English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Acute respiratory infections are the commonest disease in humans; This study is important for several reasons: First, NPL can be used as they aremost often caused by viruses and, to a smaller extent, by bacteria (Denny, 1995). Distinguishing these causes is important because viral infections are usually self-limiting and generally do not require any specific treatment while bacterial infections can often lead to complications and are treatedwith anti-bacterialmedications (Dowell et al., 1998; Gozales et al., 2001). However, ruling out a viral etiology is not trivial because of complexities of diagnostic tests and because of a poor correlation between detection of virus and symptoms even with a positive test. This is because the virus could colonize the body but not lead to an infection. One approach to identifying the infection-causing pathogen is to detect the host-response to that pathogen. Gene expression changes in the peripheral blood can be used to detect responses elicited in the host by the pathogen. Indeed, host gene expression profiles based on microarray profiling studies that can distinguish between bacterial and viral infections in patients with acute respiratory symptoms have already been developed (Tsalik et al., 2016). However, such RNA-based detectionmethods have a number of limitations including cost, time for processing and complexity. To circumvent some of these limitations, Burke et al. cameupwith an innovative approach usingmass spectrometry-based proteomics technology to detect proteins that are involved in host-response to viral infection from nasopharyngeal lavage (NPL) (Burke et al., 2017). To discover such biomarker proteins in NPL, they designed an experiment where they recruited healthy volunteers and infected them with human rhinovirus, H3N2 subtype of influenza A virus or sham. Using global mass spectrometry-based proteomics, they first identified candidate proteins that were differentially expressed in the nasal cavity upon viral infection. Next, they designed multiple reaction monitoring assays to validate a set of candidate proteins in a high-throughput and targeted fashion on a larger number of samples. Finally, they derived a classifier using a machine learning algorithm to distinguish infected from uninfected individuals to obtain a 75% true positive rate and 97.46% true negative rate.

Mass Spectrometry Reveals Respiratory Viral Infection Biomarkers