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JPMA. the Journal of the Pakistan... Jun 2024
Topics: Humans; Alzheimer Disease; Pakistan
PubMed: 38949013
DOI: 10.47391/JPMA.10703 -
Alzheimer's & Dementia : the Journal of... Jul 2024Although reproductive hormones are implicated in cerebral small vessel disease in women, few studies consider measured hormones in relation to white matter...
INTRODUCTION
Although reproductive hormones are implicated in cerebral small vessel disease in women, few studies consider measured hormones in relation to white matter hyperintensity volume (WMHV), a key indicator of cerebral small vessel disease. Even fewer studies consider estrone (E1), the primary postmenopausal estrogen, or follicle-stimulating hormone (FSH), an indicator of ovarian age. We tested associations of estradiol (E2), E1, and FSH to WMHV among women.
METHODS
Two hundred twenty-two women (mean age = 59) underwent hormone assays (E1, E2, FSH) and 3T brain magnetic resonance imaging. Associations of hormones to WMHV were tested with linear regression.
RESULTS
Higher E2 (B[standard error (SE)] = -0.17[0.06], P = 0.008) and E1 (B[SE] = -0.26[0.10], P = 0.007) were associated with lower whole-brain WMHV, and higher FSH (B[SE] = 0.26[0.07], P = 0.0005) with greater WMHV (covariates age, race, education). When additionally controlling for cardiovascular disease risk factors, associations of E1 and FSH to WMHV remained.
DISCUSSION
Reproductive hormones, particularly E1 and FSH, are important to women's cerebrovascular health.
HIGHLIGHTS
Despite widespread belief that sex hormones are important to women's brain health, little work has considered how these hormones in women relate to white matter hyperintensities (WMH), a major indicator of cerebral small vessel disease. We considered relations of estradiol (E2), estrone (E1), and follicle-stimulating hormone (FSH) to WMH in midlife women. Higher E2 and E1 were associated with lower whole-brain WMH volume (WMHV), and higher FSH with higher whole-brain WMHV. Associations of E1 and FSH, but not E2, to WMHV persisted with adjustment for cardiovascular disease risk factors. Findings underscore the importance of E2 and FSH to women's cerebrovascular health.
PubMed: 38948946
DOI: 10.1002/alz.14093 -
Frontiers in Neuroscience 2024Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke,...
INTRODUCTION
Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
METHODS
This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
RESULTS
A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
CONCLUSION
Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.
PubMed: 38948927
DOI: 10.3389/fnins.2024.1401329 -
F1000Research 2023Tyrosine-protein kinase SYK, encoded by the gene, is a non-receptor type protein kinase which mediates immune signal transduction through immunoreceptors....
Tyrosine-protein kinase SYK, encoded by the gene, is a non-receptor type protein kinase which mediates immune signal transduction through immunoreceptors. Tyrosine-protein kinase SYK expression has been associated with the development of various inflammatory diseases, cancer and neurodegenerative conditions. The reproducibility of tyrosine-protein kinase SYK research would help elucidate the mechanism in which it causes neuroinflammation as well as its potential as a novel target to treat Alzheimer's disease. This would be facilitated with the availability of high-quality tyrosine-protein kinase SYK. In this study, we characterized thirteen tyrosine-protein kinase SYK commercial antibodies for Western Blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. We identified many high-performing antibodies and encourage readers to use this report as a guide to select the most appropriate antibody for their specific needs.
Topics: Syk Kinase; Humans; Immunoprecipitation; Fluorescent Antibody Technique; Blotting, Western; Antibodies; Animals; Cell Line
PubMed: 38948505
DOI: 10.12688/f1000research.140456.2 -
Journal of Pharmacopuncture Jun 2024Cognitive impairments, ranging from mild to severe, adversely affect daily functioning, quality of life, and work capacity. Despite significant efforts in the past... (Review)
Review
OBJECTIVES
Cognitive impairments, ranging from mild to severe, adversely affect daily functioning, quality of life, and work capacity. Despite significant efforts in the past decade, more than 200 promising drug candidates have failed in clinical trials. Herbal remedies are gaining interest as potential treatments for dementia due to their long history and safety, making them valuable for drug development. This review aimed to examine the mechanisms behind the effect of on cognitive function.
METHODS
This study focused primarily on the effects of and its chemical constituents on cognitive behavioral outcomes including the Morris water maze, the passive avoidance test, and the Y maze, as well as pathogenic targets of cognitive impairment and Alzheimer's disease (AD) like amyloid deposition, amyloid precursor protein, tau hyperphosphorylation, and cognitive decline. Additionally, a thorough evaluation of the mechanisms behind 's impact on cognitive function was conducted. We reviewed the most recent data from preclinical research done on experimental models, particularly looking at 's effects on cognitive decline and AD.
RESULTS
According to recent research, and its bioactive components, stilbene, and emodin, influence cognitive behavioral results and regulate the pathological target of cognitive impairment and AD. Their mechanisms of action include reducing oxidative and mitochondrial damage, regulating neuroinflammation, halting apoptosis, and promoting increased neurogenesis and synaptogenesis.
CONCLUSION
This review serves as a comprehensive compilation of current experiments on AD and other cognitive impairment models related to the therapeutic effects of . We believe that these findings can serve as a basis for future clinical trials and have potential applications in the treatment of human neurological disorders.
PubMed: 38948308
DOI: 10.3831/KPI.2024.27.2.70 -
Frontiers in Molecular Biosciences 2024Alzheimer's disease (AD) is a progressive debilitating neurological disorder representing the most common neurodegenerative disease worldwide. Although the exact...
Alzheimer's disease (AD) is a progressive debilitating neurological disorder representing the most common neurodegenerative disease worldwide. Although the exact pathogenic mechanisms of AD remain unresolved, the presence of extracellular amyloid-β peptide 1-42 (Aβ) plaques in the parenchymal and cortical brain is considered one of the hallmarks of the disease. In this work, we investigated the Aβ fibrillogenesis timeline up to 48 h of incubation, providing morphological and chemo-structural characterization of the main assemblies formed during the aggregation process of Aβ, by atomic force microscopy (AFM) and surface enhanced Raman spectroscopy (SERS), respectively. AFM topography evidenced the presence of characteristic protofibrils at early-stages of aggregation, which form peculiar macromolecular networks over time. SERS allowed to track the progressive variation in the secondary structure of the aggregation species involved in the fibrillogenesis and to determine when the β-sheet starts to prevail over the random coil conformation in the aggregation process. Our research highlights the significance of investigating the early phases of fibrillogenesis to better understand the molecular pathophysiology of AD and identify potential therapeutic targets that may prevent or slow down the aggregation process.
PubMed: 38948077
DOI: 10.3389/fmolb.2024.1376411 -
Frontiers in Cellular Neuroscience 2024Mild traumatic brain injury (mTBI) resulting from low-intensity blast (LIB) exposure in military and civilian individuals is linked to enduring behavioral and cognitive...
Mild traumatic brain injury (mTBI) resulting from low-intensity blast (LIB) exposure in military and civilian individuals is linked to enduring behavioral and cognitive abnormalities. These injuries can serve as confounding risk factors for the development of neurodegenerative disorders, including Alzheimer's disease-related dementias (ADRD). Recent animal studies have demonstrated LIB-induced brain damage at the molecular and nanoscale levels. Nevertheless, the mechanisms linking these damages to cognitive abnormalities are unresolved. Challenges preventing the translation of preclinical studies into meaningful findings in "real-world clinics" encompass the heterogeneity observed between different species and strains, variable time durations of the tests, quantification of dosing effects and differing approaches to data analysis. Moreover, while behavioral tests in most pre-clinical studies are conducted at the group level, clinical tests are predominantly assessed on an individual basis. In this investigation, we advanced a high-resolution and sensitive method utilizing the CognitionWall test system and applying reversal learning data to the Boltzmann fitting curves. A flow chart was developed that enable categorizing individual mouse to different levels of learning deficits and patterns. In this study, rTg4510 mice, which represent a neuropathology model due to elevated levels of tau P301L, together with the non-carrier genotype were exposed to LIB. Results revealed distinct and intricate patterns of learning deficits and patterns within each group and in relation to blast exposure. With the current findings, it is possible to establish connections between mice with specific cognitive deficits to molecular changes. This approach can enhance the translational value of preclinical findings and also allow for future development of a precision clinical treatment plan for ameliorating neurologic damage of individuals with mTBI.
PubMed: 38948027
DOI: 10.3389/fncel.2024.1397046 -
Imaging Neuroscience (Cambridge, Mass.) Feb 2024Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens...
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
PubMed: 38947941
DOI: 10.1162/imag_a_00089 -
ArXiv Jun 2024Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known...
Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. In this study, we leveraged structural and functional MRI to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce SNPs as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning-based classification framework where generative module employing Cycle GANs was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable AI method, Integrated Gradients, to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our model was able to reach the SOA in the classification of CN/AD reaching an average test accuracy of $0.926\pm0.02$. For the MCI task, we achieved an average prediction accuracy of $0.711\pm0.01$ using the pre-trained model for CN/AD. The interpretability analysis revealed significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights.
PubMed: 38947922
DOI: No ID Found -
ACS Omega Jun 2024Increased deposition of amyloid-β (Aβ) plaques in the brain is a frequent pathological feature observed in human immunodeficiency virus (HIV)-positive patients....
Increased deposition of amyloid-β (Aβ) plaques in the brain is a frequent pathological feature observed in human immunodeficiency virus (HIV)-positive patients. Emerging evidence indicates that HIV regulatory proteins, particularly the transactivator of transcription (TAT) protein, could interact with Aβ peptide, accelerating the formation of Aβ plaques in the brain and potentially contributing to the onset of Alzheimer's disease in individuals with HIV infection. Nevertheless, the molecular mechanisms underlying these processes remain unclear. In the present study, we have used long all-atom molecular dynamics simulations to probe the direct interactions between the TAT protein and Aβ peptide at the molecular level. Sampling over 28.0 μs, our simulations show that TAT protein induces a shift in the Aβ monomer ensemble toward elongated conformations, exposing aggregation-prone regions on the surface and thereby inducing subsequent aggregation. TAT protein also appears to enhance the stability of preformed Aβ fibrils, while increasing the β-sheet content within these fibrils. Our atomistically detailed simulations qualitatively agree with previous in vitro and in vivo studies. Importantly, our simulations identify key interactions between Aβ and the TAT protein that drive the Aβ aggregation process and stabilize the preformed Aβ aggregates, which are particularly challenging to obtain through current experimental techniques.
PubMed: 38947850
DOI: 10.1021/acsomega.4c02643