OBJECTIVE: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).
METHODS: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.
RESULTS: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.
CONCLUSIONS: These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

The San-Ao Decoction (SAD) is a well-known Traditional Chinese Medicine (TCM) formula used to alleviate respiratory symptoms, including asthma. However, its precise mechanisms of action have remained largely unknown. In this study, we utilized computer-aided approaches to explore these mechanisms. Firstly, we conducted a comprehensive analysis of the chemical composition of SAD, which allowed us to identify the 28 main ingredients. Then, we employed computer simulations to investigate the potential active ingredients of SAD and the corresponding binding sites of transient receptor potential vanilloid 1 (TRPV1). The simulations revealed that D509 and D647 were the potential binding sites for TRPV1. Notably, molecular dynamics (MD) studies indicated that site D509 may function as an allosteric site of TRPV1. Furthermore, to validate the computer-aided predictions, we performed experimental studies, including in vitro and in vivo assays. The results of these experiments confirmed the predictions made by our computational models, providing further evidence for the mechanisms of action of San-Ao Decoction in asthma treatment. Our findings demonstrated that: i) D509 and D647 of TRPV1 are the key binding sites for the main ingredients of SAD; ii) SAD or its main ingredients significantly reduce the influx of Ca2+ through TRPV1, following the TCM principle of \"Jun, Chen, Zuo, Shi\"; iii) SAD shows efficiency in comprehensive in vivo validation. In conclusion, our computer-aided investigation of San-Ao Decoction in asthma treatment has provided valuable insights into the therapeutic mechanisms of this TCM formula. The combination of computational analysis and experimental validation has proven effective in enhancing our understanding of TCM and may pave the way for future discoveries in the field.

Adverse Outcome Pathways (AOPs) summarize mechanistic understanding of toxicological effects and have, for example, been highlighted as a promising tool to integrate data from novel in vitro and in silico methods into chemical risk assessments. Networks based on AOPs are considered the functional implementation of AOPs, as they are more representative of complex biology. At the same time, there are currently no harmonized approaches to generate AOP networks (AOPNs). Systematic strategies to identify relevant AOPs, and methods to extract and visualize data from the AOP-Wiki, are needed. The aim of this work was to develop a structured search strategy to identify relevant AOPs in the AOP-Wiki, and an automated data-driven workflow to generate AOPNs. The approach was applied on a case study to generate an AOPN focused on the Estrogen, Androgen, Thyroid, and Steroidogenesis (EATS) modalities. A search strategy was developed a priori with search terms based on effect parameters in the ECHA/EFSA Guidance Document on Identification of Endocrine Disruptors. Furthermore, manual curation of the data was performed by screening the contents of each pathway in the AOP-Wiki, excluding irrelevant AOPs. Data were downloaded from the Wiki, and a computational workflow was utilized to automatically process, filter, and format the data for visualization. This study presents an approach to structured searches of AOPs in the AOP-Wiki coupled to an automated data-driven workflow for generating AOPNs. In addition, the case study presented here provides a map of the contents of the AOP-Wiki related to the EATS-modalities, and a basis for further research, for example, on integrating mechanistic data from novel methods and exploring mechanism-based approaches to identify endocrine disruptors (EDs). The computational approach is freely available as an R-script, and currently allows for the (re)-generation and filtering of new AOP networks based on data from the AOP-Wiki and a list of relevant AOPs used for filtering.