Predictive, personalized, preventive, participatory (P4) cancer medicine

Advancements in metabolomics research in benign gallbladder diseases: A review

The burgeoning field of metabolomics has piqued the interest of researchers in the context of benign gallbladder diseases, which include conditions such as gallbladder polyps, gallstones, and cholecystitis, which are common digestive system disorders. As metabolomics continues to advance, researchers have increasingly focused their attention on its applicability in the study of benign gallbladder diseases to provide new perspectives for diagnostic, therapeutic, and prognostic evaluation. This comprehensive review primarily describes the techniques of liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance and their respective applications in the study of benign gallbladder disease. Metabolomics has made remarkable progress in various aspects of these diseases, ranging from early diagnosis, etiological research, assessment of disease progression and prognosis, and optimization of therapeutic strategies. However, challenges remain in the field of metabolomics in the study of benign gallbladder diseases. These include issues related to data processing and analysis, biomarker discovery and validation, interdisciplinary research integration, and the advancement of personalized medicine. This article attempts to summarize research findings to date, highlight future research directions, and provide a reference point for metabolomics research in benign gallbladder disease.

Romania's Pioneering Law: Establishing the Right to Personalized Medicine

This analysis and commentary discuss Romania's landmark law, the first globally, acknowledging the right of citizens and patients to personalized medicine. Initiated following the EU Council's 2015 policy on personalized medicine, the law is a result of intersectoral collaborative efforts led by the Centre for Innovation in Medicine in Romania using a quadruple (later evolved to penta) helix model involving academia, public, private, and civil society sectors. Promulgated on May 24, 2023, the law legally entitles patients to personalized health care and in ways informed by individual genetic and phenotypic consideration. The law mandates informed consent for medical interventions and ensures data protection in accordance with the General Data Protection Regulation. We suggest that this pioneering legislation paves the way for integrating personalized medicine into Romania's health care system, shaping clinical practice, research, and health policy. In all, it marks a significant step in redefining health care delivery, emphasizing individualized treatment and the political determinants of personalized medicine, and setting a precedent for future health care innovations worldwide.

Integration of persistence in the 5P-medicine approach for age-related chronic diseases

5P medicine is defined as Personalized, Predictive, Preventive, Participatory, and Population-based. 5P medicine may be improved by including a factor that could provide information about the therapeutic value of a particular drug treatment and measure its effectiveness in clinical practice. We propose that this factor may be treatment persistence, and that its addition to 5P medicine would allow to define a new improved 6P medicine. Persistence is the length of time between initiation and the last dose, which immediately precedes discontinuation, that is, a definitive suspension of the treatment. By including this sixth P, the persistence, we would be able to present the value of a treatment for each individual patient with its own characteristics, state of the disease, with more than one age-related diseases and patient journey. Persistence is a concept of the value of a treatment that includes the three main stakeholders of the pharmacotherapeutic process: Patient, Physician, and Pharmacist. Persistence is becoming a useful measure to evaluate the long-term effectiveness of therapies in real-world setting in chronic diseases. Drug treatments with longer persistence are more likely to provide better disease control and to be amenable to dose adjustment in order to optimize treatment cost in age-related chronic diseases. Long-term persistence could be a measure of a drug´s real-world performance and has been shown to aid in clinical decision-making.

Psychosocial factors contributing to value creation in value-based healthcare: a scoping review

Background

Healthcare systems constantly evolve to improve care quality and resource utilization. One way is implementing Value-Based Healthcare (VBHC) an economic approach. This scoping review aims to identify and describe the literature on VBHC, particularly its psychosocial aspects, to uncover research gaps.

Method

The review followed the PRISMA guidelines for Scoping Reviews. We took the following 14 steps: (a) defining the research question; (b) identifying relevant studies; (c) selecting studies; (d) 15 mapping data; (e) collecting, synthesizing and reporting results. A detailed Boolean search was conducted from January 2021 to August 31, 2021, across APA PsycINFO and PubMed databases using keywords such as "Value-Based Healthcare" and "psychosocial perspective." Initially, three reviewers screened 70 e-records independently, assessing titles, abstracts, and full-text against the inclusion criteria. Discrepancies regarding the evaluation of the articles were resolved through consensus sessions between the reviewers.

Results

The final review included 14 relevant e-records in English from peer-reviewed sources, focusing on quantitative and qualitative research. From the analysis, four areas emerged: (1) Value chains in Healthcare; (2) Styles, activities, and practices of value co-creation in Healthcare; (3) Value co-creation in the encounter process; (4) Value co-creation in preventive health services.

Conclusion

The scoping review findings suggest several potential key aspects, including the interdependence between patients and healthcare organizations, organizational culture in healthcare, and the role of patient-centered approaches that focus on relationships, communication, and social support in healthcare. This can be achieved through patient engagement, patient-centered care and communication, health literacy, psychosocial support services, comprehensive psychosocial assessments, care coordination, and continuity of care. Integrating psychosocial elements in VHBC enhances quality and optimizes resource use. Findings highlight the need to develop practical guidance on how to implement a culture of value in care that takes into account the psychosocial aspects that have emerged, but not fully addressed. The pandemic teaches that the workforce poorly receives sudden and unsystematic changes. This review could provide an initial basis for the redesign of value in healthcare and a paradigm shift that has already begun with patient-centered medicine and patient engagement.

Improving sexual health through medical education

This editorial comments on the article by Burton et al. in this issue.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Cluster Analysis to Identify Clinical Subtypes of Ménière's Disease

OBJECTIVE

To identify distinct clinical subtypes of Ménière's disease by analyzing data acquired from a UK registry of patients who have been diagnosed with Ménière's disease.

STUDY DESIGN

Observational study.

METHODS

Patients with Ménière's disease were identified at secondary/tertiary care clinics. Cluster analysis was performed by grouping participants sharing similar characteristics and risk factors into groups based on a defined measure of similarity.

RESULTS

A total of 411 participants were recruited into this study. Two main clusters were identified: participants diagnosed with ear infections (OR = 0.30, p < 0.014, 95% CI: 0.11-0.78) were more likely to be allocated in Cluster 1 (C1). Participants reporting tinnitus in both ears (OR = 11.89, p < 0.001, 95% CI: 4.08-34.64), low pitched tinnitus (OR = 21.09, p < 0.001, 95% CI: 7.47-59.54), and those reporting stress as a trigger for vertigo attacks (OR = 14.94, p < 0.001, 95% CI: 4.54-49.10) were significantly more likely to be in Cluster 2 (C2). Also, participants diagnosed with Benign Paroxysmal Positional Vertigo (OR = 13.14, <0.001, 95% CI: 4.35-39.74), autoimmune disease (OR = 5.97, p < 0.007, 95% CI: 1.62-22.03), depression (OR = 4.72, p < 0.056, 95% CI: 0.96-23.24), migraines (OR = 3.13, p < 0.008, 95% CI: 1.34-7.26), drug allergy (OR = 3.25, p < 0.029, 95% CI: 1.13-9.34), and hay fever (OR = 3.12, p < 0.009, 95% CI: 1.33-7.34) were significantly more likely to be clustered in C2.

CONCLUSIONS

This study supports the hypothesis that Ménière's disease is a heterogeneous condition with subgroups that may be identifiable by clinical features. Two main clusters were identified with differing putative etiological factors.

LEVEL OF EVIDENCE

3 Laryngoscope, 2024.

Analysis of comprehensive biomolecules in critically ill patients via bioinformatics technologies

Each patient with a critical illness such as sepsis and severe trauma has a different genetic background, comorbidities, age, and sex. Moreover, pathophysiology changes dynamically over time even in the same patient. Therefore, individualized treatment is necessary to account for heterogeneity in patient backgrounds. Recently, the analysis of comprehensive biomolecular information using clinical specimens has revealed novel molecular pathological classifications called subtypes. In addition, comprehensive biomolecular information using clinical specimens has enabled reverse translational research, which is a data-driven approach to the identification of drug target molecules. The development of these methods is expected to visualize the heterogeneity of patient backgrounds and lead to personalized therapy.

Pharmacomicrobiomics-Guided Precision Oncology: A New Frontier of P4 (Predictive, Personalized, Preventive, and Participatory) Medicine and Microbiome-Based Therapeutics

Pharmacomicrobiomics is a rapidly developing field that promises to make significant contributions to predictive, personalized, preventive, and participatory (P4) medicine. This is becoming evident particularly in the field of precision (P4) oncology by taking seriously the crucial role microbiome plays in health and disease. Several studies have already shown that clinicians can harness insights from the microbiome to better predict treatment response, reduce side effects, and improve overall outcomes for cancer patients. Furthermore, pharmacomicrobiomics will undoubtedly play a crucial role in shaping the future of cancer treatment in the era of P4 oncology as we continue to unravel the intricate relationships between the microbiome and cancer. This perspective and innovation analysis discusses the emerging intersection of P4 medicine and P4 oncology, as seen through a lens of pharmacomicrobiomics. A key promise of pharmacomicrobiomics is the development of personalized microbiome-based therapeutics. In all, we suggest that optimizing cancer treatment and prevention by harnessing pharmacomicrobiomics has vast potentials for precision oncology, and personalized medicine using the right drug, at the right dose, for the right patient, and at the right time.

Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review

Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

A Machine Learning Approach to Predict Weight Change in ART-Experienced People Living With HIV

INTRODUCTION

The objective of the study was to develop machine learning (ML) models that predict the percentage weight change in each interval of time in antiretroviral therapy-experienced people living with HIV.

METHODS

This was an observational study that comprised consecutive people living with HIV attending Modena HIV Metabolic Clinic with at least 2 visits. Data were partitioned in an 80/20 training/test set to generate 10 progressively parsimonious predictive ML models. Weight gain was defined as any weight change >5%, at the next visit. SHapley Additive exPlanations values were used to quantify the positive or negative impact of any single variable included in each model on the predicted weight changes.

RESULTS

A total of 3,321 patients generated 18,322 observations. At the last observation, the median age was 50 years and 69% patients were male. Model 1 (the only 1 including body composition assessed with dual-energy x-ray absorptiometry) had an accuracy greater than 90%. This model could predict weight at the next visit with an error of <5%.

CONCLUSIONS

ML models with the inclusion of body composition and metabolic and endocrinological variables had an excellent performance. The parsimonious models available in standard clinical evaluation are insufficient to obtain reliable prediction, but are good enough to predict who will not experience weight gain.

Effects of safety and care services on psychological outcomes and housing satisfaction in Korean middle-aged and older adults living alone

BACKGROUND

Korean society has witnessed a rapid increase in the number of single-person households at risk for loneliness or solitary deaths. This study aims to evaluate the effectiveness of safety and care services (SCS) on the psychological outcomes and housing satisfaction of Korean middle-aged and older adults living alone.

METHODS

This study was a randomized controlled trial on residents of public rental housing. A total of 40 people underwent a 3-month intervention. For the experimental group, a caring service IoT solution was installed in participants' houses, and coordinators provided services using IoT information. The control group received only visits by coordinators.

RESULTS

The experimental group showed significant positive changes in depressive symptoms. An interaction effect of time and condition was observed, indicating that the loneliness scores of the experimental group were significantly reduced, whereas those of their counterparts were negligible. Both groups showed significant decreases in suicidal thoughts. Housing satisfaction in both groups significantly increased over time, but group differences were observed.

CONCLUSIONS

This study demonstrated the positive effects of SCS on depressive symptoms, suicidal thoughts and housing satisfaction for people living alone, suggesting that technology can be a useful tool for helping vulnerable people.

Talking numbers: how women and providers use risk scores during and after risk counseling - a qualitative investigation from the NRG Oncology/NSABP DMP-1 study

OBJECTIVES

Little research exists on how risk scores are used in counselling. We examined (a) how Breast Cancer Risk Assessment Tool (BCRAT) scores are presented during counselling; (b) how women react and (c) discuss them afterwards.

DESIGN

Consultations were video-recorded and participants were interviewed after the consultation as part of the NRG Oncology/National Surgical Adjuvant Breast and Bowel Project Decision-Making Project 1 (NSABP DMP-1).

SETTING

Two NSABP DMP-1 breast cancer care centres in the USA: one large comprehensive cancer centre serving a high-risk population and an academic safety-net medical centre in an urban setting.

PARTICIPANTS

Thirty women evaluated for breast cancer risk and their counselling providers were included.

METHODS

Participants who were identified as at increased risk of breast cancer were recruited to participate in qualitative study with a video-recorded consultation and subsequent semi-structured interview that included giving feedback and input after viewing their own consultation. Consultation videos were summarised jointly and inductively as a team.tThe interview material was searched deductively for text segments that contained the inductively derived themes related to risk assessment. Subgroup analysis according to demographic variables such as age and Gail score were conducted, investigating reactions to risk scores and contrasting and comparing them with the pertinent video analysis data. From this, four descriptive categories of reactions to risk scores emerged. The descriptive categories were clearly defined after 19 interviews; all 30 interviews fit principally into one of the four descriptive categories.

RESULTS

Risk scores were individualised and given meaning by providers through: (a) presenting thresholds, (b) making comparisons and (c) emphasising or minimising the calculated risk. The risk score information elicited little reaction from participants during consultations, though some added to, agreed with or qualified the provider's information. During interviews, participants reacted to the numbers in four primary ways: (a) engaging easily with numbers; (b) expressing greater anxiety after discussing the risk score; (c) accepting the risk score and (d) not talking about the risk score.

CONCLUSIONS

Our study highlights the necessity that patients' experiences must be understood and put into relation to risk assessment information to become a meaningful treatment decision-making tool, for instance by categorising patients' information engagement into types.

TRIAL REGISTRATION NUMBER

NCT01399359.

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

New management strategies for primary headache disorders: Insights from P4 medicine

Primary headache disorder is the main cause of headache attacks, leading to significant disability and impaired quality of life. This disorder is increasingly recognized as a heterogeneous condition with a complex network of genetic, environmental, and lifestyle factors. However, the timely diagnosis and effective treatment of these headaches remain challenging. Precision medicine is a potential strategy based on P4 (predictive, preventive, personalized, and participatory) medicine that may bring new insights for headache care. Recent machine learning advances and widely available molecular biology and imaging data have increased the usefulness of this medical strategy. Precision medicine emphasizes classifying headaches according to their risk factors, clinical presentation, and therapy responsiveness to provide individualized headache management. Furthermore, early preventive strategies, mainly utilizing predictive tools, are critical in reducing headache attacks and improving the quality of life of individuals with headaches. The current review comprehensively discusses the potential application value of P4 medicine in headache management.

Food & You: A digital cohort on personalized nutrition

Nutrition is a key contributor to health. Recently, several studies have identified associations between factors such as microbiota composition and health-related responses to dietary intake, raising the potential of personalized nutritional recommendations. To further our understanding of personalized nutrition, detailed individual data must be collected from participants in their day-to-day lives. However, this is challenging in conventional studies that require clinical measurements and site visits. So-called digital or remote cohorts allow in situ data collection on a daily basis through mobile applications, online services, and wearable sensors, but they raise questions about study retention and data quality. "Food & You" is a personalized nutrition study implemented as a digital cohort in which participants track food intake, physical activity, gut microbiota, glycemia, and other data for two to four weeks. Here, we describe the study protocol, report on study completion rates, and describe the collected data, focusing on assessing their quality and reliability. Overall, the study collected data from over 1000 participants, including high-resolution data of nutritional intake of more than 46 million kcal collected from 315,126 dishes over 23,335 participant days, 1,470,030 blood glucose measurements, 49,110 survey responses, and 1,024 stool samples for gut microbiota analysis. Retention was high, with over 60% of the enrolled participants completing the study. Various data quality assessment efforts suggest the captured high-resolution nutritional data accurately reflect individual diet patterns, paving the way for digital cohorts as a typical study design for personalized nutrition.

Symptom-based stratification algorithm for heterogeneous symptoms of dry eye disease: a feasibility study

BACKGROUND/OBJECTIVE

To test the feasibility of a dry eye disease (DED) symptom stratification algorithm previously established for the general population among patients visiting ophthalmologists.

SUBJECT/METHODS

This retrospective cross-sectional study was conducted between December 2015 and October 2021 at a university hospital in Japan; participants who underwent a comprehensive DED examination and completed the Japanese version of the Ocular Surface Disease Index (J-OSDI) were included. Patients diagnosed with DED were stratified into seven clusters using a previously established symptom-based stratification algorithm for DED. Characteristics of the patients in stratified clusters were compared.

RESULTS

In total, 426 participants were included (median age [interquartile range]; 63 [48-72] years; 357 (83.8%) women). Among them, 291 (68.3%) participants were diagnosed with DED and successfully stratified into seven clusters. The J-OSDI total score was highest in cluster 1 (61.4 [52.2-75.0]), followed by cluster 5 (44.1 [38.8-47.9]). The tear film breakup time was the shortest in cluster 1 (1.5 [1.1-2.1]), followed by cluster 3 (1.6 [1.0-2.5]). The J-OSDI total scores from the stratified clusters in this study and those from the clusters identified in the previous study showed a significant correlation (r = 0.991, P < 0.001).

CONCLUSIONS

The patients with DED who visited ophthalmologists were successfully stratified by the previously established algorithm for the general population, uncovering patterns for their seemingly heterogeneous and variable clinical characteristics of DED. The results have important implications for promoting treatment interventions tailored to individual patients and implementing smartphone-based clinical data collection in the future.

Harnessing large language models (LLMs) for candidate gene prioritization and selection

BACKGROUND

Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.

METHODS

In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene's biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.

RESULTS

Of the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.

CONCLUSIONS

Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Genomic newborn screening: Are we entering a new era of screening?

Population newborn screening (NBS) for phenylketonuria began in the United States in 1963. In the 1990s electrospray ionization mass spectrometry permitted an array of pathognomonic metabolites to be identified simultaneously, enabling up to 60 disorders to be recognized with a single test. In response, differing approaches to the assessment of the harms and benefits of screening have resulted in variable screening panels worldwide. Thirty years on and another screening revolution has emerged with the potential for first line genomic testing extending the range of screening conditions recognized after birth to many hundreds. At the annual SSIEM conference in 2022 in Freiburg, Germany, an interactive plenary discussion on genomic screening strategies and their challenges and opportunities was conducted. The Genomics England Research project proposes the use of Whole Genome Sequencing to offer extended NBS to 100 000 babies for defined conditions with a clear benefit for the child. The European Organization for Rare Diseases seeks to include "actionable" conditions considering also other types of benefits. Hopkins Van Mil, a private UK research institute, determined the views of citizens and revealed as a precondition that families are provided with adequate information, qualified support, and that autonomy and data are protected. From an ethical standpoint, the benefits ascribed to screening and early treatment need to be considered in relation to asymptomatic, phenotypically mild or late-onset presentations, where presymptomatic treatment may not be required. The different perspectives and arguments demonstrate the unique burden of responsibility on those proposing new and far-reaching developments in NBS programs and the need to carefully consider both harms and benefits.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

Precision Medical Education

Medical schools and residency programs are increasingly incorporating personalization of content, pathways, and assessments to align with a competency-based model. Yet, such efforts face challenges involving large amounts of data, sometimes struggling to deliver insights in a timely fashion for trainees, coaches, and programs. In this article, the authors argue that the emerging paradigm of precision medical education (PME) may ameliorate some of these challenges. However, PME lacks a widely accepted definition and a shared model of guiding principles and capacities, limiting widespread adoption. The authors propose defining PME as a systematic approach that integrates longitudinal data and analytics to drive precise educational interventions that address each individual learner's needs and goals in a continuous, timely, and cyclical fashion, ultimately improving meaningful educational, clinical, or system outcomes. Borrowing from precision medicine, they offer an adapted shared framework. In the P4 medical education framework, PME should (1) take a proactive approach to acquiring and using trainee data; (2) generate timely personalized insights through precision analytics (including artificial intelligence and decision-support tools); (3) design precision educational interventions (learning, assessment, coaching, pathways) in a participatory fashion, with trainees at the center as co-producers; and (4) ensure interventions are predictive of meaningful educational, professional, or clinical outcomes. Implementing PME will require new foundational capacities: flexible educational pathways and programs responsive to PME-guided dynamic and competency-based progression; comprehensive longitudinal data on trainees linked to educational and clinical outcomes; shared development of requisite technologies and analytics to effect educational decision-making; and a culture that embraces a precision approach, with research to gather validity evidence for this approach and development efforts targeting new skills needed by learners, coaches, and educational leaders. Anticipating pitfalls in the use of this approach will be important, as will ensuring it deepens, rather than replaces, the interaction of trainees and their coaches.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

The Role of Radiomics in Fibrosis Crohn's Disease: A Review

Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn's disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.

P32: a sex- and gender-sensitive model for evidence-based precision medicine: from knowledge generation to implementation in the field of kidney transplantation

Precision medicine emerged as a promising approach to identify suitable interventions for individual patients with a particular health concern and at various time points. Technology can enable the acquisition of increasing volumes of clinical and "omics" data at the individual and population levels and support advanced clinical decision making. However, to keep pace with evolving societal realities and developments, it is important to systematically include sex- and gender-specific considerations in the research process, from the acquisition of knowledge to implementation. Building on the foundations of evidence-based medicine and existing precision medicine frameworks, we propose a novel evidence-based precision medicine framework in the form of the P3²model, which considers individual sex-related (predictive [P1], preventive [P2], and personalized [P3] medicine) and gender-related (participatory [P4], psychosocial [P5], and percipient [P6] medicine) domains and their intersection with ethnicity, geography, and other demographic and social variables, in addition to population, community, and public dimensions (population-informed [P7], partnered with community [P8], and public-engaging [P9] medicine, respectively). Through its ability to contextualize and reflect on societal realities and developments, our model is expected to promote consideration of diversity, equity, and inclusion principles and, thus, enrich science, increase reproducibility of research, and ensure its social impact.

Consensus recommendations on organization of care for individuals with Phelan-McDermid syndrome

The manifestations of Phelan-McDermid syndrome (PMS) are complex, warranting expert and multidisciplinary care in all life stages. In the present paper we propose consensus recommendations on the organization of care for individuals with PMS. We indicate that care should consider all life domains, which can be done within the framework of the International Classification of Functioning, Disability and Health (ICF). This framework assesses disability and functioning as the outcome of the individual's interactions with other factors. The different roles within care, such as performed by a centre of expertise, by regional health care providers and by a coordinating physician are addressed. A surveillance scheme and emergency card is provided and disciplines participating in a multidisciplinary team for PMS are described. Additionally, recommendations are provided for transition from paediatric to adult care. This care proposition may also be useful for individuals with other rare genetic neurodevelopmental disorders.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Developing integrated care pathways for atopic dermatitis-Challenges and unmet needs

BACKGROUND

GA² LEN-ADCARE is a branch of the largest multidisciplinary network of research centres and clinical care in allergy and asthma, GA² LEN, focussing on the field of atopic dermatitis (AD). AD is a chronic inflammatory skin disease with high burden and many comorbidities requiring different levels of treatment. The need for aligned information from all involved healthcare providers led to the discussion of an integrated care pathway (ICP) plan for AD patient care involving all stakeholders and considering the complexity and variability of the disease, with a particular focus placed on the large number of patients with milder forms of AD.

METHODS

The GA² LEN ADCARE network and all stakeholders, abbreviated the AD-ICPs working group, were involved in the discussion and preparation of the AD-ICPs during a series of subgroup workshops and meetings in years 2020 and 2021.

RESULTS

Here we discuss the unmet needs in AD, the methodology for devising an AD-ICP and the ICP action plan.

CONCLUSION

The GA² LEN ADCARE network has outlined the unmet needs in AD and provided an action plan for devising AD-ICPs, considering the complexity and variability of the disease.

Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping

Deciphering information hidden in the gene expression assays for identifying disease subtypes has significant importance in precision medicine. However, computational limitations thwart this process due to the intricacy of the biological networks and the curse of dimensionality of gene expression data. Therefore, clustering in such scenarios often becomes the first choice of exploratory data analysis to identify natural structures and intrinsic patterns in the data. However, sparse and high dimensional nature of omics data prevents conventional clustering algorithms to discover subtypes that are clinically relevant and statistically significant. Hence, non-linear dimensionality reduction techniques coupled with clustering in such scenarios often becomes imperative to improve the clustering results. In this study, we present a robust pipeline to discover disease subtypes with clinical relevance. Specifically, we focus on discovering patient sub-groups that have a residual life patterns remarkably different from other sub-groups. This is significant because by refining prognosis, subtyping can reduce uncertainty in approximating patients expected outcome. The methodology present is based on robust correlation estimation, UMAP- a non-linear dimensionality reduction method and mapper- a tool from topology. Notably, we suggest a method for improving the robustness of the correlation matrix of gene expression data for improving the clustering results. The performance of the model is evaluated by applying to five cancer datasets obtained through TCGA and comparisons are performed with some state of the art methods of NEMO, RSC-OTRI and SNF with regard to log-rank test and Restricted Life Expectancy Difference. For example in GBM dataset, the minimum separation for any two discovered subtypes is 221 days which is significantly higher than the other methodologies. We also compared the results without using the robust correlation based estimate and observed that robust correlation improves separability between survival curves significantly. From the results we infer that our methodology performs better compared to other methodologies with regard to separating survival curves of patient sub-groups despite using single omics profiles of patients compared to multiple omics profiles of SNF and NEMO. Pathway over-representation analysis is performed on the final clustering results to investigate the biological underpinnings characterizing each subtype.

The Mobile Patient Information Assistant (PIA) App during the Inpatient Surgical Hospital Stay: Evaluation of Usability and Patient Approval

Mobile eHealth apps are becoming increasingly important tools in healthcare management, capable of providing education and support at any time. There is little knowledge about surgical patients' appreciation and use of these apps. The objective of this study was to develop and evaluate a user-friendly medical app (PIA; Patient Information Assistant) for providing individual patient information before and after inpatient urological surgery. Twenty-two patients aged 35 to 75 years were provided with timely information, push notifications, and personalized agendas (e.g., date of presentation, time of surgery, time of doctor's consultation, imaging appointment) via the PIA app. Of the 22 patients, 19 evaluated the PIA app in terms of usage and usability, benefits, and potential for improvement. Of the study participants, 95% did not need any assistance to use the app, 74% confirmed that the PIA app made them feel better informed and more satisfied with their hospital stay, and 89% stated that they would like to re-use the PIA app and support the general use of medical apps in healthcare. Thus, we created an innovative digital health information tool, allowing targeted support for doctor-nurse-patient communication and offering great potential for patient support before and after surgery. Our study revealed that use of an app during the surgical hospital stay is readily accepted and benefits patients by acting as an additional informative tool.

Precision and personalized medicine: What their current definition says and silences about the model of health they promote. Implication for the development of personalized health

The US National Human Genome Research Institute defines precision medicine as follows: "Precision medicine (generally considered analogous to personalized medicine or individualized medicine) is an innovative approach that uses information about an individual's genomic, environmental, and lifestyle information to guide decisions related to their medical management. The goal of precision medicine is to provide a more precise approach for the prevention, diagnosis, and treatment of disease." In this perspective article, we question this definition of precision medicine and the risks linked to its current practice and development. We highlight that in practice, precision medicine is based on the use of large volumes of biological data for individual purposes mostly in line with the biomedical model of health, which carries the risk of the biological reductionism of the person. A more comprehensive, precise, and even "personal" approach to health would require taking into account environmental, socio-economic, psychological, and biological determinants, an approach more in line with the biopsychosocial model of health. The role of environmental exposures, in a broad sense, is highlighted more and more, notably in the field of exposome research. Not considering the conceptual framework in which precision medicine is deployed leads to the concealment of the different responsibilities that can be mobilized within the health system. Anchoring precision medicine in a model that does not limit its definition to its biological and technical components makes it possible to envisage a personalized and more precise medicine, integrating a greater share of interventions centered on the skills and life contexts of individuals.

Personalized Care for Patients with Cancer in the Precision-Medicine Era

Important advances in cancer management have been made in the beginning of the 21st century [...].

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Personalization: a new political arithmetic?

Scholarship on the history of political arithmetic highlights its significance for classical liberalism, a political philosophy in which subjects perceive themselves as autonomous individuals in an abstract system called society. This society and its component individuals became intelligible and governable in a deluge of printed numbers, assisted by the development of statistics, the emergence of a common space of measurement, and the calculation of probabilities. Our proposal is that the categories, numbers, and norms of this political arithmetic have changed in a ubiquitous culture of personalization. Today's political arithmetic, we suggest, produces a different kind of society, what Facebook CEO Mark Zuckerberg calls the 'default social'. We address this new social as a 'vague whole' and propose that it is characterized by a continuous present, the contemporary form of simultaneity or way of being together that Benedict Anderson argued is fundamental to any kind of imagined community. Like the society imagined in the earlier arithmetic, this vague whole is an abstraction that obscures forms of stratification and discrimination.

An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study

Introduction

Artificial intelligence (AI) is increasingly used in healthcare. AI-based chatbots can act as automated conversational agents, capable of promoting health and providing education at any time. The objective of this study was to develop and evaluate a user-friendly medical chatbot (prostate cancer communication assistant (PROSCA)) for provisioning patient information about early detection of prostate cancer (PC).

Methods

The chatbot was developed to provide information on prostate diseases, diagnostic tests for PC detection, stages, and treatment options. Ten men aged 49 to 81 years with suspicion of PC were enrolled in this study. Nine of ten patients used the chatbot during the evaluation period and filled out the questionnaires on usage and usability, perceived benefits, and potential for improvement.

Results

The chatbot was straightforward to use, with 78% of users not needing any assistance during usage. In total, 89% of the chatbot users in the study experienced a clear to moderate increase in knowledge about PC through the chatbot. All study participants who tested the chatbot would like to re-use a medical chatbot in the future and support the use of chatbots in the clinical routine.

Conclusions

Through the introduction of the chatbot PROSCA, we created and evaluated an innovative evidence-based health information tool in the field of PC, allowing targeted support for doctor-patient communication and offering great potential in raising awareness, patient education, and support. Our study revealed that a medical chatbot in the field of early PC detection is readily accepted and benefits patients as an additional informative tool.

Contributions of Hippocratic medicine and Plato to today's debate over health, social determinants and the authority of biomedicine

By exploring a competition for authority on health and human nature between Plato and Hippocratic medicine, this paper offers a fresh perspective on an overarching debate today involving health and the role of healthcare in its safeguarding. Economically and politically, healthcare continues to dominate the USA's handling of health, construed biophysically as the absence of disease. Yet, notoriously, in major health outcomes, the USA fares worse than other countries in the Organisation for Economic Co-operation and Development (OECD). Clearly, in giving pre-eminence to healthcare, the USA is doing far less than it could to protect and improve health. Meanwhile, mounting evidence supports the view that health impacts of social determinants besides healthcare (eg, education) surpass healthcare in heft. Circumscribed shifts in the USA's current frame will not suffice: what's needed is a change in its overall template for addressing health. Unless this is widely seen, the sway of biomedicine will likely be reduced slowly, if at all. That biomedicine's role in relation to health is raised increasingly as a question is a sign that its ongoing supremacy is not a forgone conclusion. But making the most of this opportunity requires appreciating that 'How should health's relationship to medicine be conceptualised?' is not the most fundamental query that we need to pose. Through consideration of Hippocratic medicine and Plato, I argue that the most availing answer to this particular question can come only after exploration of three larger questions involving health's status as a human good and its relationship to human flourishing. Exploration of the Greeks is, thus, valuable methodologically. What's more, it supports today's advocacy of 'health promotion', a perspective tying health closely to well-being that has yet to achieve the overall prominence that it warrants.

MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy

Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.

Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring

With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84-85% with the lowest overfit problem, which proves its potential application in other related fields.

The interplay of post-acute COVID-19 syndrome and aging: a biological, clinical and public health approach

The post-acute COVID-19 syndrome (PACS) is characterized by the persistence of fluctuating symptoms over three months from the onset of the possible or confirmed COVID-19 acute phase. Current data suggests that at least 10% of people with previously documented infection may develop PACS, and up to 50-80% of prevalence is reported among survivors after hospital discharge. This viewpoint will discuss various aspects of PACS, particularly in older adults, with a specific hypothesis to describe PACS as the expression of a modified aging trajectory induced by SARS CoV-2. This hypothesis will be argued from biological, clinical and public health view, addressing three main questions: (i) does SARS-CoV-2-induced alterations in aging trajectories play a role in PACS?; (ii) do people with PACS face immuno-metabolic derangements that lead to increased susceptibility to age-related diseases?; (iii) is it possible to restore the healthy aging trajectory followed by the individual before pre-COVID?. A particular focus will be given to the well-being of people with PACS that could be assessed by the intrinsic capacity model and support the definition of the healthy aging trajectory.

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening

BACKGROUND

Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction.

METHODS

In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS).

RESULTS

First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration.

CONCLUSIONS

Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.

The potential of predictive and prognostic breast MRI (P2-bMRI)

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.