Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

How to use live sampling tissues and archived specimens in cetacean stable isotope research

Cetaceans are unique ecological engineers,and their restoration may have a crucial impact on the future structure of aquatic ecosystems,which calls for more investigations into their trophic ecology.Among current techniques,stable isotope analysis(SIA)has the advantages of non-invasive sampling and long timescales.However,the full benefits of SIA in cetacean research may not be achieved due to issues like different types of tissue between sampling methods and use of chemical preservation solutions in historical specimens.To address these challenges,we conducted a study on Narrow-ridged Finless Porpoises(Neophocaena asiaeorientalis).Multiple tissues from freshwater and marine subspecies,as well as tissues preserved using different solutions such as ethanol and formalin were collected for SIA.Linear mixed effects models were used for data analysis.Our results showed that,except for blubber,kidney,and stomach,differences between other tissues were correctable.In tissues from live sampling,we found no significant difference between blood and muscle,and skin could also be used for isotope analysis after proper correction.Ethanol preservation caused significant positive changes in δ^(13)C and δ^(15)N values of muscle,while formalin preservation caused negative changes in δ^(13)C and δ^(15)N.Our findings provide valuable insight into unifying data from stranded carcasses and live sampling,as well as correcting for the effect of chemical preservation on museum specimens.Findings from this research support further application of stable isotope analysis in the conservation of endangered finless porpoises,offer a reference for other similar cetaceans,and also provide guidance for chemical preservation when freezing conditions are not available.