Background: Some authors suggest that working memory may underlie most of cognitive deficits found in schizophrenia and contribute to the most salient features of the disorder. Many authors further believe that, despite the differences in magnitude, profile of cognitive impairment is quite similar across schizophrenia and affective psychosis. To test the hypothesis of profile similarity between SCZ and BPD compared to healthy individuals, we carried out a comparative study applying several working memory tasks.
Subjects And Methods: A total of 64 subjects participated in the study, 20 diagnosed with schizophrenia, 18 with bipolar affective disorder and 26 healthy controls. Groups were matched according to age, sex and education, and two clinical groups were also matched according to the number of hospitalizations. To measure working memory we applied se Wisconsin Card Sorting Test (WCST), STROOP task, Trail making test (TMT), Digit span forward and backward tasks. To test the size and profile similarities of the groups, we used ANOVA and Kruskal-Wallis tests on individual measures and on factor scores.
Results: Most indicators of the WCST did not differentiate between the groups, but all of the remaining indicators indicated weaker working memory of the two clinical groups compared to the healthy controls. All applied measures could be reduced to two latent constructs provisionally named WM Attention and WM Capacity. Both clinical groups scored lower on the capacity component than controls, whereas the three groups could not be distinguished according to the attention component. Results provided no evidence of difference in either size or profile of working memory impairment in patients with SCZ and BDP.
Conclusions: The current study determined impairment of WM in patients diagnosed with SCZ and BPD compared to healthy controls. However, no difference was found regarding either the size or the profile of impairment between SCZ and BPD patients.
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http://dx.doi.org/10.24869/psyd.2019.54 | DOI Listing |
PLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Systems and Cybersecurity, University of Bisha, Bisha, KSA.
Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.
View Article and Find Full Text PDFNetwork
January 2025
Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM).
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Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.
View Article and Find Full Text PDFMetab Brain Dis
January 2025
School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
Alzheimer's disease is a complex neurodegenerative disease characterized by progressive decline in cognitive function and behaviour. Ginger is the rhizome of the plant Zingiber officinale Roscoe, has been an important ingredient of many Ayurveda formulations to treat neurological disorders. The present study aims to estimate the variation of 6-gingerol content in nine different ginger samples collected from Manipur, India, investigate the neuroprotective potential of the most potent ginger sample against scopolamine-induced cognitively impaired mice, and validate the therapeutic claim by molecular docking analysis.
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