ArticleImmunologic research2025
Study of immunological and inflammatory gene response in Indian cohort of COVID- 19 patients by NanoString technology.
Article in Immunologic research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID- 19, which has affected millions of people across the globe as a pandemic, is caused by the SARS-Cov- 2 virus which has a case fatality rate of 2.3%. The clinical outcome of those who had mild and severe infection exhibited different responses for the treatment due to differences in the host immune system. Predicting immune response with reliable biomarkers to monitor the severity and also identifying potential biomarkers that could help the clinician in decision-making would be important and also beneficial for the management of COVID- 19 in the hospital setup. In our study, we have used the NanoString nCounter gene expression assay to investigate the molecular signalling of host to COVID- 19 infection. The nCounter gene expression assay identified 29 genes that were differentially regulated and specific to COVID- 19 infection; out of which, 9 genes (ICAM3, PTAFR, CEACAM6, GBP1, C7, STAT1, CEACAM8, IL16, HLA-DPB1) exhibited strong predictive performance to differentiate COVID- 19 infection from healthy controls (AUC ≥ 0.9). We also observed that three genes (MAP4 K1, CTLA4, and HLA-DQB1) were able to differentiate COVID- 19 from patients with flu-like symptoms. A group of 11 genes (C2, CD14, CDKN1 A, CMKLR1, CYBB, HLA-A, IFNA2, LAG3, MARCO, TLR7, and IL15) showed a dysregulation trend with onset of COVID- 19 infection and settled to normal levels by day 14 as patient recovered. The outcome of our study may help in understanding the host immune response towards COVID- 19 infection.
Indexed as
Identifiers
40299133What 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.