P4 medicine: how systems medicine will transform the healthcare sector and society

The Role of Virtual Reality in Personalized Medicine: Advancing Prediction, Prevention, and Participation

The integration of virtual reality (VR) technology into medicine represents a transformative step toward the realization of 4P medicine, characterized by personalization, prediction, prevention, and participation. VR provides an immersive platform for addressing neurological and mental disorders by tailoring virtual environments to individual patient needs, enhancing diagnostic precision, and improving therapeutic outcomes. This paper explores the applications of VR within the framework of 4P medicine, emphasizing its potential in neurorehabilitation, stress reduction, and patient engagement. The personalization aspect of VR enables the design of customized scenarios for therapeutic interventions. Predictive capabilities allow for early detection and mitigation of complications through simulated environments that analyze patient responses. In preventive medicine, VR fosters stress and anxiety reduction, which directly influences patient well-being and quality of life. Participation is enhanced by VR's interactive nature, transforming patients from passive recipients of care to active participants in their treatment journey. Despite its promise, VR faces limitations, including simulator sickness, technical challenges, and accessibility barriers. These factors highlight the need for methodological standardization, improved hardware, and enhanced training for medical professionals. Additionally, a lack of longitudinal studies and safety monitoring systems restricts the widespread clinical adoption of VR. As healthcare systems continue to adapt to technological advancements, VR has the potential to emerge as a pivotal tool in personalized medicine, offering innovative solutions for complex neurological and mental health challenges. By addressing current limitations, VR may redefine patient care and mark a significant milestone in the evolution of evidence-based medical practice.

[Chronic tinnitus from a psychological perspective]

Chronic tinnitus is a common and often highly distressing phenomenon that, in its chronic phase, is primarily maintained by psychological factors. Individuals affected by tinnitus frequently attribute fluctuations in their emotional well-being causally to the tinnitus symptom. However, from a psychotherapeutic perspective, both the perception of symptoms and the resulting emotional distress are shaped by complex, dynamic interactions involving (a) personal life experiences, (b) personality traits, (c) the subjective interpretation of intrapsychic and interpersonal stimuli, (d) emotional states, and (e) coping strategies at both intrapsychic and interpersonal levels. This review begins with a brief introduction and then connects a well-established psychological theory of chronic tinnitus development and maintenance with a broader vulnerability-stress-coping model. Within this framework, we present key research findings from both somatic and psychological perspectives, ultimately offering insights for psychotherapeutic prevention and treatment approaches.

Unlocking the Mysteries of Rare Disease Drug Development: A Beginner's Guide for Clinical Pharmacologists

Clinical pharmacologists face unique challenges when developing drugs for rare diseases. These conditions are characterized by small patient populations, diverse disease progression patterns, and a limited understanding of underlying pathophysiology. This tutorial serves as a comprehensive guide, offering practical insights and strategies to navigate its complexities. In this tutorial, we outline global regulatory incentives and resources available to support rare disease research, describe some considerations for designing a clinical development plan for rare diseases, and we highlight the role of biomarkers, real-world data, and modeling and simulations to navigate rare disease challenges. By leveraging these tools and understanding regulatory pathways, clinical pharmacologists can significantly contribute to advancing therapeutic options for rare diseases.

Adherence to Compression Stockings for Venous Leg Ulcer Prevention: A Pilot Randomised Controlled Trial and Health Economic Analysis, Evaluating a New Multidimensional Tool (PAMCAI)

Venous leg ulcer recurrence can be prevented with daily compression stocking wear; however, stocking effectiveness is often hindered by poor patient adherence. The 'Personalised and Multi-dimensional Compression Assessment and Intervention' (PAMCAI) is a multidimensional decision aid, delivered by clinicians via an iPad application, designed to improve adherence. This study piloted the methodology and feasibility of PAMCAI's efficacy and cost-effectiveness for evaluation in a larger randomised controlled trial (RCT). Using a two-arm, single-blinded pilot RCT with cost-utility analysis, PAMCAI was compared with usual care in a tertiary hospital occupational therapy outpatient clinic. The primary outcome was adherence to wearing compression stockings, measured on a 4-point scale. Twenty participants were randomised to receive PAMCAI (n = 10) or usual care (n = 10). Recruitment and pilot methodology appear feasible for use in a larger study. Compression stocking adherence was greater with PAMCAI compared to usual care (p = 0.002) and PAMCAI is likely cost-effective, with an incremental cost-effectiveness ratio of $3379.36 per quality-adjusted life year (QALY) gained. Resolution of identified barriers to stocking adherence was positively associated with improvements in patient adherence. These findings support further investigation of PAMCAI in a larger RCT and its potential to improve patient adherence and the cost-effectiveness of compression therapy for venous leg ulcer prevention.

A roadmap to precision medicine through post-genomic electronic medical records

The promise of integrating Electronic Medical Records (EMR) and genetic data for precision medicine has largely fallen short due to its omission of environmental context over time. Post-genomic data can bridge this gap by capturing the real-time dynamic relationship between underlying genetics and the environment. This perspective highlights the pivotal role of integrating EMR and post-genomics for personalized health, reflecting on lessons from past efforts, and outlining a roadmap of challenges and opportunities that must be addressed to realize the potential of precision medicine.

The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review

OBJECTIVE

This narrative review aims to explore the current applications and future prospects of AI within the subfields of oral and maxillofacial surgery (OMS), emphasizing its potential benefits and anticipated challenges.

METHODS

A detailed review of the literature was conducted to evaluate the role of AI in oral and maxillofacial surgery. All domains within OMS were reviewed with a focus on diagnostic, therapeutic and prognostic interventions.

RESULTS

AI has been successfully integrated into surgical specialties to enhance clinical outcomes. In OMS, AI demonstrates potential to improve clinical and administrative workflows in both ambulatory and hospital-based settings. Notable applications include more accurate risk prediction, minimally invasive surgical techniques, and optimized postoperative management.

CONCLUSION

OMS stands to benefit enormously from the integration of AI. However, significant roadblocks, such as ethical concerns, data security, and integration challenges, must be addressed to ensure effective adoption. Further research and innovation are needed to fully realize the potential of AI in this specialty.

Medical AI and AI for Medical Sciences

Digital transformation of healthcare is rapidly progressing. Digital transformation improves the quality of services and access to health information for users, reduces the workload and associated costs for healthcare providers, and supports clinical decision-making. Data and artificial intelligence (AI) play a key role in this process. The AI used for this purpose is called medical AI. Medical AI is currently undergoing a shift from task-specific to general-purpose models. Large language models have the potential to systematize existing medical knowledge in a standardized way. The usage of AI in medicine is not limited to digital transformation; it plays a pivotal role in fundamentally changing the state of medical science. This approach, known as "AI for Medical Science," focuses on pioneering a form of medical science that predicts the onset and progression of disease based on the underlying causes of disease. The key to such predictive medicine is the concept of "states," which can be sought through machine learning. Using states instead of symptoms not only dramatically improves the accuracy of identification (diagnosis) and prediction (prognosis) but also potentially pioneers P4 medicine by integrating it with empirical knowledge and theories based on natural principles.

Complete Lifestyle Medicine Intervention Program-Ontario: Implementation Protocol for a Rural Study

BACKGROUND

Sedentary lifestyles, poor nutritional choices, inadequate sleep, risky substance use, limited social connections, and high stress contribute to the growing prevalence of chronic diseases. Lifestyle medicine, emphasizing therapeutic lifestyle changes for prevention and treatment, has demonstrated effectiveness but remains underutilized in clinical settings. The Complete Lifestyle Medicine Intervention Program-Ontario (CLIP-ON) was developed to educate the rural population of Northern Ontario in lifestyle medicine to improve health outcomes and engagement.

OBJECTIVE

This study evaluates the implementation and effectiveness of the CLIP-ON program for patients with chronic diseases in the Parry Sound area, focusing on lifestyle behaviors, health outcomes, enrollment, retention rates, and interdisciplinary team engagement.

METHODS

This observational cohort study guided by the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) includes pre- and postintervention assessments from participants and health care providers. A hybrid type II mixed methods design evaluates the intervention's effectiveness and implementation process in real-world settings through quantitative and qualitative data collection. CLIP-ON is tailored to the residents of the Parry Sound catchment area in Northern Ontario. Participants (≥18 years old) with chronic conditions such as prediabetes, type II diabetes, systemic hypertension, cardiovascular vascular disease, dyslipidemia, or high BMI (≥25) will be recruited through self-referral or provider referral. Approximately 10 participants per cohort will be enrolled in the CLIP-ON program, consisting of 22 weeks of weekly group sessions and monthly individual consultations with physicians, health coaches, kinesiologists, and registered dieticians either in person or through a web-based platform. CLIP-ON will cover the 6 pillars of lifestyle medicine through 14 group sessions followed by an 8-week supervised exercise program. Anthropometric and cardiometabolic variables will be measured before and after the program. Participants will be surveyed on lifestyle habits, wellness, perceived barriers, and program satisfaction at 3 and 6 months. Focus groups and dropout interviews with participants (n=10 per cohort) and providers (n=6 per cohort) will guide program adaptations. Quantitative and qualitative data collected at baseline and follow-up will assess the program's implementation and identify barriers and opportunities for improvement.

RESULTS

This study was approved by the Laurentian University Research Ethics Board (6021397) on July 6, 2023. The first cohort was enrolled in late 2023 and is still under evaluation. The second cohort began in mid-2024, and data collection is currently underway. A mixed methods analysis will be used at enrollment, program completion (22 weeks), and follow-up (6 months after program completion). Focus groups assessing the program's effectiveness and implementation will take place after the 22-week intervention. Data will be analyzed in early 2025.

CONCLUSIONS

This protocol provides insights into the implementation of this lifestyle medicine program and its impact on participants' health. The findings will guide future advancements and establish a scalable model for other communities.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06192251; https://clinicaltrials.gov/study/NCT06192251.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59179.

Mendelian randomization reveals predictive, preventive, and personalized insights into inflammatory bowel disease: the role of gut microbiome and circulating inflammatory proteins

Background

A chronic illness with increasing global frequency, inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), profoundly affects patients' quality of life and healthcare systems. IBD pathogenesis consists of changes in gut microbiota, immune system dysregulation, and genetic predisposition. Although emerging data suggests that gut microbiota and circulating inflammatory proteins play critical roles in IBD, their utility as biomarkers for predictive, preventive, and personalized medicine (PPPM) remains incompletely understood.

Working hypothesis and methods

We hypothesized that specific gut microbiota and inflammatory proteins causally influence IBD risk and mediate pathways between gut microbiota and IBD development. We employed Mendelian randomization (MR) using genome-wide association studies (GWAS) to explore these causal relationships, including further analyses on UC and CD subtypes.

Results

We identified eight gut microbiota species linked to IBD, with four protective and four increasing risk. Nine inflammatory proteins were also associated, six increasing risk and three protective. MMP-10 and IL-10Rα mediated the effects of Clostridiaceae1 on IBD risk. For UC, five microbiota species were protective, five were risk factors, and two proteins increased risk while three were protective. IL-10Rα mediated the effects of Clostridiaceae1 on UC risk. For CD, eight microbiota species were protective, four increased risk, and nine proteins were implicated. However, no mediation pathways were supported by multivariable MR.

Conclusions

This study highlights specific gut microbiota and inflammatory proteins that may serve as therapeutic targets for PPPM in IBD, UC, and CD. These findings offer new insights into IBD pathogenesis and suggest potential avenues for improved prevention, early detection, and personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00384-2.

Rheumatology in the digital health era: status quo and quo vadis?

Rheumatology faces a critical shortage of health-care professionals, exacerbated by an ageing patient population and escalating costs, resulting in widening gaps in care. Exponential advances in digital health technologies (DHTs) in the past 5 years offer new opportunities to address these challenges and could contribute to overall improved health care. However, keeping pace with innovations and integrating them into clinical practice can be challenging. This Review explores the transformative potential of DHTs for rheumatology in reshaping the entire patient pathway and redefining the roles of patients and providers, and discusses the potential barriers to DHT integration. Key technologies, such as large language models, clinical decision-support systems, digital therapeutics, electronic patient-reported outcomes, digital biomarkers, robots, self-sampling devices and artificial intelligence-based scribes, can be implemented along the patient pathway. A digital-first hybrid stepped-care patient pathway could combine in-person and remote care, enabling personalized and continuous monitoring through a digital safety net. The potential benefits and risks of transforming the traditional patient-provider relationship into a digital health triad with technology are discussed. Collaborative efforts are needed to navigate the evolving digital health landscape and harness the potential of DHTs to improve rheumatology care.

Precision medicine in primary care: How GPs envision "old" and "new" forms of personalization

Visions of precision or personalized medicine (PM) are gaining currency around the globe. While the potential of PM in specialist medicine has been in focus, primary care is also considered to be a fruitful area for the application of PM. "Low-tech" forms of personalization and attention to individual patients are already central features of primary care practice, and primary care thus constitutes an area in which "old" and "new" forms of personalization (may) come together. Against this backdrop, we explore general practitioners' (GPs) views on PM and how they envision the future of personalization in primary care. We draw on 45 qualitative interviews with GPs from Austria, Denmark, and the United States. Along the lines of major "promises" of PM-tailoring treatment decisions, improving disease prevention, empowering patients-we show that in some areas GPs consider PM to be a continuation or extension of existing practices of personalization, while in other cases, GPs envision that PM may negatively disrupt current forms of personalization in primary care. We suggest that this ambivalent stance towards PM can be understood through the lens of GPs' views on core values and practices of primary care.

Precision medicine activities and opportunities for shaping maternal and neonatal health in Qatar

Precision Medicine (PM) is a transformative clinical medicine strategy that aims to revolutionize healthcare by leveraging biological information and biomarkers. In the context of maternal and neonatal health, PM enables personalized care from preconception through the postnatal period. Qatar has emerged as a key player in PM research, with dedicated programs driving advancements and translating cutting-edge research into clinical applications. This article delves into neonatal and maternal health in Qatar, emphasizing PM programs and initiatives that have been implemented. It also features noteworthy clinical cases that demonstrate the effectiveness of precision interventions. Furthermore, the article highlights the role of pharmacogenomics in addressing various maternal health conditions. The review further explores potential advancements in the application of PM in maternal and neonatal healthcare in Qatar.

Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis

Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM remain elusive. To address these challenges, we propose an innovative approach that leverages data-driven methods to unveil the concealed connections between therapy effectiveness and patient data, collected through a randomized controlled trial (DRKS00011159; 10th October 2016). Our findings reveal that machine learning algorithms can successfully identify informative features that distinguish between healthy and sick states. These hold promise as potential markers for disease classification and severity stratification, as well as offering a continuous and data-driven "multidimensional" Sequential Organ Failure Assessment (SOFA) score. The positive impact of TDM on patient recovery rates is demonstrated by unraveling the intricate connections between therapy effectiveness and clinically relevant data via machine learning.

Prediction of Hearing Help Seeking to Design a Recommendation Module of an mHealth Hearing App: Intensive Longitudinal Study of Feature Importance Assessment

BACKGROUND

Mobile health (mHealth) solutions can improve the quality, accessibility, and equity of health services, fostering early rehabilitation. For individuals with hearing loss, mHealth apps might be designed to support the decision-making processes in auditory diagnostics and provide treatment recommendations to the user (eg, hearing aid need). For some individuals, such an mHealth app might be the first contact with a hearing diagnostic service and should motivate users with hearing loss to seek professional help in a targeted manner. However, personalizing treatment recommendations is only possible by knowing the individual's profile regarding the outcome of interest.

OBJECTIVE

This study aims to characterize individuals who are more or less prone to seeking professional help after the repeated use of an app-based hearing test. The goal was to derive relevant hearing-related traits and personality characteristics for personalized treatment recommendations for users of mHealth hearing solutions.

METHODS

In total, 185 (n=106, 57.3% female) nonaided older individuals (mean age 63.8, SD 6.6 y) with subjective hearing loss participated in a mobile study. We collected cross-sectional and longitudinal data on a comprehensive set of 83 hearing-related and psychological measures among those previously found to predict hearing help seeking. Readiness to seek help was assessed as the outcome variable at study end and after 2 months. Participants were classified into help seekers and nonseekers using several supervised machine learning algorithms (random forest, naïve Bayes, and support vector machine). The most relevant features for prediction were identified using feature importance analysis.

RESULTS

The algorithms correctly predicted action to seek help at study end in 65.9% (122/185) to 70.3% (130/185) of cases, reaching 74.8% (98/131) classification accuracy at follow-up. Among the most important features for classification beyond hearing performance were the perceived consequences of hearing loss in daily life, attitude toward hearing aids, motivation to seek help, physical health, sensory sensitivity personality trait, neuroticism, and income.

CONCLUSIONS

This study contributes to the identification of individual characteristics that predict help seeking in older individuals with self-reported hearing loss. Suggestions are made for their implementation in an individual-profiling algorithm and for deriving targeted recommendations in mHealth hearing apps.

Non-Hereditary Obesity Type Networks and New Drug Targets: An In Silico Approach

Obesity, a chronic, preventable disease, has significant comorbidities that are associated with a great human and financial cost for society. The aim of the present work is to reconstruct the interactomes of non-hereditary obesity to highlight recent advances of its pathogenesis, and discover potential therapeutic targets. Obesity and biological-clock-related genes and/or gene products were extracted from the biomedical literature databases PubMed, GeneCards and OMIM. Their interactions were investigated using STRING v11.0 (a database of known and predicted physical and indirect associations among genes/proteins), and a high confidence interaction score of >0.7 was set. We also applied virtual screening to discover natural compounds targeting obesity- and circadian-clock-associated proteins. Two updated and comprehensive interactomes, the (a) stress- and (b) inflammation-induced obesidomes involving 85 and 93 gene/gene products of known and/or predicted interactions with an average node degree of 9.41 and 10.8, respectively, were produced. Moreover, 15 of these were common between the two non-hereditary entities, namely, ADIPOQ, ADRB2/3, CCK, CRH, CXCL8, FOS, GCG, GNRH1, IGF1, INS, LEP, MC4R, NPY and POMC, while phelligridin E, a natural product, may function as a potent FOX1-DBD interaction blocker. Molecular networks may contribute to the understanding of the integrated regulation of energy balance/obesity pathogenesis and may associate chronopharmacology schemes with natural products.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Precision medicine approaches in obstructive sleep apnoea: The role of dentist-sleep physician partnerships

Obstructive Sleep Apnoea (OSA) is a common heterogenous sleep disorder that is associated with a wide range of comorbidities and consequences, including the development of neurocognitive and cardiometabolic disorders. The heterogeneity of OSA necessitates a precision medicine approach to accurately diagnose this condition and to effectively manage patients. One of the primary models of precision medicine is described by the P4 approach of predicting those who are susceptible to disease, preventing the occurrence of disease, personalizing treatment, and encouraging patients to participate in their individual healthcare journey. Recent advances in oral appliance therapy and OSA monitoring techniques have fostered an exciting opportunity for enhanced collaboration between dentists and sleep physicians to optimize OSA precision medicine care. This review aims to discuss the sources of heterogeneity among OSA patients, provide an overview of the growing applications of oral appliance therapy and tailored monitoring programs for OSA that are shifting treatment to a more personalized and participatory model of care, and outline the pivotal role of dentists in managing patients with OSA.

Wellness or medicine? Use and perception of Ayurveda in Germany: data from an online-representative cross-sectional study

Introduction

Ayurveda, South Asia's largest and most relevant system of Traditional Medicine, holds a legal status akin to conventional Western medicine in India and elsewhere. There is an almost complete lack of data on the use of Ayurveda in Germany. The aim of this study was to investigate Ayurveda's utilization patterns, entry points, and factors influencing its use and the perception of Ayurveda among the German population.

Methods

Basis of this manuscript was an online-representative survey which involved 4,065 participants aged 18-75 about the use and acceptance of Traditional, Complementary and Integrative Medicine (TCIM) in Germany. The survey was conducted online using Computer Assisted Web Interview (CAWI) in 2022. The dataset was analyzed descriptively and inferentially.

Results

Altogether 9.3% (n = 377) of all survey participants (n = 4,065) had already used Ayurveda somehow, either more often (1.7%) or at least once in a lifetime (7.6%). Responders associated Ayurveda primarily with Indian Medicine (27.7%) and wellness (18%). Commonly used Ayurvedic services included non-medical treatments at wellness resorts/spas (48.3%), in outpatient practices (27.1%), and hotels (23.6%). 30.2% of the participants believe in Ayurveda's therapeutic potential. 76.7% of Ayurveda users find healthy nutrition important or very important. Nine predictors were found to classify Ayurveda users vs. non-users with spirituality and belief in Ayurveda's therapeutic efficacy as the most relevant ones. Ayurveda seems to be primarily used by well-educated and female individuals, often from higher-income groups and with a rather modern social milieu-orientation.

Conclusion

Study results suggest that about every tenth German citizen has used Ayurveda in the past and about one third believes in its therapeutic potential. Because Ayurvedic therapies are often not evidence-based, there is an urgent need to perform high quality randomized controlled trials to investigate potential effects and safety of Ayurveda and how evidence-based Ayurveda treatments can be integrated into the German healthcare system.

Digital Remote Monitoring Using an mHealth Solution for Survivors of Cancer: Protocol for a Pilot Observational Study

BACKGROUND

Healthy lifestyle interventions have a positive impact on multiple disease trajectories, including cancer-related outcomes. Specifically, appropriate habitual physical activity, adequate sleep, and a regular wholesome diet are of paramount importance for the wellness and supportive care of survivors of cancer. Mobile health (mHealth) apps have the potential to support novel tailored lifestyle interventions.

OBJECTIVE

This observational pilot study aims to assess the feasibility of mHealth multidimensional longitudinal monitoring in survivors of cancer. The primary objective is to test the compliance (user engagement) with the monitoring solution. Secondary objectives include recording clinically relevant subjective and objective measures collected through the digital solution.

METHODS

This is a monocentric pilot study taking place in Bangor, Wales, United Kingdom. We plan to enroll up to 100 adult survivors of cancer not receiving toxic anticancer treatment, who will provide self-reported behavioral data recorded via a dedicated app and validated questionnaires and objective data automatically collected by a paired smartwatch over 16 weeks. The participants will continue with their normal routine surveillance care for their cancer. The primary end point is feasibility (eg, mHealth monitoring acceptability). Composite secondary end points include clinically relevant patient-reported outcome measures (eg, the Edmonton Symptom Assessment System score) and objective physiological measures (eg, step counts). This trial received a favorable ethical review in May 2023 (Integrated Research Application System 301068).

RESULTS

This study is part of an array of pilots within a European Union funded project, entitled "GATEKEEPER," conducted at different sites across Europe and covering various chronic diseases. Study accrual is anticipated to commence in January 2024 and continue until June 2024. It is hypothesized that mHealth monitoring will be feasible in survivors of cancer; specifically, at least 50% (50/100) of the participants will engage with the app at least once a week in 8 of the 16 study weeks.

CONCLUSIONS

In a population with potentially complex clinical needs, this pilot study will test the feasibility of multidimensional remote monitoring of patient-reported outcomes and physiological parameters. Satisfactory compliance with the use of the app and smartwatch, whether confirmed or infirmed through this study, will be propaedeutic to the development of innovative mHealth interventions in survivors of cancer.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/52957.

Serum biomarker levels in smokers and non-smokers following periodontal therapy. A prospective cohort study

BACKGROUND

To compare presence and levels of serum cytokines in smokers and non-smokers with periodontitis following periodontal therapy.

METHODS

Thirty heavy smokers and 30 non-smokers with stage III or IV periodontitis were included in this prospective cohort study. Clinical data and blood serum were collected at baseline (T0), after step I-III (T1), and after 12 months step IV periodontal therapy (T2). Cytokine IL-1β, IL-6, IL-8, TNF-α, IL-10, and IP-10 levels were measured using multiplex kit Bio-Plex Human Pro™ Assay. Linear regression models with cluster robust variance estimates to adjust for repeated observations were used to test intra- and intergroup levels for each marker, IL-6 and IL-8 defined as primary outcomes.

RESULTS

Clinical outcomes improved in both groups following therapy (p < 0.05). IL-6 levels increased with 75.0% from T0-T2 among smokers (p = 0.004). No significant intra- or intergroup differences were observed for IL-8. Higher levels of TNF-α (44.1%) and IL-10 (50.6%) were detected in smokers compared with non-smokers at T1 (p = 0.007 and p = 0.037, respectively). From T1-T2, differences in mean change over time for levels of TNF-α and IL-10 were observed in smokers compared with non-smokers (p = 0.005 and p = 0.008, respectively).

CONCLUSION

Upregulated levels of serum cytokines in smokers indicate a systemic effect of smoking following periodontal therapy. Differences in cytokine levels between smokers and non-smokers demonstrate a smoking induced modulation of specific systemic immunological responses in patients with severe periodontitis.

Biosensors for melanoma skin cancer diagnostics

Skin cancer is a critical global public health concern, with melanoma being the deadliest variant, correlated to 80% of skin cancer-related deaths and a remarkable propensity to metastasize. Despite notable progress in skin cancer prevention and diagnosis, the limitations of existing methods accentuate the demand for precise diagnostic tools. Biosensors have emerged as valuable clinical tools, enabling rapid and reliable point-of-care (POC) testing of skin cancer. This review offers insights into skin cancer development, highlights essential cutaneous melanoma biomarkers, and assesses the current landscape of biosensing technologies for diagnosis. The comprehensive analysis in this review underscores the transformative potential of biosensors in revolutionizing melanoma skin cancer diagnosis, emphasizing their critical role in advancing patient outcomes and healthcare efficiency. The increasing availability of these approaches supports direct diagnosis and aims to reduce the reliance on biopsies, enhancing POC diagnosis. Recent advancements in biosensors for skin cancer diagnosis hold great promise, with their integration into healthcare expected to enhance early detection accuracy and reliability, thereby mitigating socioeconomic disparities.

VISIBL-MS: A bilingual educational framework to increase awareness of early multiple sclerosis

BACKGROUND

Despite advancements in treatments of multiple sclerosis (MS), there is a lack of awareness of early MS symptoms, especially in students and the public, contributing to delays in diagnosis and treatment. This review aims to identify gaps in tools to increase awareness and to provide a bilingual framework to facilitate recognition of early MS symptoms.

METHODS

We performed a literature review to determine the use of English and Spanish mnemonics in MS education for medical students and patients.

RESULTS

There is no educational tool to help remember the early signs of MS at present. Here we present a framework for early awareness encompassed in the bilingual mnemonics VISIBLY (English) and VISIBLE (Spanish). VISIBLY stands for (1) Vision changes: Painful vision loss, loss of color vision or double vision; (2) Belly or Back numbness and Balance issues; (3) Limb weakness or Numbness; (4), Young people. Spanish version is included in the manuscript.

CONCLUSION

We posit that VISIBL-MS provides a framework for MS awareness that addresses the interconnection between language, culture, health literacy, and health outcomes and can be a useful educational tool to tackle the effects of health literacy on diverse communities.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Rapid and Self-Administrable Capillary Blood Microsampling Demonstrates Statistical Equivalence with Standard Venous Collections in NMR-Based Lipoprotein Analysis

We investigated plasma and serum blood derivatives from capillary blood microsamples (500 μL, MiniCollect tubes) and corresponding venous blood (10 mL vacutainers). Samples from 20 healthy participants were analyzed by 1H NMR, and 112 lipoprotein subfraction parameters; 3 supramolecular phospholipid composite (SPC) parameters from SPC1, SPC2, and SPC3 subfractions; 2 N-acetyl signals from α-1-acid glycoprotein (Glyc), GlycA, and GlycB; and 3 calculated parameters, SPC (total), SPC3/SPC2, and Glyc (total) were assessed. Using linear regression between capillary and venous collection sites, we explained that agreement (Adj. R2 ≥ 0.8, p < 0.001) was witnessed for 86% of plasma parameters (103/120) and 88% of serum parameters (106/120), indicating that capillary lipoprotein, SPC, and Glyc concentrations follow changes in venous concentrations. These results indicate that capillary blood microsamples are suitable for sampling in remote areas and for high-frequency longitudinal sampling of the majority of lipoproteins, SPCs, and Glycs.

Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management

This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables-demographic, lifestyle and family history, personal health device, and laboratory data-organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.

Don't Just Stand There. Rethinking the Ideal Body Posture for Otorhinolaryngologists

Surgeons have a high rate of work-related musculoskeletal injuries; an area that has received little attention. These injuries result in surgeons performing less efficiently, needing to take time off work, suffering higher rates of burnout, and may ultimately lead surgeons to retire earlier than planned. Otorhinolaryngologists are at particular risk for work-related musculoskeletal injuries. Beyond the clinician, sustaining such injuries can negatively impact patient safety. Ergonomic interventions have been used effectively to reduce work-related musculoskeletal injuries in other professions, yet not in surgery. With traditional teachings of ideal body postures to avoid injury and manual handling training being re-evaluated, it is important to explore evidence based interventions for reducing work-related musculoskeletal injuries in otorhinolaryngologists. New research encourages us to shift the focus away from the traditional one-size-fits-all approach to ergonomics and toward postural recommendations and education that promote a dynamic, individualized approach to avoiding sustained, static and awkward postures.

Lipoprotein(a): from Causality to Treatment

PURPOSE OF REVIEW

This paper reviews the evidence why lipoprotein(a) (Lp(a)) is a causal risk factor for cardiovascular disease and how high Lp(a) concentrations should be managed now and with an outlook to the future.

REVIEW FINDINGS

No optimal and widely available animal models exist to study the causality of the association between Lp(a) and cardiovascular disease. This has been a major handicap for the entire field. However, genetic studies turned the page. Already in the early 1990s, the principle of Mendelian randomization studies was applied for the first time ever (even if they were not named so at that time). Genetic variants of the LPA gene such as the apolipoprotein(a) isoform size, the number and sum of kringle IV repeats and later single nucleotide polymorphisms are strongly associated with life-long exposure to high Lp(a) concentrations as well as cardiovascular outcomes. This evidence provided a basis for the development of specific Lp(a)-lowering drugs that are currently in clinical testing phase. Lp(a) is one of the most important genetically determined risk factors for cardiovascular disease. With the specific Lp(a)-lowering therapies, we might get tools to fight this common risk factor in case the outcome trials will be positive.

Modern medical schools curricula: Necessary innovations and priorities for change

Medical schools' curricula have expanded over the decades to incorporate important new medical breakthroughs and discoveries. Their current focus and overall structures remain, however, stubbornly captive of early 20th-century thinking, with changes having been undertaken in a piecemeal fashion. Indeed, since the notable Flexner reform in 1910, medical schools' study plans have suffered successive and typically always partial adjustments which have failed to keep up with scientific, technological and sociological change. This difficulty may be attributable to the well-known conservatism of medical schools, where updating study plans is a process that invariably encounters numerous barriers to change. These observations were afforded detailed attention some 15 years ago when de Oliveira wrote: 'it is now perfectly demonstrated that public medical schools have not been able to adapt their operation in depth and in due time to the new demands of teaching dictated by an explosive scientific and technological development'. Recent advances in communication and information technologies, as well as the introduction of new pedagogical techniques, have the potential to bring significant benefits to medical practice and healthcare systems, but these have not in the main become properly taught and utilized. The proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was initially regarded as highly speculative, yet systems approaches to biology and medicine are now beginning to provide experience of both health and disease at the molecular, cellular and organ levels. Medicine is a broad scientific field. In contrast to the 19th century, current medical 'sectarianism' is a positive by-product of rapid and gratifying medical progress, and the multiplicity of new models means that the lines of evidence legitimately bearing on practice and health policymaking are already highly diverse and likely to become ever more variegated over time. Put simply, most sound decisions, by definition, will be evidence-informed and not evidence-based, where divergence may be as informative as convergence. Here, the most enduring lesson of history is, perhaps, that clinical medicine is constantly rediscovering its humanistic core. Complexities create opportunities for innovation. In innovative environments, high-performing organizations are finding ways to create a culture that supports a diverse workforce preparing to deliver different models of care, with direct implications for excellence of patient experience and strong repercussions for medical education. The COVID-19 crisis saw major increases in the use of telemedicine, virtual office visits and other forms of online contact, and these are likely to increase considerably. This particular transformation will not be easy or comfortable to make. But reconfiguration of medical education seems inevitable, fuelled by online educational technology and the need to transform clinical training to more outpatient settings with promotion based on competency and person-centeredness, not simply time. As we prepare to enter 2024, this is an exciting time to be working in healthcare. We have more evidence than ever about how to provide high quality, person-centered care, and to keep patients safe. Shame on us if there is any hesitation about applying this knowledge to make the healthcare experience better for patients and providers. Embracing change and making continuous improvements are essential and urgent priorities for medicine and healthcare and, as we describe in the current article, will become more and more indispensably important in our rapidly changing world.

Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy

Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vehicles (EVs) have received significant attention in recent times as emerging biomarkers and subjects of transformational studies. The three main branches of liquid biopsy have evolved from the three primary tumor liquid biopsy detection targets-CTC, ctDNA, and EVs-each with distinct benefits. CTCs are derived from circulating cancer cells from the original tumor or metastases and may display global features of the tumor. ctDNA has been extensively analyzed and has been used to aid in the diagnosis, treatment, and prognosis of neoplastic diseases. EVs contain tumor-derived material such as DNA, RNA, proteins, lipids, sugar structures, and metabolites. The three provide different detection contents but have strong complementarity to a certain extent. Even though they have already been employed in several clinical trials, the clinical utility of three biomarkers is still being studied, with promising initial findings. This review thoroughly overviews established and emerging technologies for the isolation, characterization, and content detection of CTC, ctDNA, and EVs. Also discussed were the most recent developments in the study of potential liquid biopsy biomarkers for cancer diagnosis, therapeutic monitoring, and prognosis prediction. These included CTC, ctDNA, and EVs. Finally, the potential and challenges of employing liquid biopsy based on CTC, ctDNA, and EVs for precision medicine were evaluated.

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

Direct-to-consumer tests advertised online in Australia and their implications for medical overuse: systematic online review and a typology of clinical utility

OBJECTIVES

The objective of this study is to map the range and variety of direct-to-consumer (DTC) tests advertised online in Australia and analyse their potential clinical utility and implications for medical overuse.

DESIGN

Systematic online search of DTC test products in Google and Google Shopping. DTC test advertisements data were collected and analysed to develop a typology of potential clinical utility of the tests at population level, assessing their potential benefits and harms using available evidence, informed by concepts of medical overuse.

RESULTS

We identified 484 DTC tests (103 unique products), ranging from $A12.99 to $A1947 in cost (mean $A197.83; median $A148.50). Using our typology, we assigned the tests into one of four categories: tests with potential clinical utility (10.7%); tests with limited clinical utility (30.6%); non-evidence-based commercial 'health checks' (41.9%); and tests whose methods and/or target conditions are not recognised by the general medical community (16.7%). Of the products identified, 56% did not state that they offered pretest or post-test consultation, and 51% did not report analytical performance of the test or laboratory accreditation.

CONCLUSIONS

This first-in-Australia study shows most DTC tests sold online have low potential clinical utility, with healthy consumers constituting the main target market. Harms may be caused by overdiagnosis, high rates of false positives and treatment decisions led by non-evidence-based tests, as well as financial costs of unnecessary and inappropriate testing. Regulatory mechanisms should demand a higher standard of evidence of clinical utility and efficacy for DTC tests. Better transparency and reporting of health outcomes, and the development of decision-support resources for consumers are needed.

Mass Spectrometric ITEM-ONE and ITEM-TWO Analyses Confirm and Refine an Assembled Epitope of an Anti-Pertuzumab Affimer

Intact Transition Epitope Mapping-One-step Non-covalent force Exploitation (ITEM-ONE) analysis reveals an assembled epitope on the surface of Pertuzumab, which is recognized by the anti-Pertuzumab affimer 00557_709097. It encompasses amino acid residues NSGGSIYNQRFKGR, which are part of CDR2, as well as residues FTLSVDR, which are located on the variable region of Pertuzumab's heavy chain and together form a surface area of 1381.46 Å2. Despite not being part of Pertuzumab's CDR2, the partial sequence FTLSVDR marks a unique proteotypic Pertuzumab peptide. Binding between intact Pertuzumab and the anti-Pertuzumab affimer was further investigated using the Intact Transition Epitope Mapping-Thermodynamic Weak-force Order (ITEM-TWO) approach. Quantitative analysis of the complex dissociation reaction in the gas phase afforded a quasi-equilibrium constant (KD m0g#) of 3.07 × 10-12. The experimentally determined apparent enthalpy (ΔHm0g#) and apparent free energy (ΔGm0g#) of the complex dissociation reaction indicate that the opposite reaction-complex formation-is spontaneous at room temperature. Due to strong binding to Pertuzumab and because of recognizing Pertuzumab's unique partial amino acid sequences, the anti-Pertuzumab affimer 00557_709097 is considered excellently suitable for implementation in Pertuzumab quantitation assays as well as for the accurate therapeutic drug monitoring of Pertuzumab in biological fluids.

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

BACKGROUND

Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios.

METHODS

This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria.

RESULTS

The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent.

CONCLUSIONS

Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.

[Chronic tinnitus: An interplay between somatic and psychological factors]

Chronic tinnitus is a common, sometimes highly distressing phenomenon that can be triggered and maintained by an interplay of physical and psychological factors. Partnering with clinical psychology and psychosomatic medicine, modern otolaryngology integrates both medical (e.g., hearing loss) and psychological influences (e.g., interactions between biographical experiences, personality traits, subjective evaluation of intrapsychic and interpersonal stimuli, emotional states, and intrapsychic or interpersonal emotion regulation strategies). Both groups of variables can influence the intensity and course of chronic tinnitus symptomatology both directly and indirectly, whereby the quality and relative degrees of psychological and physical components in a person's self-experience can fluctuate. With this in mind, the present article distinguishes between chronic tinnitus symptomatology with or without hearing loss-and strongly advocates for an integrated understanding of the symptomatology within a holistic psychological frame of reference. After a brief introduction to the principles of psychosomatic medicine and psychotherapy, the article discusses psychological case conceptualization using a vulnerability-stress-coping (VSC) model as an example, outlines clinical aspects and diagnostics of chronic tinnitus symptomatology, and concludes with a conceptualization of chronic tinnitus-related distress as a function of person-centered VSC interactions.

Combined surveillance and treatment register for children with cerebral palsy: the protocol of the Netherlands CP register

INTRODUCTION

Cerebral palsy (CP) is a childhood onset, lifelong, condition. Early detection and timely treatment of potential problems during the child's development are important to prevent secondary impairments and improve function. Clinical management of children with CP requires a spectrum of multidisciplinary interventions, which have an impact on short-term and long-term outcomes. However, there is a lack of knowledge about a personalised approach in this heterogeneous population. Various CP registers with different aims have been developed worldwide, which has made an important contribution to our understanding of CP. The purpose of this protocol is to describe the unique design of a combined multidisciplinary surveillance and treatment register for children with CP in the Netherlands, which aims to improve quality of care and to enhance an individual treatment approach.

METHODS AND ANALYSIS

The Netherlands CP Register combines a multidisciplinary surveillance programme with a standardised protocol for treatment registry. The register systematically collects real-life surveillance and treatment data of children with CP. The register contributes to daily care at the individual level by screening for potential secondary impairments using a decision-support tool, by visualising individual development using a dashboard, and by supporting goal setting and shared decision-making for interventions. The register provides a platform at the national level for quality of care improvement and a comprehensive database of real-life data allowing multicentre studies with a long-term follow-up. People with lived experience of CP, healthcare professionals from different disciplines and researchers collaborated in the development of the register.

ETHICS AND DISSEMINATION

The Netherlands CP register was submitted to the Medical Ethics Review Committee of VU University Medical Center (Amsterdam, the Netherlands), who judged the register not to be subject to the Medical Research Involving Human Subjects Act. A scientific board reviews requests for dissemination of data from the register for specific research questions.

Walking the path of treatable traits in interstitial lung diseases

Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, and disease phenotypes. The identification of specific "treatable traits" in ILDs, which are clinically relevant and modifiable disease characteristics, may improve patient's outcomes. Treatable traits in ILDs may be classified into four different domains (pulmonary, aetiological, comorbidities, and lifestyle), which will facilitate identification of related assessment tools, treatment options, and expected benefits. A multidisciplinary care team model is a potential way to implement a "treatable traits" strategy into clinical practice with the aim of improving patients' outcomes. Multidisciplinary models of care, international registries, and the use of artificial intelligence may facilitate the implementation of the "treatable traits" approach into clinical practice. Prospective studies are needed to test potential therapies for a variety of treatable traits to further advance care of patients with ILD.

Digital Twins in Healthcare: An Architectural Proposal and Its Application in a Social Distancing Case Study

The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.

A three-level model for therapeutic drug monitoring of antimicrobials at the site of infection

The silent pandemic of bacterial antimicrobial resistance is a leading cause of death worldwide, prolonging hospital stays and raising health-care costs. Poor incentives to develop novel pharmacological compounds and the misuse of antibiotics contribute to the bacterial antimicrobial resistance crisis. Therapeutic drug monitoring (TDM) based on blood analysis can help alleviate the emergence of bacterial antimicrobial resistance and effectively decreases the risk of toxic drug concentrations in patients' blood. Antibiotic tissue penetration can vary in patients who are critically or chronically ill and can potentially lead to treatment failure. Antibiotics such as β-lactams and glycopeptides are detectable in non-invasively collectable biofluids, such as sweat and exhaled breath. The emergence of wearable sensors enables easy access to these non-invasive biofluids, and thus a laboratory-independent analysis of various disease-associated biomarkers and drugs. In this Personal View, we introduce a three-level model for TDM of antibiotics to describe concentrations at the site of infection (SOI) by use of wearable sensors. Our model links blood-based drug measurement with the analysis of drug concentrations in non-invasively collectable biofluids stemming from the SOI to characterise drug concentrations at the SOI. Finally, we outline the necessary clinical and technical steps for the development of wearable sensing platforms for SOI applications.

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.

Dual-Aptamer Drift Canceling Techniques to Improve Long-Term Stability of Real-Time Structure-Switching Aptasensors

This paper presents a dual-aptamer scheme to mitigate signal drifts caused by structure-switching aptamers during long-term monitoring. Electrochemical aptamer-based (E-AB) biosensors have recently shown great potential for continuous in vivo monitoring. However, the accuracy of detection is often limited by signaling drifts. Traditional approaches rely on kinetic differential measurements (KDM) coupled with square-wave voltammetry to eliminate these drifts. Yet, we have discovered that KDM does not apply universally to all aptamers, as their responses at different SWV frequencies heavily rely on their structure-switching characteristics and the electron transfer (ET) kinetics of the redox reporters. In light of this, we propose a "dual-aptamer" scheme that utilizes two aptamers, each responding differently to the same target molecule to cancel out drift. These paired aptamers are identified through (1) screening from an existing pool of aptamers and (2) engineering the signaling behavior of the redox reporters. We demonstrate the differential signaling of the aptamer pair in the presence of ampicillin and ATP molecules and show that the pair exhibits similar drifts in undiluted goat serum. By implementing drift cancelation, sensor drift is reduced by a factor of 370. Additionally, the differential signaling enables an increased recording throughput by leveraging differential readout electronics. The authors believe that the proposed technique holds significant benefits for long-term in vivo monitoring.

Differential Spectrum of Albumin Glycation, Oxidation, and Truncation in Type 2 and Type 1 Diabetes: Clinical and Biological Implications

Background: Albumin, the most abundant and physiologically vital serum protein, accumulates a range of chemical modifications, as consequence of encounters with large number of reactive molecules whose concentrations increase in serum under pathological conditions. Methods: In a "proof of concept" study, mass spectrometric analysis was utilized to quantitate albumin post-translational modifications (glycation, oxidation, and truncation; individual isoforms and total) in four informative subject groups [type 1 diabetes (T1DM), type 2 diabetes (T2DM), prediabetes-obesity and healthy; all with estimated glomerular filtration rate ≥60 mL/(min·m2)]. Besides glycated albumin (GA/mass spectrometry), glycated serum protein (GSP/nitro blue tetrazolium colorimetry), and glycated hemoglobin (HbA1c/high-performance liquid chromatography) were also measured. Results: A wide spectrum of albumin molecular modifications was identified in diabetes, with significant differences between T2DM and T1DM. Albumin glycation: GA correlated more strongly with HbA1c in T1DM, compared to T2DM. Higher albumin glycation isoforms (human serum albumin +3G/2G) were more stable and discriminative markers of mean glycemia. Albumin oxidation: T2DM, in comparison with T1DM, showed enhanced oxidative and dual (glycation plus oxidation) modifications, representing extreme molecular pathology. Albumin truncation: There was dramatic reduction ("deficiency") of truncated albumin isoforms in T2DM, and significant reduction in T1DM. Albumin truncation negatively correlated with severity of albumin glycation (mean glycemia) and albumin oxidation (cysteinylation). Possible mechanisms of insulin resistance, with associated increased free fatty acids binding to albumin, in stimulating albumin oxidation and inhibiting albumin glycation ("metabolic cross talks") are reviewed. Conclusions: Albumin molecular modifications in diabetes, together with significant differences between T2DM and T1DM, suggest possible role for insulin resistance in their genesis and consequent cell, tissue, and vascular dysfunction/damage. Albumin molecular fingerprinting can provide valuable insights into pathogenesis, diagnosis, monitoring, and future therapies for diabetes. Identification of biomarker battery ("albuminomics," "diabetomics") driven diverse "healthy," prediabetes, obesity, and T2DM phenotypes represents additional novel step toward precision medicine in diabetes and related disorders.

Reviewing the state of biosensors and lab-on-a- chip technologies: opportunities for extreme environments and space exploration

The space race is entering a new era of exploration, in which the number of robotic and human missions to various places in our solar system is rapidly increasing. Despite the recent advances in propulsion and life support technologies, there is a growing need to perform analytical measurements and laboratory experiments across diverse domains of science, while keeping low payload requirements. In this context, lab-on-a-chip nanobiosensors appear to be an emerging technology capable of revolutionizing space exploration, given their low footprint, high accuracy, and low payload requirements. To date, only some approaches for monitoring astronaut health in spacecraft environments have been reported. Although non-invasive molecular diagnostics, like lab-on-a-chip technology, are expected to improve the quality of long-term space missions, their application to monitor microbiological and environmental variables is rarely reported, even for analogous extreme environments on Earth. The possibility of evaluating the occurrence of unknown or unexpected species, identifying redox gradients relevant to microbial metabolism, or testing for specific possible biosignatures, will play a key role in the future of space microbiology. In this review, we will examine the current and potential roles of lab-on-a-chip technology in space exploration and in extreme environment investigation, reporting what has been tested so far, and clarifying the direction toward which the newly developed technologies of portable lab-on-a-chip sensors are heading for exploration in extreme environments and in space.

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Digital Emergency Management for a Complex One Health Landscape: the Need for Standardization, Integration, and Interoperability

OBJECTIVE

Planning reliable long-term planning actions to handle disruptive events requires a timely development of technological infrastructures, as well as the set-up of focused strategies for emergency management. The paper aims to highlight the needs for standardization, integration, and interoperability between Accident & Emergency Informatics (A&EI) and One Digital Health (ODH), as fields capable of dealing with peculiar dynamics for a technology-boosted management of emergencies under an overarching One Health panorama.

METHODS

An integrative analysis of the literature was conducted to draw attention to specific foci on the correlation between ODH and A&EI, in particular: (i) the management of disruptive events from private smart spaces to diseases spreading, and (ii) the concepts of (health-related) quality of life and well-being.

RESULTS

A digitally-focused management of emergency events that tackles the inextricable interconnectedness between humans, animals, and surrounding environment, demands standardization, integration, and systems interoperability. A consistent and finalized process of adoption and implementation of methods and tools from the International Standard Accident Number (ISAN), via findability, accessibility, interoperability, and reusability (FAIR) data principles, to Medical Informatics and Digital Health Multilingual Ontology (MIMO) - capable of looking at different approaches to encourage the integration between the ODH framework and the A&EI vision, provides a first answer to these needs.

CONCLUSIONS

ODH and A&EI look at different scales but with similar goals for converging health and environmental-related data management standards to enable multi-sources, interdisciplinary, and real-time data integration and interoperability. This allows holistic digital health both in routine and emergency events.