Observational studyPloS one2026
Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.
Observational study in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers of post-acute infection syndrome: a systematic literature review.Frontiers in immunology · 2026Pooled it
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
backgroundCOVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. While many patients recover fully, others develop long-COVID, a heterogeneous condition marked by persistent symptoms beyond the acute phase. The immunological pathomechanisms between long-COVID and other post-acute recovery states remain unclear.
objectiveTo characterize and compare clinical, pulmonary, and proteomic profiles of patients with long-COVID (LC) and those recovering from severe COVID-19 without long-COVID (post-severe-COVID, PC), and to evaluate the predictive potential of machine learning-based biomarker analysis.
methodsIn this monocentric, prospective observational study with a cross-sectional design, patients undergoing rehabilitation were included at admission. Clinical data, detailed symptom profiles, and lung function testing, including diffusing capacity of the lungs, were collected. Serum proteomics covering immune response and inflammation panels was performed, and a Random Forest classifier was applied to identify biomarkers differentiating LC and PC.
resultsLC (n = 24) patients were younger (52 years vs. 58 years in PC), predominantly female (66.7% vs. 30.0% in PC), and reported fatigue, neurocognitive symptoms, and exercise intolerance, whereas PC (n = 40) patients showed greater pulmonary impairment, as shown by reduced diffusing capacity (46% vs. 72.5% in LC p<0.001). Proteomic profiling revealed distinct immune and inflammatory signatures between groups. Applying a random forest classification algorithm, we were able to distinguish between the LC and the PC group with a high degree of accuracy of around 89%, using LAMP3 (Lysosome-associated membrane glycoprotein 3), CKAP4 (cytoskeleton associated protein 4) and KRT19 (Keratin 19).
conclusionsThis study introduces a novel characterization of patients recovering from severe COVID-19 without long-COVID, enabling clearer differentiation between persistent and recovering trajectories. Combining clinical data, pulmonary function, and proteomic machine learning analysis provides insight into post-acute COVID-19 biology and identifies candidate biomarkers for improved diagnosis.
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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 Socratic graph.