A Bayesian Model for the Prediction and Early Diagnosis of Alzheimer's Disease

A diagnosis model of dementia via machine learning

As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods.

Dietary Alterations in Impaired Mitochondrial Dynamics Due to Neurodegeneration

Alzheimer's disease is still an incurable disease with significant social and economic impact globally. Nevertheless, newly FDA-approved drugs and non-pharmacological techniques may offer efficient disease treatments. Furthermore, it is widely accepted that early diagnosis or even prognosis of Alzheimer's disease using advanced computational tools could offer a compelling alternative way of management. In addition, several studies have presented an insight into the role of mitochondrial dynamics in Alzheimer's development. In combination with diverse dietary and obesity-related diseases, mitochondrial bioenergetics may be linked to neurodegeneration. Considering the probabilistic expectations of Alzheimer's disease development or progression due to specific risk factors or biomarkers, we designed a Bayesian model to formulate the impact of diet-induced obesity with an impaired mitochondrial function and altered behavior. The applied probabilities are based on clinical trials globally and are continuously subject to updating and redefinition. The proposed multiparametric model combines various data types based on uniform probabilities. The program simulates all the variables with a uniform distribution in a sample of 1000 patients. First, the program initializes the variable age (30-95) and the four different diet types ("HFO_diet," "Starvation," "HL_diet," "CR") along with the factors that are related to prodromal or mixed AD (ATP, MFN1, MFN2, DRP1, FIS1, Diabetes, Oxidative_Stress, Hypertension, Obesity, Depression, and Physical_activity). Besides the known proteins related to mitochondrial dynamics, our model includes risk factors like Age, Hypertension, Oxidative Stress, Obesity, Depression, and Physical Activity, which are associated with Prodromal Alzheimer's. The outcome is the disease progression probability corresponding to a random individual ID related to diet choices and mitochondrial dynamics parameters. The proposed model and the programming code are adjustable to different parameters and values. The program is coded and executed in Python and is fully and freely available for research purposes and testing the correlation between diet type and Alzheimer's disease progression regarding various risk factors and biomarkers.

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.

Peripheral Inflammatory Biomarkers of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease of unknown pathological origin. The clinical diagnosis of AD is time-consuming and needs to a combination of clinical evaluation, psychological testing, and imaging assessments. Biomarkers may be good indicators for the clinical diagnosis of AD; hence, it is important to identify suitable biomarkers for the diagnosis and treatment of AD. Peripheral inflammatory biomarkers have been the focus of research in recent years. This review summarizes the role of inflammatory biomarkers in the disease course of AD.

BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension

A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.

Symptomatic, Genetic, and Mechanistic Overlaps between Autism and Alzheimer's Disease

Autism spectrum disorder (ASD) and Alzheimer's disease (AD) are neurodevelopmental and neurodegenerative disorders affecting two opposite ends of life span, i.e., childhood and old age. Both disorders pose a cumulative threat to human health, with the rate of incidences increasing considerably worldwide. In the context of recent developments, we aimed to review correlated symptoms and genetics, and overlapping aspects in the mechanisms of the pathogenesis of ASD and AD. Dementia, insomnia, and weak neuromuscular interaction, as well as communicative and cognitive impairments, are shared symptoms. A number of genes and proteins linked with both disorders have been tabulated, including MECP2, ADNP, SCN2A, NLGN, SHANK, PTEN, RELN, and FMR1. Theories about the role of neuron development, processing, connectivity, and levels of neurotransmitters in both disorders have been discussed. Based on the recent literature, the roles of FMRP (Fragile X mental retardation protein), hnRNPC (heterogeneous ribonucleoprotein-C), IRP (Iron regulatory proteins), miRNAs (MicroRNAs), and α-, β0, and γ-secretases in the posttranscriptional regulation of cellular synthesis and processing of APP (amyloid-β precursor protein) have been elaborated to describe the parallel and overlapping routes and mechanisms of ASD and AD pathogenesis. However, the interactive role of genetic and environmental factors, oxidative and metal ion stress, mutations in the associated genes, and alterations in the related cellular pathways in the development of ASD and AD needs further investigation.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling

BACKGROUND

Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.

OBJECTIVE

This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk.

METHODS

Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class.

RESULTS

Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters.

CONCLUSION

We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

Bayesian Statistics to Estimate Diagnostic Probability of Scaphoid Fractures from Clinical Examinations: A Meta-Analysis

BACKGROUND

Management of suspected scaphoid fractures includes repeated evaluation and casting in symptomatic patients with nondiagnostic radiographs. In this systematic review and meta-analysis, the authors compare the diagnostic accuracy of clinical examinations for scaphoid fractures and create a decision guide using Bayesian statistics.

METHODS

The MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature databases were queried for studies that evaluated clinical index tests and their diagnostic accuracies for scaphoid fracture. Summary estimates were achieved by a bivariate random effects model and used in Bayes' theorem. The authors varied the scaphoid fracture prevalence for sensitivity analysis.

RESULTS

Fourteen articles with 22 index tests and 1940 patients were included. Anatomical snuffbox pain/tenderness (11 studies, 1363 patients), pain with axial loading (eight studies, 995 patients), and scaphoid tubercle tenderness (five studies, 953 patients) had sufficient data for pooled analysis. Anatomical snuffbox pain/tenderness was the most sensitive test (0.93; 95 percent CI, 0.87 to 0.97), and pain with axial loading was the most specific test (0.66; 95 percent CI, 0.41 to 0.85), but all three tests had lower estimated specificities compared with sensitivities. In the base case, the probability of fracture was approximately 60 percent when a patient presented with all three findings after acute wrist injury.

CONCLUSIONS

The posttest probability of scaphoid fracture was sensitive to both prevalence and diagnostic accuracy of individual clinical index tests. In a population with a fracture prevalence of 20 percent, patients presenting with concurrent anatomical snuffbox pain/tenderness, pain on axial loading, and scaphoid tubercle tenderness may benefit from early advanced imaging to rule out scaphoid fractures if initial radiographs are nondiagnostic.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Diagnostic, II.

Molecular subtyping of Alzheimer's disease with consensus non-negative matrix factorization

Alzheimer's disease (AD) is a heterogeneous disease and exhibits diverse clinical presentations and disease progression. Some pathological and anatomical subtypes have been proposed. However, these subtypes provide a limited mechanistic understanding for AD. Leveraging gene expression data of 222 AD patients from The Religious Orders Study and Memory and Aging Project (ROSMAP) Study, we identified two AD molecular subtypes (synaptic type and inflammatory type) using consensus non-negative matrix factorization (NMF). Synaptic type is characterized by disrupted synaptic vesicle priming and recycling and synaptic plasticity. Inflammatory type is characterized by disrupted IL2, interferon alpha and gamma pathways. The two AD molecular subtypes were validated using independent data from Gene Expression Omnibus. We further demonstrated that the two molecular subtypes are associated with APOE genotypes, with synaptic type more prevalent in AD patients with E3E4 genotype and inflammatory type more prevalent in AD patients with E3E3 genotype (p = 0.031). In addition, two molecular subtypes are differentially represented in male and female AD, with synaptic type more prevalent in male and inflammatory type in female patients (p = 0.051). Identification of AD molecular subtypes has potential in facilitating disease mechanism understanding, clinical trial design, drug discovery, and precision medicine for AD.

Role of Adaptive Immune and Impacts of Risk Factors on Adaptive Immune in Alzheimer's Disease: Are Immunotherapies Effective or Off-Target?

The pathogenesis of Alzheimer's disease (AD) is complex. Still it remains unclear, which resulted in all efforts for AD treatments with targeting the pathogenic factors unsuccessful over past decades. It has been evidenced that the innate immune is strongly implicated in the pathogenesis of AD. However, the role of adaptive immune in AD remains mostly unknown and the results obtained were controversial. In the review, we summarized recent studies and showed that the molecular and cellular alterations in AD patients and its animal models involving T cells and B cells as well as immune mediators of adaptive immune occur not only in the peripheral blood but also in the brain and the cerebrospinal fluid. The risk factors that cause AD contribute to AD progress by affecting the adaptive immune, indicating that adaptive immunity proposes a pivotal role in this disease. It may provide a possible basis for applying immunotherapy in AD and further investigates whether the immunotherapies are effective or off-target?

RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking

As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to the process of preparing necessary medications in advance according to the predicted disease progression of patients. However, predicting preventive or even life-saving medicine for each patient is a non-trivial task. Existing models usually overlook the implicit hierarchical relation between patient's predicted diseases and medications, and mainly focus on single tasks (medication recommendation or disease prediction). To tackle this limitation, we propose a relation augmented hierarchical multi-task learning framework, named RAHM. which is capable of learning multi-level relation-aware patient representation for reasonable medication stocking. Specifically, the framework first leverages the underlying structural relations of Electronic Health Record (EHR) data to learn the low-level patient visit representation. Then, it uses a regular LSTM to encode the historical temporal disease information for disease-level patient representation learning. Further, a relation-aware LSTM (R-LSTM) is proposed to handle the relations between diseases and medication in longitudinal patient records, which can better integrate the historical information into the medication-level patient representation. In the learning process, two pseudo residual structures are introduced to mitigate the error propagation and preserve the valuable relation information of EHRs. To validate our method, extensive experiments have been conducted based on the real-world clinical dataset. The results demonstrate a consistent superiority of our framework over several baselines in suggesting reasonable stock medication.

Characterization of network switching in disorder of consciousness at multiple time scales

OBJECTIVE

Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels.

APPROACH

By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels.

MAIN RESULTS

Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales.

SIGNIFICANCE

These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.

Data science in neurodegenerative disease: its capabilities, limitations, and perspectives

PURPOSE OF REVIEW

With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed. In this review, we argue that computational models can be both relevant and useful in neurodegenerative disease research and although the current established models have limitations in clinical practice, artificial intelligence has the potential to overcome deficiencies encountered by these models, which in turn can improve our understanding of disease.

RECENT FINDINGS

In recent years, diverse computational approaches have been used to shed light on different aspects of neurodegenerative disease models. For example, linear and nonlinear mixed models, self-modeling regression, differential equation models, and event-based models have been applied to provide a better understanding of disease progression patterns and biomarker trajectories. Additionally, the Cox-regression technique, Bayesian network models, and deep-learning-based approaches have been used to predict the probability of future incidence of disease, whereas nonnegative matrix factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes. Furthermore, the interpretation of neurodegenerative disease data is possible through knowledge-based models which use prior knowledge to complement data-driven analyses. These knowledge-based models can include pathway-centric approaches to establish pathways perturbed in a given condition, as well as disease-specific knowledge maps, which elucidate the mechanisms involved in a given disease. Collectively, these established models have revealed high granular details and insights into neurodegenerative disease models.

SUMMARY

In conjunction with increasingly advanced computational approaches, a wide spectrum of neurodegenerative disease models, which can be broadly categorized into data-driven and knowledge-driven, have been developed. We review the state of the art data and knowledge-driven models and discuss the necessary steps which are vital to bring them into clinical application.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data

BACKGROUND

Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.

RESULTS

We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.

CONCLUSIONS

Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.

The Relationship between Subjective Cognitive Impairment and Activity Participation: A Systematic Review

This systematic review synthesizes current evidence to determine how subjective cognitive impairment (SCI) relates to physical, cognitive, and social activity participation in older adults. Nine peer-reviewed articles were reviewed and appraised for evidence quality. Most were cross-sectional and had high methodological quality. Higher levels of SCI were almost universally associated with lower levels of physical and social activity participation. These findings suggest that older adults who report higher SCI engage in fewer activities. Examining these relationships longitudinally is an important next step to determine whether SCI precedes withdrawing from activities in older adults.

Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health

The human gastrointestinal (gut) microbiota comprises diverse and dynamic populations of bacteria, archaea, viruses, fungi, and protozoa, coexisting in a mutualistic relationship with the host. When intestinal homeostasis is perturbed, the function of the gastrointestinal tract and other organ systems, including the brain, can be compromised. The gut microbiota is proposed to contribute to blood-brain barrier disruption and the pathogenesis of neurodegenerative diseases. While progress is being made, a better understanding of interactions between gut microbes and host cells, and the impact these have on signaling from gut to brain is now required. In this review, we summarise current evidence of the impact gut microbes and their metabolites have on blood-brain barrier integrity and brain function, and the communication networks between the gastrointestinal tract and brain, which they may modulate. We also discuss the potential of microbiota modulation strategies as therapeutic tools for promoting and restoring brain health.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

BACKGROUND

Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.

OBJECTIVE

Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.

METHODS

Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.

RESULTS

GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.

CONCLUSION

In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Effects of hCG on reduced numbers of hCG receptors in the prefrontal cortex and cerebellum of rat models of Alzheimer's disease

Age-associated changes in the levels of luteinizing hormone and human chorionic gonadotropin (hCG) are potential risk factors for Alzheimer's disease (AD); hCG concentration is related to the incidence of AD. The highest density of hCG receptors is in zones of the brain that are vulnerable to AD and streptozotocin (STZ) can decrease the density of this receptor. We investigated the effects of different doses of hCG on hCG receptor density in the prefrontal cortex and cerebellum in a rat model of STZ-induced AD. AD was induced by intracerebroventricular injection of 3 mg/kg STZ. The resulting AD rats were treated for 3 days with 50, 100 or 200 IU/200 μl hCG, or with saline as a control. Sections of prefrontal cortex and cerebellum were stained immunohistochemically and hCG receptor-immunoreactive (ir) neurons were counted. STZ injected into the lateral ventricles of rat brains reduced the density of hCG receptor-ir neurons in the prefrontal cortex and cerebellum. hCG administration resulted in a significant dose-dependent increase in the number of hCG receptor-ir neurons in the prefrontal cortex and cerebellum. The maximum increase in the number of receptors occurred following the 200 IU dose of hCG. Administration of hCG ameliorated the lowered density of hCG receptor-ir neurons in the cerebellum and prefrontal cortex in STZ-induced AD rats.

Role of GTPases in the Regulation of Mitochondrial Dynamics in Alzheimer's Disease and CNS-Related Disorders

Data obtained from several studies have shown that mitochondria are involved and play a central role in the progression of several distinct pathological conditions. Morphological alterations and disruptions on the functionality of mitochondria may be related to metabolic and energy deficiency in neurons in a neurodegenerative disorder. Several recent studies demonstrate the linkage between neurodegeneration and mitochondrial dynamics in the spectrum of a promising era called precision mitochondrial medicine. In this review paper, an analysis of the correlation between mitochondria, Alzheimer's disease, and other central nervous system (CNS)-related disorders like the Parkinson's disease and the autism spectrum disorder is under discussion. The role of GTPases like the mfn1, mfn2, opa1, and dlp1 in mitochondrial fission and fusion is also under investigation, influencing mitochondrial population and leading to oxidative stress and neuronal damage.

Genetic Relationship Between IL-6 rs1800796 Polymorphism and Susceptibility to Periodontitis

BACKGROUND

There are accumulating reports for the potential role of Interleukin-6 (IL-6) rs1800796 polymorphism in the risk of periodontitis. However, distinct conclusions are observed. In this study, we have an interest in comprehensively analyzing the genetic relationship between IL-6 rs1800796 and the susceptibility to periodontitis.

METHODS

We retrieved the eligible case-control studies from on-line database and conducted a meta-analysis. P-value of association test, OR (odd ratios) and 95% CI (confidence interval) were calculated for the assessment of potential genetic association.

RESULTS

We enrolled a total of 20 case-control studies for pooling analysis. A positive association between periodontitis cases and controls was observed in the overall meta-analysis under all genetic models (all P < 0.05, OR > 1). Similar results were detected in the "population-based, PB" and "China" subgroups (all P < 0.05, OR > 1). In the "Asian" subgroup, there is an increased periodontitis risk under the allele, homozygote, heterozygote, dominant and carrier models (all P < 0.05, OR > 1). Nevertheless, negative results were found in the "Caucasian" subgroup under all models [all P > 0.05]. In addition, a positive association between IL-6 rs1800796 and the risk of chronic periodontitis was detected under the models of allele [G vs. C], GG vs. CC, GG vs. CC+ CG and carrier [G vs. C] (all P < 0.05, OR > 1).

CONCLUSION

IL-6 rs1800796 may serve as one genetic risk factor for periodontitis patients in the Asian population, especially the Chinese population. G/G genotype of IL-6 rs1800796 appears to be associated with an increased risk of chronic periodontitis.

Impact of age on acute post-TBI neuropathology in mice expressing humanized tau: a Chronic Effects of Neurotrauma Consortium Study

OBJECTIVES

We hypothesized that polypathology is more severe in older than younger mice during the acute phase following repetitive mild traumatic brain injury (r-mTBI).

METHODS

Young and aged male and female mice transgenic for human tau (hTau) were exposed to r-mTBI or a sham procedure. Twenty-four hours post-last injury, mouse brain tissue was immunostained for alterations in astrogliosis, microgliosis, tau pathology, and axonal injury.

RESULTS

Quantitative analysis revealed a greater percent distribution of glial fibrillary acid protein and Iba-1 reactivity in the brains of all mice exposed to r-mTBI compared to sham controls. With respect to axonal injury, the number of amyloid precursor protein-positive profiles was increased in young vs aged mice post r-mTBI. An increase in tau immunoreactivity was found in young and aged injured male hTau mice.

CONCLUSIONS

We report the first evidence in our model that r-mTBI precipitates a complex sequelae of events in aged vs young hTau mice at an acute time point, typified by an increase in phosphorylated tau and astroglisosis, and a diminished microgliosis response and axonal injury in aged mice. These findings suggest differential age-dependent effects in TBI pathobiology.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database

BACKGROUND

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI.

NEW METHOD

Based on preprocessed MRI images from the organizers of a neuroimaging challenge,³ we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme.

RESULTS

In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition.

COMPARISON WITH EXISTING METHOD(S)

The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature.

CONCLUSIONS

Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.