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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Immunometabolic differences between DS-TB and MDR-TB | Using a prospective, single-center, observational study design, it is planned to enroll 30 patients divided into drug-susceptible tuberculosis and multidrug-resistant tuberculosis. CyTOF technology was used to analyze the differences in immune subsets and metabolic functions. | ||
| To assess the effect of immune status on NTM | A total of 15 patients over the age of 18 diagnosed with non-tuberculous mycobacteria were included, and peripheral blood samples were collected after 2 months of treatment to analyze the changes in immune status and metabolic status of non-tuberculous mycobacterial patients in healthy people. | ||
| Significance of studying the immunometabolic status of tuberculous pleurisy | A total of 20 patients over the age of 18 diagnosed with tuberculous pleurisy were included in the plan, divided into high-symptom and low-symptomatic groups, and pleural fluid and peripheral blood samples were collected before and after treatment to analyze the changes in their immune status and metabolic status before and after treatment. | ||
| Study of immunometabolic status in different states of tuberculosis | A total of 15 patients over the age of 18 diagnosed with active pulmonary tuberculosis and 10 patients with latent pulmonary tuberculosis were enrolled, and peripheral blood samples were collected before and after treatment to analyze the changes in their immune status and metabolic status before and after treatment. | ||
| A study of exhaled air condensate in NTM patients versus CAP patients |
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| Measure | Description | Time Frame |
|---|---|---|
| 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. | 3 days before treatment and 2 months after treatment |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria and Exclusion Criteria:
Inclusion Criteria：
Exclusion Criteria：
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chao Cao | Contact | +86-0574-87089878 | caodoctor@163.com | |
| Shiyi He | Contact | +86-0574-87089878 | shiyihii@163.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The First Affiliated Hospital of Ningbo University | Recruiting | Ningbo | China |
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| ID | Term |
|---|---|
| D014376 | Tuberculosis |
| ID | Term |
|---|---|
| D009164 | Mycobacterium Infections |
| D000193 | Actinomycetales Infections |
| D016908 | Gram-Positive Bacterial Infections |
| D001424 | Bacterial Infections |
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A total of 30 patients over the age of 18 diagnosed with nontuberculous mycobacteria and 30 healthy or community pneumonia patients were enrolled, and their exhaled air condensate was collected before or within 2 weeks after treatment to analyze its composition. |
| 3 days before treatment and 2 months after treatment |
| 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. | 3 days before treatment and 2 months after treatment |
| 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. | 3 days before treatment and 2 months after treatment |
| 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. | 3 days before treatment and 2 months after treatment |
| To explore the differences of peripheral blood immune subsets between TP patients and healthy people before 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. | 3 days before treatment and 2 months after treatment |
| To explore the metabolic differences of three major nutrients between TP patients and healthy people before treatment | Using CyTOF with an antibody panel including metabolic markers such as GLUT1 and CPT1A, the expression levels of these markers were measured in peripheral blood immune subsets (CD4+ T cells, CD8+ T cells, monocytes, etc.) from healthy controls and untreated patients with tuberculous pleurisy. The median fluorescence intensity (MdFI) of GLUT1 and CPT1A on each subset was used as the primary metric to quantify differences in glucose metabolism and fatty acid oxidation capacity. | 3 days before treatment and 2 months after treatment |
| Differences in metabolic function between multidrug-resistant tuberculosis group and drug-sensitive tuberculosis group | In this study, CyTOF and a preconfigured panel consisting of 41 metal-conjugated antibodies were used for specimen detection. After data normalization and doublet removal, multiple machine learning algorithms were utilized for cell clustering analysis. We quantitatively compared the proportional differences of various immune subsets in peripheral blood CD45⁺ leukocytes among drug-resistant tuberculosis (DR-TB), drug-susceptible tuberculosis (DS-TB) and healthy control groups, including Th1 cells, Th17 cells, classical monocytes, CD4⁺ effector memory T cells and CD8⁺ effector memory T cells. This study aims to characterize treatment-induced quantitative changes in immune subsets and provide evidence for screening novel biomarkers. | 3 days before treatment and 2 months after treatment |
| Influence of immune status on the efficacy of NTM | 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. | 3 days before treatment and 2 months after treatment |
| D001423 | Bacterial Infections and Mycoses |
| D007239 | Infections |