Background: Certain antipsychotics elevate prolactin levels in patients with schizophrenia spectrum disorders (SSD), potentially affecting cognition, symptoms, and hormone levels. This study examines the association between prolactin, testosterone, and estrogen and cognition and symptoms in men with SSD, considering antipsychotic medication.
Methods: This cross-sectional study included 128 men with SSD and 44 healthy men from two trials. Patients were divided into a prolactin-sparing (n = 53) and prolactin-raising group (n = 75) based on antipsychotic medication. We examined the association between hormones (testosterone, estrogen and prolactin), and cognition and symptoms using backward linear regression. Three domains of cognition were assessed including: processing speed, verbal fluency, and working memory, while symptoms were measured using the Positive and Negative Syndrome Scale (PANSS).
Results: Prolactin levels were highest in the prolactin-raising group, followed by the control group, and lowest in the prolactin-sparing group (H = 45.279, p < .001). Testosterone and estrogen levels did not differ significantly between groups. In the prolactin-raising group, prolactin negatively correlated with testosterone (r(73) = -0.32, p = .005). Higher testosterone predicted better cognitive functioning (working memory: β = 0.20, p = .007, verbal fluency: β = 0.30, p = .001) and lower symptom scores (total: β = -0.21, p = .001; negative: β = -0.24, p = .002) in men with SSD. Conversely, higher estrogen levels related to slower processing speed (β = -0.22, p < .001) and higher symptoms scores (β = 0.23, p = .010) in men with SSD.
Conclusion: The results suggest positive associations between testosterone and cognition and symptoms in men with SSD, while suggesting that high prolactin levels could relate to lower testosterone levels, possibly worsening cognition and symptoms in men with SSD.
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http://dx.doi.org/10.1016/j.schres.2024.06.022 | DOI Listing |
Viruses
December 2024
Department of Medical Oncology, Medical University of Sofia, University Hospital "Tsaritsa Yoanna", 1527 Sofia, Bulgaria.
Central nervous system (CNS) infections caused by SARS-CoV-2 are uncommon. This case report describes the clinical progression of a 92-year-old female who developed a persistent neuroinfection associated with SARS-CoV-2. The patient initially presented with progressive fatigue, catarrhal symptoms, and a fever (38.
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December 2024
The Sheba Pandemic Preparedness Research Institute (SPRI), Sheba Medical Center, Tel Hashomer, Ramat Gan 52621, Israel.
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December 2024
Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Hepatitis C virus (HCV) disproportionately affects certain sub-populations, including people with experience of incarceration (PWEI). Little is known about how perceptions of HCV and treatment have changed despite simplifications in testing and treatment in carceral settings. Nineteen semi-structured interviews were conducted with people living with or having a history of HCV infection released from Quebec provincial prison.
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December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance.
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December 2024
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
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