Background: There is a crucial link between the gut microbiota and the host central nervous system, and the communication between them occurs via a bidirectional pathway termed the “microbiota-gut-brain axis.” The gut microbiome in the modern environment has markedly changed in response to environmental factors. These changes may affect a broad range of host psychiatric disorders, such as depression, by interacting with the host through metabolic, immune, neural, and endocrine pathways. Nevertheless, the general aspects of the links between the gut microbiota and depression have not been systematically investigated through bibliometric analysis.Aim: This study aimed to analyze the current status and developing trends in gut microbiota research in the depression field through bibliometric and visual analysis.Methods: A total of 1,962 publications published between 1999 and 2019 were retrieved from the Web of Science Core Collection. CiteSpace (5.6 R5) was used to perform collaboration network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection.Results: The number of publications has been rapidly growing since 2010. The collaboration network analysis revealed that the USA, University College Cork, and John F. Cryan were the most influential country, institute, and scholar, respectively. The most productive and co-cited journals were Brain Behavior and Immunity and Proceedings of the National Academy of Sciences of the United States of America, respectively. The co-citation analysis of references revealed that the most recent research focus was in the largest theme cluster, “cytokines,” thus reflecting the important research foundation in this field. The co-occurrence analysis of keywords revealed that “fecal microbiota” and “microbiome” have become the top two research hotspots since 2013. The citation burst detection for keywords identified several keywords, including “Parkinson's disease,” “microbiota-gut-brain axis,” “microbiome,” “dysbiosis,” “bipolar disorder,” “impact,” “C reactive protein,” and “immune system,” as new research frontiers, which have currently ongoing bursts.Conclusions: These results provide an instructive perspective on the current research and future directions in the study of the links between the gut microbiota and depression, which may help researchers choose suitable cooperators or journals, and promote their research illustrating the underlying molecular mechanisms of depression, including its etiology, prevention, and treatment.

Background: The objective of this study was to investigate the serum concentrations of olanzapine in relation to age, sex, and other factors in Chinese patients aged between 10 and 90 years. Methods: Data for 884 olanzapine patients, deposited between 2016 and 2017, were retrieved from the therapeutic drug monitoring database of the Affiliated Brain Hospital of Guangzhou Medical University. The effects of covariates on serum olanzapine concentration, dose-normalized concentration (C/D ratio), and normalized concentration (C/D/weight) were investigated. Results: Generally, male patients had lower olanzapine concentration, C/D ratio, and C/D/weight than female patients (P < 0.001). Smoking and drinking reduced olanzapine concentration, C/D ratio, and C/D/weight (P < 0.001). Coadministration with valproate decreased olanzapine concentration, C/D ratio, and C/D/weight by about 16%, 30%, and 40%, respectively (P < 0.001). Patients younger than 60 years had higher olanzapine concentrations (P < 0.05) but lower C/D ratios and C/D/weight (P < 0.001) than patients older than 60 years. Age was correlated with olanzapine concentration (r = −0.082, P < 0.05), C/D ratio (r = 0.196, P < 0.001), and C/D/weight (r = 0.169, P < 0.001). Sample timing after dose and diagnostic factors also contributed to the olanzapine concentrations. Multiple linear regression analysis revealed significant influences of dosage, age, sex, valproate comedication, smoking, postdose interval, and schizophrenia (vs bipolar affective disorders) on serum olanzapine concentrations. Conclusions: The metabolism of olanzapine may be altered by several factors. Patients characterized with a combination of factors may benefit from therapeutic drug monitoring for the adjustment of olanzapine dose to minimize adverse reactions.

Quantum metrology can achieve far better precision than classical metrology, and is one of the most important applications of quantum technologies in the real world. To attain the highest precision promised by quantum metrology, all steps of the schemes need to be optimized, which include the state preparation, parametrization, and measurement. Here the recent progresses on the optimization of these steps, which are essential for the identification and achievement of the ultimate precision limit in quantum metrology, are reviewed. It is hoped this provides a useful reference for the researchers in quantum metrology and related fields.

No abstract

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort,” and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees’ regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL−1 g−1 day), as illustrated by a minimal bias (mean relative error (%) =  + 3%), good precision (MAE = 8.7 μg mL−1 g−1 day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions.

No abstract

No abstract

No abstract