Effect of Mycobacterial Infection on Immune Status (EMIIS)
Studienübersicht
Status
Status
Bedingungen
Bedingungen
Studientyp
Studientyp
Einschreibung (Geschätzt)
Einschreibung
Kontakte und Standorte
Studienkontakt
Studienkontakt
- Name: Shiyi He
- Telefonnummer: +86-0574-87089878
- E-Mail: shiyihii@163.com
Studieren Sie die Kontaktsicherung
- Name: Chao Cao
- Telefonnummer: +86-0574-87089878
- E-Mail: caodoctor@163.com
Studienorte
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Ningbo, China
- Rekrutierung
- The First Affiliated Hospital of Ningbo University
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Teilnahmekriterien
Zulassungskriterien
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Probenahmeverfahren
Studienpopulation
Beschreibung
Inclusion Criteria and Exclusion Criteria:
Inclusion Criteria:
- 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.
- Voluntarily join this study and sign the informed consent form.
- Patients whose drug susceptibility test or NGS results indicate resistance to at least isoniazid and rifampicin (MDR-TB).
- Patients whose drug susceptibility test or NGS results indicate sensitivity to first-line anti-tuberculosis drugs.
- Patients whose drug susceptibility test or NGS results indicate resistance to only one anti-tuberculosis drug.
- Patients with newly identified nontuberculous mycobacterial infection (within less than 1 week) by sputum culture or NGS.
Exclusion Criteria:
- Immunosuppressive conditions including HIV infection, long-term use (>1 month) of immunosuppressive agents or corticosteroids, severe malnutrition, etc.
- Concurrent other lung diseases, severe liver or kidney dysfunction, severe endocrine diseases, hematological diseases, or malignant tumors that may affect the study outcomes.
- Patients with diabetes mellitus.
- Pregnant or lactating women.
- Patients unable or unwilling to provide informed consent, or with poor compliance.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Anzahl der Gruppen / Kohorten
Kohorten und Interventionen
Gruppe / KohorteGruppe / Kohorte |
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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.
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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.
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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.
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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.
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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.
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Was misst die Studie?
Primäre Ergebnismessungen
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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To establish a multi-immune pathway interaction network and composite biomarkers in mycobacterial infection thing
Zeitfenster: 3 days before treatment and 2 months after treatment
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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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3 days before treatment and 2 months after treatment
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Sekundäre Ergebnismessungen
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
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Immune cell subsets and mechanisms of possible effects of anti-tuberculosis drugs
Zeitfenster: 3 days before treatment and 2 months after treatment
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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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3 days before treatment and 2 months after treatment
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Differences in immune subsets between normal persons and patients with active pulmonary tuberculosis
Zeitfenster: 3 days before treatment and 2 months after treatment
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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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3 days before treatment and 2 months after treatment
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To explore whether the peripheral blood before treatment contains a certain marker can predict the short-term efficacy
Zeitfenster: 3 days before treatment and 2 months after treatment
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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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3 days before treatment and 2 months after treatment
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Comparison of the dynamic changes of immune subsets in peripheral blood and pleural effusion of TP patients before and after treatment
Zeitfenster: 3 days before treatment and 2 months after treatment
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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.
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3 days before treatment and 2 months after treatment
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To explore the differences of peripheral blood immune subsets between TP patients and healthy people before treatment
Zeitfenster: 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.
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3 days before treatment and 2 months after treatment
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To explore the metabolic differences of three major nutrients between TP patients and healthy people before treatment
Zeitfenster: 3 days before treatment and 2 months after treatment
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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.
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3 days before treatment and 2 months after treatment
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Differences in metabolic function between multidrug-resistant tuberculosis group and drug-sensitive tuberculosis group
Zeitfenster: 3 days before treatment and 2 months after treatment
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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.
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3 days before treatment and 2 months after treatment
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Influence of immune status on the efficacy of NTM
Zeitfenster: 3 days before treatment and 2 months after treatment
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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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3 days before treatment and 2 months after treatment
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Mitarbeiter und Ermittler
Sponsor
Sponsor
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Studienbeginn
Primärer Abschluss (Geschätzt)
Primärer Abschluss
Studienabschluss (Geschätzt)
Studienabschluss
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
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Zuerst gepostet
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes Update gepostet
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
Andere Studien-ID-Nummern
- 2025-157A-01
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