Objective:To study the changes in serum small molecule metabolites after brucella infection in humans using untargeted metabolomics methods, and screening representative biomarkers. Methods:A total of 109 serum samples collected from January 2019 to December 2021 at the Brucellosis Clinic of the Baotou Center for Disease Control and Prevention were divided into acute phase group ( n = 40), chronic phase group ( n = 35) of brucellosis, and healthy group ( n = 34) based on clinical diagnosis. Ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry technology was used to test serum samples and screen for differential metabolites. Receiver operating characteristic curve was used to evaluate the predictive ability of differential metabolites for brucellosis. Enriched pathways were screened using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to identify metabolic pathways significantly affected. Results:A total of 17 differential metabolites were screened between the acute phase group and the healthy group, and 12 differential metabolites were screened between the chronic phase group and the healthy group. There were a total of 5 differential metabolites (oleamide, linoleamide, stearamide, palmitoleic acid, α-linolenic acid) statistically significant among the three groups ( F = 16.84, 17.52, 14.31, 13.01, 20.76, P < 0.05). KEGG pathway analysis showed that the differential metabolites in the acute phase group were enriched in metabolic pathways such as ether lipid metabolism, glycerophosphate metabolism, sphingolipid signal and sphingolipid metabolism. The differential metabolites in the chronic phase group were enriched in metabolic pathways such as glycerophosphate metabolism, ether lipid metabolism, protein digestion and absorption metabolism. Conclusion:Untargeted metabolomics methods can screen out serum small molecule metabolites that undergo changes after brucella infection in the human body, including oleamide, linoleamide, stearamide, palmitoleic acid, α-linolenic acid can serve as potential biomarkers to distinguish brucellosis patients from healthy people.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers