Evidence map›Paper›PMID 40352038›Full record

ArticleCureus2025

Mycobacterium avium Complex-Related Pulmonary Parenchyma Destruction: A Case Report.

Aishwarya Viswanath, Arjun Manimaran, Jack Brown, Jhiamluka Solano

Abstract readCase Reports
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Aishwarya ViswanathInternal Medicine, Scunthorpe General Hospital, Scunthorpe, GBR.
Arjun ManimaranInternal Medicine, Northern Lincolnshire and Goole NHS Trust, Scunthorpe, GBR.
Jack BrownCritical Care Medicine, Northern Lincolnshire and Goole NHS Trust, Scunthorpe, GBR.
Jhiamluka SolanoResident Doctor Committee, Royal College of Physicians, London, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-tuberculous mycobacteria, particularly Mycobacterium avium complex (MAC), have become significant causes of pulmonary infections, especially in immunocompromised individuals. MAC lung disease presents a diagnostic challenge due to its clinical overlap with other pulmonary conditions, including Mycobacterium tuberculosis infections. The need for microbiological confirmation and radiological evaluation complicates early diagnosis. New molecular diagnostic methods, such as PCR, have improved detection but are not universally accessible. Treatment for MAC lung disease typically involves a multidrug regimen including macrolides, rifamycins, and ethambutol. However, the disease often proves resistant to standard therapies, and treatment failure is common due to drug resistance, delayed diagnosis, and poor adherence. Recent research has highlighted the need for personalized treatment strategies and alternative therapies, such as intravenous antibiotics and host-directed treatments, to improve outcomes. The prognosis for MAC lung disease remains poor, especially in patients with preexisting lung conditions like bronchiectasis or rheumatoid arthritis, who are at increased risk of disseminated infection. This case report and review underscore the importance of early recognition and intervention, highlighting the role of a multidisciplinary approach in managing complex infections. Additionally, it emphasizes the need for continued research to identify more effective treatment options and improve patient outcomes in this growing patient population.

Indexed as

host-directed therapiesmortality factorsmycobacterium aviumnon-tuberculous mycobacterial infectionpulmonary disease

Identifiers

PMID40352038
PMCPMC12066151

What OpenQuestion holds

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Registered trials

None linked

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.