The Role of Networks in Advancing Human Rights: Making Human Connections
The Role of Networks in Advancing Human Rights: Making Human Connections
Suicidal Behaviour Prior to First Episode Psychosis: Wider and More Widespread Grey-Matter Alterations
Volume 29, Issue 4, October-December 2025, Page 982-996
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Understanding the Trajectory of Depressive Symptoms in Newly Diagnosed High‐Grade Glioma Patients—A Meta‐Analysis
ABSTRACT
Introduction
High-grade gliomas, the most common and aggressive brain cancer, are associated with significant neurological disability and are almost uniformly fatal. Though the diagnosis of brain cancer is represented as one of the most stressful life events for patients as well as for their caregivers, the prevalence of depression as a longitudinal event during and after the initial diagnosis and sequential lines of treatment is under-researched.
Objectives
To inform clinical practice, we assembled published, time-specific estimates of the prevalence of depression in adult high-grade glioma patients to test the idea that depression prevalence varies across therapeutic trajectory milestones.
Methods
We a priori defined five time points in the clinical course of first-line therapy. After an exhaustive search of the current literature, we extracted time point-specific estimates of depression prevalence, pooled the data by time point across studies, and constructed 95% confidence intervals on depression prevalence at each time point. A total of 822 patients were identified and entered into our analyses.
Results
The prevalence of depression in adult high-grade glioma is about 16%–27% between surgery and the end of temozolomide maintenance therapy, which is higher than the 9% estimated for the general population. However, when assessed in the time interval between the initial diagnostic tumor imaging and the confirmatory surgery, at least 30% of these patients are depressed.
Conclusions
Because depression worsens the patient’s quality of life and is treatable, the multidisciplinary treatment teams involved in the care of high-grade glioma patients should assess depression throughout the disease trajectory, and especially immediately after the first imaging study showing a suspicious intracranial mass.
Effects of exercise interventions on mental health in people with insomnia symptoms: A systematic review and meta-analysis
Is Addiction Research Addicted to Artificial Intelligence? Mapping the Intersection of Artificial Intelligence, Substance Use and Mental Health Through a Bibliometric Analysis
ABSTRACT
Issues
From extracting insights from large-scale, multimodal data to prevention and support, there is growing interest in the applications and implications of recent advances in Artificial Intelligence (AI) within the fields of addiction, substance use and mental health, which we refer to as ASUM. However, due to the absence of a structured mapping of AI for ASUM, it remains unclear how this interest is translated into concrete research results.
Approach
This paper addresses this gap by conducting a bibliometric analysis of AI for ASUM, exploring: (i) the scale of ASUM-related research (number of publications, authors, institutions and countries); (ii) the evolution of ASUM‘s research productivity over time, both in absolute terms and relative to its parent disciplines; (iii) the key topics within ASUM and their interrelations.
Key Findings
Results, supplemented by a comparison of similar fields, show that, while ASUM is an emerging and rapidly expanding domain (with a 25-fold increase in research output since 2012, attracting growing attention relative to parent disciplines as well as appearing to rely on applying more advanced AI methods than related fields), it remains largely fragmented through a dispersed group of infrequent contributors.
Implications
An integration of the findings suggests two dominant trajectories through which AI for ASUM is currently being realised: as AI-driven analytic support and as innovative research and therapeutic methods (e.g., virtual reality, chatbots).
Conclusions
The paper concludes by situating AI for ASUM as an emerging scientific field, outlining the scientific and practical challenges and opportunities that are likely to arise, and high-potential research areas open for exploration.