James D. Geyer

Background/Objectives: Pediatric obstructive sleep apnea (OSA) is a complex disorder with a variable presentation and often challenging diagnostic testing. The history and physical examination in pediatric OSA frequently differ from those in adults. The treatment options are multifaceted and must be tailored to the individual patient. Artificial intelligence (AI) modalities currently employed in pediatric sleep medicine face several important limitations: modality fragmentation, lack of explainability, and limited semantic integration. Method: Our team proposes a new vision for AI and pediatric sleep medicine. This platform is based on a knowledge graph (KG) framework integrating structured and unstructured data to enable reasoning, personalization, and clinical decision support. Results: This framework represents a conceptual architecture; it has not yet been empirically implemented, and the use cases described herein are illustrative of its intended capabilities. Components of the infrastructure developed for similar applications have been successfully implemented. The quantitative feasibility pilot KG represented 100% multimodal data with >90% semantic completeness. Conclusions: Fully realized and deployed into the clinical space, this pediatric OSA KG system will enhance tertiary care programs and help project tertiary-level pediatric care into underserved regions.

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts.

Narcolepsy is a chronic neurologic state in which patients experience an irrepressible need to sleep or lapses into sleep. The hallmark symptom of narcolepsy is excessive daytime sleepiness, but patients may also experience other symptoms, such as cataplexy, sleep attacks, disturbed nighttime sleep, or sleep-related hallucinations. Narcolepsy has 2 distinct classifications, narcolepsy type 1 and narcolepsy type 2, and only patients with narcolepsy type 1 experience cataplexy. Treatment of narcolepsy must be individualized to patient-specific variables, including symptoms, co-occurring conditions, adverse effects, drug interactions, pregnancy potential, and lifestyle factors. These variables introduce complexities that must be appropriately acknowledged and monitored. Monitoring adverse effects, individualized medication selection, management of key interactions, and care for narcolepsy in patients with reproductive potential are explored through 3 patient cases.

Introduction: Telehealth approaches have demonstrated benefits in improving positive airway pressure (PAP) compliance in patients with obstructive sleep apnea (OSA), improving access to healthcare resources, and improving health outcomes for rural communities. Methods: This quality improvement (QI) pilot study implemented weekly telemedicine visits for four weeks of PAP therapy in rural patients newly diagnosed with OSA. Epworth Sleepiness Scale (ESS) scores were compared prior to and at one month of therapy. PAP compliance was compared between rural patients who received the telemedicine intervention and a group of patients not receiving the telemedicine intervention. Results: Compliance rates were higher in the intervention group. There was not a significant difference in compliance for the intervention group (M = 63.22, SD = 32.78) versus the control group (M = 46.40, SD = 36.24), t (46) = 1.69, p = 0.099. ESS scores were significantly greater prior to one month of therapy (M = 8.38, SD = 5.70) compared to after one month of therapy (M = 2.83, SD = 2.65), t (23) = 5.22, p < 0.001, d = 1.07. Discussion: This QI pilot study utilized telemedicine to remove barriers to care, improve PAP compliance, and improve health outcomes for this underserved, rural population.

Background: Short sleep and obstructive sleep apnea are underrecognized strains on the public health infrastructure. In the United States, over 35% of adults report short sleep and more than 80% of individuals with obstructive sleep apnea remain undiagnosed. The associations between inadequate sleep and cardiometabolic disease risk factors have garnered increased attention. However, challenges persist in modeling sleep-associated cardiometabolic disease risk factors. Objective: This study aimed to report early findings from the Short Sleep Undermines Cardiometabolic Health-Public Health Observational study (SLUMBRx-PONS). Methods: Data for the SLUMBRx-PONS study were collected cross-sectionally and longitudinally from a nonclinical, rural community sample (n=47) in the southeast United States. Measures included 7 consecutive nights of wrist-based actigraphy (eg, mean of 7 consecutive nights of total sleep time [TST7N]), 1 night of sleep apnea home testing (eg, apnea-hypopnea index [AHI]), and a cross-sectional clinical sample of anthropometric (eg, BMI), cardiovascular (eg, blood pressure), and blood-based biomarkers (eg, triglycerides and glucose). Correlational analyses and regression models assessed the relationships between the cardiometabolic disease risk factors and the sleep indices (eg, TST7N and AHI). Linear regression models were constructed to examine associations between significant cardiometabolic indices of TST7N (model 1) and AHI (model 2). Results: Correlational assessment in model 1 identified significant associations between TST7N and AHI (r=–0.45, P=.004), BMI (r=–0.38, P=.02), systolic blood pressure (r=0.40, P=.01), and diastolic blood pressure (r=0.32, P=.049). Pertaining to model 1, composite measures of AHI, BMI, systolic blood pressure, and diastolic blood pressure accounted for 25.1% of the variance in TST7N (R2adjusted=0.25; F2,38=7.37; P=.002). Correlational analyses in model 2 revealed significant relationships between AHI and TST7N (r=–0.45, P<.001), BMI (r=0.71, P<.001), triglycerides (r=0.36, P=.03), and glucose (r=0.34, P=.04). Results from model 2 found that TST7N, triglycerides, and glucose accounted for 37.6% of the variance in the composite measure of AHI and BMI (R2adjusted=0.38; F3,38=8.63; P<.001). Conclusions: Results from the SLUMBRx-PONS study highlight the complex interplay between sleep-associated risk factors for cardiometabolic disease. Early findings underscore the need for further investigations incorporating the collection of clinical, epidemiological, and ambulatory measures to inform public health, health promotion, and health education interventions addressing the cardiometabolic consequences of inadequate sleep. International Registered Report Identifier (IRRID): RR2-10.2196/27139

BACKGROUND Short sleep and obstructive sleep apnea are underrecognized strains on the public health infrastructure. In the United States, over 35% of adults report short sleep and more than 80% of individuals with obstructive sleep apnea remain undiagnosed. The associations between inadequate sleep and cardiometabolic disease risk factors have garnered increased attention. However, challenges persist in modeling sleep-associated cardiometabolic disease risk factors. OBJECTIVE This study aimed to report early findings from the Short Sleep Undermines Cardiometabolic Health-Public Health Observational study (SLUMBRx-PONS). METHODS Data for the SLUMBRx-PONS study were collected cross-sectionally and longitudinally from a nonclinical, rural community sample (n=47) in the southeast United States. Measures included 7 consecutive nights of wrist-based actigraphy (eg, mean of 7 consecutive nights of total sleep time [TST7N]), 1 night of sleep apnea home testing (eg, apnea-hypopnea index [AHI]), and a cross-sectional clinical sample of anthropometric (eg, BMI), cardiovascular (eg, blood pressure), and blood-based biomarkers (eg, triglycerides and glucose). Correlational analyses and regression models assessed the relationships between the cardiometabolic disease risk factors and the sleep indices (eg, TST7N and AHI). Linear regression models were constructed to examine associations between significant cardiometabolic indices of TST7N (model 1) and AHI (model 2). RESULTS Correlational assessment in model 1 identified significant associations between TST7N and AHI (r=–0.45, P=.004), BMI (r=–0.38, P=.02), systolic blood pressure (r=0.40, P=.01), and diastolic blood pressure (r=0.32, P=.049). Pertaining to model 1, composite measures of AHI, BMI, systolic blood pressure, and diastolic blood pressure accounted for 25.1% of the variance in TST7N (R2adjusted=0.25; F2,38=7.37; P=.002). Correlational analyses in model 2 revealed significant relationships between AHI and TST7N (r=–0.45, P<.001), BMI (r=0.71, P<.001), triglycerides (r=0.36, P=.03), and glucose (r=0.34, P=.04). Results from model 2 found that TST7N, triglycerides, and glucose accounted for 37.6% of the variance in the composite measure of AHI and BMI (R2adjusted=0.38; F3,38=8.63; P<.001). CONCLUSIONS Results from the SLUMBRx-PONS study highlight the complex interplay between sleep-associated risk factors for cardiometabolic disease. Early findings underscore the need for further investigations incorporating the collection of clinical, epidemiological, and ambulatory measures to inform public health, health promotion, and health education interventions addressing the cardiometabolic consequences of inadequate sleep. INTERNATIONAL REGISTERED REPORT RR2-10.2196/27139

The study of human microbiomes has yielded insights into basic science, and applied therapeutics are emerging. However, conflicting definitions of what microbiomes are and how they affect the health of the “host” are less understood. A major impediment towards systematic design, discovery, and implementation of targeted microbiome therapeutics is the continued reliance on taxonomic indicators to define microbiomes in health and disease. Such reliance often confounds analyses, potentially suggesting associations where there are none, and conversely failing to identify significant, causal relationships. This review article discusses recent discoveries pointing towards a molecular understanding of microbiome “dysbiosis” and away from a purely taxonomic approach. We highlight the growing role of systems biological principles in the complex interrelationships between the gut microbiome and host cells, and review current approaches commonly used in targeted microbiome therapeutics, including fecal microbial transplant, bacteriophage therapies, and the use of metabolic toxins to selectively eliminate specific taxa from dysbiotic microbiomes. These approaches, however, remain wholly or partially dependent on the bacterial taxa involved in dysbiosis, and therefore may not capitalize fully on many therapeutic opportunities presented at the bioactive molecular level. New technologies capable of addressing microbiome-associated diseases as molecular problems, if solved, will open possibilities of new classes and categories of targeted microbiome therapeutics aimed, in principle, at all dysbiosis-driven disorders.

Background: Human norovirus (HuNoV) is the leading viral cause of diarrhea, with GII.4 as the predominant genotype of HuNoV outbreaks globally. However, new genogroup variants emerge periodically, complicating the development of anti-HuNoV vaccines; other prophylactic or therapeutic medications specifically for HuNoV disease are lacking. Passive immunization using oral anti-HuNoV antibodies may be a rational alternative. Here, we explore the feasibility of using avian immunoglobulins (IgY) for preventing HuNoV infection in vitro in a human intestinal enteroid (HIE) model. Methods: Hens were immunized with virus-like particles (VLP) of a GII.4 HuNoV strain (GII.4/CHDC2094/1974/US) by intramuscular injection. The resulting IgY was evaluated for inhibition of binding to histo-blood group antigens (HBGA) and viral neutralization against representative GII.4 and GII.6 clinical isolates, using an HIE model. Results: IgY titers were detected by three weeks following initial immunization, persisting at levels of 1:2²¹ (1:2,097,152) from 9 weeks to 23 weeks. Anti-HuNoV IgY significantly (p < 0.05) blocked VLP adhesion to HBGA up to 1:12,048 dilution (0.005 mg/mL), and significantly (p < 0.05) inhibited replication of HuNoV GII.4[P16] Sydney 2012 in HIEs up to 1:128 dilution (0.08 mg/mL). Neutralization was not detected against genotype GII.6. Conclusions: We demonstrate the feasibility of IgY for preventing infection of HIE by HuNoV GII.4. Clinical preparations should cover multiple circulating HuNoV genotypes for comprehensive effects. Plans for animal studies are underway.

Restless legs syndrome (RLS) is a chronic disorder causing clinically significant discomfort to approximately 3% of adults. Although RLS was first identified centuries ago, our understanding of this disorder, its causes, and its treatments is still evolving. In particular, our knowledge of the potential negative effects of RLS treatments, including dopaminergic augmentation, continues to expand. Augmentation, which refers to a paradoxical treatment-related increase in RLS symptoms, has been associated with all three dopamine agonists approved for the treatment of RLS – rotigotine, pramipexole, and ropinirole. This review presents key information on prevention and treatment of dopaminergic augmentation from the recently published consensus-based guidelines issued by the International RLS Study Group task force in conjunction with the European RLS Study Group and the RLS Foundation for first-line treatment of RLS/Willis-Ekbom disease. If dopamine agonists are used to treat RLS, it is recommended that the dosage should be kept as low as possible without exceeding the maximum dose recommended for RLS treatment. As the frequency of augmentation with the rotigotine patch may only be slightly lower than that associated with pramipexole or ropinirole, medications that are effective and have little risk of augmentation, such as alpha-2-delta ligands, may be considered for initial RLS treatment. In addition, we present our clinical experience with treating patients with dopaminergic augmentation by highlighting 2 case studies and practical considerations when treating different patient populations. Applying current RLS augmentation diagnosis and treatment guidelines, as well as collecting detailed histories of worsening RLS symptoms, is critical for patient safety and effective management of RLS augmentation.