Evidence map›Paper›PMID 42488414›Full record

ArticleFrontiers in cellular and infection microbiology2026

Population-level and longitudinal MIC distributions for antimicrobials against drug-resistant

Song Gao, Fan Tu, Huan Ding, Qi Zhang, Shipeng Zhang, Shiya Shen, Jun Liu, Zhuping Xu

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

8 authors.

Song Gao *Department of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.
Fan Tu *Department of Laboratory Medicine, Affiliated Wuxi Fifth Hospital of Jiangnan University, The Fifth People's Hospital of Wuxi, Wuxi, China.
Huan Ding *Department of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.
Qi ZhangDepartment of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.
Shipeng ZhangDepartment of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.
Shiya ShenDepartment of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.
Jun LiuDepartment of Laboratory Medicine, Affiliated Wuxi Fifth Hospital of Jiangnan University, The Fifth People's Hospital of Wuxi, Wuxi, China.
Zhuping XuDepartment of Chronic Infectious Disease Control and Prevention, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Drug-resistant tuberculosis (DR-TB) remains a significant global health challenge, with increasing resistance to both first- and second-line anti-tuberculosis drugs. Understanding the demographic distribution, temporal trends, and resistance profiles of DR-TB is essential for improving treatment strategies and public health interventions. Objective: This study aimed to examine the demographic characteristics, temporal distribution, and minimum inhibitory concentration (MIC) patterns of drug-resistant Methods: A total of 402 drug-resistant Results: The majority of patients were male (72.14%), and the most common form of DR-TB was isoniazid-resistant TB (23.14%). The study found significant seasonal variation, with higher DR-TB incidence in winter and spring months, and the temporal distribution was affected by COVID-19. Resistance rates were highest for isoniazid (40.30%), cycloserine (47.76%), and para-aminosalicylic acid (52.24%). Across the DR-TB cohorts, MICs for isoniazid, ethionamide, para-aminosalicylic acid, and amikacin demonstrated a significant upward trend, whereas a declining pattern was observed for ethambutol. The longitudinal comparison of paired Conclusion: This study highlights the increasing burden of DR-TB, with increasing MICs and relatively high resistance rates for a few key drugs indicating reduced drug susceptibility over time. The increasing MICs highlight the need for continuous resistance monitoring and enhanced DR-TB control efforts.

Indexed as

Antitubercular AgentsMycobacterium tuberculosisTuberculosis, Multidrug-ResistantAdultChinaDrug Resistance, Multiple, BacterialFemaleHumansIsoniazidLongitudinal StudiesMaleMicrobial Sensitivity TestsRetrospective StudiesAntitubercular AgentsIsoniaziddrug resistancedrug-resistant tuberculosisMICresistance patternstemporal trendstuberculosis epidemiology

Identifiers

PMID42488414
PMCPMC13388225

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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.