrichment>
    <author>
      <firstName>Muhammad Moman</firstName>
      <lastName>Khan</lastName>
    </author>
    <submitter>
      <firstName>Aamir</firstName>
      <lastName>Ali</lastName>
    </submitter>
    <author>
      <firstName>Aamir</firstName>
      <lastName>Ali</lastName>
    </author>
    <author>
      <firstName>Rafał</firstName>
      <lastName>Kolenda</lastName>
    </author>
    <author>
      <firstName>Olugbenga Adekunle</firstName>
      <lastName>Olowe</lastName>
    </author>
    <author>
      <firstName>Jörg</firstName>
      <lastName>Weinreich</lastName>
    </author>
    <author>
      <firstName>Ganwu</firstName>
      <lastName>Li</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Schierack</lastName>
    </author>
    <collection role="institutes" number="2109">FG Multiparameterdiagnostik</collection>
  </doc>
  <doc>
    <id>36349</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>11</pageLast>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>15</volume>
    <type>articler</type>
    <publisherName>Nature Publishing Group UK</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-07-01</completedDate>
    <publishedDate>2025-07-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CytoBead ANA 2 assay : a novel method for the detection of antinuclear antibodies</title>
    <abstract language="eng">Detection of anti-nuclear autoantibodies (ANA) is based on a two-step algorithm including indirect immunofluorescence (IIF) on HEp2 cells and subsequent reflex/confirmatory testing for specific autoantibodies. Simultaneous cell- and microbead-based autoantibody detection by IIF may be utilized for the evaluation of systemic autoimmune rheumatic diseases (SARDs). In the present study, we assessed the performance of CytoBead ANA 2 in the detection of ANA and ANA-specific autoantibodies, compared to ANA IIF and BioPlex™ 2200. We also tested the ability of CytoBead ANA DFS-70 to identify dense-fine speckled (DFS) pattern associated with anti-DFS70 antibodies in non-SARDs patients. Hundred-twelve routine sera samples were assessed by manual CytoBead ANA 2 for the presence of ANA and specific autoantibodies. In parallel, these samples were analyzed by HEp2 ANA IIF test and a subsequent multiplexed assay BioPlex™ 2200 ANA. Twenty-nine ANA-positive samples obtained from non-SARDs patients and exhibiting DFS pattern by ANA IIF were further tested by CytoBead ANA DFS-70. A substantial agreement was observed between classical ANA IIF and manual CytoBead ANA 2 for the detection of ANA (k = 0.74). Discordant results were mainly associated with the presence of anti-SSA/Ro antibodies detected by CytoBead ANA 2 in ANA IIF negative patients. A good to almost perfect agreement was found between CytoBead ANA 2 and BioPlex™ 2200 for detection of specific antibodies with kappa values ranging from 0.70 to 0.90. Twenty samples (68.9%) obtained from 29 ANA IIF positive without SARDs patients exhibited DFS pattern in CytoBead ANA DFS-70, which confirmed the presence of anti-DFS70 antibodies. The diagnostic performance of manual CytoBead ANA 2 for ANA screening and detection of ANA specific antibodies is comparable to the diagnostic performance of ANA IIF followed by BioPlex™ 2200. This novel one-step assay enables simultaneous ANA screening and confirmation and represents a promising alternative approach to the time-consuming and costly two-tier ANA analysis.</abstract>
    <parentTitle language="eng">Scientific reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="doi">10.1038/s41598-025-04583-3</identifier>
    <enrichment key="opus.import.date">2025-07-29T11:46:15+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">aa6dc787e81c5bd2b7fcc0cef94247d5</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>
      <firstName>Tulchinsky</firstName>
      <lastName>Roei</lastName>
    </author>
    <author>
      <firstName>Gilburd</firstName>
      <lastName>Boris</lastName>
    </author>
    <author>
      <firstName>Shovman</firstName>
      <lastName>Yehuda</lastName>
    </author>
    <author>
      <firstName>Tocut</firstName>
      <lastName>Milena</lastName>
    </author>
    <author>
      <firstName>Zeruya</firstName>
      <lastName>Eleanor</lastName>
    </author>
    <author>
      <firstName>Binyaminov</firstName>
      <lastName>Ariel</lastName>
    </author>
    <author>
      <firstName>Davidson</firstName>
      <lastName>Tima</lastName>
    </author>
    <author>
      <firstName>Thomas</firstName>
      <lastName>Büttner</lastName>
    </author>
    <author>
      <firstName>Abdullah</firstName>
      <lastName>Nasser</lastName>
    </author>
    <author>
      <firstName>Juliane</firstName>
      <lastName>Michel</lastName>
    </author>
    <author>
      <firstName>Stefan</firstName>
      <lastName>Rödiger</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Schierack</lastName>
    </author>
    <author>
      <firstName>Amital</firstName>
      <lastName>Howard</lastName>
    </author>
    <author>
      <firstName>Dirk</firstName>
      <lastName>Roggenbuck</lastName>
    </author>
    <author>
      <firstName>Shoenfeld</firstName>
      <lastName>Yehuda</lastName>
    </author>
    <author>
      <firstName>Shovman</firstName>
      <lastName>Ora</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Anti-nuclear antibodies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Anti-DFS70 antibodies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indirect immunofluorescence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiplexed assay</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CytoBead technology</value>
    </subject>
    <collection role="institutes" number="2109">FG Multiparameterdiagnostik</collection>
  </doc>
  <doc>
    <id>36755</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>43</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>articler</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Milano</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-11-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">MicroRNAs in neurodegenerative diseases : from molecular mechanisms to clinical biomarkers, detection methods and therapeutic strategies—advances and challenges</title>
    <abstract language="eng">Neurodegenerative diseases (NDDs) pose significant challenges in early detection and treatment due to their complex pathophysiology and heterogeneous clinical presentations. MicroRNAs (miRNAs), small noncoding RNAs that regulate gene expression, have emerged as promising diagnostic biomarkers and therapeutic targets in NDDs. Pathological examination of affected tissues reveals early synaptic dysfunction, protein misfolding, and neuroinflammation occur prior to overt clinical symptoms, highlighting the importance of sensitive diagnostics approaches in prodromal stages. This review summarizes for researchers on the role of miRNAs in NDDs by examining their diagnostic potential in biofluids such as blood and cerebrospinal fluid, and their therapeutic applicability through inhibition or replacement strategies. Literature from peer-reviewed databases was assessed with a focus on recent advances in molecular detection platforms, computational modeling of miRNA–mRNA interactions, and preclinical/clinical investigations.&#13;
&#13;
More than 2600 human miRNAs have been identified, collectively regulating over half of mammalian protein-coding genes. Quantitative methodologies, particularly reverse transcription quantitative PCR (RT-qPCR), enable reliable miRNA profiling, facilitating early diagnosis and prognosis of NDDs. Therapeutic strategies, including antagomirs, mimics, sponges and viral or non-viral delivery systems, show promise in modulating disease pathways. However, significant challenges remain, including variability in miRNA extraction and quantification protocols, off-target effects, delivery barriers across the blood brain barrier and limited reproducibility across studies.&#13;
&#13;
MiRNAs represent a class of molecular tools with potential to transform diagnostics and therapeutics in NDDs. Future research should prioritize methodological standardization, validation in large multicenter cohorts, and improved computational approaches to elucidate miRNA-mediated regulatory networks in NDDs. Replication studies and translational research are essential harnessing the the full clinical utility of miRNAs in the management of Alzheimer disease, Parkinson disease and other NDDs.</abstract>
    <parentTitle language="deu">Neurological sciences</parentTitle>
    <identifier type="doi">10.1007/s10072-025-08419-w</identifier>
    <identifier type="issn">1590-3478</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Referiert">Beitrag ist referiert / Article peer-reviewed</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <submitter>
      <firstName>Rödiger</firstName>
      <lastName>Stefan</lastName>
    </submitter>
    <author>
      <firstName>Hafiz Muhammad Husnain</firstName>
      <lastName>Azam</lastName>
    </author>
    <author>
      <firstName>Mehvish</firstName>
      <lastName>Mumtaz</lastName>
    </author>
    <author>
      <firstName>Stefan</firstName>
      <lastName>Rödiger</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Schierack</lastName>
    </author>
    <author>
      <firstName>Nazim</firstName>
      <lastName>Hussain</lastName>
    </author>
    <author>
      <firstName>Ambreen</firstName>
      <lastName>Aisha</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MicroRNA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neurodegenerative diseases</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Alzheimer’s disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parkinson’s disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Huntington’s disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Amyotrophic lateral sclerosis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diagnostic biomarkers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>MiRNA-based therapeutics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Molecular diagnostics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gene regulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Biofluid biomarkers</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Delivery technologies</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neuroinflammation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RT-qPCR</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NGS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Clinical translation</value>
    </subject>
    <collection role="institutes" number="2109">FG Multiparameterdiagnostik</collection>
  </doc>
  <doc>
    <id>37827</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>11</pageLast>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>104</volume>
    <type>articler</type>
    <publisherName>Elsevier BV</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-02-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fecal microbiota landscape of commercial poultry farms in Faisalabad, Pakistan : a 16S rRNA gene-based metagenomics study</title>
    <abstract language="eng">This study explores the microbiota of broiler and layer farms, aiming to understand how genetic breed, age, and farm type influence microbial communities in commercial settings. Fecal samples from 18 poultry farms (twelve layers and six broilers) in Faisalabad, Pakistan were analyzed using 16S rRNA gene sequencing of the V3-V4 region to evaluate bacterial composition. The dominant phylum, Firmicutes, accounted for 58.72 % of the microbial population, with Lactobacillus being the most abundant genus in both broilers and layers. The total abundance of potentially pathogenic genera was also assessed with Enterococcus and Corynebacterium being the most prevalent across all farms, regardless of bird type. Layers exhibited greater microbial richness and diversity than broilers, while the Karachi cage system (KCS) farm type showed higher richness than Floor system (FS). Although the breed significantly influenced microbial diversity, age was not a determining factor. Co-occurrence analyses revealed close interactions among phyla (Actinobacteriota, Proteobacteria, Firmicutes, Fusobacteriota, and Bacteroidota) and genera (Lactobacillus, Brevibacterium, Enterococcus), suggesting their pivotal roles within the microbial community. Additionally, functional analysis detected important metabolic pathways and traced microbial signatures of key pathogenic bacteria, enhancing our understanding of microbial contributions to poultry health. Despite limitations such as the need for broader geographic sampling and accounting for diet and medication, this study advances microbiome research in Pakistan's poultry sector, emphasizing consistent taxa and opening avenues for future investigations into microbiome manipulations for improved food safety and achieve better sustainable practices.</abstract>
    <parentTitle language="eng">Poultry science</parentTitle>
    <identifier type="doi">10.1016/j.psj.2025.105089</identifier>
    <identifier type="issn">0032-5791</identifier>
    <identifier type="issn">1525-3171</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:39:03Z","timestamp":1769834343800,"version":"3.49.0"},"reference-count":40,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Poultry Science"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.psj.2025.105089","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T14:15:30Z","timestamp":1742739330000},"page":"105089","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fecal microbiota landscape of commercial poultry farms in Faisalabad, Pakistan: A 16S rRNA gene-based metagenomics study"],"prefix":"10.1016","volume":"104","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-1563","authenticated-orcid":false,"given":"Muhammad Moman","family":"Khan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6930-7960","authenticated-orcid":false,"given":"Muhammad Ahmed","family":"Mushtaq","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Suleman","sequence":"additional","affiliation":[]},{"given":"Umer","family":"Ahmed","sequence":"additional","affiliation":[]},{"given":"Muhammad Faisal","family":"Ashraf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5065-4462","authenticated-orcid":false,"given":"Rizwan","family":"Aslam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5460-5780","authenticated-orcid":false,"given":"Mashkoor","family":"Mohsin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1441-6512","authenticated-orcid":false,"given":"Stefan","family":"R\u00f6diger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9816-7674","authenticated-orcid":false,"given":"Yasra","family":"Sarwar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6445-3506","authenticated-orcid":false,"given":"Peter","family":"Schierack","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8433-4328","authenticated-orcid":false,"given":"Aamir","family":"Ali","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.psj.2025.105089_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.psj.2022.102401","article-title":"Metagenomics analysis of the morphological aspects and bacterial composition of broiler feces","volume":"102","author":"Alvarenga","year":"2023","journal-title":"Poult. Sci."},{"key":"10.1016\/j.psj.2025.105089_bib0002","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.anifeedsci.2016.05.004","article-title":"Interaction between chicken intestinal microbiota and protein digestion","volume":"221","author":"Apajalahti","year":"2016","journal-title":"Anim. Feed. Sci. Technol."},{"key":"10.1016\/j.psj.2025.105089_bib0003","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/s40168-018-0470-z","article-title":"Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2\u2019s q2-feature-classifier plugin","volume":"6","author":"Bokulich","year":"2018","journal-title":"Microbiome"},{"key":"10.1016\/j.psj.2025.105089_bib0004","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/nmeth.3869","article-title":"DADA2: high resolution sample inference from Illumina amplicon data","volume":"13","author":"Callahan","year":"2016","journal-title":"Nat. Methods"},{"key":"10.1016\/j.psj.2025.105089_bib0005","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s13765-018-0358-4","article-title":"Blood biochemical profile of four fast-growing broiler strains under high ambient temperature","volume":"61","author":"Chand","year":"2018","journal-title":"Appl. Biol. Chem."},{"key":"10.1016\/j.psj.2025.105089_bib0006","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.3382\/ps\/pex359","article-title":"The gastrointestinal microbiome and its association with the control of pathogens in broiler chicken production: a review","volume":"97","author":"Clavijo","year":"2018","journal-title":"Poult. Sci."},{"key":"10.1016\/j.psj.2025.105089_bib0007","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2017.01310","article-title":"Age-related variations in intestinal microflora of free-range and caged hens","volume":"8","author":"Cui","year":"2017","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0008","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1080\/03079457.2022.2086447","article-title":"Omics technologies in poultry health and productivity - part 1: current use in poultry research","volume":"51","author":"Dehau","year":"2022","journal-title":"Avian Pathology"},{"key":"10.1016\/j.psj.2025.105089_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2023.109698","article-title":"Profiling bacterial communities and foodborne pathogens on food-associated surface following contact with raw beef, chicken and pork using 16S amplicon metagenomics","volume":"149","author":"Emamjomeh","year":"2023","journal-title":"Food Control"},{"key":"10.1016\/j.psj.2025.105089_bib0010","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s42523-019-0017-z","article-title":"Development of the duodenal, ileal, jejunal and caecal microbiota in chickens","volume":"1","author":"Glendinning","year":"2019","journal-title":"Anim. Microbiome"},{"key":"10.1016\/j.psj.2025.105089_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-019-0540-1","article-title":"Amplicon sequencing provides more accurate microbiome information in healthy children compared to culturing","volume":"2","author":"Gupta","year":"2019","journal-title":"Commun. Biol."},{"key":"10.1016\/j.psj.2025.105089_bib0012","first-page":"689","article-title":"An overview of poultry industry in Pakistan. World's","volume":"71","author":"Hussain","year":"2015","journal-title":"Poultry Science Journal"},{"key":"10.1016\/j.psj.2025.105089_bib0013","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2018.00235","article-title":"Host and environmental factors affecting the intestinal microbiota in chickens","author":"Kers","year":"2018","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0014","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1093\/nar\/gks808","article-title":"Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies","volume":"41","author":"Klindworth","year":"2013","journal-title":"Nucleic. Acids. Res."},{"key":"10.1016\/j.psj.2025.105089_bib0015","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41385-020-00365-4","article-title":"Introduction to host microbiome symbiosis in health and disease","volume":"14","author":"Malard","year":"2021","journal-title":"Mucosal. Immunol."},{"key":"10.1016\/j.psj.2025.105089_bib0016","doi-asserted-by":"crossref","first-page":"4727","DOI":"10.1111\/1462-2920.13363","article-title":"Insights into the biodiversity of the gut microbiota of broiler chickens","volume":"18","author":"Mancabelli","year":"2016","journal-title":"Environ. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0017","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41587-023-01845-1","article-title":"Greengenes2 unifies microbial data in a single reference tree","volume":"42","author":"McDonald","year":"2024","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.psj.2025.105089_bib0018","series-title":"Data Structures for Statistical Computing in Python","first-page":"51","author":"McKinney","year":"2010"},{"key":"10.1016\/j.psj.2025.105089_bib0019","doi-asserted-by":"crossref","first-page":"e61217","DOI":"10.1371\/journal.pone.0061217","article-title":"phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data","volume":"8","author":"McMurdie","year":"2013","journal-title":"PLoS. One"},{"key":"10.1016\/j.psj.2025.105089_bib0020","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/24701394.2018.1484118","article-title":"Population structure, genetic diversity and phylogenetic analysis of different rural and commercial chickens of Pakistan using complete sequence of mtDNA D-loop","volume":"30","author":"Nisar","year":"2019","journal-title":"Mitochondrial DNA Part A"},{"key":"10.1016\/j.psj.2025.105089_bib0021","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1038\/s41598-019-39323-x","article-title":"16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan","volume":"9","author":"Ocejo","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.psj.2025.105089_bib0022","series-title":"OECD-FAO Agricultural Outlook 2020-2029","year":"2020"},{"key":"10.1016\/j.psj.2025.105089_bib0023","series-title":"Government of Pakistan (2023-24). Federal Bureau of Statistics, Economic Adviser's Wing, Finance Division","year":"2023"},{"key":"10.1016\/j.psj.2025.105089_bib0024","doi-asserted-by":"crossref","first-page":"108","DOI":"10.4161\/gmic.26945","article-title":"Intestinal microbiome of poultry and its interaction with host and diet","volume":"5","author":"Pan","year":"2014","journal-title":"Gut. Microbes."},{"key":"10.1016\/j.psj.2025.105089_bib0025","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s13205-019-1834-1","article-title":"Comparative metagenomic sequencing analysis of cecum microbiotal diversity and function in broilers and layers","volume":"9","author":"Qi","year":"2019","journal-title":"3. Biotech."},{"key":"10.1016\/j.psj.2025.105089_bib0026","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1128\/AEM.02549-15","article-title":"Bacterial succession in the broiler gastrointestinal tract","volume":"82","author":"Ranjitkar","year":"2016","journal-title":"Appl. Environ. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0027","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s12866-015-0388-6","article-title":"Comparison of fecal and cecal microbiotas reveals qualitative similarities but quantitative differences","volume":"15","author":"Stanley","year":"2015","journal-title":"BMC. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0028","doi-asserted-by":"crossref","first-page":"e84290","DOI":"10.1371\/journal.pone.0084290","article-title":"Highly variable microbiota development in the chicken gastrointestinal tract","volume":"8","author":"Stanley","year":"2013","journal-title":"PLoS. One"},{"key":"10.1016\/j.psj.2025.105089_bib0029","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0273821","article-title":"Veterinary consumption of highest priority critically important antimicrobials and various growth promoters based on import data in Pakistan","volume":"17","author":"Umair","year":"2022","journal-title":"PLoS. One"},{"key":"10.1016\/j.psj.2025.105089_bib0030","doi-asserted-by":"crossref","first-page":"598","DOI":"10.3390\/antibiotics10050598","article-title":"Quantification and trends of antimicrobial use in commercial broiler chicken production in Pakistan","volume":"10","author":"Umair","year":"2021","journal-title":"Antibiotics"},{"key":"10.1016\/j.psj.2025.105089_bib0031","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.29261\/pakvetj\/2019.037","article-title":"The under reported issue of antibiotic-resistance in food-producing animals in Pakistan","volume":"39","author":"Ur Rahman","year":"2019","journal-title":"Pakistan Veterinary Journal"},{"key":"10.1016\/j.psj.2025.105089_bib0032","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1038\/s41396-019-0367-2","article-title":"The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens","volume":"13","author":"Wen","year":"2019","journal-title":"ISMe J."},{"key":"10.1016\/j.psj.2025.105089_bib0033","series-title":"ggplot2: Elegant Graphics for Data Analysis","author":"Wickham","year":"2016"},{"key":"10.1016\/j.psj.2025.105089_bib0034","unstructured":"Wickham, H., R. Fran\u00e7ois, L. Henry, K. M\u00fcller, D. Vaughan, P. Software, and PBC. 2023. dplyr: a grammar of data manipulation."},{"key":"10.1016\/j.psj.2025.105089_bib0035","unstructured":"Wickham, H., D. Vaughan, M. Girlich, K. Ushey, P. Software, and PBC. 2024. tidyr: tidy Messy Data."},{"key":"10.1016\/j.psj.2025.105089_bib0036","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2017.01089","article-title":"Characterization of the microbiome along the gastrointestinal tract of growing turkeys","author":"Wilkinson","year":"2017","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0037","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1111\/jam.15558","article-title":"Layers, broiler chickens and their F1 cross develop distinctly different caecal microbial communities when hatched and reared together","volume":"133","author":"Willson","year":"2022","journal-title":"J. Appl. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0038","article-title":"Gut microbiota co-microevolution with selection for host humoral","author":"Yang","year":"2017","journal-title":"Immunity. Front. Microbiol."},{"key":"10.1016\/j.psj.2025.105089_bib0039","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1017\/S1466252312000138","article-title":"The microbiome of the chicken gastrointestinal tract","volume":"13","author":"Yeoman","year":"2012","journal-title":"Anim. Health Res. Rev."},{"key":"10.1016\/j.psj.2025.105089_bib0040","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1038\/srep01163","article-title":"Quantitative genetic background of the host influences gut microbiomes in chickens","volume":"3","author":"Zhao","year":"2013","journal-title":"Sci. Rep."}],"container-title":["Poultry Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0032579125003281?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0032579125003281?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T02:26:11Z","timestamp":1749003971000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0032579125003281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["S0032579125003281"],"URL":"https:\/\/doi.org\/10.1016\/j.psj.2025.105089","relation":{},"ISSN":["0032-5791"],"issn-type":[{"value":"0032-5791","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fecal microbiota landscape of commercial poultry farms in Faisalabad, Pakistan: A 16S rRNA gene-based metagenomics study","name":"articletitle","label":"Article Title"},{"value":"Poultry Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.psj.2025.105089","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc. on behalf of Poultry Science Association Inc.","name":"copyright","label":"Copyright"}],"article-number":"105089"}}</enrichment>
    <enrichment key="opus_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorIdentifierOrcid_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorIdentifierOrcid_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorIdentifierOrcid_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PersonAuthorIdentifierOrcid_9,PersonAuthorFirstName_10,PersonAuthorLastName_10,PersonAuthorIdentifierOrcid_10,PersonAuthorFirstName_11,PersonAuthorLastName_11,PersonAuthorIdentifierOrcid_11,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Issue,Volume,PublishedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Referiert">Beitrag ist referiert / Article peer-reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>
      <firstName>Muhammad Moman</firstName>
      <lastName>Khan</lastName>
    </author>
    <submitter>
      <firstName>Stefan</firstName>
      <lastName>Rödiger</lastName>
    </submitter>
    <author>
      <firstName>Muhammad Ahmed</firstName>
      <lastName>Mushtaq</lastName>
    </author>
    <author>
      <firstName>Muhammad</firstName>
      <lastName>Suleman</lastName>
    </author>
    <author>
      <firstName>Umer</firstName>
      <lastName>Ahmed</lastName>
    </author>
    <author>
      <firstName>Muhammad Faisal</firstName>
      <lastName>Ashraf</lastName>
    </author>
    <author>
      <firstName>Rizwan</firstName>
      <lastName>Aslam</lastName>
    </author>
    <author>
      <firstName>Mashkoor</firstName>
      <lastName>Mohsin</lastName>
    </author>
    <author>
      <firstName>Stefan</firstName>
      <lastName>Rödiger</lastName>
    </author>
    <author>
      <firstName>Yasra</firstName>
      <lastName>Sarwar</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Schierack</lastName>
    </author>
    <author>
      <firstName>Aamir</firstName>
      <lastName>Ali</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>16S rRNA</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Broiler</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Farm-type</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Layer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Metagenomics</value>
    </subject>
    <collection role="institutes" number="2109">FG Multiparameterdiagnostik</collection>
  </doc>
</export-example>
