Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary.

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions. CITATION: Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.

Meeting the challenges in conducting research in vulnerable older adults with self-neglect-notes from a field team.

Gaining a systematic understanding of possible ways to increase the quality and lifespan of older adults experiencing self-neglect has unique challenges. These challenges include identifying self-neglect in the community and navigating levels of cognitive, physical, and/or psychological difficulties in this population that impact recruitment, consent, and accurate data collection. Conducting quality research under some of the environmental self-neglect conditions such as squalor, animal and insect infestations and no utilities can also challenge planned study protocols and study validity. This manuscript presents details of these overarching challenges and some of the workable solutions noted and implemented by research field-team members who have enrolled over 300 adults experiencing self-neglect for various studies. Usual research methodology must overcome these barriers to work to create consciousness about the self-neglect population. The classic series of cases is still a good alternative when describing self-neglect. Considerations for conducting future self-neglect research are presented.

Artificial Intelligence for Biology.

Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect, and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines. They will make possible both targeted (testing specific hypotheses) and untargeted discoveries. AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century. The difficulties, however, are many, including data curation and assembly, development of new science in the form of theories that connect the subdisciplines, and new predictive and interpretable AI models that are more suited to biology than existing machine learning and AI techniques. Development efforts will require strong collaborations between biological and computational scientists. This white paper provides a vision for AI for Biology and highlights some challenges.

Development of a Conceptual Framework for Severe Self-Neglect (SN) by Modifying the CREST Model for Self-Neglect.

Self-neglect is an inability or refusal to meet one's own basic needs as accepted by societal norms and is the most common report received by state agencies charged with investigating abuse, neglect and exploitation of vulnerable adults. Self-neglect is often seen in addition to one or multiple conditions of frailty, mild to severe dementia, poor sleep and depression. While awareness of elder self-neglect as a public health condition and intervention has significantly risen in the past decade as evidenced by the increasing amount of literature available, research on self-neglect still lacks comprehensiveness and clarity since its inception to the medical literature in the late 1960s. With the burgeoning of the older adult population, commonness of self-neglect will most likely increase as the current incidence rate represents only the "tip of the iceberg" theory given that most cases are unreported. The COVID-19 pandemic has exacerbated the incidence of self-neglect in aged populations and the need for the use of intervention tools for aging adults and geriatric patients living alone, many of which may include in-home artificial intelligence systems. Despite this, little research has been conducted on aspects of self-neglect other than definition and identification. Substantial further study of this disorder's etiology, educating society on early detection, and conceivably preventing this syndrome altogether or at least halting progression and abating its severity is needed. The purpose of this research is to provide a definition of severe self-neglect, identify key concepts related to self-neglect, comprehensively describe this syndrome, present a conceptual framework and analyze the model for its usefulness, generalizability, parsimony, and testability.

Integrating CRISPR-Cas9 Technology into Undergraduate Courses: Perspectives from a National Science Foundation (NSF) Workshop for Undergraduate Faculty, June 2018.

As CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 technology becomes more mainstream in life science research, it becomes critical for undergraduate instructors to devise engaging ways to bring the technology into their classrooms. To help meet this challenge, the National Science Foundation sponsored a workshop for undergraduate instructors in June 2018 at The Ohio State University in conjunction with the annual Association of Biology Laboratory Educators meeting based on a workflow developed by the workshop's facilitators. Over the course of two and a half days, participants worked through a modular workflow for the use of CRISPR-Cas9 in a course-based (undergraduate) research experience (CURE) setting while discussing the barriers each of their institutions had to implementing such work, and how such barriers could be overcome. The result of the workshop was a team with newfound energy and confidence to implement CRISPR-Cas9 technology in their courses and the development of a community of undergraduate educators dedicated to supporting each other in the implementation of the workflow either in a CURE or modular format. In this article, we review the activities and discussions from the workshop that helped each participant devise their own tailored approaches of how best to bring this exciting new technology into their classes.

Identifying Cognitive Impairment in Hospitalized Older Adults to Prevent Readmission: Two Case Studies.

OBJECTIVE: To describe two patient outcomes post-discharge from an acute hospital admission. Both patients underwent cognitive testing during hospitalization. METHODS: A battery of cognitive tests was administered to two hospitalized older adult patients. Both patients were evaluated in their homes within 72 hours of discharge and again at 14- and 30-days by a nurse practitioner. RESULTS: One of the patients was readmitted within 30 days of hospital discharge due to complications from an amputation. This patient did not perform well on cognitive measures which may have been related to his pain levels and/or his medication regimen. CONCLUSIONS: Not all readmissions are avoidable; however, if readmissions are related to cognitive impairment, implementing strategies tailored to this population may reduce readmission rates. CLINICAL IMPLICATIONS: Risk factors for readmission should be identified so the discharge team can develop a tailored plan of care. Including both the patient and an informal caregiver may reduce the chance of a hospital readmission in older adults with cognitive impairment regardless of the etiology.

Sleep disturbances and post-traumatic stress disorder in women.

Sleep disturbances are found in a majority of individuals diagnosed with posttraumatic stress disorder (PTSD). The purpose of this literature review is to provide information about PTSD, in addition to assessing sleep quality. Current research observes that the lifetime prevalence of PTSD diagnosis in women is increasing. Although there are several studies that have been conducted to assess PTSD and sleep, there is a gap in the research that pertains to women, PTSD, and sleep quality. The current study will compile information on the subject to aid in decreasing the gender disparity in PTSD research, which is important for treating the entire PTSD population. Using the PubMed and PsycINFO databases, a comprehensive search was conducted to find relevant research about sleep difficulties and PTSD. Sleep disturbances such as insomnia, re-current nightmares, REM sleep dysfunction, and obstructive sleep apnea (OSA) affect sleep quality in PTSD patients. The implications of this study suggest that more research should be conducted pertaining to women and PTSD with sleep difficulties. This research is needed to decrease both PTSD symptoms and sleep-related disorders.

Period-amplitude analysis reveals wake-dependent changes in the electroencephalogram during sleep deprivation.

STUDY OBJECTIVES: Electroencephalographic slow wave activity (SWA) during non-rapid eye movement (NREM) sleep results from the synchronous oscillation of cortical neurons and is the standard measurement of sleep homeostasis. SWA is not a direct measure of sleep pressure accumulation, but rather a measure of the NREM-sleep response to accumulated sleep pressure. Currently, no practical standard for the direct measurement of sleep pressure accumulation exists. Recently, it was demonstrated that rat cortical neurons undergo oscillations during wake that are similar to the cortical oscillations responsible for SWA. Furthermore, these oscillations increase in number as time awake increases. Here we hypothesize that period-amplitude analysis of the electroencephalogram (EEG), which treats the EEG as a series of discrete waves, can measure these cortical oscillations, and thus, is a measure of sleep-pressure accumulation during extended wake. DESIGN: Mice were sleep deprived for 24 h by confinement to a slowly rotating wheel in order to assess wake-dependent changes in EEG wave incidence. MEASUREMENTS AND RESULTS: Continuous period-amplitude analysis of the waking EEG across 24 h of sleep deprivation revealed that the incidence of 2 to 6 Hz waves increased exponentially over the deprivation period. This increase in wave incidence appeared to occur in two phases with exponential time constants of approximately 0.12 h and 3 h. Further analysis revealed that the changes in wave incidence were significantly correlated with two established markers of sleep pressure, SWA and NREM sleep latency. CONCLUSIONS: The data suggest that wave incidence is an effective method of measuring sleep homeostasis in the waking EEG that provides better temporal resolution than spectral power analysis.

4-Aminobiphenyl downregulation of NAT2 acetylator genotype-dependent N- and O-acetylation of aromatic and heterocyclic amine carcinogens in primary mammary epithelial cell cultures from rapid and slow acetylator rats.

Aromatic and heterocyclic amine carcinogens present in the diet and in cigarette smoke induce breast tumors in rats. N-acetyltransferase 1 (NAT1) and N-acetyltransferase 2 (NAT2) enzymes have important roles in their metabolic activation and deactivation. Human epidemiological studies suggest that genetic polymorphisms in NAT1 and/or NAT2 modify breast cancer risk in women exposed to these carcinogens. p-Aminobenzoic acid (selective for rat NAT2) and sulfamethazine (SMZ; selective for rat NAT1) N-acetyltransferase catalytic activities were both expressed in primary cultures of rat mammary epithelial cells. PABA, 2-aminofluorene, and 4-aminobiphenyl N-acetyltransferase and N-hydroxy-2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine and N-hydroxy-2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline O-acetyltransferase activities were two- to threefold higher in mammary epithelial cell cultures from rapid than slow acetylator rats. In contrast, SMZ (a rat NAT1-selective substrate) N-acetyltransferase activity did not differ between rapid and slow acetylators. Rat mammary cells cultured in the medium supplemented 24 h with 10muM ABP showed downregulation in the N-and O-acetylation of all substrates tested except for the NAT1-selective substrate SMZ. This downregulation was comparable in rapid and slow NAT2 acetylators. These studies clearly show NAT2 acetylator genotype-dependent N- and O-acetylation of aromatic and heterocyclic amine carcinogens in rat mammary epithelial cell cultures to be subject to downregulation by the arylamine carcinogen ABP.

Laboratory variability does not preclude identification of biological functions impacted by hydroxyurea.

The multi-lab International Life Sciences Institute (ILSI) project on the application of genomics to risk assessment offered the unique opportunity to investigate the influence of variability within and between laboratories on identifying biologically relevant gene expression changes. We assessed the gene expression profiles of mouse lymphoma L5178Y cells treated with hydroxyurea (HU) in three independent studies from two different laboratories, Sanofi Aventis and Procter and Gamble. Cells were dosed for 4 hr and harvested immediately at the end of the treatment or after a 20-hr recovery period. Cytotoxicity and genotoxicity were evaluated by standard assays. Statistical analysis of these data revealed that, while gene expression responses to HU treatment were markedly different at 4 hr vs. 24 hr, there was otherwise a consistent pattern of dose-response across the three studies. Therefore, the studies were merged and each time point was evaluated separately. At 4 hr, we identified 173 (P < 0.0001) dose-responsive genes with a common trend in all three studies. These were mainly associated with the cell cycle, DNA repair and DNA metabolism, and in particular, the intra-S and G2/M phase checkpoints. At 24 hr, we identified 434 dose-responsive genes common across studies. These genes were involved in lymphocyte-specific activities and the activation of apoptosis via the caspase cascade. Our results show that despite inter-laboratory variability, combining the three studies in a single statistical analysis identifies more significantly-modulated genes than in any of the individual studies, due to improved statistical sensitivity. The genes identified in our study provide information that is relevant to HU biology.