Evidence map›Paper›PMID 41613602›Full record

ArticleFrontiers in cellular and infection microbiology2025

The pathological correlation between pulmonary tuberculosis and sarcoidosis patients and the impact of presence of nodules on pulmonary tuberculosis patients.

Yunfeng Sheng, Zhijian Bao, Xiaojing Zhang, Haibo Hua, Yuxin Guo, Wei Gai, Yanfei Cui

Abstract read
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Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Case reports in infectious diseases · 2026
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yunfeng ShengTuberculosis Department, Hangzhou Red Cross Hospital, Hangzhou, China.
Zhijian BaoTuberculosis Department, Hangzhou Red Cross Hospital, Hangzhou, China.
Xiaojing ZhangWillingMed Technology Beijing Co., Ltd, Beijing, China.
Haibo HuaTuberculosis Department, Hangzhou Red Cross Hospital, Hangzhou, China.
Yuxin GuoWillingMed Technology Beijing Co., Ltd, Beijing, China.
Wei GaiWillingMed Technology Beijing Co., Ltd, Beijing, China.
Yanfei CuiTuberculosis Department, Hangzhou Red Cross Hospital, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Both pulmonary tuberculosis (PTB) and sarcoidosis (SA) are chronic, systemic, granulomatous diseases. Due to their similar clinical and radiological features, as well as similar pathological characteristics, it is difficult to distinguish. This study aims to explore the pathological correlation between PTB and SA and the impact of nodules formation on the occurrence of PTB. Methods: We retrospective enrolled 307 patients admitted to the tuberculosis department between January 2022 and March 2024. After applying the inclusion and exclusion criteria, 170 patients were divided into three groups and analyzed: sarcoid tuberculosis group (TB-N, n=59), non-sarcoid tuberculosis group (TB-NoN, n=74), and sarcoidosis group (SA, n=37). Comparative analysis was performed on the clinical characteristics, pathogen profiles, and pulmonary microbial composition differences among the three groups. Results: Patients in the TB-N and SA group predominantly presented with multiple nodules. Among samples testing positive by both mNGS and conventional microbiological tests (CMT), the proportion of partially matched results was higher in the TB-N group than in the TB-NoN group, with a greater diversity of pathogenic bacteria detected in the TB-N group. ACE index analysis revealed significantly higher microbial richness in the TB-NoN group compared to both SA and TB-N groups. Regarding treatment regimens, combination therapy was more frequently administered in the TB-N group, while single drug treatment predominated in the TB-NoN group. Although the duration of anti-tuberculosis treatment was longer in the TB-N group, this difference did not reach statistical significance. Discussion: Significant differences in imaging manifestations were observed between TB-N and SA groups. The presence of nodules was associated with a more complex pathogen profile in PTB patients; however, the pulmonary microbial diversity was lower in TB-N than in TB-NoN. PTB patients with nodules predominantly received combination therapy.

Indexed as

SarcoidosisTuberculosis, PulmonaryAdultAgedFemaleHumansLungMaleMiddle AgedMycobacterium tuberculosisRetrospective Studiesmedication guidancemycobacterium tuberculosis complexpulmonary microbiotapulmonary tuberculosissarcoidosis

Identifiers

PMID41613602
PMCPMC12847324

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.