Closely associated with aging and age-related disorders, cellular senescence (CS) is the inability of cells to proliferate due to accumulated unrepaired cellular damage and irreversible cell cycle arrest. Senescent cells are characterized by their senescence-associated secretory phenotype that overproduces inflammatory and catabolic factors that hamper normal tissue homeostasis. Chronic accumulation of senescent cells is thought to be associated with intervertebral disc degeneration (IDD) in an aging population. This IDD is one of the largest age-dependent chronic disorders, often associated with neurological dysfunctions such as, low back pain, radiculopathy, and myelopathy. Senescent cells (SnCs) increase in number in the aged, degenerated discs, and have a causative role in driving age-related IDD. This review summarizes current evidence supporting the role of CS on onset and progression of age-related IDD. The discussion includes molecular pathways involved in CS such as p53-p21CIP1, p16INK4a, NF-κB, and MAPK, and the potential therapeutic value of targeting these pathways. We propose several mechanisms of CS in IDD including mechanical stress, oxidative stress, genotoxic stress, nutritional deprivation, and inflammatory stress. There are still large knowledge gaps in disc CS research, an understanding of which will provide opportunities to develop therapeutic interventions to treat age-related IDD.

Alzheimer’s Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age.. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients’ subjective experience. In this study, we developed a rule-based NLP algorithm and machine learning models to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the clinical notes of patients diagnosed with AD. We trained and validated the proposed models on the clinical notes retrieved from the University of Pittsburgh of Medical Center (UPMC). The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.

Background: Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. Objectives: The main objective of this review is to identify and analyze published research on clinical IR, including the methods, techniques, and tools used to retrieve and analyze clinical information from various sources. We aim to provide a comprehensive overview of the current state of clinical IR research and guide future research efforts in this field. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted a thorough search of multiple databases, including Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, from January 1st, 2012, to January 4th, 2023. The screening process involved multiple reviewers, and we included 184 papers for the final review. Results: We conducted a detailed analysis and discussion of various aspects of clinical IR research, including publication year, data sources, methods, techniques, evaluation metrics, shared tasks, and applications. Our analysis revealed key research areas in clinical IR, such as indexing, ranking, and query expansion, and identified opportunities for future research in these areas.