Delaware journal of public health最新文献

This literature review aims to highlight new drug discovery specifically in the United States, and introduce how artificial intelligence can be used to help reduce development time and costs.

The Learning Lab serves as a resource for students to come into a laboratory space and work with genomic scientists on cutting-edge CRISPR research. This opportunity was created to reach students with fewer resources in their classroom. We hope to expand this program further in the coming years throughout all of Delaware to further our mission of inclusion and equity in education.

Objective: At the forefront of machine learning research since its inception has been natural language processing, also known as text mining, referring to a wide range of statistical processes for analyzing textual data and retrieving information. In medical fields, text mining has made valuable contributions in unexpected ways, not least by synthesizing data from disparate biomedical studies. This rapid scoping review examines how machine learning methods for text mining can be implemented at the intersection of these disparate fields to improve the workflow and process of conducting systematic reviews in medical research and related academic disciplines.

Methods: The primary research question that this investigation asked, "what impact does the use of machine learning have on the methods used by systematic review teams to carry out the systematic review process, such as the precision of search strategies, unbiased article selection or data abstraction and/or analysis for systematic reviews and other comprehensive review types of similar methodology?" A literature search was conducted by a medical librarian utilizing multiple databases, a grey literature search and handsearching of the literature. The search was completed on December 4, 2020. Handsearching was done on an ongoing basis with an end date of April 14, 2023.

Results: The search yielded 23,190 studies after duplicates were removed. As a result, 117 studies (1.70%) met eligibility criteria for inclusion in this rapid scoping review.

Conclusions: There are several techniques and/or types of machine learning methods in development or that have already been fully developed to assist with the systematic review stages. Combined with human intelligence, these machine learning methods and tools provide promise for making the systematic review process more efficient, saving valuable time for systematic review authors, and increasing the speed in which evidence can be created and placed in the hands of decision makers and the public.

The prevalence of allergic diseases is rising rapdly in the US and the world. While antibody drugs and corticosteroids can provide symptom relief, they cannot cure allergic diseases. Described herein is a novel approach to treating severe atopic allergic diseases - chimeric antigen receptor-engineered T cells - that target and eliminate the cells that produce the causative agent of all atopic allergic diseases, immunoglubulin E (IgE).

U.S. presidential elections can be stressful for many Americans; however, there is little research as to how elections might influence mental health of undocumented immigrants specifically. The 2020 U.S. Presidential Election had the potential to dramatically influence immigration policies with the Democratic candidate promising a pathway toward citizenship for undocumented immigrants who arrived in the U.S. as minors (i.e., dreamers), and the incumbent Republican candidate threatening to terminate the DACA program. Using an online survey method, this exploratory longitudinal study examined whether dreamers' mental health changed following the U.S. presidential election, while also examining risk factors associated with their mental health. We employed GAD-7 and PHQ-9 questionnaires as preclinical screens for anxiety and depression. We found that the mean anxiety and depression scores decreased significantly following the election, i.e., when the democratic candidate was declared the winner. Risk factors for mental health problems also differed before and after the election. Risk factors for depression before the election included being female, Hispanic white, having a low self-reported status on the subjective social ladder, and having high perceived discrimination; risk factors for depression after the election included coming to the U.S. at an older age and high perceived discrimination. Risk factors for anxiety before the election included being female, having more siblings, both parents working, and high perceived discrimination. Risk factors for anxiety after the election included low self-reported status on the subjective social ladder, being a freshman, and high perceived discrimination. Preliminary results suggest that mental health of dreamers improved after the election. In addition, while risk factors differed before and after the election, perceived everyday discrimination remained a consistent risk factor for mental health issues.

As a leading cause of death and long-term disability, stroke care is a complex endeavor, requiring a coalition of healthcare professionals. As part of a multi-disciplinary team, social workers help the patient to reach individual goals and facilitate their return to and stability in their community at their highest possible functional, social, and economic level.

Stroke continues to be a leading cause of adult disability, contributing to immense healthcare costs. Even after discharge from rehabilitation, post-stroke individuals continue to have persistent gait impairments, which in turn adversely affect functional mobility and quality of life. Multiple factors, including biomechanics, energy cost, psychosocial variables, as well as the physiological function of corticospinal neural pathways influence stroke gait function and training-induced gait improvements. As a step toward addressing this challenge, the objective of the current perspective paper is to outline knowledge gaps pertinent to the measurement and retraining of stroke gait dysfunction. The paper also has recommendations for future research directions to address important knowledge gaps, especially related to the measurement and rehabilitation-induced modulation of biomechanical and neural processes underlying stroke gait dysfunction. We posit that there is a need for leveraging emerging technologies to develop innovative, comprehensive, methods to measure gait patterns quantitatively, to provide clinicians with objective measure of gait quality that can supplement conventional clinical outcomes of walking function. Additionally, we posit that there is a need for more research on how the stroke lesion affects multiple parts of the nervous system, and to understand the neuroplasticity correlates of gait training and gait recovery. Multi-modal clinical research studies that can combine clinical, biomechanical, neural, and computational modeling data provide promise for gaining new information about stroke gait dysfunction as well as the multitude of factors affecting recovery and treatment response in people with post-stroke hemiparesis.

This review article discusses medical management of acute cerebral ischemia including recent advances. Expansion of the thrombolysis eligibility criteria are discussed. Tenecteplase as a promising new thrombolytic is explored and the evidence supporting the use of Mobile Stroke Units is presented.

Stroke affects close to 800,000 Americans every year and is a major cause of disability and mortality. Prompt, accurate diagnosis and treatment of stroke is of critical importance in minimizing these deleterious effects. Recent advances in computer technology have allowed the development artificial intelligence technology that can be applied to the diagnosis, treatment and rehabilitation of victims of stroke.