A Brief Review on the Potential of Psychedelics for Treating Alzheimer's Disease and Related Depression.

Alzheimer's disease (AD), the most common form of senile dementia, is poised to place an even greater societal and healthcare burden as the population ages. With few treatment options for the symptomatic relief of the disease and its unknown etiopathology, more research into AD is urgently needed. Psychedelic drugs target AD-related psychological pathology and symptoms such as depression. Using microdosing, psychedelic drugs may prove to help combat this devastating disease by eliciting psychiatric benefits via acting through various mechanisms of action such as serotonin and dopamine pathways. Herein, we review the studied benefits of a few psychedelic compounds that may show promise in treating AD and attenuating its related depressive symptoms. We used the listed keywords to search through PubMed for relevant preclinical, clinical research, and review articles. The putative mechanism of action (MOA) for psychedelics is that they act mainly as serotonin receptor agonists and induce potential beneficial effects for treating AD and related depression.

Network Medicine Approach for Analysis of Alzheimer's Disease Gene Expression Data.

Alzheimer's disease (AD) is the most widespread diagnosed cause of dementia in the elderly. It is a progressive neurodegenerative disease that causes memory loss as well as other detrimental symptoms that are ultimately fatal. Due to the urgent nature of this disease, and the current lack of success in treatment and prevention, it is vital that different methods and approaches are applied to its study in order to better understand its underlying mechanisms. To this end, we have conducted network-based gene co-expression analysis on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. By processing and filtering gene expression data taken from the blood samples of subjects with varying disease states and constructing networks based on that data to evaluate gene relationships, we have been able to learn about gene expression correlated with the disease, and we have identified several areas of potential research interest.

Roles of ß-Endorphin in Stress, Behavior, Neuroinflammation, and Brain Energy Metabolism.

ß-Endorphins are peptides that exert a wide variety of effects throughout the body. Produced through the cleavage pro-opiomelanocortin (POMC), ß-endorphins are the primarily agonist of mu opioid receptors, which can be found throughout the body, brain, and cells of the immune system that regulate a diverse set of systems. As an agonist of the body's opioid receptors, ß-endorphins are most noted for their potent analgesic effects, but they also have their involvement in reward-centric and homeostasis-restoring behaviors, among other effects. These effects have implicated the peptide in psychiatric and neurodegenerative disorders, making it a research target of interest. This review briefly summarizes the basics of endorphin function, goes over the behaviors and regulatory pathways it governs, and examines the variability of ß-endorphin levels observed between normal and disease/disorder affected individuals.

Simultaneous Monitoring of Multi-Enzyme Activity and Concentration in Tumor Using a Triply Labeled Fluorescent In Vivo Imaging Probe.

The use of fluorescent imaging probes that monitor the activity of proteases that experience an increase in expression and activity in tumors is well established. These probes can be conjugated to nanoparticles of iron oxide, creating a multimodal probe serving as both a magnetic resonance imaging (MRI) agent and an indicator of local protease activity. Previous works describe probes for cathepsin D (CatD) and metalloproteinase-2 (MMP2) protease activity grafted to cross-linked iron oxide nanoparticles (CLIO). Herein, we have synthesized a triply labeled fluorescent iron oxide nanoparticle molecular imaging (MI) probe, including an AF750 substrate concentration reporter along with probes for cathepsin B (CatB) sand MMP2 protease activity. The reporter provides a baseline signal from which to compare the activity of the two proteases. The activity of the MI probe was verified through incubation with the proteases and tested in vitro using the human HT29 tumor cell line and in vivo using female nude mice injected with HT29 cells. We found the MI probe had the appropriate specificity to the activity of their respective proteases, and the reporter dye did not activate when incubated in the presence of only MMP2 and CatB. Probe fluorescent activity was confirmed in vitro, and reporter signal activation was also noted. The fluorescent activity was also visible in vivo, with injected HT29 cells exhibiting fluorescence, distinguishing them from the rest of the animal. The reporter signal was also observable in vivo, which allowed the signal intensities of the protease probes to be corrected; this is a unique feature of this MI probe design.

A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC50 of the target compounds. The performance of the models was assessed primarily through analysis of the Q2 values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.

A Novel Dual Fluorochrome Near-Infrared Imaging Probe for Potential Alzheimer's Enzyme Biomarkers-BACE1 and Cathepsin D.

A molecular imaging probe to fluorescently image the ß-site of the amyloid precursor protein (APP) cleaving enzyme 1 (BACE1) and cathepsin D (CatD) enzymes associated with Alzheimer's disease (AD) was designed and synthesized. This imaging probe was built upon iron oxide nanoparticles (cross-linked dextran iron oxide nanoparticles, or CLIO). Peptide substrates containing a terminal near-infrared fluorochrome (fluorophore emitting at 775 nm for CatD or fluorophore emitting at 669 nm for BACE1) were conjugated to the CLIO nanoparticles. The CatD substrate contained a phenylalanine-phenylalanine cleavage site more specific to CatD than BACE1. The BACE1 substrate contained the sequence surrounding the leucine-asparagine cleavage site of the BACE1 found in the Swedish mutation of APP, which is more specific to BACE1 than CatD. These fluorescently-labeled peptide substrates were then conjugated to the nanoparticle. The nanoparticle probes were purified by gel filtration, and their fluorescence intensities were determined using a fluorescence plate reader. The CatD peptide substrate demonstrated a 15.5-fold increase in fluorescence when incubated with purified CatD enzyme, and the BACE1 substrate exhibited a 31.5-fold increase in fluorescence when incubated with purified BACE1 enzyme. Probe specificity was also demonstrated in the human H4 neuroglioma cells and the H4 cells stably transfected with BACE1 in which the probe monitored enzymatic cleavage. In the H4 and H4-BACE1 cells, BACE1 and active CatD activity increased, an occurrence that was reflected in enzyme expression levels as determined by immunoblotting. These results demonstrate the applicability of this probe for detecting potential Alzheimer's enzyme biomarkers.

NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis through Cross-Referencing with Genomic and Imaging Data.

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen, and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of NCI's Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from more than 160 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this article, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource. SIGNIFICANCE: The Proteomic Data Commons (PDC) plays a crucial role in advancing cancer research by providing a centralized repository of high-quality cancer proteomic data, enriched with extensive clinical annotations. By integrating and cross-referencing with complementary genomic and imaging data, the PDC facilitates multi-omics analyses, driving comprehensive insights, and accelerating discoveries across various cancer types.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

An Overview of ICA/BSS-Based Application to Alzheimer's Brain Signal Processing.

Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.

Overcoming Alzheimer's Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies.

Alzheimer's disease (AD) imposes a considerable burden on those diagnosed. Faced with a neurodegenerative decline for which there is no effective cure or prevention method, sufferers of the disease are subject to judgement, both self-imposed and otherwise, that can have a great deal of effect on their lives. The burden of this stigma is more than just psychological, as reluctance to face an AD diagnosis can lead people to avoid early diagnosis, treatment, and research opportunities that may be beneficial to them, and that may help progress towards fighting AD and its progression. In this review, we discuss how recent advents in information technology may be employed to help fight this stigma. Using artificial intelligence (AI) technologies, specifically natural language processing (NLP), to classify the sentiment and tone of texts, such as those of online posts on various social media sites, has proven to be an effective tool for assessing the opinions of the general public on certain topics. These tools can be used to analyze the public stigma surrounding AD. Additionally, there is much concern among individuals that an AD diagnosis, or evidence of pre-clinical AD such as a biomarker or imaging test results, may wind up unintentionally disclosed to an entity that may discriminate against them. The lackluster security record of many medical institutions justifies this fear to an extent. Adopting more secure and decentralized methods of data transfer and storage, and giving patients enhanced ability to control their own data, such as a blockchain-based method, may help to alleviate some of these fears.

Current Cognition Tests, Potential Virtual Reality Applications, and Serious Games in Cognitive Assessment and Non-Pharmacological Therapy for Neurocognitive Disorders.

As the global population ages, the incidence of major neurocognitive disorders (major NCDs), such as the most common geriatric major NCD, Alzheimer's disease (AD), has grown. Thus, the need for more definitive cognitive assessment or even effective non-pharmacological intervention for age-related NCDs is becoming more and more pressing given that no definitive diagnostics or efficacious therapeutics are currently unavailable for them. We evaluate the current state of the art of cognitive assessment for major NCDs, and then briefly glance ahead at potential application of virtual reality (VR) technologies in major NCD assessment and in cognition training of visuospatial reasoning in a 3D environment, as well as in the alleviation of depression and other symptoms of cognitive disorders. We believe that VR-based technologies have tremendous potentials in cognitive assessment and non-pharmacological therapy for major NCDs.

Designing Socially Assistive Robots for Alzheimer's Disease and Related Dementia Patients and Their Caregivers: Where We are and Where We are Headed.

Over the past few years there has been a large rise in the field of robotics. Robots are being in used in many industries, but there has not been a large surge of robots in the medical field, especially the robots for healthcare use. However, as the aging population keeps growing, current medical staff and healthcare providers are increasingly burdened by caring for the ever-growing number of senior patients, especially those with cognitive impairment of Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) patients. As a result, we can expect to see a large increase in the field of medical robotics, especially in forms of socially assistive robots (SARs) for senior patients and healthcare providers. In fact, SARs can alleviate AD and ADRD patients and their caregivers' unmet medical needs. Herein, we propose a design outline for such a SAR, based on a review of the current literature. We believe the next generation of SARs will enhance health and well-being, reduce illness and disability, and improve quality of life for AD and ADRD patients and their caregivers.

Exposure of CuO Nanoparticles Contributes to Cellular Apoptosis, Redox Stress, and Alzheimer's Aß Amyloidosis.

Fe2O3, CuO and ZnO nanoparticles (NP) have found various industrial and biomedical applications. However, there are growing concerns among the general public and regulators about their potential environmental and health impacts as their physio-chemical interaction with biological systems and toxic responses of the latter are complex and not well understood. Herein we first reported that human SH-SY5Y and H4 cells and rat PC12 cell lines displayed concentration-dependent neurotoxic responses to insults of CuO nanoparticles (CuONP), but not to Fe2O3 nanoparticles (Fe2O3NP) or ZnO nanoparticles (ZnONP). This study provides evidence that CuONP induces neuronal cell apoptosis, discerns a likely p53-dependent apoptosis pathway and builds out the relationship between nanoparticles and Alzheimer's disease (AD) through the involvement of reactive oxygen species (ROS) and increased Aß levels in SH-SY5Y and H4 cells. Our results implicate that exposure to CuONP may be an environmental risk factor for AD. For public health concerns, regulation for environmental or occupational exposure of CuONP are thus warranted given AD has already become a pandemic.

A Narrative Review of Alzheimer's Disease Stigma.

As the most common form of senile dementia, Alzheimer's disease (AD) is accompanied by a great deal of uncertainty which can lead to fear and stigma for those identified with this devastating disease. As the AD definition evolves from a syndromal to a biological construct, and early diagnoses becomes more commonplace, more confusion and stigma may result. We conducted a narrative review of the literature on AD stigma to consolidate information on this body of research. From the perspective of several stigma theories, we identified relevant studies to inform our understanding of the way in which implementation of the new framework for a biological based AD diagnosis may have resulted in new and emerging stigma. Herein, we discuss the emergence of new AD stigma as our understanding of the definition of the disease changes. We further propose recommendations for future research to reduce the stigma associated with AD.

Exposure to CuO Nanoparticles Mediates NFκB Activation and Enhances Amyloid Precursor Protein Expression.

Amyloid precursor protein (APP) is directly related to Aß amyloidosis-a hallmark of Alzheimer's disease (AD). However, the impact of environmental factors upon APP biology and Aß amyloid pathology have not been well studied. The increased use of nanoparticles (NPs) or engineered nanomaterials (ENMs) has led to a growing body of evidence suggesting that exposure to metal/metal oxide NPs, such as Fe2O3, CuO, and ZnO, may contribute to the pathophysiology of neurodegenerative diseases such as AD through neuroinflammation. Our previous studies indicated that exposure to CuO nanoparticles (CuONPs) induce potent in vitro neurotoxicity. Herein, we investigated the effects on APP expression in neuronal cells exposed to different metal oxide NPs. We found a low dose of CuONPs effectively activated the NFκB signaling pathway and increased APP expression. Moreover, the inhibition of p65 expression using siRNA abolished CuONP-mediated APP expression, suggesting that NFκB-regulated APP expression in response to CuONP exposure may be associated with AD pathology.

A Preliminary Study of Cu Exposure Effects upon Alzheimer's Amyloid Pathology.

A large body of evidence indicates that dysregulation of cerebral biometals (Fe, Cu, Zn) and their interactions with amyloid precursor protein (APP) and Aß amyloid may contribute to the Alzheimer's disease (AD) Aß amyloid pathology. However, the molecular underpinnings associated with the interactions are still not fully understood. Herein we have further validated the exacerbation of Aß oligomerization by Cu and H2O2 in vitro. We have also reported that Cu enhanced APP translations via its 5' untranslated region (5'UTR) of mRNA in SH-SY5Y cells, and increased Aß amyloidosis and expression of associated pro-inflammatory cytokines such as MCP-5 in Alzheimer's APP/PS1 doubly transgenic mice. This preliminary study may further unravel the pathogenic role of Cu in Alzheimer's Aß amyloid pathogenesis, warranting further investigation.

Nanoneurotoxicity and Potential Nanotheranostics for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common form of senile dementia and it is characterized by cognitive, motor and memory impairments. AD neuropathology includes toxic biomarkers, such as Aß amyloid protein buildup between neurons disrupting connections, tau protein fibrillization and neuronal demise. These biomarkers are exacerbated with exposure to environmental borne or man-made nanoparticles or engineered nanomaterials (ENMs) as these nanoparticles are becoming more widely adopted for industrial applications. Studies suggest a link between nanoparticle exposure and neurotoxic responses, thus suggesting a contribution to AD pathology. This review summarizes research in the field of nanoparticles in terms of neurotoxic changes in the nervous system, as well as its relation to AD pathology. Studies involving silver, silica, copper oxide and iron oxide nanoparticles in mice suggest ranging neurotoxic reactions, such as disrupted neural connections, neuroinflammation, neuron cell death, redox stress, impairment of the blood-brain barrier (BBB), decrease in motor performance, demyelination of axons, decrease in long-term potentiation (LTP) and damage to DNA and brain structures. This review also examines beneficial effects of certain nanoparticles as potential therapeutic or diagnostic tools for AD.