Effect of Mycobacterial Infection on Immune Status
ConditionTuberculosis
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorFirst Affiliated Hospital of Ningbo University
About this trial
This study, titled "Effect of Mycobacterial Infection on Immune Status" (EMIIS), investigates the immune-driven mechanisms of mycobacterial infections, focusing on the dynamic immune characteristics of multidrug-resistant tuberculosis (MDR-TB), nontuberculous mycobacterial (NTM) infections, and tuberculous pleurisy. Mycobacterial infections (including the Mycobacterium tuberculosis complex and nontuberculous mycobacteria) remain a major global public health threat. EMIIS is a single-center, randomized, single-blind,prospective study. The study recruited 120 participants, divided into groups of healthy individuals/community-acquired pneumonia patients, active pulmonary tuberculosis patients, latent tuberculosis infection patients, tuberculous pleurisy patients, and nontuberculous mycobacteria patients. Blood samples were collected from all groups within 3 days before treatment and 2-3 months after treatment. Pleural effusion samples were additionally collected from the tuberculous pleurisy group within 3 days before treatment and 2 months after treatment. Exhaled breath condensate (EBC) was collected from the nontuberculous mycobacteria group. Utilizing mass cytometry (CyTOF) and multi-dimensional indicators, the study aims to elucidate the immune-driven mechanisms of mycobacterial infections and provide new strategies for individualized treatment.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
Age ≥ 18 years, all genders and races accepted.
Patients with active pulmonary tuberculosis diagnosed clinically or by bronchoscopy within less than 1 week.
Patients with latent tuberculosis infection (positive T-SPOT test but no evidence of active tuberculosis infection).
Patients with tuberculous pleurisy with onset within less than 1 week.
Trial population
Patients with clinical diagnosis including (active tuberculosis, latent tuberculosis, multidrug-resistant tuberculosis, tuberculous pleurisy, nontuberculous mycobacteria), older than 18 years, meeting the inclusion criteria and no exclusion criteria.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
120 Participants
are grouped into 5 trial groupsGroup A: Immunometabolic differences between DS-TB and MDR-TB
Group B: To assess the effect of immune status on NTM
Group C: Significance of studying the immunometabolic status of tuberculous pleurisy
Group D: Study of immunometabolic status in different states of tuberculosis
Group E: A study of exhaled air condensate in NTM patients versus CAP patients
Trial outcomes
Primary outcomes
1
To establish a multi-immune pathway interaction network and composite biomarkers in mycobacterial infection thing
This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.
Time frame
3 days before treatment and 2 months after treatmentSecondary outcomes
1
Immune cell subsets and mechanisms of possible effects of anti-tuberculosis drugs
This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.
Time frame
3 days before treatment and 2 months after treatment2
Differences in immune subsets between normal persons and patients with active pulmonary tuberculosis
This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.
Time frame
3 days before treatment and 2 months after treatment3
To explore whether the peripheral blood before treatment contains a certain marker can predict the short-term efficacy
This study utilized mass cytometry (CyTOF) and a pre-designed panel containing 41 metal-tagged antibodies for detection. After data normalization and doublet exclusion, multiple machine learning algorithms were applied for clustering analysis to quantitatively compare the proportions of various immune subsets (such as Th1 cells, Th17 cells, classical monocytes, CD4TEM cells, CD8TEM cells,etc.) among CD45+ leukocytes in the peripheral blood of healthy individuals and patients with active tuberculosis.
Time frame
3 days before treatment and 2 months after treatment4
Comparison of the dynamic changes of immune subsets in peripheral blood and pleural effusion of TP patients before and after treatment
Using CyTOF with a 41-metal-labeled antibody panel, peripheral blood samples from healthy controls and untreated patients with tuberculous pleurisy were analyzed. After data normalization and debarcoding, clustering was applied to determine the percentages of CD45+ leukocyte subsets (Th1, Th17, classical monocytes, CD4+/CD8+ effector memory T cells, and NK cells). Patients were divided into high- and low-symptom groups based on symptom severity. Immune subset proportions were compared between each patient group and healthy controls, as well as between the two patient groups.
Time frame
3 days before treatment and 2 months after treatmentSponsors and contacts
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Source ClinicalTrials.gov