Neuroimaging Studies of Essential Tremor: How Well Do These Studies Support/Refute the Neurodegenerative Hypothesis?

Background: Tissue‐based research has recently led to a new patho‐mechanistic model of essential tremor (ET)—the cerebellar degenerative model. We are not aware of a study that has reviewed the current neuroimaging evidence, focusing on whether the studies support or refute the neurodegenerative hypothesis of ET. This was our aim. Methods: References for this review were identified by searches of PubMed (1966 to February 2014). Results: Several neuroimaging methods have been used to study ET, most of them based on magnetic resonance imaging (MRI). The methods most specific to address the question of neurodegeneration are MRI‐based volumetry, magnetic resonance spectroscopy, and diffusion‐weighted imaging. Studies using each of these methods provide support for the presence of cerebellar degeneration in ET, finding reduced cerebellar brain volumes, consistent decreases in cerebellar N‐acetylaspartate, and increased mean diffusivity. Other neuroimaging techniques, such as functional MRI and positron emission tomography (PET) are less specific, but still sensitive to potential neurodegeneration. These techniques are used for measuring a variety of brain functions and their impairment. Studies using these modalities also largely support cerebellar neuronal impairment. In particular, changes in 11C‐flumazenil binding in PET studies and changes in iron deposition in an MRI study provide evidence along these lines. The composite data point to neuronal impairment and likely neuronal degeneration in ET. Discussion: Recent years have seen a marked increase in the number of imaging studies of ET. As a whole, the combined data provide support for the presence of cerebellar neuronal degeneration in this disease

Reproducibility and effect of tissue composition on cerebellar GABA MRS in an elderly population.

Magnetic resonance spectroscopy (MRS) provides a valuable tool to non-invasively detect brain gamma-amino butyric acid (GABA) in vivo. GABAergic dysfunction has been observed in the aging cerebellum. Studying cerebellar GABA changes is of considerable interest in understanding certain age-related motor disorders. However, little is known about the reproducibility of GABA MRS in an aged population. Therefore, this study aimed to explore the feasibility and reproducibility of GABA MRS in the aged cerebellum at 3.0 Tesla and to examine the effect of differing tissue composition on GABA measurements. MRI and 1H MRS exams were performed on 10 healthy elderly volunteers (mean age 75.2 years ± 6.5 years) using a 3.0 Tesla Siemens Tim Trio scanner. Among them, 5 subjects were scanned twice to assess short-term reproducibility. The MEGA-PRESS J-editing sequence was used for GABA detection in two volumes of interest (VOIs) in left and right cerebellar dentate. MRS data processing and quantification were performed with LCModel 6.3-0L using two separate basis sets, generated from density matrix simulations using published values for chemical shifts an

Individual-level precision diagnosis for coronavirus disease 2019 related severe outcome: an early study in New York

Abstract Because of inadequate information provided by the on-going population level risk analyses for Coronavirus disease 2019 (COVID-19), this study aimed to evaluate the risk factors and develop an individual-level precision diagnostic method for COVID-19 related severe outcome in New York State (NYS) to facilitate early intervention and predict resource needs for patients with COVID-19. We analyzed COVID-19 related hospital encounter and hospitalization in NYS using Statewide Planning and Research Cooperative System hospital discharge dataset. Logistic regression was performed to evaluate the risk factors for COVID-19 related mortality. We proposed an individual-level precision diagnostic method by taking into consideration of the different weights and interactions of multiple risk factors. Age was the greatest risk factor for COVID-19 related fatal outcome. By adding other demographic variables, dyspnea or hypoxemia and multiple chronic co-morbid conditions, the model predictive accuracy was improved to 0.85 (95% CI 0.84–0.85). We selected cut-off points for predictors and provided a general recommendation to categorize the levels of risk for COVID-19 related fatal outcome, which can facilitate the individual-level diagnosis and treatment, as well as medical resource prediction. We further provided a use case of our method to evaluate the feasibility of public health policy for monoclonal antibody therapy

Data capture and visualization for a canine influenza outbreak — New York City, 2018

ObjectiveThe objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza H3N2 outbreak in New York City (NYC) and provide aggregate data back to the veterinary community as an interactive dashboard.IntroductionData-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical.1 Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner. Dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the public.2During Spring 2018, detection of canine influenza H3N2 among dogs in NYC caused concern in the veterinary community. Canine influenza is a highly contagious respiratory infection caused by an influenza A virus.3 However, no central database existed in NYC to monitor the outbreak and no single agency was responsible for data capture. Our team at the NYC Department of Health and Mental Hygiene (DOHMH) partnered with the NYC Veterinary Medical Association (VMA) to monitor the canine influenza H3N2 outbreak by building a web-based reporting platform and interactive dashboard.MethodsThe NYC DOHMH built and deployed a web-based reporting platform to aid veterinarians in reporting cases of canine influenza. We leveraged REDCap Cloud, a cloud-based graphical user interface data capture and management software. REDCap Cloud collected information regarding the provider, owner, dog, residence of dog, illness history, and influenza testing. We leveraged REDCap QA functionality in the form of mandatory questions to ensure data completeness. Several different field types — including dropdown menus, mutually exclusive radio buttons, and multi-select check boxes — were used to ensure data standardization. Skip logic was incorporated to guide users through unique sequences of questions based on the answers they entered. Reporting was voluntary.ResultsAfter requirements were gathered, the REDCap web-based reporting platform was rapidly deployed in approximately two business days. Over the course of one week, multiple versions of the dashboard were produced and the final iteration was completed. The entire system was built on server-side software that is available as free or open-source for individual licenses. The dashboard can be found at the following link: http://www.vmanyc.org/canine_influenza_dashboard.html.A total of 28 cases were reported by 6 providers during June–August 2018. All of the 28 cases were reported from 2 of the 5 NYC counties (boroughs); 17/28 (60.7%) were reported from Brooklyn and 11/28 (39.3%) were reported from Manhattan. We were able to collect mostly complete data by leveraging REDCap QA functionality. The reporting facility was listed in all cases, and an owner was listed in all but two cases. All reported cases used a PCR test for the detection of canine influenza H3N2. One reported case indicated polymerase chain reaction (PCR) test results as “not detected” which suggests that one negative case was reported through the system.ConclusionsUsing REDCap Cloud and R, we were able to rapidly build and deploy a web-based reporting platform and dynamic data visualization during an emergency response to an outbreak of canine influenza H3N2. Our system was used by veterinarians to report 28 cases of canine influenza. Future emergency responses for human disease outbreaks will likely benefit from the experience our team gained during our partnership with the NYC VMA.References1. Centers for Disease Control and Prevention. Public Health Emergency Response Guide for State, Local, and Tribal Public Health Directors. https://emergency.cdc.gov/planning/pdf/cdcresponseguide.pdf.2. Meyer M. The Rise of Healthcare Data Visualization. http://journal.ahima.org/2017/12/21/the-rise-of-healthcare-data-visualization/.3. American Veterinary Medical Association. Canine Influenza FAQ. https://www.avma.org/KB/Resources/FAQs/Pages/Control-of-Canine-Influenza-in-Dogs.aspx.4. Wickham H. R packages. http://r-pkgs.had.co.nz/

Community-setting pneumonia-associated hospitalizations by level of urbanization-New York City versus other areas of New York State, 2010-2014.

BackgroundNew York City (NYC) reported a higher pneumonia and influenza death rate than the rest of New York State during 2010-2014. Most NYC pneumonia and influenza deaths are attributed to pneumonia caused by infection acquired in the community, and these deaths typically occur in hospitals.MethodsWe identified hospitalizations of New York State residents aged ≥20 years discharged from New York State hospitals during 2010-2014 with a principal diagnosis of community-setting pneumonia or a secondary diagnosis of community-setting pneumonia if the principal diagnosis was respiratory failure or sepsis. We examined mean annual age-adjusted community-setting pneumonia-associated hospitalization (CSPAH) rates and proportion of CSPAH with in-hospital death, overall and by sociodemographic group, and produced a multivariable negative binomial model to assess hospitalization rate ratios.ResultsCompared with non-NYC urban, suburban, and rural areas of New York State, NYC had the highest mean annual age-adjusted CSPAH rate at 475.3 per 100,000 population and the highest percentage of CSPAH with in-hospital death at 13.7%. NYC also had the highest proportion of CSPAH patients residing in higher-poverty-level areas. Adjusting for age, sex, and area-based poverty, NYC residents experienced 1.3 (95% confidence interval [CI], 1.2-1.4), non-NYC urban residents 1.4 (95% CI, 1.3-1.6), and suburban residents 1.2 (95% CI, 1.1-1.3) times the rate of CSPAH than rural residents.ConclusionsIn New York State, NYC as well as other urban areas and suburban areas had higher rates of CSPAH than rural areas. Further research is needed into drivers of CSPAH deaths, which may be associated with poverty

Reduced intrinsic DNA curvature leads to increased mutation rate

Background: Mutation rates vary across the genome. Many trans factors that influence mutation rates have been identified, as have specific sequence motifs at the 1–7-bp scale, but cis elements remain poorly characterized. The lack of understanding regarding why different sequences have different mutation rates hampers our ability to identify positive selection in evolution and to identify driver mutations in tumorigenesis. Results: Here, we use a combination of synthetic genes and sequences of thousands of isolated yeast colonies to show that intrinsic DNA curvature is a major cis determinant of mutation rate. Mutation rate negatively correlates with DNA curvature within genes, and a 10% decrease in curvature results in a 70% increase in mutation rate. Consistently, both yeast and humans accumulate mutations in regions with small curvature. We further show that this effect is due to differences in the intrinsic mutation rate, likely due to differences in mutagen sensitivity and not due to differences in the local activity of DNA repair. Conclusions: Our study establishes a framework for understanding the cis properties of DNA sequence in modulating the local mutation rate and identifies a novel causal source of non-uniform mutation rates across the genome