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Purpose of Review This review aims to present the current state of the art in measuring frailty in patients with heart failure (HF), covering the entire spectrum from diagnosis to advanced stages of the disease. Frailty is a critical factor that significantly impacts outcomes in heart failure, and accurate assessment is essential for guiding treatment and improving prognosis. Recent Findings Frailty is increasingly recognized as a key determinant of morbidity and mortality in HF patients. Various tools are available for assessing frailty, but there is no consensus on the optimal method. The assessment of frailty needs to be multidimensional, incorporating physical, cognitive, and social domains. Early detection of frailty, coupled with personalized interventions, has the potential to improve patient outcomes. Summary Integrating routine frailty assessments into the clinical care of heart failure patients is essential for optimizing treatment. Future research should focus on standardizing frailty assessment tools and integrating innovative technologies, such as artificial intelligence, to enhance the precision and applicability of these assessments in clinical practice.

Sleep disturbances and autonomic dysregulation are common in autism spectrum disorder (ASD), yet few studies have examined long-term nocturnal heart rate variability (HRV) in home settings. This study evaluated the feasibility of one-month home-based HRV monitoring using smart clothing in a preschooler with ASD, and explored whether nocturnal HRV predicts next-day problem behaviors. HRV was recorded nightly for 25 valid days using a garment-type wearable ECG. Problem behaviors were reported daily by caregivers. HRV indices were compared between nights preceding days with and without problem behaviors using Wilcoxon signed-rank tests. No significant differences in total sleep time or HRV indices were found between the two day types. Although HRV did not predict next-day behavior, the study demonstrates the feasibility and methodological transparency of long-term home-based physiological monitoring in young children with ASD.

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Metabolic alterations in the critically ill have been studied for more than a century, but the heterogeneity of the critically ill patient population, the varying duration and severity of the acute phase of illness, and the many confounding factors have hindered progress in the field. These factors may explain why management of metabolic alterations and related conditions in critically ill patients has for many years been guided by recommendations based essentially on expert opinion. Over the last decade, a number of randomized controlled trials have been conducted, providing us with important population-level evidence that refutes several longstanding paradigms. However, between-patient variation means there is still substantial uncertainty when translating population-level evidence to individuals. A cornerstone of metabolic care is nutrition, for which there is a multifold of published guidelines that agree on many issues but disagree on others. Using a series of nine questions, we provide a review of the latest data in this field and a background to promote efforts to address the need for international consistency in recommendations related to the metabolic care of the critically ill patient. Our purpose is not to replace existing guidelines, but to comment on differences and add perspective.

Purpose Despite advances in personalizing the efficacy of cancer therapy, our ability to identify patients at risk of severe treatment side effects and provide individualized supportive care is limited. This is particularly the case for mucositis (oral and gastrointestinal), with no comprehensive risk evaluation strategies to identify high-risk patients. We, the Multinational Association for Supportive Care in Cancer/International Society for Oral Oncology (MASCC/ISOO) Mucositis Study Group, therefore aimed to systematically review current evidence on that factors that influence mucositis risk to provide a foundation upon which future risk prediction studies can be based. Methods We identified 11,018 papers from PubMed and Web of Science, with 197 records extracted for full review and 113 meeting final eligibility criteria. Data were then synthesized into tables to highlight the level of evidence for each risk predictor. Results The strongest level of evidence supported dosimetric parameters as key predictors of mucositis risk. Genetic variants in drug-metabolizing pathways, immune signaling, and cell injury/repair mechanisms were also identified to impact mucositis risk. Factors relating to the individual were variably linked to mucositis outcomes, although female sex and smoking status showed some association with mucositis risk. Conclusion Mucositis risk reflects the complex interplay between the host, tumor microenvironment, and treatment specifications, yet the large majority of studies rely on hypothesis-driven, single-candidate approaches. For significant advances in the provision of personalized supportive care, coordinated research efforts with robust multiplexed approaches are strongly advised.

[Display omitted] •Nutrigenetics and nutrigenomics study nutrition-gene interaction and genome-based patterns of taking bioactive food components regulating the internal OMICS (transcriptomics, proteomics, and metabolomics) environment.•Personalized health care diagnosis using nutrigenetics and nutrigenomics in conjunction with omics technologies can result in optimum nutritional therapy and better individual-centric care.•Using omics data, comprising transcriptomics, proteomics, and metabolomics, can give a personalized diagnosis for a preventive rather than reactive treatment approach. Nutrigenetics and nutrigenomics, combined with the omics technologies, are a demanding and an increasingly important field in personalizing nutrition-based care to understand an individual's response to nutrition-guided therapy. Omics is defined as the analysis of the large data sets of the biological system featuring transcriptomics, proteomics, and metabolomics and providing new insights into cell regulation. The effect of combining nutrigenetics and nutrigenomics with omics will give insight into molecular analysis, as human nutrition requirements vary per individual. Omics measures modest intraindividual variability and is critical to exploit these data for use in the development of precision nutrition. Omics, combined with nutrigenetics and nutrigenomics, is instrumental in the creation of goals for improving the accuracy of nutrition evaluations. Although dietary-based therapies are provided for various clinical conditions such as inborn errors of metabolism, limited advancement has been done to expand the omics data for a more mechanistic understanding of cellular networks dependent on nutrition-based expression and overall regulation of genes. The greatest challenge remains in the clinical sector to integrate the current data available, overcome the well-established limits of self-reported methods in research, and provide omics data, combined with nutrigenetics and nutrigenomics research, for each individual. Hence, the future seems promising if a design for personalized, nutrition-based diagnosis and care can be implemented practically in the health care sector.

The dementia landscape has evolved, with earlier diagnoses, improved prevention understanding (eg, modifiable factors), and new treatments. Emerging digital technologies (eg, wearables, smart home systems, and mobile apps) offer self‑management opportunities; yet, gaps persist regarding integration into the care needs and preferences of people with dementia. Broader gaps remain concerning intervention design; adaptation; and implementation, including effectiveness, study quality, and accessibility. This systematic review aims to synthesize and critically appraise existing literature on digital self-management technologies (wearables, smart home systems, and mobile apps) intended to reduce dementia-associated behaviors, enhance self-management, and improve quality of life (QoL). It evaluates intervention characteristics, effectiveness, accessibility, study design, and methodological quality according to international standards. A systematic search across 9 databases (PubMed, Scopus, ACM Digital Library, CINAHL, PsycInfo, Web of Science, IEEE Xplore, Embase, and MEDLINE) identified relevant English‑language studies published between January 1, 2013, and September 30, 2023. Search terms covered dementia, QoL, behavioral and self‑management strategies, and digital technologies. Eligible studies involved adults with dementia using wearable, smart home, or mobile technologies targeting QoL, behavior, and autonomy. Two reviewers independently appraised study design, hardware, and intervention purpose. Outcomes were mapped to the Nursing Outcomes Classification and benchmarked against National Institute for Health and Care Excellence quality standard 184. Accessibility was evaluated by availability, cost, usability, and context. Bias mitigation included a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)‑guided strategy and PROSPERO registration. Methodological quality and bias were assessed using the Mixed Methods Appraisal Tool, the Critical Appraisal Skills Program, and a bespoke characterization framework. Twenty-four studies evaluated interventions based on wearables, smart home systems, or apps for people with dementia and carers. Outcomes centered on neurocognition (24/24, 100%), self-care (17/24, 71%), and health behavior (13/24, 54%). Identified needs included managing distress (15/24, 62%) and supporting carers (15/24, 62%). Technologies included commercial tools (activity trackers, health-based wearables, and digital prompters) but were often inaccessible due to complex setup requirements and ongoing support needs. Substantial methodological heterogeneity precluded meta-analysis, necessitating narrative synthesis. Study quality was generally good to excellent, but samples were small, reporting incomplete, and outcomes unblinded. Only 1 (17%) of 6 randomized controlled trials reported effect sizes, indicating moderate decline in QoL at 24 months; effectiveness in other studies remains uncertain. Research on digital technologies for dementia self‑management shows benefits, particularly with off‑the‑shelf devices and mobile apps supporting person‑centered outcomes. Notable limitations include inadequate participant diversity (eg, atypical dementias and minoritized populations) and insufficient high‑quality research on QoL and behavioral outcomes, such as symptom management and self‑control. Future research must prioritize innovative solutions enhancing accessibility and usability, emphasizing simplified configuration, personalized adaptability, and effective training and support structures. PROSPERO CRD42023461841; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023461841.