Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents

Stroke destroys neurons and their connections leading to focal neurological deficits. Although limited, many patients exhibit a certain degree of spontaneous functional recovery. Structural remodeling of the intracortical axonal connections is implicated in the reorganization of cortical motor representation maps, which is considered to be an underlying mechanism of the improvement in motor function. Therefore, an accurate assessment of intracortical axonal plasticity would be necessary to develop strategies to facilitate functional recovery following a stroke. The present study developed a machine learning-assisted image analysis tool based on multi-voxel pattern analysis in fMRI imaging. Intracortical axons originating from the rostral forelimb area (RFA) were anterogradely traced using biotinylated dextran amine (BDA) following a photothrombotic stroke in the mouse motor cortex. BDA-traced axons were visualized in tangentially sectioned cortical tissues, digitally marked, and converted to pixelated axon density maps. Application of the machine learning algorithm enabled sensitive comparison of the quantitative differences and the precise spatial mapping of the post-stroke axonal reorganization even in the regions with dense axonal projections. Using this method, we observed a substantial extent of the axonal sprouting from the RFA to the premotor cortex and the peri-infarct region caudal to the RFA. Therefore, the machine learningassisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.

Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network

MOTIVATION

Polypharmacy side effects should be carefully considered for new drug development. However, considering all the complex drug-drug interactions that cause polypharmacy side effects is challenging. Recently, graph neural network (GNN) models have handled these complex interactions successfully and shown great predictive performance. Nevertheless, the GNN models have difficulty providing intelligible factors of the prediction for biomedical and pharmaceutical domain experts.

METHOD

A novel approach, graph feature attention network (GFAN), is presented for interpretable prediction of polypharmacy side effects by emphasizing target genes differently. To artificially simulate polypharmacy situations, where two different drugs are taken together, we formulated a node classification problem by using the concept of line graph in graph theory.

RESULTS

Experiments with benchmark datasets validated interpretability of the GFAN and demonstrated competitive performance with the graph attention network in a previous work. And the specific cases in the polypharmacy side-effect prediction experiments showed that the GFAN model is capable of very sensitively extracting the target genes for each side-effect prediction.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SunjooBang/Polypharmacy-side-effect-prediction.

Comorbidity Scoring with Causal Disease Networks

In recent years, there has been numerous studies constructing a disease network with diverse sources of data. Many researchers attempted to extend the usage of the disease network by employing machine learning algorithms on various problems such as prediction of comorbidity. The relations between diseases can further be specified into causal relations. When causality is laid on the edges in the network, prediction for comorbid diseases can be more improved. However, not many machine learning algorithms have been developed to concern causality. In this study, we exploit a network based machine learning algorithm that generates comorbidity scores from a causal disease network. In order to find comorbid diseases, semi-supervised scoring for causal networks is proposed. It computes scores of entire nodes in the network when a specific node is labeled. Each score is calculated one at a time and affects to the others along causal edges. The algorithm iterates until it converges. We compared the scoring results of the causal disease network and those of simple association network. As a gold standard, we referenced the values of relative risk from prevalence database, HuDiNe. Scoring by the proposed method provides clearer distinguishability between the top-ranked diseases in the comorbidity list. This is a benefit because it allows the choosing of the most significant ones on an easier fashion. To present typical use of the resulting list, comorbid diseases of Huntington disease and pnuemonia are validated via PubMed literature, respectively.

Microscopic hematuria is a risk factor of incident chronic kidney disease in the Korean general population: a community-based prospective cohort study

BACKGROUND

Although asymptomatic microscopic hematuria (MH) is a common finding in clinical practice, its long-term outcome remains unknown.

AIM

This study evaluated the clinical implication of MH in the general population using a large-scale long-term longitudinal cohort database.

METHODS

This study included 8719 participants from the Korean Genome and Epidemiology Study between 2001 and 2014. MH was defined as ≥5 red blood cells per high-power field in random urinalysis without evidence of pyuria. The primary study outcome measure was incident chronic kidney disease (CKD), defined as estimated glomerular filtration rate <60 ml min-1⋅1.73⋅m-2.

RESULTS

During a median follow-up of 11.7 years, CKD occurred in 677 (7.8%) subjects. In Cox regression after adjustment for multiple confounders, subjects with MH had a significantly higher risk of incident CKD than those without [hazard ratio (HR) 1.45, 95% confidence interval (CI) 1.12-1.87; P = 0.005]. Isolated MH without proteinuria was also a risk factor of incident CKD (HR 1.37, 95% CI 1.04-1.79; P = 0.023) and the risk was further increased in MH with concomitant proteinuria (HR 5.41, 95% CI 2.54-11.49; P < 0.001). In propensity score matching analysis after excluding subjects with proteinuria, multi-variable stratified Cox regression analysis revealed that subjects with isolated MH had a significantly higher risk of incident CKD than those without (HR 1.83, 95% CI 1.14-2.94; P = 0.012).

CONCLUSION

The presence of MH is associated with an increased risk of incident CKD in the general population. Therefore, attentive follow-up is warranted in persons with MH for early detection of CKD.