Effect of Mycobacterial Infection on Immune Status (EMIIS)
2026年6月4日 更新者：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.
調査の概要
状態
募集
条件
研究の種類
観察的
入学 (推定)
120
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究連絡先
- 名前：Shiyi He
- 電話番号：+86-0574-87089878
- メール：shiyihii@163.com
研究連絡先のバックアップ
- 名前：Chao Cao
- 電話番号：+86-0574-87089878
- メール：caodoctor@163.com
研究場所
-
-
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Ningbo、中国
- 募集
- The First Affiliated Hospital of Ningbo University
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参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
いいえ
サンプリング方法
確率サンプル
調査対象母集団
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.
説明
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.
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
|---|
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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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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To establish a multi-immune pathway interaction network and composite biomarkers in mycobacterial infection thing
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Immune cell subsets and mechanisms of possible effects of anti-tuberculosis drugs
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
|
協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (実際)
2025年7月9日
一次修了 (推定)
2026年7月20日
研究の完了 (推定)
2026年7月20日
試験登録日
最初に提出
2026年5月19日
QC基準を満たした最初の提出物
2026年6月4日
最初の投稿 (実際)
2026年6月10日
学習記録の更新
投稿された最後の更新 (実際)
2026年6月10日
QC基準を満たした最後の更新が送信されました
2026年6月4日
最終確認日
2026年6月1日
詳しくは
本研究に関する用語
追加の関連 MeSH 用語
その他の研究ID番号
- 2025-157A-01
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