ReviewScientifica2025
Diagnostic Challenges in Fungal Coinfections Associated With Global COVID-19.
Review in Scientifica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Article
- Diagnostic Insights Into Pathogen Spectrum and Mixed Microbial Detection in Critically Ill Patients With Pulmonary Infection Using Targeted Next-Generation Sequencing.Canadian respiratory journal · 2026Article
- Rapid and simultaneous detection ofFrontiers in cellular and infection microbiology · 2026Article
- Virulence factors of Candida spp. isolated from COVID-19 patients: hydrolytic enzyme activity and biofilm formation.BMC microbiology · 2025Article
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The early diagnosis of opportunistic infections is a critical concern for patient care worldwide, particularly in the context of the COVID-19 pandemic. This review examines the challenges and advancements in the management and early diagnosis of opportunistic fungal infections, which have become increasingly prominent during the pandemic. Using multiple sources, including curated databases such as PubMed and Scopus, as well as Google Scholar for broader literature searches, we systematically reviewed studies on COVID-19-associated fungal infections, with a focus on candidiasis, mucormycosis, and aspergillosis. The inclusion criteria encompassed peer-reviewed articles, clinical case reports, and cohort studies that discussed diagnostic methods, clinical outcomes, and treatment responses. Data were systematically extracted and analyzed to identify key trends and gaps in current diagnostic practices. Given the significance of opportunistic fungal infections-particularly the selected species-this review provides a comprehensive analysis of diagnostic challenges and advancements in the context of COVID-19 and beyond. Currently, there is no definitive strategy for effectively addressing these opportunistic pathogens, highlighting the need for continued research and innovation. Despite advancements in medical technology, opportunistic fungal infections continue to pose significant challenges to early and accurate diagnosis. The COVID-19 pandemic has exacerbated these challenges, with secondary fungal infections contributing to increased morbidity and mortality rates. This review highlights the complexities of diagnosing fungal coinfections and emphasizes the urgent need for improved diagnostic strategies. Enhancing the early and accurate detection of these infections is critical for effective patient management, particularly during viral pandemics. Addressing the challenges outlined in this review requires innovative diagnostic approaches to improve patient outcomes and reduce the burden of opportunistic infections on global healthcare systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.