A5013904506- COVID-19 epidemiological studies
- Sirtuins and Resveratrol in Medicine
- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- Data-Driven Disease Surveillance
- COVID-19 diagnosis using AI
- COVID-19 and Mental Health
- Neurogenesis and neuroplasticity mechanisms
- Calcium signaling and nucleotide metabolism
- COVID-19 Clinical Research Studies
- Neuroinflammation and Neurodegeneration Mechanisms
- Nicotinic Acetylcholine Receptors Study
- Genetics and Neurodevelopmental Disorders
- Bioinformatics and Genomic Networks
- Infection Control and Ventilation
- Vaccine Coverage and Hesitancy
- Neuroscience and Neuropharmacology Research
- Alzheimer's disease research and treatments
- vaccines and immunoinformatics approaches
- COVID-19 Pandemic Impacts
- Viral Infections and Outbreaks Research
- Biochemical effects in animals
- Nuclear Receptors and Signaling
- Health and Medical Research Impacts
- Dementia and Cognitive Impairment Research
NeuroHealing Pharmaceuticals (United States)
2025
Institute for the Future
2016-2024
University of California, San Francisco
2021-2024
Massachusetts Institute of Technology
2016-2022
Massachusetts General Hospital
2016
Harvard University
2016
Bridgewater State University
2016
University of Pittsburgh
2006-2010
Parkinson's disease is characterized by progressive death of dopaminergic neurons, leading to motor and cognitive dysfunction. Epidemiological studies consistently show that the use tobacco reduces risk Parkinson's. We report nicotine abundance SIRT6 in neuronal culture brain tissue. find reduction partly responsible for neuroprotection afforded nicotine. Additionally, greater patient brains, decreased brains users. also identify SNPs promote expression simultaneously associate with an...
Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens case prediction models have been proposed but their accuracy over time by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, first categorizing them then calculating mean absolute percent error, both wave-wise on the complete timeline. We compare estimates to government-reported numbers, one another, as...
Abstract Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level in given population. This study uses symptoms data collected Global COVID-19 Trends and Impact Surveys (UMD CTIS), on variants sequencing from GISAID. work, conducted January 2022 during emergence Omicron variant (subvariant BA.1), aims to improve quality available use resulting estimates levels assess changes vaccine efficacy change...
Advanced age and the APOE ε4 allele are two biggest risk factors for Alzheimer’s disease (AD) declining cognitive function. We describe a universal gauge to measure molecular brain using transcriptome analysis of four human postmortem cohorts (n = 673, ages 25–97) free neurological disease. In fifth cohort older subjects with or without 438, 67–108), we show that brains deviating in direction from what would be expected based on chronological an increase AD, Parkinson’s disease, decline....
ABSTRACT Accurate predictive modeling of pandemics is essential for optimally distributing resources and setting policy. Dozens case predictions models have been proposed but their accuracy over time by model type remains unclear. In this study, we analyze all US CDC COVID-19 forecasting models, first categorizing them then calculating mean absolute percent error, both wave-wise on the complete timeline. We compare estimates to government-reported numbers, one another, as well two baseline...
Abstract Data collected in the Global COVID-19 Trends and Impact Surveys (UMD CTIS), data on variants sequencing from GISAID, are used to evaluate impact of Omicron variant (in South Africa other countries) prevalence among unvaccinated vaccinated population, general discriminating by number doses. In Africa, we observe that December (with strong presence Omicron) population is comparable during previous wave August-September), which Delta was with largest presence. However, vaccinated, much...
Abstract Having accurate and timely data on active COVID-19 cases is challenging, since it depends the availability of an appropriate infrastructure to perform tests aggregate their results. In this paper, we consider a case be if infectious, propose methods estimate number infectious from official (of confirmed fatalities) public survey data. We show that latter viable option in countries with reduced testing capacity or infrastructures.
By sharing their experiences, early-career scientists can help to make the case for increased government funding researchers.
The COVID-19 pandemic spurred many computational modeling efforts. Many mistakes were made and lessons learned. This study attempts to list the key learned from a perspective, highlighting both successes shortcomings observed during pandemic. Additionally, this work compile set of critical steps best practices that authors believe would prove helpful should be implemented before start next avoid inaccuracies in scenarios. will help improve preparedness ensure models can more effectively...
Abstract Data collected in the Global COVID-19 Trends and Impact Surveys (UMD CTIS), data on variants sequencing from GISAID, are used to evaluate impact of Omicron variant (in South Africa other countries) prevalence among unvaccinated vaccinated population, general discriminating by number doses. In Africa, we observe that December (with strong presence Omicron) population is comparable during previous wave August-September), which Delta was with largest presence. However, vaccinated, much...
Abstract Objective To investigate the abrogation of COVID-19 case declines from predicted rates in US relationship to viral variants and mutations. Design Epidemiological prediction time series study by State. Setting Community testing sequencing US. Participants Time data Johns Hopkins University CSSE database. Variant Mutation GISAID Main outcome measures Primary outcomes were statistical modeling state deviations epidemiological predictions, percentage variants, mutations, reported...
Abstract Public health intervention techniques have been highly significant in reducing the negative impact of several epidemics and pandemics. Among all wide-spread diseases, one most dangerous has severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or Coronavirus disease 2019 (COVID-19). The virus observed over 200 countries leading to hospitalizations deaths millions people. Currently existing COVID-19 risk estimation tools provided general public variable during pandemic due its...
The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions people. Public health interventions, such as risk estimators, can reduce spread pandemics epidemics through influencing behavior, which impacts exposure infection. Current publicly available COVID-19 estimation tools have had variable effectiveness during pandemic due their...