Patients with ALS show highly correlated progression rates in left and right limb muscles.

ObjectiveAmyotrophic lateral sclerosis (ALS) progresses at different rates between patients, making clinical trial design difficult and dependent on large cohorts of patients. Currently, there are few data showing whether the left and right limbs progress at the same or different rates. This study addresses rates of decline in specific muscle groups of patients with ALS and assesses whether there is a relationship between left and right muscles in the same patient, regardless of overall progression.MethodsA large cohort of patients was used to assess decline in muscle strength in right and left limbs over time using 2 different methods: The Tufts Quantitative Neuromuscular Exam and Accurate Test of Limb Isometric Strength protocol. Then advanced linear regression statistical methods were applied to assess progression rates in each limb.ResultsThis report shows that linearized progression models can predict general slopes of decline with good accuracy. Critically, the data demonstrate that while overall decline is variable, there is a high degree of correlation between left and right muscle decline in ALS. This implies that irrespective of which muscle starts declining soonest or latest, their rates of decline following onset are more consistent.ConclusionsFirst, this study demonstrates a high degree of power when using unilateral treatment approaches to detect a slowing in disease progression in smaller groups of patients, thus allowing for paired statistical tests. These findings will be useful in transplantation trials that use muscle decline to track disease progression in ALS. Second, these findings discuss methods, such as tactical selection of muscle groups, which can improve the power efficiency of all ALS clinical trials

Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit

USGS/NOAA Workshop on Mycobacteriosis in Striped Bass, May 7-10, 2006, Annapolis, Maryland

As a Federal trust species, the well-being of the striped bass (Morone saxatilis) population along the Eastern Seaboard is of major concern to resource users. Striped bass are an extremely valuable commercial and recreational resource. As a principal piscivore in Chesapeake Bay, striped bass directly or indirectly interact with multiple trophic levels within the ecosystem and are therefore very sensitive to biotic and abiotic ecosystem changes. For reasons that have yet to be defined, the species has a high intrinsic susceptibility to mycobacteriosis. This disease has been impacting Chesapeake Bay striped bass since at least the 1980s as indicated by archived tissue samples. However, it was not until heightened incidences of fish with skin lesions in the Pocomoke River and other tributaries of the Chesapeake Bay were reported in the summer and fall of 1996 and 1997 that a great deal of public and scientific interest was stimulated about concerns for fish disease in the Bay. (PDF contains 50 pages

Understanding Disease Heterogeneity and Patient Characteristics in Patients with Amyotrophic Lateral Sclerosis (ALS)

Background: Amytrophic lateral sclerosis (ALS) is a fatal neurologic disease that is projected to double in worldwide incidence in the next 20 years. The heterogenic nature of the disease and relatively limited research data, compared to non-rare diseases, have made it difficult for clinician researchers to alter the course of the disease within the short life expectancy after symptom onset. Method: This was a mixed-method retrospective review and live sampling study using three distinct data sources. Retrospective data was abstracted from the electronic medical record systems for a select group of ALS patients seen at the University of California, Irvine Neuromuscular Center (UCI NMC). Additional retrospective datasets curated by the Pooled Resources Open-Access Clinical Trials (PRO-ACT) database were also analyzed. Observational data was collected using a 9-item survey developed on Google Forms and disseminated through the ALS Association Golden West Chapter. The items measured symptom onset, diagnostic journey, and patient demographics.Results: The analyses confirmed current reports of higher disease incidence in Caucasian populations, usually comprising at least 60% of each dataset. The gender prevalence towards males was only observed in the PRO-ACT dataset. There was also a difference in mean age between PRO-ACT (56 years), UCI (61 years), and Online Questionnaire respondents (66 years). Discussion: Ultimately retrospective data analyses were limited by substantial missing, not at random data. Large data repositories can bridge the gap between non-rare and rare disease research, but only with robust and methodologic data collection across all participating sites

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: A systematic literature review

OBJECTIVE: Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS: We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS: There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION: Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research

Strategies for the treatment of Hepatitis C in an era of interferon-free therapies: what public health outcomes do we value most?

Objective: The expense of new therapies for HCV infection may force health systems to prioritise the treatment of certain patient groups over others. Our objective was to forecast the population impact of possible prioritisation strategies for the resource-rich setting of Scotland. Design: We created a dynamic Markov simulation model to reflect the HCV-infected population in Scotland. We determined trends in key outcomes (e.g. incident cases of chronic infection and severe liver morbidity (SLM)) until the year 2030, according to treatment strategies involving prioritising, either: (A) persons with moderate/advanced fibrosis or (B) persons who inject drugs (PWID). Results: Continuing to treat the same number of patients with the same characteristics will give rise to a fall in incident infection (from 600 cases in 2015 to 440 in 2030) and a fall in SLM (from 195 cases in 2015 to 145 in 2030). Doubling treatment-uptake and prioritising PWID will reduce incident infection to negligible levels (<50 cases per year) by 2025, while SLM will stabilise (at 70–75 cases per year) in 2028. Alternatively, doubling the number of patients treated, but, instead, prioritising persons with moderate/advanced fibrosis will reduce incident infection less favourably (only to 280 cases in 2030), but SLM will stabilise by 2023 (i.e. earlier than any competing strategy). Conclusions: Prioritising treatment uptake among PWID will substantially impact incident transmission, however, this approach foregoes the optimal impact on SLM. Conversely, targeting those with moderate/advanced fibrosis has the greatest impact on SLM but is suboptimal in terms of averting incident infection