Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts

Neuroprotective effects of gemfibrozil in neurological disorders: Focus on inflammation and molecular mechanisms

BACKGROUND

Gemfibrozil (Gem) is a drug that has been shown to activate PPAR-α, a nuclear receptor that plays a key role in regulating lipid metabolism. Gem is used to lower the levels of triglycerides and reduce the risk of coronary heart disease in patients. Experimental studies in vitro and in vivo have shown that Gem can prevent or slow the progression of neurological disorders (NDs), including cerebral ischemia (CI), Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). Neuroinflammation is known to play a significant role in these disorders.

METHOD

The literature review for this study was conducted by searching Scopus, Science Direct, PubMed, and Google Scholar databases.

RESULT

The results of this study show that Gem has neuroprotective effects through several cellular and molecular mechanisms such as: (1) Gem has the ability to upregulate pro-survival factors (PGC-1α and TFAM), promoting the survival and function of mitochondria in the brain, (2) Gem strongly inhibits the activation of NF-κB, AP-1, and C/EBPβ in cytokine-stimulated astroglial cells, which are known to increase the expression of iNOS and the production of NO in response to proinflammatory cytokines, (3) Gem protects dopamine neurons in the MPTP mouse model of PD by increasing the expression of PPARα, which in turn stimulates the production of GDNF in astrocytes, (4) Gem reduces amyloid plaque pathology, reduces the activity of glial cells, and improves memory, (5) Gem increases myelin genes expression (MBP and CNPase) via PPAR-β, and (6) Gem increases hippocampal BDNF to counteract depression.

CONCLUSION

According to the study, Gem was investigated for its potential therapeutic effect in NDs. Further research is needed to fully understand the therapeutic potential of Gem in NDs.

Speech and language processing with deep learning for dementia diagnosis: A systematic review

Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.

Automated detection of progressive speech changes in early Alzheimer's disease

Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research.

HIGHLIGHTS

Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.

Disparities and resilience: analyzing online Health information provision, behaviors and needs of LBGTQ + elders during COVID-19

BACKGROUND

Prior studies indicate that older members of LGBTQ+ communities have specific health provision and health information needs related to coping with COVID-19, its long-term effects, and the social and economic impact of the pandemic. This study addresses the issue of a lack of timely, complete, and high-quality data about this population's healthcare and healthcare information needs and behaviors. Recognizing also that this is a diverse population made up of multiple communities and identities with different concerns and experiences, this research seeks to develop and refine a method that can provide additional nuanced data and insights that can support improved and more closely targeted health interventions and online information provision.

METHODS

We use computational discourse analysis, which is based on NLP algorithms, to build and analyze a digital corpus of online search results containing rich, wide-ranging content such as quotes and anecdotes from older members of LGBTQ+ communities as well as practitioners, advice, and recommendations from policymakers and healthcare experts, and research outcomes. In our analysis, we develop and apply an innovative disparities and resilience (D&R) framework to identify external and internal perspectives and understand better disparities and resilience as they pertain to this population.

RESULTS

Results of this initial study support previous research that LGBTQ+ elders experience aggravated health and related social-economic disparities in comparison to the general population of older people. We also find that LGBTQ+ elders leverage individual toughness and community closeness, and quickly adapt mentally and technologically, despite inadequate social infrastructure for sharing health information and elders' often low social economic status. The methods used therefore are able to surface distinctive resilience in the face of distinctive disparities.

CONCLUSIONS

Our study provides evidence that methodological innovation in gathering and analyzing digital data relating to overlooked, disparately affected, and socially and economically marginalized intersectional communities such as LGBTQ+ elders can result in increased external and self-knowledge of these populations. Specifically, it demonstrates the potential of computational discourse analysis to surface hidden and emerging issues and trends relating to a multi-faceted population that has important concerns about public exposure in highly timely and automated ways. It also points to the potential benefits of triangulating data gathered through this approach with data gathered through more traditional mechanisms such as surveys and interviews.

TRIAL REGISTRATION

Not Applicable.

A Perspective: Challenges in Dementia Research

Although dementia is a common and devastating disease that has been studied intensely for more than 100 years, no effective disease modifying treatment has been found. At this impasse, new approaches are important. The purpose of this paper is to provide, in the context of current research, one clinician's perspective regarding important challenges in the field in the form of specific challenges. These challenges not only illustrate the scope of the problems inherent in finding treatments for dementia, but can also be specific targets to foster discussion, criticism and new research. One common theme is the need to transform research activities from small projects in individual laboratories/clinics to larger multinational projects, in which each clinician and researcher works as an integral part. This transformation will require collaboration between researchers, large corporations, regulatory/governmental authorities and the general population, as well as significant financial investments. However, the costs of transforming the approach are small in comparison with the cost of dementia.