Factors Associated With Emergency Medical Services Providers' Acceptance of the Seasonal Influenza Vaccine.

OBJECTIVE: Influenza vaccination remains the most effective primary prevention strategy for seasonal influenza. This research explores the percentage of emergency medical services (EMS) clinicians who received the seasonal flu vaccine in a given year, along with their reasons for vaccine acceptance and potential barriers. METHODS: A survey was distributed to all EMS clinicians in Virginia during the 2018-2019 influenza season. The primary outcome was vaccination status. Secondary outcomes were attitudes and perceptions toward influenza vaccination, along with patient care behaviors when treating an influenza patient. RESULTS: Ultimately, 2796 EMS clinicians throughout Virginia completed the survey sufficiently for analysis. Participants were mean 43.5 y old, 60.7% male, and included the full range of certifications. Overall, 79.4% of surveyed EMS clinicians received a seasonal flu vaccine, 74% had previously had the flu, and 18% subjectively reported previous side effects from the flu vaccine. Overall, 54% of respondents believed their agency has influenza or respiratory specific plans or procedures. CONCLUSIONS: In a large, state-wide survey of EMS clinicians, overall influenza vaccination coverage was 79.4%. Understanding the underlying beliefs of EMS clinicians remains a critical priority for protecting these frontline clinicians. Agencies should consider practical policies, such as on-duty vaccination, to increase uptake.

Small molecule inhibitors of α-synuclein oligomers identified by targeting early dopamine-mediated motor impairment in C. elegans.

BACKGROUND: Parkinson's disease is a disabling neurodegenerative movement disorder characterized by dopaminergic neuron loss induced by α-synuclein oligomers. There is an urgent need for disease-modifying therapies for Parkinson's disease, but drug discovery is challenged by lack of in vivo models that recapitulate early stages of neurodegeneration. Invertebrate organisms, such as the nematode worm Caenorhabditis elegans, provide in vivo models of human disease processes that can be instrumental for initial pharmacological studies. METHODS: To identify early motor impairment of animals expressing α-synuclein in dopaminergic neurons, we first used a custom-built tracking microscope that captures locomotion of single C. elegans with high spatial and temporal resolution. Next, we devised a method for semi-automated and blinded quantification of motor impairment for a population of simultaneously recorded animals with multi-worm tracking and custom image processing. We then used genetic and pharmacological methods to define the features of early motor dysfunction of α-synuclein-expressing C. elegans. Finally, we applied the C. elegans model to a drug repurposing screen by combining it with an artificial intelligence platform and cell culture system to identify small molecules that inhibit α-synuclein oligomers. Screen hits were validated using in vitro and in vivo mammalian models. RESULTS: We found a previously undescribed motor phenotype in transgenic α-synuclein C. elegans that correlates with mutant or wild-type α-synuclein protein levels and results from dopaminergic neuron dysfunction, but precedes neuronal loss. Together with artificial intelligence-driven in silico and in vitro screening, this C. elegans model identified five compounds that reduced motor dysfunction induced by α-synuclein. Three of these compounds also decreased α-synuclein oligomers in mammalian neurons, including rifabutin which has not been previously investigated for Parkinson's disease. We found that treatment with rifabutin reduced nigrostriatal dopaminergic neurodegeneration due to α-synuclein in a rat model. CONCLUSIONS: We identified a C. elegans locomotor abnormality due to dopaminergic neuron dysfunction that models early α-synuclein-mediated neurodegeneration. Our innovative approach applying this in vivo model to a multi-step drug repurposing screen, with artificial intelligence-driven in silico and in vitro methods, resulted in the discovery of at least one drug that may be repurposed as a disease-modifying therapy for Parkinson's disease.

Using artificial intelligence to identify anti-hypertensives as possible disease modifying agents in Parkinson's disease.

PURPOSE: Drug repurposing is an effective means of increasing treatment options for diseases, however identifying candidate molecules for the indication of interest from the thousands of approved drugs is challenging. We have performed a computational analysis of published literature to rank existing drugs according to predicted ability to reduce alpha synuclein (aSyn) oligomerization and analyzed real-world data to investigate the association between exposure to highly ranked drugs and PD. METHODS: Using IBM Watson for Drug Discoveryâ (WDD) we identified several antihypertensive drugs that may reduce aSyn oligomerization. Using IBM MarketScanâ Research Databases we constructed a cohort of individuals with incident hypertension. We conducted univariate and multivariate Cox proportional hazard analyses (HR) with exposure as a time-dependent covariate. Diuretics were used as the referent group. Age at hypertension diagnosis, sex, and several comorbidities were included in multivariate analyses. RESULTS: Multivariate results revealed inverse associations for time to PD diagnosis with exposure to the combination of the combination of angiotensin receptor II blockers (ARBs) and dihydropyridine calcium channel blockers (DHP-CCB) (HR = 0.55, p < 0.01) and angiotensin converting enzyme inhibitors (ACEi) and diuretics (HR = 0.60, p-value <0.01). Increased risk was observed with exposure to alpha-blockers alone (HR = 1.81, p < 0.001) and the combination of alpha-blockers and CCB (HR = 3.17, p < 0.05). CONCLUSIONS: We present evidence that a computational approach can efficiently identify leads for disease-modifying drugs. We have identified the combination of ARBs and DHP-CCBs as of particular interest in PD.

Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson.

BACKGROUND: Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship. METHODS: Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers. RESULTS: Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%. CONCLUSION: Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation.

Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.

Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kinases so far. One of these, NEK2, was studied in detail. A known mitosis promoter, NEK2 was shown to phosphorylate p53 at Ser315 in vitro and in vivo and to functionally inhibit p53. These bona fide validations of text-based predictions of p53 phosphorylation, and the discovery of an inhibitory p53 kinase of pharmaceutical interest, suggest that automated reasoning using a large body of literature can generate valuable molecular hypotheses and has the potential to accelerate scientific discovery.

Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease with no effective treatments. Numerous RNA-binding proteins (RBPs) have been shown to be altered in ALS, with mutations in 11 RBPs causing familial forms of the disease, and 6 more RBPs showing abnormal expression/distribution in ALS albeit without any known mutations. RBP dysregulation is widely accepted as a contributing factor in ALS pathobiology. There are at least 1542 RBPs in the human genome; therefore, other unidentified RBPs may also be linked to the pathogenesis of ALS. We used IBM Watson® to sieve through all RBPs in the genome and identify new RBPs linked to ALS (ALS-RBPs). IBM Watson extracted features from published literature to create semantic similarities and identify new connections between entities of interest. IBM Watson analyzed all published abstracts of previously known ALS-RBPs, and applied that text-based knowledge to all RBPs in the genome, ranking them by semantic similarity to the known set. We then validated the Watson top-ten-ranked RBPs at the protein and RNA levels in tissues from ALS and non-neurological disease controls, as well as in patient-derived induced pluripotent stem cells. 5 RBPs previously unlinked to ALS, hnRNPU, Syncrip, RBMS3, Caprin-1 and NUPL2, showed significant alterations in ALS compared to controls. Overall, we successfully used IBM Watson to help identify additional RBPs altered in ALS, highlighting the use of artificial intelligence tools to accelerate scientific discovery in ALS and possibly other complex neurological disorders.

Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing.

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.