<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04490746</url>
  </required_header>
  <id_info>
    <org_study_id>研2020-556</org_study_id>
    <nct_id>NCT04490746</nct_id>
  </id_info>
  <brief_title>Identifcation of Biomarkers for Active Pulmonary Tuberculosis</brief_title>
  <official_title>Identifcation of Biomarkers for Active Pulmonary Tuberculosis Using a Metabolomics Approach: a Prospective Multicenter Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Second Affiliated Hospital, School of Medicine, Zhejiang University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Second Affiliated Hospital, School of Medicine, Zhejiang University</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      This experiment is designed to analyze the metabolites in body fluids from patients with&#xD;
      active tuberculosis and latent tuberculosis infection also with health volunteer, then&#xD;
      combine the metabolic product features with clinical characteristics, to develop a kind of&#xD;
      metabolites targets to evaluate the progress in tuberculosis, thus provide a basis for&#xD;
      further utilization of humoral metabolites in the diagnosis of tuberculosis.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Not yet recruiting</last_known_status>
  <start_date type="Anticipated">October 1, 2020</start_date>
  <completion_date type="Anticipated">October 1, 2022</completion_date>
  <primary_completion_date type="Anticipated">October 1, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>metabolic product</measure>
    <time_frame>24 hours after sampling</time_frame>
    <description>features up-regulated or down-regulated</description>
  </primary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Anticipated">100</enrollment>
  <condition>Tuberculosis</condition>
  <condition>Diagnosis</condition>
  <arm_group>
    <arm_group_label>active</arm_group_label>
    <description>patients with active tuberculosis</description>
  </arm_group>
  <arm_group>
    <arm_group_label>latent</arm_group_label>
    <description>patients with latent tuberculosis infection</description>
  </arm_group>
  <arm_group>
    <arm_group_label>negtive control</arm_group_label>
    <description>healthy volunteers</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>with no intervention, Collect samples</intervention_name>
    <description>only with observation,there is no need to intervene</description>
    <arm_group_label>active</arm_group_label>
    <arm_group_label>latent</arm_group_label>
    <arm_group_label>negtive control</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        patients with active tuberculosis and latent tuberculosis infection&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  patients with active tuberculosis and latent tuberculosis infection&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  have other serious health problems&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Pintong Huang</last_name>
    <role>Principal Investigator</role>
    <affiliation>Second Affiliated Hospital, School of Medicine, Zhejiang University</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Yajing Liu</last_name>
    <phone>18358152521</phone>
    <email>liuyajingyyyyy@126.com</email>
  </overall_contact>
  <location>
    <facility>
      <name>Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University</name>
      <address>
        <city>Hangzhou</city>
        <state>Zhejiang</state>
        <zip>0571</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 26, 2020</study_first_submitted>
  <study_first_submitted_qc>July 26, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 29, 2020</study_first_posted>
  <last_update_submitted>July 26, 2020</last_update_submitted>
  <last_update_submitted_qc>July 26, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 29, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Tuberculosis</mesh_term>
    <mesh_term>Tuberculosis, Pulmonary</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>individual participant data (IPD) can be shared after the main unit publish articals</ipd_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
    <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
    <ipd_info_type>Clinical Study Report (CSR)</ipd_info_type>
    <ipd_info_type>Analytic Code</ipd_info_type>
    <ipd_time_frame>by request</ipd_time_frame>
    <ipd_access_criteria>by request</ipd_access_criteria>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04490746
org study id: 研2020-556
nct id: NCT04490746
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This experiment is designed to analyze the metabolites in body fluids from patients with
      active tuberculosis and latent tuberculosis infection also with health volunteer, then
      combine the metabolic product features with clinical characteristics, to develop a kind of
      metabolites targets to evaluate the progress in tuberculosis, thus provide a basis for
      further utilization of humoral metabolites in the diagnosis of tuberculosis.
observational model: Cohort
time perspective: Prospective
measure: metabolic product
time frame: 24 hours after sampling
description: features up-regulated or down-regulated
arm group label: active
description: patients with active tuberculosis
arm group label: latent
description: patients with latent tuberculosis infection
arm group label: negtive control
description: healthy volunteers
intervention type: Other
intervention name: with no intervention, Collect samples
description: only with observation,there is no need to intervene
arm group label: active
arm group label: latent
arm group label: negtive control
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Pintong Huang
role: Principal Investigator
affiliation: Second Affiliated Hospital, School of Medicine, Zhejiang University
last name: Yajing Liu
phone: 18358152521
email: liuyajingyyyyy@126.com
facility: 
country: China
responsible party type: Sponsor
mesh term: Tuberculosis
mesh term: Tuberculosis, Pulmonary
sharing ipd: Yes
ipd description: individual participant data (IPD) can be shared after the main unit publish articals
ipd info type: Study Protocol
ipd info type: Statistical Analysis Plan (SAP)
ipd info type: Informed Consent Form (ICF)
ipd info type: Clinical Study Report (CSR)
ipd info type: Analytic Code
ipd time frame: by request
ipd access criteria: by request