Salivary Interleukin-6 and C-Reactive Protein/Mean Platelet Volume Ratio in the Diagnosis of Late-Onset Neonatal Pneumonia

Omran, Ahmed; Ali, Yasmin; Abdalla, Mohamed; El-Sharkawy, Sonya; Rezk, Ahmed R.; Khashana, Abdelmoneim

doi:10.1155/2021/8495889

Cited by 2 publications

(2 citation statements)

References 56 publications

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“…Regarding IL6, a negative correlation was observed between respiratory rate and axillary temperature. In this context, elevated levels of IL-6 can be useful, for example, in the diagnosis and/or prognosis of neonatal sepsis, appendicitis, and pneumonia (Liu et al, 2021; Omran, Ali et al, 2021; Qiu et al, 2018; Sun et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of Inflammatory Mediators in Saliva Samples From Hospitalized Newborns: Potential Biomarkers?

Rocha,

Cruz-Machado,

Silva

et al. 2024

Clin Nurs Res

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Saliva measurements serve as a noninvasive tool for clinically monitoring newborns (NB) and children, a vulnerable population with promising potential for both research and clinical practice. Saliva acts as a repository for various inflammatory biomarkers involved in diverse biological functions. Particularly for children, it offers numerous advantages when compared to plasma and urine sampling. Nevertheless, there is a significant knowledge gap regarding detectable levels of cytokines in the saliva of newborns and children, as well as studies aiming to assess the relationship of this content with physiological and pathological processes. Objectives: To characterize the levels of 11 inflammatory mediators (IFNg, IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17, TNF, and VEGF) in saliva samples from NB on the first and second day of hospitalization in the Neonatal Intensive Care Unit (NICU). Method: Exploratory study, descriptive, nested within a primary clinical, observational, and prospective study, conducted in the NICU of a public hospital in São Paulo, Brazil. Demographic data and vital signs were recorded in the clinical records of 90 NB, and five saliva samples from 5 NB were collected between the first and second day of life (D1–D2) at approximately 8-hr intervals (8–9 am, 4–5 pm, and 11–12 pm). Saliva samples were used for the measurement of 11 cytokines (IFNg, IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17, TNF, and VEGF). Results: Five NBs participated in this exploratory study, and the vital signs showed variability from the first (D1) to the second day (D2) of hospitalization, variability similar to that of the total population of the primary study. The presence and levels of the 11 cytokines were detected in the saliva samples, as well as a statistical correlation between 10 cytokines (IFNg, IL1b, IL2, IL4, IL6, IL10, IL12, IL17, TNF, and VEGF) and vital signs. Conclusions: The novelty of measuring inflammatory mediators in saliva samples from hospitalized NBs in the NICU is highlighted, providing support and new perspectives for the development of clinical and experimental research and an opportunity for developing and implementing new salivary biomarkers in different population segments.

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Section: Discussionmentioning

confidence: 99%

Identification of Inflammatory Mediators in Saliva Samples From Hospitalized Newborns: Potential Biomarkers?

Rocha,

Cruz-Machado,

Silva

et al. 2024

Clin Nurs Res

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“…Therefore, it is very important to diagnose neonatal pneumonia in pregnant women with diabetes [ 8 ]. The current research focuses on the effect of neonatal ventilators on neonatal pneumonia, and the prediction models used are mainly logistic regression model [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

Lin,

Chen,

Zhong

et al. 2023

BMC Med Res Methodol

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Objective To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia. Method Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set. Results In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%). Conclusion KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.

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Salivary Interleukin-6 and C-Reactive Protein/Mean Platelet Volume Ratio in the Diagnosis of Late-Onset Neonatal Pneumonia

Cited by 2 publications

References 56 publications

Identification of Inflammatory Mediators in Saliva Samples From Hospitalized Newborns: Potential Biomarkers?

Identification of Inflammatory Mediators in Saliva Samples From Hospitalized Newborns: Potential Biomarkers?

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

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