Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature

Posterior cingulate cortex reveals an expression profile of resilience in cognitively intact elders

The posterior cingulate cortex, a key hub of the default mode network, underlies autobiographical memory retrieval and displays hypometabolic changes early in Alzheimer disease. To obtain an unbiased understanding of the molecular pathobiology of the aged posterior cingulate cortex, we performed RNA sequencing (RNA-seq) on tissue obtained from 26 participants of the Rush Religious Orders Study (11 males/15 females; aged 76-96 years) with a pre-mortem clinical diagnosis of no cognitive impairment and post-mortem neurofibrillary tangle Braak Stages I/II, III, and IV. Transcriptomic data were gathered using next-generation sequencing of RNA extracted from posterior cingulate cortex generating an average of 60 million paired reads per subject. Normalized expression of RNA-seq data was calculated using a global gene annotation and a microRNA profile. Differential expression (DESeq2, edgeR) using Braak staging as the comparison structure isolated genes for dimensional scaling, associative network building and functional clustering. Curated genes were correlated with the Mini-Mental State Examination and semantic, working and episodic memory, visuospatial ability, and a composite Global Cognitive Score. Regulatory mechanisms were determined by co-expression networks with microRNAs and an overlap of transcription factor binding sites. Analysis revealed 750 genes and 12 microRNAs significantly differentially expressed between Braak Stages I/II and III/IV and an associated six groups of transcription factor binding sites. Inputting significantly different gene/network data into a functional annotation clustering model revealed elevated presynaptic, postsynaptic and ATP-related expression in Braak Stages III and IV compared with Stages I/II, suggesting these pathways are integral for cognitive resilience seen in unimpaired elderly subjects. Principal component analysis and Kruskal-Wallis testing did not associate Braak stage with cognitive function. However, Spearman correlations between genes and cognitive test scores followed by network analysis revealed upregulation of classes of synaptic genes positively associated with performance on the visuospatial perceptual orientation domain. Upregulation of key synaptic genes suggests a role for these transcripts and associated synaptic pathways in cognitive resilience seen in elders despite Alzheimer disease pathology and dementia.

Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records

BACKGROUND

Many studies have used Taiwan's National Health Insurance Research database (NHIRD) to conduct psychiatric research. However, the accuracy of the diagnostic codes for psychiatric disorders in NHIRD is not validated, and the symptom profiles are not available either. This study aimed to evaluate the accuracy of diagnostic codes and use text mining to extract symptom profile and functional impairment from electronic health records (EHRs) to overcome the above research limitations.

METHODS

A total of 500 discharge notes were randomly selected from a medical center's database. Three annotators reviewed the notes to establish gold standards. The accuracy of diagnostic codes for major psychiatric illness was evaluated. Text mining approaches were applied to extract depressive symptoms and function profiles and to identify patients with major depressive disorder.

RESULTS

The accuracy of the diagnostic code for major depressive disorder, schizophrenia, and dementia was acceptable but that of bipolar disorder and minor depression was less satisfactory. The performance of text mining approach to recognize depressive symptoms is satisfactory; however, the recall for functional impairment is lower resulting in lower F-scores of 0.774-0.753. Using the text mining approach to identify major depressive disorder, the recall was 0.85 but precision was only 0.69.

CONCLUSIONS

The accuracy of the diagnostic code for major depressive disorder in discharge notes was generally acceptable. This finding supports the utilization of psychiatric diagnoses in claims databases. The application of text mining to EHRs might help in overcoming current limitations in research using claims databases.