Background And Aims: Dementia is becoming a major global public health menace in the aging population affecting 47 million people globally. Dementia has no cure and effective interventions. Treatment of dementia is a big problem. The most common symptomatic medications for cognition, behavior, and global functioning among patients with dementia currently are cholinesterase inhibitors and memantine. However, Information on the effectiveness of cholinesterase inhibitors for dementia is conflicting and controversial. Thus, this makes it difficult for decision-makers, healthcare providers, patients, and caregivers to decide on the most effective intervention. The current meta-analysis sought to investigate the efficacy of pharmacologic interventions to improve cognitive and behavioral symptoms in people with living dementia.
Methods: This current systematic review and meta-analysis used the preferred reporting items for systematic reviews and meta-analyses to ensure accuracy and comprehensiveness. The Cochrane MEDLINE, Database of Systematic Reviews, and other databases were thoroughly searched for relevant studies. We selected Studies such as randomized controlled trials published in English with a sample size of at least 20 subjects. We selected and applied the random-effects meta-analysis as the most preferred model because of the heterogeneity across studies. The computation of the weighted effect size was based on the result from the test of heterogeneity.
Results: Twenty-two studies were finally used in the meta-analysis. The study subjects who received donepezil 5 mg/day, donepezil 10 mg/day, and galantamine 24 mg/day had improved cognition symptoms (ADAS-cog) score of -1.46 (95% CI = -2.24, -0.68, = 3.67, < 0.001), -2.31 (95% CI = -3.30, -1.31, = 5.45, < 0.001) and -3.04 (95% CI = -4.16, -1.92, = 5.31, < 0.001) respectively.
Conclusion: The current meta-analysis suggests significant benefits of cholinesterase inhibitors such as donepezil (5 and 10 mg/day) and galantamine on cognitive symptoms.
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http://dx.doi.org/10.1002/hsr2.913 | DOI Listing |
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December 2024
Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey.
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously.
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December 2024
Department of Production Engineering, KTH Royal Institute of Technology, 11428, Stockholm, Sweden.
This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics.
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December 2024
Department of Informatics, University of Hamburg, Hamburg, Germany.
Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.
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December 2024
Department of Orthopaedics and Traumatology, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Establishing normative values and understanding how proprioception varies among body parts is crucial. However, the variability across individuals, especially adolescents, makes it difficult to establish norms. This prevents further investigation into classifying patients with abnormal proprioception.
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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