Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Hogan, Catherine A.; Rajpurkar, Pranav; Sowrirajan, Hari; Phillips, Nicholas; Le, Anthony; Wu, Manhong; Garamani, Natasha; Sahoo, Malaya K.; Wood, Mona; Huang, ChunHong; Ng, Andrew Y.; Mak, Justin; Cowan, Tina M.; Pinsky, Benjamin A.

doi:10.1016/j.ebiom.2021.103546

Cited by 24 publications

(17 citation statements)

References 43 publications

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“…Zhang et al constructed a predictive model for recurrence-free survival after first treatment in patients with stage T4 primary NPC by extracting features from tumor regions in MRI findings for 360 patients with primary NPC and validating them with a training cohort [ 48 ]. Furthermore, a machine learning approach based on PET-CT was used to identify recurrence and inflammatory changes after NPC treatment [ 49 ].…”

Section: Clinical Symptom and Diagnosis Of Recurrent Npcmentioning

confidence: 99%

Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge

Peng

Wang

Fan

et al. 2022

Cancers

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Recurrent nasopharyngeal carcinoma (NPC), which occurs in 10–20% of patients with primary NPC after the initial treatment modality of intensity-modulated radiation therapy (IMRT), is one of the major causes of death among NPC patients. Patients with recurrent disease without distant metastases still have a chance to be saved, but re-treatment often carries more serious toxicities or higher risks. For this group of patients, both otolaryngologists and oncologists are committed to developing more appropriate treatment regimens that can prolong patient survival and improve survival therapy. Currently, there are no international guidelines for the treatment of patients with recurrent NPC. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC.

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Section: Clinical Symptom and Diagnosis Of Recurrent Npcmentioning

confidence: 99%

Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge

Peng

Wang

Fan

et al. 2022

Cancers

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“…Descriptive analysis was performed by Chi-squared test or Fisher’s exact test for variables with less than five data points per cell, and Mann–Whitney U test for continuous variables, using Stata v15.1 (Stata Corp, College Station, TX, United States). A multivariable analysis was used to investigate the significance of the a priori determined potential confounder’s age and sex in the analysis, as previously described using R version 4.0.2 ( Hogan et al, 2021 ). The significance of each predictor was determined using the value of p from this regression.…”

Section: Methodsmentioning

confidence: 99%

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2

Wu²,

Khan³

et al. 2023

Front. Microbiol.

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IntroductionThe routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection.MethodsWe studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS–MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance.ResultsA total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95%CI 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95).DiscussionThis approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.

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“…On the day of the health examination, oropharyngeal specimens were collected from healthy controls and patients with IAV pneumonia sterile swabs (155C, COPAN, Murrieta, CA, USA) within 24 h after hospital admission ( Hogan et al., 2021 ; Ma et al., 2021 ). We collected samples in the morning, and participants refrained from brushing their teeth 12 hours before sampling, rinsed or drank water two hours before sampling to remove oral debris, followed by a two-hour fast to reduce the impact on the oropharyngeal microbiota and metabolome.…”

Section: Methodsmentioning

confidence: 99%

Multi-omics association analysis reveals interactions between the oropharyngeal microbiome and the metabolome in pediatric patients with influenza A virus pneumonia

Liu

Fan³

et al. 2022

Front. Cell. Infect. Microbiol.

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Children are at high risk for influenza A virus (IAV) infections, which can develop into severe illnesses. However, little is known about interactions between the microbiome and respiratory tract metabolites and their impact on the development of IAV pneumonia in children. Using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and 16S rRNA gene sequencing, we analyzed the composition and metabolic profile of the oropharyngeal microbiota in 49 pediatric patients with IAV pneumonia and 42 age-matched healthy children. The results indicate that compared to healthy children, children with IAV pneumonia exhibited significant changes in the oropharyngeal macrobiotic structure (p = 0.001), and significantly lower microbial abundance and diversity (p < 0.05). These changes came with significant disturbances in the levels of oropharyngeal metabolites. Intergroup differences were observed in 204 metabolites mapped to 36 metabolic pathways. Significantly higher levels of sphingolipid (sphinganine and phytosphingosine) and propanoate (propionic acid and succinic acid) metabolism were observed in patients with IAV pneumonia than in healthy controls. Using Spearman’s rank-correlation analysis, correlations between IAV pneumonia-associated discriminatory microbial genera and metabolites were evaluated. The results indicate significant correlations and consistency in variation trends between Streptococcus and three sphingolipid metabolites (phytosphingosine, sphinganine, and sphingosine). Besides these three sphingolipid metabolites, the sphinganine-to-sphingosine ratio and the joint analysis of the three metabolites indicated remarkable diagnostic efficacy in children with IAV pneumonia. This study confirmed significant changes in the characteristics and metabolic profile of the oropharyngeal microbiome in pediatric patients with IAV pneumonia, with high synergy between the two factors. Oropharyngeal sphingolipid metabolites may serve as potential diagnostic biomarkers of IAV pneumonia in children.

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Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Cited by 24 publications

References 43 publications

Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge

Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge

Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2

Multi-omics association analysis reveals interactions between the oropharyngeal microbiome and the metabolome in pediatric patients with influenza A virus pneumonia

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