Objectives: This study aimed to investigate the role of estrogen in the differential diagnosis of depression and schizophrenia and its relationship with the curative effects, adverse events.
Methods: From 2017 to 2019, patients with depression or schizophrenia treated with modern electroconvulsive therapy (MECT) were studied retrospectively. Their serum estrogen levels, Hamilton Depression Scale, and Brief Psychiatric Rating Scale scores were collected. Differences in the estrogen levels between patients with depression and schizophrenia before and after treatment and the correlation of the estrogen level with curative effect and adverse events was evaluated. In total, 67 patients with depression and 61 with schizophrenia were included.
Results: There were no significant differences in the baseline characteristics, except the estrogen level (p < 0.001). Serum estrogen levels increased in both groups after MECT (117 vs. 141 pmol/L, p < 0.001; 42 vs. 46 pmol/L, respectively; p < 0.001), and higher estrogen levels were positively correlated with better outcomes (p < 0.001).
Conclusion: Post-MECT estrogen levels were not associated with the incidence rate of adverse events of MECT. Estrogen plays a promising role in distinguishing depression and schizophrenia and evaluating the therapeutic efficacy of MECT.
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BMJ Open
December 2024
School of Health & Wellbeing, University of Glasgow, Glasgow, UK.
Introduction: Fear of recurrence is a transdiagnostic problem experienced by people with psychosis, which is associated with anxiety, depression and risk of future relapse events. Despite this, there is a lack of available psychological interventions for fear of recurrence, and psychological therapies for schizophrenia are often poorly implemented in general. However, low-intensity psychological therapy is available for people who experience fear of recurrence in the context of cancer, which means there is an opportunity to learn what has worked in a well-implemented psychological therapy to see if any learning can be adapted for schizophrenia care.
View Article and Find Full Text PDFSchizophr Res
December 2024
Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address:
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials.
View Article and Find Full Text PDFZh Nevrol Psikhiatr Im S S Korsakova
December 2024
Mental Health Research Centre, Moscow, Russia.
Objective: Identification of therapeutic targets in the treatment of adolescent depression with attenuated symptoms of schizophrenia and assessment of the effectiveness of therapeutic interventions.
Material And Methods: One hundred and twenty-three patients (mean age 19.6±2.
Eur Arch Psychiatry Clin Neurosci
December 2024
Department of Psychiatry, University of Muenster, Muenster, Germany.
Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal.
View Article and Find Full Text PDFCurr Issues Mol Biol
November 2024
Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain.
Neurological disorders such as Autism Spectrum Disorder (ASD), Schizophrenia (SCH), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) affect millions of people worldwide, yet their molecular mechanisms remain poorly understood. This study describes the application of the Comparative Analysis of Shapley values (CASh) to transcriptomic data from nine datasets associated with these complex disorders, demonstrating its effectiveness in identifying differentially expressed genes (DEGs). CASh, which combines Game Theory with Bootstrap resampling, offers a robust alternative to traditional statistical methods by assessing the contribution of each gene in the broader context of the complete dataset.
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