Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption

A Machine learning classification framework using fused fractal property feature vectors for Alzheimer's disease diagnosis

Alzheimer's disease (AD) profoundly affects brain tissue and network structures. Analyzing the topological properties of these networks helps to understand the progression of the disease. Most studies focus on single-scale brain networks, but few address multiscale brain networks. In this study, the renormalization group approach was applied to rescale the gray matter brain networks of AD patients and cognitively normal (CN) into three scales: the original, once-renormalized, and twice-renormalized networks. Based on the fractal property of these networks at different scales, a novel framework for classifying Alzheimer's disease using fractal and renormalization group was proposed. We integrated the fractal metrics across different scales to create fused feature vectors, which served as inputs for the classification framework aimed at diagnosing Alzheimer's disease. The experimental result indicates that the original and once-renormalized networks of both CN and AD exhibit the fractal property. The classification framework performed best when using the fused feature vector, including the average connection ratio of the original and once-renormalized networks. Using the fused feature vector of the average connection ratio, the One-Dimensional Convolution Neural Network model achieved an accuracy of 92.59% and an F1 score of 91.19%. This marks an improvement of approximately 10% in accuracy and 5% in F1 score compared to results using feature fusion of the average degree, average path length, and clustering coefficient.

Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade

BACKGROUND

Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects.

OBJECTIVES

This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data.

DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS

We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data.

RESULTS

Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories.

CONCLUSION

Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.

Effect of Silver Nanoparticles and L-Carnitine Supplement on Mixed Vaginitis Caused by Candida albicans/ Staphylococcus aureus in Mouse Models: An Experimental Study

The evolution of antimicrobial-resistant pathogens is a global health and development threat. Nanomedicine is rapidly becoming the main driving force behind ongoing changes in antimicrobial studies. Among nanoparticles, silver (AgNPs) have attracted attention due to their versatile properties. The study aimed to investigate the effects of AgNPs and L-carnitine (LC) on mixed Candida albicans and Staphylococcus aureus in the mice vaginitis model. Study of antimicrobial activity of AgNPs evaluated by Minimum Inhibitory Concentration (MIC) and Minimum Biocidal Concentration (MBC) assays. AgNPs inhibited biofilm formation of microbial strains, which was tested by using crystal violet staining. In the current study, we evaluated the effects of AgNPs and LC in NMRI mice infected intravaginally with C. albicans/ S. aureus for two weeks. The proportion of mice in each stage of the estrous cycle (proestrus, estrus, metestrus, and diestrus) was examined. Histological properties were assessed by hematoxylin/ eosin (H&E) staining of formalin-fixed, paraffin-embedded vaginal tissue sections. Based on the results, MICs of AgNPs against S. aureus, C. albicans, and their combination were 252.3, 124.8, and 501.8 ppm, and their minimum biofilm inhibitory concentration (MBIC) was 500, 250, and 1000 ppm, respectively. The estrous cycle in the treated group was similar to the control. Vaginal histology and cytology showed that LC can improve tissue damages caused by vaginitis and AgNPs. This study demonstrates the promising use of AgNPs as antimicrobial agents and the combination of AgNPs/ LC could be a great future alternative in the control of vaginitis.

A mini-network balance model for evaluating the progression of cardiovascular complications in Goto-Kakizaki rats

Cardiovascular complications represent a leading cause of mortality in patients with type 2 diabetes mellitus (T2DM). During such complicated progression, subtle variations in the cardiovascular risk (CVR)-related biomarkers have been used to identify cardiovascular disease at the incipient stage. In this study we attempt to integrally characterize the progression of cardiovascular complications and to assess the beneficial effects of metformin combined with salvianolic acid A (Sal A), in Goto-Kakizaki (GK) rats with spontaneous T2DM. The rats were treated with metformin (200 mg·kg^-1·d^-1, ig) alone or in combination with Sal A (1 mg·kg^-1·d^-1, ip) at ages from 8 to 22 weeks. During the treatment, the levels of asymmetric dimethylarginine, L-arginine, superoxide dismutase, malondialdehyde, glucose, high density lipoprotein and low density lipoprotein were assessed. Based on alterations in these biomarkers, a mini-network balance model was established using matrixes and vectors. Radar charts were created to visually depict the disruption of CVR-related modules (endothelial function, oxidative stress, glycation and lipid profiles). The description for the progression of cardiovascular disorder was quantitatively represented by u, the dynamic parameter of the model. The modeling results suggested that untreated GK rats tended to have more severe cardiovascular complications than the treatment groups. Metformin monotherapy retarded disease deterioration, whereas the combination treatment ameliorated the disease progression via restoring the balance. The current study, which focused on the balance of the mini-network and interactions among CVR-related modules, proposes a novel method for evaluating the progression of cardiovascular complications in T2DM as well as a more beneficial intervention strategy.

A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is a precursor phase of Alzheimer’s disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.