Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis

Zhang, Peng; Wang, Zhangxing; Qiu, Huixian; Zhou, Wenhao; Wang, Mingbang; Cheng, Guoqiang

doi:10.1016/j.csbj.2021.05.024

Cited by 16 publications

(11 citation statements)

References 49 publications

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“…Furthermore, in a recent study, PA was associated with an increased risk of incident T2DM 58 . In addition, proline metabolism homeostasis is essential for glucose homeostasis 59 and neurological dysfunction 60 , which might partly explain why insulin resistance-related proteins and inflammation-related cytokines were altered in LDR + HFD mice compared to HFD mice. These data also indicate that the impact of LDR on proline metabolism may be one mechanism by which LDR affects the gut microbiota to potentiate HFD-induced metabolic impairment, particularly gut barrier dysfunction, inflammation and insulin resistance.…”

Section: Discussionmentioning

confidence: 99%

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

et al. 2022

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Co-exposure of High-fat-diet (HFD) behavior and environmental low-dose radiation (LDR) is common among majority occupational workers, but the synergism of this co-exposure in metabolic health is poorly understood. This study aimed to investigate the impact of gut microbiota and its metabolites on the regulation of HFD accompanied by LDR-associated with metabolic dysfunction and insulin resistance. Here, we reported that Parasutterella was markedly elevated in the gut microbiota of mice in co-exposure of HFD and LDR, accompanied by increased pyrrolidinecarboxylic acid (PA) level in both intestine and plasma. Transplantation of fecal microbiota from mice with co-exposure HFD and LDR with metabolic dysfunction resulted in increased disruption of metabolic dysfunction, insulin resistance and increased PYCR1 (Pyrroline-5-carboxylate reductase 1) expression. Mechanistically, intestinal barrier was damaged more serious in mice with co-exposure of HFD and LDR, leading high PA level in plasma, activating PYCR1 expression to inhibit insulin Akt/mTOR (AKT kinase-transforming protein/Serine threonine-protein kinase) signaling pathway to aggravate HFD-induced metabolic impairments. This study suggests a new avenue for interventions against western diet companied with low dose radiation exposure-driven metabolic impairments.

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Section: Discussionmentioning

confidence: 99%

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

et al. 2022

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“…LC-MS was performed as previously described [14] , [15] . The collected stool samples were freeze-dried to remove water, then approximately 30 mg of the stool was weighted and added to 600 µL of 50% acetonitrile/water extract containing 5 µM chlorosulfonylurea (internal standard), mixed thoroughly and sonicated at room temperature for 30 min.…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the methodology of metabolomic analysis, we mainly refer to our previous publication [14] , [15] and the raw data were preprocessed by Compound Discoverer software (ThermoFisher Scientific, USA) for LC/MS data （detailed in Supplementary Appendix ),in short, the extracted data normalized to the sum of the peak area before analysis, multivariate statistical analysis was performed using SIMCA-P Software (Umetrics AB, Umea, Sweden), including PCA analysis, PLS-DA analysis and OPLS-DA analysis. Differential metabolites were screened by OPLS-DA model VIP (variable weight) value > 1 and T-test P value (P < 0.05).…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning was performed as previously described [14] , [16] to determine which meconium metabolites could be used as neonatal hyperbilirubinemia biomarkers. As the random forest method allows for ranking the importance of the selected features.We used the Random Forest Classifier function of scikit learn (version 0.23.1) to determine the importance of the meconium metabolites.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia

Zeng

Wang

Zhang

et al. 2022

Computational and Structural Biotechnology Journal

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“…Based on this concept, AI-assisted screening systems have been developed to analyze the electronic health record of individuals for the detection of fragile X syndrome [ 124 ]. In [ 125 ], regarding sensitivity and specificity measures, random forest was found to outperform K-nearest neighbor, SVM, backpropagation, and deep learning in classifying Autism spectrum disorders (ASDs) in children and adolescents. The early detection of ASDs helps children at high risk undergo targeted screenings.…”

Section: Applications Of Ai For Neurological Disordersmentioning

confidence: 99%

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

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Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis

Abstract: Graphical abstract

Cited by 16 publications

References 49 publications

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

Low-dose radiation exaggerates HFD-induced metabolic dysfunction by gut microbiota through PA-PYCR1 axis

Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

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