Journal of Alzheimer's disease reports最新文献

Background: Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research.

Objective: The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE).

Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies.

Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ₄₂ to Aβ₄₀. Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R² = 0.8 to 0.95).

Conclusions: Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition.

Background: Alzheimer's disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020. Early diagnosis is crucial for managing these diseases, but their complexity poses a challenge. Convolutional neural networks have shown promise in accurate diagnosis.

Objective: The main objective of this research is to diagnose Alzheimer's disease and mild cognitive impairment in healthy individuals using convolutional neural networks.

Methods: This study utilized three different convolutional neural network models, two of which were pre-trained models, namely AlexNet and DenseNet, while the third model was a CNN1D-LSTM neural network.

Results: Among the neural network models used, the AlexNet demonstrated the highest accuracy, exceeding 98%, in diagnosing mild cognitive impairment and Alzheimer's disease in healthy individuals. Furthermore, the accuracy of the DenseNet and CNN1D-LSTM models is 88% and 91.89%, respectively.

Conclusions: The research highlights the potential of convolutional neural networks in diagnosing mild cognitive impairment and Alzheimer's disease. The use of pre-trained neural networks and the integration of various patient data contribute to achieving accurate results. The high accuracy achieved by the AlexNet neural network underscores its effectiveness in disease classification. These findings pave the way for future research and improvements in the field of diagnosing these diseases using convolutional neural networks, ultimately aiding in early detection and effective management of mild cognitive impairment and Alzheimer's disease.

Diffusion tensor imaging along perivascular spaces (DTI-ALPS) is a novel MRI method for assessing brain interstitial fluid dynamics, potentially indexing glymphatic function. Failed glymphatic clearance is implicated in Alzheimer's disease (AD) pathophysiology. We assessed the contribution of age and female sex (strong AD risk factors) to DTI-ALPS index in healthy subjects. We also for the first time assessed the effect of head size. In accord with prior studies, we show reduced DTI-ALPS index with aging, and in men compared to women. However, head size may be a major contributing factor to this counterintuitive sex difference.

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder linked to the loss of dopaminergic neurons in the substantia nigra. Mitophagy, mitochondrial selective autophagy, is critical in maintaining mitochondrial and subsequently neuronal homeostasis. Its impairment is strongly implicated in PD and is associated with accelerated neurodegeneration.

Objective: To study the positive effect of dimethyl fumarate (DMF) on mitophagy via the NRF2/BNIP3/PINK1 axis activation in PD disease models.

Methods: The neuroprotective effect of DMF was explored in in vitro and in vivo PD models. MTT assay was performed to determine the DMF dose followed by JC-1 assay to study its mitoprotective effect in MPP⁺ exposed SHSY5Y cells. For the in vivo study, C57BL/6 mice were divided into six groups: Normal Control (NC), Disease Control (DC), Sham (Saline i.c.v.), Low Dose (MPP⁺ iodide+DMF 15 mg/kg), Mid Dose (MPP⁺ iodide+DMF 30 mg/kg), and High Dose (MPP⁺ iodide+DMF 60 mg/kg). The neuroprotective effect of DMF was assessed by performing rotarod, open field test, and pole test, and biochemical parameter analysis using immunofluorescence, western blot, and RT-PCR.

Results: DMF treatment significantly alleviated the loss of TH positive dopaminergic neurons and enhanced mitophagy by increasing PINK1, Parkin, BNIP3, and LC3 levels in the MPP⁺ iodide-induced PD mice model. DMF treatment groups showed good locomotor activity and rearing time when compared to the DC group.

Conclusions: DMF confers neuroprotection by activating the BNIP3/PINK1/Parkin pathway, enhancing the autophagosome formation via LC3, and improving mitophagy in PD models, and could be a potential therapeutic option in PD.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease with cognitive decline and behavioral dysfunction. AD will become a global public health concern due to its increasing prevalence brought on by the severity of global aging. It is critical to understand the pathogenic mechanisms of AD and investigate or pursue a viable therapy strategy in clinic. Amyloid-β (Aβ) accumulation and abnormally hyperphosphorylated tau protein are the main regulating variables in the pathological phase of AD. And neuroinflammation brought on by activated microglia was found to be one risk factor contributing to changes in Aβ and tau pathology. It is important to investigate the unique biomarkers of early diagnosis and advanced stage, which may help to elucidate the specific pathological process of AD and provide potential novel therapeutic targets or preventative measures.

Background: Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial.

Objective: To develop a new composite score to improve the prediction of outcome change.

Methods: We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression.

Results: The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs.

Conclusions: ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.

Background: Subjective cognitive complaints (SCC) may be an early indicator of future cognitive decline. However, findings comparing SCC and objective cognitive performance have varied, particularly in the memory domain. Even less well established is the relationship between subjective and objective complaints in non-amnestic domains, such as in executive functioning, despite evidence indicating very early changes in these domains. Moreover, particularly early changes in both amnestic and non-amnestic domains are apparent in those carrying the Apolipoprotein-E ɛ4 allele, a primary genetic risk for Alzheimer's disease (AD).

Objective: This study investigated the role of the ɛ4 allele in the consistency between subjective and objective executive functioning in 54 healthy, cognitively intact, middle-aged and older adults.

Methods: Participants (M_age = 64.07, SD = 9.27, range = 48-84; ɛ4+ = 18) completed the Frontal Systems Behavior Scale (FrSBe) Executive Dysfunction Scale (EXECDYS) to measure subjective executive functioning (SEF) and multiple executive functioning tasks, which were condensed into a single factor.

Results: After accounting for age, depression, and anxiety, objective executive functioning performance significantly predicted SEF. Importantly, ɛ4 moderated this effect. Specifically, those carrying the ɛ4 allele had significantly less accurate self-awareness of their executive functioning compared to ɛ4 non-carriers.

Conclusions: Utilizing an approach that integrates self-evaluation of executive functioning with objective neurocognitive assessment may help identify the earliest signs of impending cognitive decline, particularly in those with genetic risk for AD. Such an approach could sensitively determine those most prone to future cognitive decline prior to symptom onset, when interventions could be most effective.