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Effect of Mycobacterial Infection on Immune Status (EMIIS)

4 giugno 2026 aggiornato da: Chao Cao, Ph.D., First Affiliated Hospital of Ningbo University
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.

Panoramica dello studio

Stato

Reclutamento

Condizioni

Tipo di studio

Osservativo

Iscrizione (Stimato)

120

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Contatto studio

Backup dei contatti dello studio

Luoghi di studio

  • Cina
      • Ningbo, Cina
        • Reclutamento
        • The first affiliated Hospital of Ningbo University

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

  • Adulto
  • Adulto più anziano

Accetta volontari sani

No

Metodo di campionamento

Campione di probabilità

Popolazione di studio

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.

Descrizione

Inclusion Criteria and Exclusion Criteria:

Inclusion Criteria：

  1. Age ≥ 18 years, all genders and races accepted.
  2. Patients with active pulmonary tuberculosis diagnosed clinically or by bronchoscopy within less than 1 week.
  3. Patients with latent tuberculosis infection (positive T-SPOT test but no evidence of active tuberculosis infection).
  4. Patients with tuberculous pleurisy with onset within less than 1 week.
  5. Voluntarily join this study and sign the informed consent form.
  6. Patients whose drug susceptibility test or NGS results indicate resistance to at least isoniazid and rifampicin (MDR-TB).
  7. Patients whose drug susceptibility test or NGS results indicate sensitivity to first-line anti-tuberculosis drugs.
  8. Patients whose drug susceptibility test or NGS results indicate resistance to only one anti-tuberculosis drug.
  9. Patients with newly identified nontuberculous mycobacterial infection (within less than 1 week) by sputum culture or NGS.

Exclusion Criteria：

  1. Immunosuppressive conditions including HIV infection, long-term use (>1 month) of immunosuppressive agents or corticosteroids, severe malnutrition, etc.
  2. Concurrent other lung diseases, severe liver or kidney dysfunction, severe endocrine diseases, hematological diseases, or malignant tumors that may affect the study outcomes.
  3. Patients with diabetes mellitus.
  4. Pregnant or lactating women.
  5. Patients unable or unwilling to provide informed consent, or with poor compliance.

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Coorti e interventi

Gruppo / Coorte
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
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.

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
To establish a multi-immune pathway interaction network and composite biomarkers in mycobacterial infection thing
Lasso di tempo: 3 days before treatment and 2 months after treatment
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

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Immune cell subsets and mechanisms of possible effects of anti-tuberculosis drugs
Lasso di tempo: 3 days before treatment and 2 months after treatment
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
Differences in immune subsets between normal persons and patients with active pulmonary tuberculosis
Lasso di tempo: 3 days before treatment and 2 months after treatment
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
Lasso di tempo: 3 days before treatment and 2 months after treatment
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
Lasso di tempo: 3 days before treatment and 2 months 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
Lasso di tempo: 3 days before treatment and 2 months 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 metabolic differences of three major nutrients between TP patients and healthy people before treatment
Lasso di tempo: 3 days before treatment and 2 months after 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
Lasso di tempo: 3 days before treatment and 2 months after treatment
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
Lasso di tempo: 3 days before treatment and 2 months after treatment
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

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

First Affiliated Hospital of Ningbo University

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Effettivo)

9 luglio 2025

Completamento primario (Stimato)

20 luglio 2026

Completamento dello studio (Stimato)

20 luglio 2026

Date di iscrizione allo studio

Primo inviato

19 maggio 2026

Primo inviato che soddisfa i criteri di controllo qualità

4 giugno 2026

Primo Inserito (Effettivo)

10 giugno 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

10 giugno 2026

Ultimo aggiornamento inviato che soddisfa i criteri QC

4 giugno 2026

Ultimo verificato

1 giugno 2026

Maggiori informazioni

Termini relativi a questo studio

Termini MeSH pertinenti aggiuntivi

Altri numeri di identificazione dello studio

  • 2025-157A-01

Piano per i dati dei singoli partecipanti (IPD)

Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?

NO

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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