Prevention and delay in the onset of memory disorders will have a great impact on society by reducing the disease burden and finances. Drugs available for the treatment of learning and memory disorders are few. There is need to develop a better drug, several studies have shown the therapeutic effectiveness of herbal extracts for the learning and memory disorders because of their neuroprotective effects, hence herbs should be evaluated scientifically to form a basis for the future discovery of newer drugs. In this study, effect of Trigonella-foenum graecum L. seeds methanol extract (TFGS-ME) was evaluated in mice on learning and memory process by both exteroceptive and interoceptive behavioral models at three different doses. Elevated plus maze test was employed to assess the effect on learning and memory as an exteroceptive behavioral test. Scopolamine-induced amnesia was performed to assess effect on learning and memory as interoceptive behavior test. In both tests, it was found that animals received extract at 200 mg/kg exhibited a highly noteworthy decline in transfer latency on both acquisition and retention days in contrast to control animals, suggestive of improved learning and memory process. Results were equivalent to the standard drug piracetam at similar dose indicating that TFGS-ME improves learning and memory process and has significant potential as an antiamnesic agent. Hence there is need to separate the dietary components which may play a vibrant role in the future invention of novel drugs.
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http://dx.doi.org/10.1007/s11011-018-0235-1 | DOI Listing |
Geroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
View Article and Find Full Text PDFNat Mater
January 2025
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
As a multivariate time series, the prediction of curling trajectories is crucial for athletes to devise game strategies. However, the wide prediction range and complex data correlations present significant challenges to this task. This paper puts forward an innovative deep learning approach, CasLSTM, by introducing integrated inter-layer memory, and establishes an encoder-predictor curling trajectory forecasting model accordingly.
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