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Introduction Amyotrophic Lateral Sclerosis (ALS) is a paralyzing, multifactorial neurodegenerative disease with limited therapeutics and no known cure. The study goal was to determine which pathophysiological treatment targets appear most beneficial. Methods A big data approach was used to analyze high copy SOD1 G93A experimental data. The secondary data set comprised 227 published studies and 4,296 data points. Treatments were classified by pathophysiological target: apoptosis, axonal transport, cellular chemistry, energetics, neuron excitability, inflammation, oxidative stress, proteomics, or systemic function. Outcome assessment modalities included onset delay, health status (rotarod performance, body weight, grip strength), and survival duration. Pairwise statistical analysis (two-tailed t -test with Bonferroni correction) of normalized fold change (treatment/control) assessed significant differences in treatment efficacy. Cohen’s d quantified pathophysiological treatment category effect size compared to “all” (e.g., all pathophysiological treatment categories combined). Results Inflammation treatments were best at delaying onset ( d = 0.42, p > 0.05). Oxidative stress treatments were significantly better for prolonging survival duration ( d = 0.18, p < 0.05). Excitability treatments were significantly better for prolonging overall health status ( d = 0.22, p < 0.05). However, the absolute best pathophysiological treatment category for prolonging health status varied with disease progression: oxidative stress was best for pre-onset health ( d = 0.18, p > 0.05); excitability was best for prolonging function near onset ( d = 0.34, p < 0.05); inflammation was best for prolonging post-onset function ( d = 0.24, p > 0.05); and apoptosis was best for prolonging end-stage function ( d = 0.49, p > 0.05). Finally, combination treatments simultaneously targeting multiple pathophysiological categories (e.g., polytherapy) performed significantly ( p < 0.05) better than monotherapies at end-stage. Discussion In summary, the most effective pathophysiological treatments change as function of assessment modality and disease progression. Shifting pathophysiological treatment category efficacy with disease progression supports the homeostatic instability theory of ALS disease progression. 

Clinical Cohort Studies (CCS) are a great source of documented clinical research. Ideally, a clinical expert will interpret these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 CCS articles are published on PubMed every day. As a result, it can take days for a doctor to find articles and extract relevant information. Can we find a way to quickly sift through the long list of these articles faster and document the crucial takeaways from each of these articles? In this work, we propose CCS Explorer, an end-to-end system for relevance prediction of sentences, extractive summarization, and patient, outcome, and intervention entity detection from CCS. CCS Explorer is packaged in a web-based graphical user interface where the user can provide any disease name. CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end. CCS Explorer fine-tunes pre-trained language models based on transformers with additional layers for each of these tasks. We evaluate the models using two publicly available datasets. CCS Explorer obtains a recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus, CCS Explorer can reliably extract relevant information to summarize articles, saving time by ∼ 660×.