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.

Cosmetic surgery is ever more affordable and accessible, but carries physical and psychological risks. Yet, no study to date has directly examined risk-taking behaviour under controlled conditions, beyond self-report and in relation to cosmetic surgery attitudes. We used the Balloon Analogue Risk Task and advanced computational modelling to measure decision-making behaviour and identify the latent parameters driving behaviour associated with cosmetic surgery attitudes in women with no cosmetic surgery history (N = 265) and a subsample of women with a cosmetic surgery history (N = 24). Risk taking was higher in women with greater acceptance and history of cosmetic surgery. Computational modelling revealed increased risk taking in women with greater acceptance of cosmetic surgery when decisions were made with greater knowledge of loss (risk) and not when the likelihood of loss was unknown (uncertainty). When women with greater acceptance of cosmetic surgery made decisions, they also placed less emphasis on possible losses (reduced loss aversion). Our findings suggest that women seeking cosmetic procedures may be less sensitive to losses and thus make more risky decisions. Greater emphasis should be placed on communicating potential losses rather than just the associated risks to women considering cosmetic procedures.No Level Assigned This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways neural networks can be useful to social neuroscientists: (i) building statistical models to predict behavior from brain activity; (ii) quantifying naturalistic stimuli and social interactions; and (iii) generating cognitive models of social brain function. These applications have the potential to enhance the clinical value of neuroimaging and improve the generalizability of social neuroscience research. We also discuss the significant practical challenges, theoretical limitations and ethical issues faced by deep learning. If the field can successfully navigate these hazards, we believe that artificial neural networks may prove indispensable for the next stage of the field\'s development: deep social neuroscience.

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.

Gold-centered carbene-metal-amides (CMAs) containing cyclic (alkyl)(amino)carbenes (CAACs) are promising emitters for thermally activated delayed fluorescence (TADF). Aiming at the design and optimization of new TADF emitters, we report a density functional theory study of over 60 CMAs with various CAAC ligands, systematically evaluating computed parameters in relation to photoluminescence properties. The CMA structures were primarily selected based on experimental synthesis prospects. We demonstrate that TADF efficiency of the CMA materials originates from a compromise between oscillator strength coefficients and exchange energy (ΔEST). The latter is governed by the overlap of HOMO and LUMO orbitals, where HOMO is localized on the amide and LUMO over the Au-carbene bond. The S0 ground and excited T1 states of the CMAs adopt approximately coplanar geometry of carbene and amide ligands, but rotate perpendicular in the excited S1 states, resulting in degeneracy or near-degeneracy of S1 and T1, accompanied by a decrease in the S1-S0 oscillator strength from its maximum at coplanar geometries to near zero at rotated geometries. Based on the computations, promising new TADF emitters are proposed and synthesized. Bright CMA complex (Et2CAAC)Au(carbazolide) is obtained and fully characterized in order to demonstrate that excellent stability and high radiative rates up to 106 s-1 can be obtained for the gold-CMA complexes with small CAAC-carbene ligands.

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.

Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline.
We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits.
There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal).
Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

A variety of OH containing molecules in their different modes of adsorption onto the rutile TiO2(110) are studied by means of density functional theory. A special focus is given to ethanol, ethylene glycol and glycerol. The different species were analyzed with respect to the adsorption energy, work function, and atomic Bader charges. Our results show that dissociated adsorption is favored in all cases. Within these modes, the strongest binding is observed in the case of bidentate fully dissociated adsorption, followed by bidentate partially dissociated then the monodentate dissociated modes. The dependence is also noted upon charge transfer analysis. Species adsorbing with two dissociated OH groups show a negative charge which is roughly twice as large compared to those exhibiting only one dissociated group. In the case of molecular adsorption, we find a small positive charge on the adsorbate. The change in work functions obtained is found to be negative in all studied cases. We observe a trend of the work function change being more negative for glycerol (3 OH groups) followed by ethylene glycol (2 OH groups) and the remaining alcohols (1 OH group), thus indicating that the number of OH groups present is an important factor in regards to work function changes. For the complete series of adsorbates studied (methanol, ethanol, isopropanol, ethylene glycol, glycerol, hydrogen peroxide and formic acid) there is a linear relationship between the change in the work function and the adsorption energy for the molecular adsorption mode. The relationship is less pronounced for the dissociated adsorption mode for the same series.

The development of foot ulcers is a common consequence of severe diabetes. Due to vascular disorders and impeded healing caused by the disease, most foot ulcers have been reported to be affected by body weight and progress with time. Also, abnormal distribution of plantar pressures has been observed to cause the formation of additional ulcers, which may collectively lead to traumatic amputations. While a study of such pathophysiology is not possible through experiments, a few computational modelling works have investigated diabetic foot ulcers. To date, ulcers with a few sizes and locations have been studied, and their effect on the plantar stresses has been quantified. In this work, we have attempted to study the effect of all possible ulcer locations on the generated plantar peak stresses and peak stress locations where additional ulcers may form. Also, the effect of ulcer location on the possible ulcer growth was investigated. A full-scale foot model was developed and a total of 52 ulcer locations were simulated separately, with standing and walking loads. The generated stresses were normalised with the foot size and statistically analysed to develop novel formulations for predicting peak plantar stresses and their locations for any known ulcer location. The results from this study are anticipated to provide important guidelines to doctors and medical practitioners for predicting foot ulcer progression in diabetic patients with existing ulcers and allow the administration of timely preventive interventions.

According to the current perception, symptomatic, presymptomatic and asymptomatic infectious persons can infect the healthy population susceptible to the SARS-CoV-2. More importantly, various reports indicate that the number of asymptomatic cases can be several-fold higher than the reported symptomatic cases. In this article, we take the reported cases in India and various states within the country till September 1, as the specimen to understand the progression of the COVID-19. Employing a modified SEIRD model, we predict the spread of COVID-19 by the symptomatic as well as asymptomatic infectious population. Considering reported infection primarily due to symptomatic, we compare the model predicted results with the available data to estimate the dynamics of the asymptomatically infected population. Our data indicate that in the absence of the asymptomatic infectious population, the number of symptomatic cases would have been much less. Therefore, the current progress of the symptomatic infection can be reduced by quarantining the asymptomatically infectious population via extensive or random testing. This study is motivated strictly toward academic pursuit; this theoretical investigation is not meant for influencing policy decisions or public health practices.