Anxiety and depressive symptoms in adults with new-onset seizures: A scoping review

Prevalence and Determinants of Anxiety and Depression Among Healthcare Workers in Liaoning Province, China

Purpose

To assess the anxiety and depression and their predictors among healthcare workers in Liaoning Province, China.

Methods

In order to explore the influencing factors and prevalence of anxiety and depression among healthcare workers, a cross-sectional research design was used to survey 500 healthcare workers using the 14-item Hospital Anxiety Depression Scale (HADS), the Rosenberg Self-Esteem Scale (RSES), the Multidimensional Scale of Perceived Social Support (MSPSS), and the Survey of Perceived Organizational Support (SPOS).

Results

About 47.12% of the healthcare workers suffered from anxiety and 71.63% suffered from depression. In our study, we found that the health status (OR: 0.540, 95% CI: 0.298-0.976), self-esteem (OR: 0.395, 95% CI: 0.251-0.619), PSS (OR: 0.621, 95% CI: 0.388-0.994), organizational support (OR: 0.533, 95% CI. 0.333-0.854) were protective factors for healthcare workers suffering from anxiety, and resistance to COVID-19 (OR: 1.703, 95% CI: 1.082-2.681) was a risk factor for healthcare workers suffering from anxiety, while good quality of life (OR: 0.385, 95% CI: 0.206-0.719) self-esteem (OR: 0.187, 95% CI: 0.110-0.317), and PSS (OR: 0.475, 95% CI: 0.267-0.847) were protective factors for healthcare workers suffering from depression, and at the age of 35-40 years (OR: 2.475, 95% CI: 1.140-5.369) and resistance to COVID-19 (OR: 2.219, 95% CI: 1.313-3.751) were risk factors for healthcare workers suffering from depression.

Conclusion

The anxiety and depression status of healthcare workers in China is poor, and hospital administrators should take positive measures to support healthcare workers and give positive expectations to alleviate negative emotions such as anxiety and depression.

Potential merits and flaws of large language models in epilepsy care: A critical review

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

Fear of disease in patients with epilepsy - a network analysis

Background

Disease-related fear among patients with epilepsy has significantly impacted their quality of life. The Disease-Related Fear Scale (D-RFS), comprising three dimensions, serves as a relatively well-established tool for assessing fear in these patients. However, certain problems potentially exist within the D-RFS's attribution of items, and its internal structure is still unclear. To establish an appropriate dimensional structure and gain deeper comprehension of its internal structure-particularly its core variables-is vital for developing more effective interventions aimed at alleviating disease-related fear among patients with epilepsy.

Methods

This study employed a cross-sectional survey involving 609 patients with epilepsy. All participants underwent assessment using the Chinese version of the D-RFS. We used exploratory network analysis to discover a new structure and network analysis to investigate the interrelationships among fear symptom domains. In addition to the regularized partial correlation network, we also estimated the node and bridge centrality index to identify the importance of each item within the network. Finally, it was applied to analyze the differences in network analysis outcomes among epilepsy patients with different seizure frequencies.

Results

The research findings indicate that nodes within the network of disease-related fear symptoms are interconnected, and there are no isolated nodes. Nodes within groups 3 and 4 present the strongest centrality. Additionally, a tight interconnection exists among fear symptoms within each group. Moreover, the frequency of epileptic episodes does not significantly impact the network structure.

Conclusion

In this study, a new 5-dimension structure was constructed for D-RFS, and the fear of disease in patients with epilepsy has been conceptualized through a network perspective. The goal is to identify potential targets for relevant interventions and gain insights for future research.

Targeting GABA receptors with chalcone derivative compounds, what is the evidence?

INTRODUCTION

In medicinal chemistry, privileged structures have been frequently exploited as a successful template for drug discovery. Common simple scaffolds like chalcone are present in a wide range of naturally occurring chemicals. Chalcone exhibits extensive biological activity and has drawn attention in this context due to its function in the GABA receptor. Epilepsy and GABA receptors are related. It is a chronic neurological condition that affects globally.

AREAS COVERED

Numerous neurological disorders, including anxiety and epilepsy, have been related to GABA, the brain's most prevalent inhibitory neurotransmitter. We go through the role of GABA receptors in anxiety and epilepsy in this review. The structure-activity relationship of chalcone and its derivatives on the GABA receptor is covered in our final section.

EXPERT OPINION

GABA is a potential therapeutic target for issues associated with the nervous system. We talk about the potential effects of chalcone as a treatment for epilepsy and anxiety on the GABA receptor. Therefore, thorough research is necessary in this regard; the value of in silico tools in developing and enhancing GABA agonists is significant.