Childhood trauma in patients with epileptic vs nonepileptic seizures.

OBJECTIVE: Childhood trauma has been implicated as a risk factor for the etiology of psychogenic nonepileptic seizures (PNES). Relatively little attention has been paid to whether profiles of specific trauma types differ between patients with epilepsy and PNES. Investigating childhood trauma profiles in these patient groups may identify psychological vulnerabilities that predispose to developing PNES, and aid early diagnoses, prevention, and treatment. METHODS: Data were collected from two cohorts (nRetrospective  = 203; nProspective  = 209) admitted to video-electroencephalography (EEG) monitoring units in Melbourne Australia. The differences in Childhood Trauma Questionnaire domain score between patient groups were investigated using standardized effect sizes and general linear mixed-effects models (GLMMs). Receiver-operating characteristic curves were used to investigate classification accuracy. RESULTS: In the retrospective cohort, patients diagnosed with PNES reported greater childhood emotional abuse, emotional neglect, physical abuse, sexual abuse, and physical neglect relative to patients with epilepsy. These differences were replicated in the prospective cohort, except for physical abuse. GLMMs revealed significant main effects for group in both cohorts, but no evidence for any group by domain interactions. Reported sexual abuse showed the best screening performance of PNES, although no psychometric scores were adequate as isolated measures. SIGNIFICANCE: Patients with PNES report a greater frequency of childhood trauma than patients with epilepsy. This effect appears to hold across all trauma types, with no strong evidence emerging for a particular trauma type that is more prevalent in PNES. From a practical perspective, inquiry regarding a history of sexual abuse shows the most promise as a screening measure.

Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning.

Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection with the behavior of road users. However, safety isn't always as it appears, and there is a scarcity of studies examining the association and mismatch between road traffic safety and road safety perceptions at the city scale, primarily due to the time-consuming nature of data acquisition. In this study, we applied an advanced deep learning model and street view images to predict and map human perception scores of road safety in Manhattan. We then explored the association and mismatch between these perception scores and traffic crash rates, while also interpreting the influence of the built environment on this disparity. The results showed that there was heterogeneity in the distribution of road safety perception scores. Furthermore, the study found a positive correlation between perception scores and crash rates, indicating that higher perception scores were associated with higher crash rates. In this study, we also concluded four perception patterns: "Safer than it looks", "Safe as it looks", "More dangerous than it looks", and "Dangerous as it looks". Wall view index, tree view index, building view index, distance to the nearest traffic signals, and street width were found to significantly influence these perception patterns. Notably, our findings underscored the crucial role of traffic lights in the "More dangerous than it looks" pattern. While traffic lights may enhance people's perception of safety, areas in close proximity to traffic lights were identified as potentially accident-prone regions.

How do urban socio-economic characteristics shape a city's social recovery? An empirical study of COVID-19 shocks in China.

The COVID-19 pandemic outbreak significantly challenged the cities' abilities to recover from shocks, and cities' responses have widely differed. Understanding these disparate responses has been insufficient, especially from a social recovery perspective. In this study, we propose the concept of social recovery and develop a comprehensive perspective on how a city's socioeconomic characteristics affect it. The analytical framework is applied to 296 prefecture-level cities in China, with social recovery measured by the changes in intercity intensity between the pre-pandemic baseline (2019 Q1 and Q2) and the period in which the pandemic slightly abated (2020 Q1 and Q2) through anonymized location-based big data. The results indicate that the social recovery of Chinese cities during the COVID-19 pandemic are significantly spatially correlated. Cities with larger populations, a higher proportion of GDP in the secondary industry, higher road density or more adequate medical resources tend to recover socially better. Moreover, these municipal characteristics have significant spatial spillover effects. Specifically, city size, government intervention and industrial structure show negative spillover effects on neighboring areas while information dissemination efficiency, road density, and the number of community health services per capita have positive spillover. This study fills the knowledge gap regarding the different performances of cities when they face pandemic shocks. The assessment of a city's social recovery is an insight into the theoretical framework of vulnerability that aids in translating it into urban resilience. Hence our findings provide practice implications for China and beyond as the interest in urban-resilience development surges around the post-pandemic world.

Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks.

Autism spectrum disorder (ASD) affects large number of children and adults in the US, and worldwide. Early and quick diagnosis of ASD can improve the quality of life significantly both for patients and their families. Prior research provides strong evidence that structural and functional magnetic resonance imaging (MRI) data collected from individuals with ASD exhibit distinguishing characteristics that differ in local and global, spatial and temporal neural patterns of the brain - and therefore can be used for diagnostic purposes for various mental disorders. However, the data from MRI are high-dimensional and advanced methods are needed to make sense out of these datasets. In this paper, we present a novel model based on graph convolutional network (GCN) that can utilize resting state fMRI (rs-fMRI) data to classify ASD subjects from health controls (HC). In addition to using the graph from traditional correlation matrices, our proposed GCN model incorporates graphlet topological counting as one of the training features. Our results show that graphlets can preserve the topological information of the graphs obtained from fMRI data. Combined with our GCN, the graphlets retain enough topological information to differentiate between the ASD and HC. Our proposed model gives an average accuracy of 64.27% on the whole ABIDE-I data sets (1035 subjects) and highest site-specific accuracy of 75.9%, which is comparable to other state-of-the-art methods - while potentially open to being more interpretable.

Core Altered Microorganisms in Colitis Mouse Model: A Comprehensive Time-Point and Fecal Microbiota Transplantation Analysis.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is characterized by chronic and relapsing inflammation within the gastrointestinal tract. Antibiotics have been used to treat IBD, primarily utilizing metronidazole. Although there does seem to be a treatment effect, the broad-spectrum antibiotics that have been used to date are crude tools and have many adverse effects. Available evidence suggests that the host microbiome is implicated in the pathogenesis of IBD, though the key bacteria remain unknown. If the bacterial population can be modified appropriately, the use of antibiotics will have a better therapeutic effect. In this study, mice were fed dextran sodium sulfate (DSS) solution for 5 days, followed by 5 days of normal drinking water, to investigate the gut microbiota response to colitis and the initial alteration of microbiota in recovery phase. Day 0 was considered the normal control, while day 5 and day 10 were considered the colitis mouse model progressive phase and recovery phase, respectively. Results showed that inflammation could induce proportional changes in the gut microbiota. Furthermore, transplanting the microbiota in progressive phase to antibiotic-induced microbiota-depleted mice could induce inflammation similar to colitis, which proves the importance of initial alteration of the microbiota for IBD recovery and the potential of the microbiota as a target for the treatment of IBD. Meanwhile, we have also identified three possible target microorganisms in the development of colitis, namely genera Muribaculaceae (negative correlation), Turicibacter (positive correlation) and Lachnospiraceae (negative correlation) in inflammation status through comprehensive analysis.

Use of the Antimicrobial Peptide Sublancin with Combined Antibacterial and Immunomodulatory Activities To Protect against Methicillin-Resistant Staphylococcus aureus Infection in Mice.

Methicillin-resistant Staphylococcus aureus (MRSA) is the major pathogen causing serious hospital infections worldwide. With the emergence and rapid spread of drug-resistant bacteria, there is extraordinary interest in antimicrobial peptides (AMPs) as promising candidates for the treatment of antibiotic-resistant bacterial infections. Sublancin, a glycosylated AMP produced by Bacillus subtilis 168, has been reported to possess protective activity against bacterial infection. This study was performed to evaluate the efficacy of sublancin in the prevention of MRSA ATCC43300 intraperitoneal infection in mice. We determined that sublancin had a minimal inhibitory concentration of 15 µM against MRSA ATCC43300. The antimicrobial action of sublancin involved the destruction of the bacterial cell wall. Dosing of mice with sublancin greatly alleviated (p < 0.05) the bacterial burden caused by MRSA intraperitoneal infection and considerably reduced the mortality and weight loss (19.2 ± 0.62 g vs 20.6 ± 0.63 g for MRSA vs 2.0 mg/kg sublancin, respectively, on day 3) of MRSA-challenged mice (p < 0.05). Sublancin was further found to balance the immune response during infection and relieve intestinal inflammation through inhibition of NF-κB activation (p < 0.01). With their combined antibacterial and immunomodulatory activities, sublancin may have potent therapeutic potential for drug-resistant infections and sepsis.