Precision Medicine: From Science To Value

The case for integrating genetic counselors into primary care: A paradigm shift for our profession

The integration of genetic counselors (GCs) into primary care represents an opportunity for a transformative shift in healthcare delivery, bridging the gap between the historical medical genetics delivery model and the increasing need for genetic services. This paradigm aligns the holistic ethos of primary care with the specialized expertise of genetic counseling and frontline access to preventive care, addressing critical barriers in genetic services. Current genetic service delivery models, concentrated in tertiary care settings, face limitations, including access disparities, fragmented care, and inefficiencies that disproportionately affect underserved populations. Embedding GCs within primary care leverages GCs' unique skills to enhance personalized healthcare delivery, improve risk assessment, and facilitate the implementation of precision medicine. GCs in primary care can streamline referrals, manage routine genetic concerns, and provide genetic continuity of care across the patient's lifespan. This integration ensures that genetic insights are contextualized within patients' day-to-day healthcare, fostering equitable and efficient access to genomic medicine. We explore the potential impact of primary care genetic counselors (PCGCs) on healthcare systems, emphasizing the alignment of their scope of practice with primary care principles such as accessibility, comprehensiveness, and continuity. By addressing evolving patient needs and collaborating with primary care teams, PCGCs can increase patient access, reduce system inefficiencies, alleviate pressures on specialty genetics services, and improve health equity. This paper advocates for a collaborative model where GCs are embedded within primary care, enabling proactive, prevention-focused interventions and enhancing patient outcomes. By integrating genetics into primary care settings, we reimagine genetic healthcare delivery to maximize the benefits of genomic medicine for all individuals. This paradigm shift underscores the urgency of addressing systemic barriers and advancing the role of GCs in healthcare to improve patient and clinician experiences, better population health, and achieve greater health equity.

Addressing the knowledge and recruitment gap in Alzheimer's disease and precision medicine research among Native people: an innovative randomized controlled trial

BACKGROUND

The American Indian and Alaska Native (AI/AN) population is increasing and AI/AN people are living longer than ever before. Although aging-related health issues such as cognitive impairment, dementia, and Alzheimer's disease (AD) are becoming more visible among this population, reliable data on AD prevalence and risk factors among AI/AN people are nearly nonexistent. Concurrently, precision medicine (PM) has demonstrated significant potential for detecting and treating diseases such as AD. For PM to promote health equity for underserved populations, it must not exacerbate existing health disparities and bias in research. There is also little information about preferences among AI/AN people for communicating information regarding AD, PM, or recruitment into clinical trials. Communication barriers and few known facilitators contribute to low rates of AI/AN research participation. This study seeks to address the gaps in AD and PM research among AI/AN communities and promote knowledge of, attitudes towards, and interest and participation in AD-related PM research efforts.

METHODS

We designed a three-armed RCT to determine the effect of a culturally tailored brochure and video compared to non-tailored recruitment materials. Participants were recruited in Rapid City, South Dakota and were required to meet the following eligibility criteria: 1) identify as AI/AN; 2) be able to speak, read, and understand English; 3) be aged 40 or older; and 4) have the cognitive and decisional capacity to consent and sign and date the informed consent document.

RESULTS

We enrolled 914 in the RCT and 812 have been randomized to a study condition. The mean age is 54 years (standard deviation = 10.3 years); 62% are female. Overall, 22% reported a parent, grandparent or sibling have been diagnosed with AD, and 22% reported a family member with an other type of dementia. One quarter (25%) of participants reported having an undiagnosed memory problem themselves, and 22% reported having a family member with undiagnosed memory problems. Of randomized participants, 743 (72%) enrolled in the research registry.

CONCLUSIONS

This study will inform future recruitment efforts for ADRD-focused clinical trials. Enrollment of AI/AN participants in an Alzheimer's Disease-Precision Medicine (AD-PM) Registry will provide opportunity for future research on this topic in partnership with this population.

Enhancing biomarker based oncology trial matching using large language models

Clinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patient's eligibility for enrollment is scattered in large amounts of unstructured text. Genomic biomarkers are especially important in precision medicine and targeted therapies, making them essential for matching patients to appropriate trials. Large language models (LLMs) offer a promising solution for extracting this information from clinical trial study descriptions (e.g., brief summary, eligibility criteria), aiding in identifying suitable patient matches in downstream applications. In this study, we explore various strategies for extracting genetic biomarkers from oncology trials. Therefore, our focus is on structuring unstructured clinical trial data, not processing individual patient records. Our results show that open-source language models, when applied out-of-the-box, effectively capture complex logical expressions and structure genomic biomarkers, outperforming closed-source models such as GPT-4. Furthermore, fine-tuning these open-source models with additional data significantly enhances their performance.

An outlier approach: advancing diagnosis of neurological diseases through integrating proteomics into multi-omics guided exome reanalysis

Neurodevelopmental disorders (NDDs) often have unknown genetic causes. Current efforts in identifying disease-related genetic variants using exome or genome sequencing still lead to an excessive number of variants of uncertain significance (VUS). There is an increasing interest in transcriptomics and, more recently, proteomics for variant detection and interpretation. In this study, we integrated quantitative liquid chromatography-mass spectrometry proteomics, RNA sequencing, and exome reanalysis to resolve VUS and detect novel causal variants in 34 patients with undiagnosed NDDs, using the software PROTRIDER and DROP to detect protein outliers and RNA outliers, respectively. We obtained a diagnosis in 11 cases (32%) resulting from the increased amount of information provided by the two additional levels of omics (n = 5) and the updated literature evidence (n = 6). Our experience suggests the potential of this outlier-detection multi-omics workflow for improving diagnostic yield in NDDs and other rare disorders.

Genetic Services in Appalachia Conference Series

This study examined the data generated as part of a seven-session webinar series that focused on genetics care provision in the medically underserved, rural Appalachian region and examined how these services have adapted to challenging practice environments. Barriers and facilitators to care in our region were considered. Data included a baseline survey of registrants, transcripts of sessions, and feedback about sessions. We analyzed data with a sequential and concurrent mixed methods approach. Registrants (n = 137) were disproportionately healthcare providers with genetic services expertise (37.2%). Approximately half (43.8%) of registrants were from KY, WV, and TN in the central Appalachian region. Our baseline survey found that the most noted barriers were the cost of services, lack of providers, and access to care. The most common facilitator was telehealth. Analysis of transcripts identified barriers that were consistent with those noted in the baseline survey, but additional support and network opportunities were discussed to allow for learning across services. Numerous barriers to service delivery were noted; however, despite challenges, participants identified opportunities and resources in the community. These insights will inform a research agenda aimed at advancing genetics services in rural Appalachia, addressing challenges, and leveraging assets for improved healthcare access and outcomes.

What traditional neuropsychological assessment got wrong about mild traumatic brain injury. IV: clinical applications and future directions

PRIMARY OBJECTIVE

Part IV concludes this four-part review of 'What Traditional Neuropsychological Assessment Got Wrong About Mild Traumatic Brain Injury,' with a focus on clinical applications and future directions.

METHODS AND PROCEDURES

These reviews have highlighted the limitations of traditional neuropsychological assessment methods, particularly in the evaluation of the patient with mild traumatic brain injury (mTBI), and especially within the context of all of the 21st Century advances in neuroimaging, quantification and network neuroscience.

MAIN OUTCOME AND RESULTS

How advanced neuroimaging technology and contemporary network neuroscience can be applied to assessing the mTBI patient at this time along with neuroimaging of the future are reviewed. The current status of computerized neuropsychological test (CNT) development is reviewed as it applies to mTBI assessment. Likewise, how the future of various types of virtual reality (VR), artificial intelligence (AI), wearable sensors, and markerless gaming technology could enhance the mTBI CNT assessment tool box of the future is reviewed.

CONCLUSIONS

The review concludes with some aspirational statements about how improvements along with novel CNT methods could be developed and integrated with advanced neuroimaging technologies in the future to be tailored to meet the needs of the mTBI patient.

Safeguarding the future of biomedical science in the United States

NIH's abrupt decision to cap indirect cost reimbursement at 15% threatens the critical infrastructure supporting groundbreaking biomedical research in the United States. This policy jeopardizes America's global leadership in science and medicine. Urgent action is needed to advocate for its immediate and permanent reversal to protect the future of science.

The integration of AI in nursing: addressing current applications, challenges, and future directions

Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.

A review of precision medicine in developing pharmaceutical products: Perspectives and opportunities

Over the next decade, Precision Medicine (PM) is poised to become the standard of care in pharmaceutical therapy, necessitating a fundamental transformation in the design and development of innovative custom-made drug products. To date, a comprehensive review linking PM with practical personalized drug formulations is missing. This review attempts to provide an overview of state-of-the-art formulation approaches capable of translating PM evaluation and resulting recommendations (clinical research) into tailored drug products (non-clinical research) for real-world patients. Comprehensive literature searches in four scientific databases (Scopus, SciFinder, Web of Science, and PubMed) were performed. Current approaches to point-of-care PM formulations and needs-based locally distributed manufacturing presently under research & development (R&D) as alternatives to conventional large-scale manufacturing of one-size-fits-all drug products are discussed. The following methods were identified as the most promising PM formulation strategies: tablet splitting, liquid dispensing, compounding pharmacies, additive manufacturing, drug impregnation, drug extrusion, and orodispersible films (ODFs). The challenges and opportunities of current state-of-the-art formulation technologies that can enable making PM routinely accessible in practice settings will be discussed. Additionally, light will be shed on point-of-use manufacturing (Pharmacy on Demand) as an uncharted territory for PM and its pathway towards practical implementation.

Genomic Testing in Adults With Undiagnosed Rare Conditions: Improvement of Diagnosis Using Clinical Exome Sequencing as a First-Tier Approach

Adult patients with undiagnosed genetic disorders suffer most from diagnostic delay and seldom appear in cohort studies investigating the diagnostic yield in medical genetic clinical practice. Here we present the results of the diagnostic activity performed in a referral center on 654 consecutive, unselected adult subjects presenting with molecularly unsolved conditions. More than 50% of the referred individuals were affected by syndromic or isolated intellectual disability. Different molecular approaches, including clinical/whole exome sequencing (CES/WES), chromosomal microarray analysis (CMA), and/or targeted gene or gene panel sequencing were used to analyze patients' DNA. Definitive diagnosis was obtained in over 30% of individuals. The most sensitive methodology was CES/WES, which allowed us to reach a diagnosis in over 50% of the 162 solved cases. Despite the great variety of clinical presentations, our results represent a reliable picture of the "real world" daily routine in an outpatient medical genetics clinic dedicated to diagnostic activity, and contribute to better understand the great value of a definitive molecular diagnosis in adults, either for the affected individuals and their families. This retrospective analysis demonstrates the importance of adopting a genomic-first approach within the diagnostic process for adults affected with unsolved rare conditions.

Current status and future direction of cancer research using artificial intelligence for clinical application

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI-equipped software as a medical device (AI-SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data-driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI-based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology.

Implementation of Cancer Genomics in the United States: Views of Payers and Other Stakeholders on Challenges and the Role of Payers in Solutions

PURPOSE

Genomic testing is crucial in cancer risk identification, diagnosis, and treatment. However, health care implementation is challenging, even for tests covered by insurance. US payers are important health care participants and may contribute to addressing implementation challenges. We explored whether and how payers consider their role in genomic test implementation, and the perspectives of nonpayer stakeholders on payers' participation.

METHODS

We conducted a group interview with private payers (N = 12) to elucidate views on their role in genomic test implementation, implementation challenges, and potential solutions. Subsequently, we conducted individual interviews (N = 10) with nonpayer stakeholders-five cancer advocacy groups and five medical societies working in cancer, examining their reactions to payer input and capturing additional ideas. Qualitative research methods were used to frame the study and analyze results.

RESULTS

Payers considered cancer genomics implementation important and expressed willingness and ability to be involved. They articulated specific challenges relevant to them, including underutilization of covered tests in clinical practice, inequitable test use, and inconsistencies across guidelines, and suggested specific solutions to collaborate on with other stakeholders. Stakeholders viewed payers' participation in implementation as appropriate and feasible, expressed willingness to work with payers where relevant, and concurred with the key challenges noted by payers. Stakeholders' agreement with payers' solutions varied, but they offered additional ideas for addressing challenges.

CONCLUSION

Both payers and other stakeholders considered payers' role in addressing genomics implementation challenges appropriate and feasible, and offered specific avenues for payer participation. Our findings inform efforts by payers and other stakeholders to address broad health care implementation. They may also help precision oncology professionals, cancer centers, and health systems to frame their own implementation efforts and influence a broader policy and implementation agenda.

Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks

Genetic screening analyzes an individual's genetic information to assess disease risk and provide personalized health recommendations. This article introduces the public to genetic screening, explaining its definition, principles, history, and common types, including prenatal, newborn, adult disease risk, cancer, and pharmacogenetic screening. It elaborates on the benefits of genetic screening, such as early risk detection, personalized prevention, family risk assessment, and reproductive decision-making. The article also notes limitations, including result interpretation uncertainty, psychological and ethical issues, and privacy and discrimination risks. It provides advice on selecting suitable screening, consulting professionals, choosing reliable institutions, and understanding screening purposes and limitations. Finally, it discusses applying screening results through lifestyle adjustments, regular check-ups, and preventive treatments. By comprehensively introducing genetic screening, the article aims to raise public awareness and encourage utilizing this technology to prevent disease and maintain health.

A data management system for precision medicine

Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level. LogiqSuite is certified and compliant with international medical data and privacy legislations. This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. The MedDMS is evaluated in seven precision medicine data science projects in prehospital triage, cardiovascular disease, pulmonology, and oncology. The P4O2 consortium uses the MedDMS as an electronic case report form (eCRF) that allows real-time data management and analytics in long covid and pulmonary diseases. In an acute myeloid leukaemia, study data from different sources were integrated to facilitate easy descriptive analytics for various research questions. In the AIDPATH project, LogiqCare is used to process patient data, while LogiqScience is used for pseudonymous CAR-T cell production for cancer treatment. In both these oncological projects the data in LogiqAnalytics is also used to facilitate machine learning to develop new prediction models for clinical-decision support (CDS). The MedDMS is also evaluated for real-time recording of CDS data from U-Prevent for cardiovascular risk management and from the Stroke Triage App for prehospital triage. The MedDMS is discussed in relation to other solutions for privacy-by-design, integrated data stewardship and real-time data analytics in precision medicine. LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. The integrated MedDMS application supports data management for care and research in precision medicine.

Community Collaboration in Public Health Genetic Literacy: Methods for Co-Designing Educational Resources for Equitable Genomics Research and Practice

Introduction

Unequal representation in genetic and genomic research is due to various factors including historically inequitable and unjust institutional research practices, potential mistrust of biomedical research among underrepresented populations, and lack of access to or awareness of research opportunities. Facilitating sustainable dialogue between diverse communities and genetic researchers can cultivate trusting, bidirectional relationships, potentially encouraging greater participation in research. Herein, we describe the co-creation of public health educational materials and dissemination plans using an approach designed to address inequities and foster community dialogue.

Methods

In this Methods paper, we describe the iterative co-creation of Genetics and Genomics educational modules by genetics clinicians, researchers, and community members. The goal of these modules is to enhance genetic literacy of the lay population to facilitate informed decision-making regarding genetic research and health services. We used Designing for Dissemination and Sustainability, grounded in Dissemination and Implementation science, and its Fit to Context process framework to guide the process. This approach ensures that the public health context and writing for a diverse audience are considered throughout the modules' development.

Conclusion

This article offers an evidence-based template for adoption or adaptation by other community-engaged groups, aimed at bolstering equity and sustainability in the development of health care interventions and with an emphasis on accessible public health literacy. The co-creation by researchers and community members of both materials and dissemination plans may improve the cultural appropriateness and relevance of public health genetics campaigns. Ongoing research is needed to assess the impact of this approach on receptiveness and participation.

Systematic review and meta-analysis of adverse events in clinical trials of mental health apps

Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and ProQuest databases to identify clinical trials of mental health apps. The risk of bias was assessed using the Cochrane Risk of Bias tool. Only 55 of 171 identified clinical trials reported AEs. AEs were more likely to be reported in trials sampling schizophrenia and delivering apps with symptom monitoring technology. The meta-analytic deterioration rate from 13 app conditions was 6.7% (95% CI = 4.3, 10.1, I2 = 75%). Deterioration rates did not differ between app and control groups (OR = 0.79, 95% CI = 0.62-1.01, I2 = 0%). Reporting of AEs was heterogeneous, in terms of assessments used, events recorded, and detail provided. Overall, few clinical trials of mental health apps report AEs. Those that do often provide insufficient information to properly judge risks related to app use.

SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine

Cloud computing allows storing the ever-growing genotype-phenotype datasets crucial for precision medicine. Due to the sensitive nature of this data and varied laws and regulations, additional security measures are needed to ensure data privacy. We develop SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. SQUiD allows storage and secure querying of data in a low-security, low-cost public cloud using homomorphic encryption in a multi-client setting. We demonstrate SQUiD's practical usability and scalability using synthetic and UK Biobank data.

Perspectives on Integrating Biological Assessments to Address the Health Effects of Childhood Adversities

ABSTRACT

A majority of adults in the United States (US) report a range of stressful and potentially traumatic childhood experiences (e.g., physical or sexual abuse, witnessing violence, neglect). Such adversities are associated with a range of mental (e.g., anxiety, mood, and behavioral difficulties) and physical (e.g., cardiovascular illnesses, diabetes, asthma) health problems. Increasingly, precision medicine approaches seek to prevent and treat such multifinal downstream health problems by identifying common etiological pathways (e.g., inflammation and immune pathways) and candidate biomarkers to target interventions. In this context, we review the rationale for continued research to identify biomarkers of childhood adversity. Building on the bioecological theory, we emphasize that individual neurobiological profiles develop within multiple ecological levels (individual, family, neighborhood, macrosocial) that confer both risk and protective factors that can attenuate or amplify biological effects of childhood adversity. Given the limited data on adversity-associated biomarkers for children and adolescents, we discuss future recommendations for research, implications for clinical care, and ethical considerations. Preventing childhood adversity and supporting adversity- and trauma-informed systemic intervention approaches remains our primary recommendation. We highlight the continued need to consider both biomarkers of risk and protective factors across ecological levels in future research.

The precision medicine strategy to treat COPD pulmonary traits in clinical practice: The role of N-acetylcysteine

Chronic obstructive pulmonary disease (COPD) is a progressive lung condition and a leading cause of physical decline and death. COPD prevalence is expected to increase steadily in the coming years, and as a result, the healthcare and social burden of this condition will intensify. In this scenario, a patient-centric approach, the treatable trait (TT) strategy, based on the identification of traits that are clinically relevant, identifiable, monitorable and treatable, has emerged. The TT strategy, which considers behavioral/risk factors, as well as pulmonary and extrapulmonary traits, has shown to be a promising strategy in COPD management. This work reviews the TT strategy in COPD, giving special attention to the most relevant pulmonary traits, such as frequent productive cough, chronic bronchitis, type 2 inflammation, neutrophilic inflammation, lung hyperinflation, bronchiectasis, exacerbations and non-reversible airflow limitation. N-acetylcysteine (NAC), a widely used mucolytic agent, might be a major player in this strategy. Indeed, through a thorough review of the literature, it has been possible to highlight that, besides being essential in the treatment of frequent productive cough, NAC could bring benefits in case of airflow limitations, airways inflammation, exacerbations and bronchiectasis. A clinical case in which the TT strategy was able to reduce symptoms and improve lung function and quality of life, minimizing unnecessary medication and side effects, is also presented. The identification of TTs and their proper treatment through personalized medicine remarkably ameliorates COPD management. Of note, the mucolytic, antioxidant, and anti-inflammatory activities of NAC might have beneficial effects on several TTs.

A framework for sharing of clinical and genetic data for precision medicine applications

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

Precision medicine path at personalized medicine research center/endocrinology and metabolism research institute: A systematic review

Background

Precision medicine is an evolving predictive, early preventive, and treatment method that takes into account human gene heterogeneity for each individual. Personalized Medicine Research Center (PMRC) of Endocrinology and Metabolism Research Institute (EMRI) has been officially founded by the authority of the Ministry of Health and Medical Education on 18 December 2016 with the primary purpose of research, education, and dissemination of the personalized medicine approach throughout the country. In this review, we aimed to introduce the Personalized Medicine Research Center activities in the field of precision medicine since the establishment.

Methods

We systematically reviewed PubMed, Scopus, and WoS from 2016 to December 2019 to identify our relevant topics with PMRC affiliation.

Results

After screening processes, a total of 18 studies met eligibility criteria to include in the systematic review.

Conclusions

Personalized medicine that interchangeably called precision medicine is a new concept in medicine, the implementation of which needs to organize some infrastructures which are physical and the required education for healthcare professionals in different countries.

Development of an accelerometer age- and sex-specific approach based on population-standardized values for physical activity surveillance: A proof of concept

BACKGROUND

A shift from self-reports to wearable sensors for global physical activity (PA) surveillance has been recommended. The conventional use of a generic cut-point to assess moderate-to-vigorous PA (MVPA) is problematic as these cut-points are often derived from non-representative samples under non-ecological laboratory conditions. This study aimed to develop age- and sex- (age-sex) specific cut-points for MVPA based on population-standardized values as a feasible approach to assess the adherence to PA guidelines and to investigate its associations with all-cause mortality.

METHODS

A total of 7601 participants (20-85+ years) were drawn from the 2003-2004 and 2005-2006 National Health and Nutrition Examination Surveys (NHANES). Minutes per week of MVPA were assessed with a hip-worn accelerometer. Counts per minute (CPM) were used to define an age-sex specific target intensity, representing the intensity each person should be able to reach based on their age and sex. Age-sex specific MVPA cut-points were defined as any activity above 40% of the target intensity. These population- and free-living-based age-sex specific cut-points overcome many of the limitations of the standard generic cut-point approach. For comparison, we also calculated MVPA with a generic cut-point of 1952 CPM. Both approaches were compared for assessing adherence to PA guidelines and association of MVPA with all-cause mortality (ascertained through December 2015).

RESULTS

Both approaches indicated that 37% of the sample met the 150+ min/week guideline. The generic cut-point approach showed a trend to inactivity with age, which was less pronounced using the age-sex specific cut-points. Overall mortality rates were comparable using generic cut-point (hazard ratio (HR) = 0.61, 95% confidence interval (95%CI): 0.50‒0.73) or age-sex specific cut-points (HR = 0.57, 95%CI: 0.50‒0.66) for the entire sample. The generic cut-point method revealed an age- and sex-related gap in the benefits of achieving 150+ min/week of MVPA, with older adults showing an 18% greater reduction in mortality rates than younger adults, and a larger difference in women than in men. This disparity disappeared when using age-sex specific cut-points.

CONCLUSION

Our findings underscore the value of age-sex specific cut-points for global PA surveillance. MVPA defined with age-sex specific thresholds was associated with all-cause mortality and the dose‒response was similar for all ages and sexes. This aligns with the single recommendation of accumulating 150+ min/week MVPA for all adults, irrespective of age and sex. This study serves as a proof of concept to develop this methodology for PA surveillance over more advanced open-source acceleration metrics and other national and international cohorts.

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.

Escalating costs of innovative medicines: perspective and proposals

Public healthcare systems are challenged by the soaring costs of medications that require increasing resources, often at the expense of other investments. The increasing pharmaceutical budget poses a threat to the allocation of funds for essential preventive and primary healthcare services while also raising concerns about equitable access, particularly in models where patients bear part of the costs out of their own pockets. Proposals on how to ensure ongoing and long-term accessibility, efficiency, and financial stability are required. The escalating costs of medicines may be explained in part by the mismatch between the traditional value-based pricing and reimbursement frameworks and the type of clinical development of targeted therapies and precision medicine in clinical practice. New appraisal methods and managed access strategies should be adapted to therapies targeting small populations and addressing increased uncertainty. Fair pricing strategies, transparent healthcare investments based on problems and outcomes, regulatory reforms, international cooperation, and critically examining the drug acquisition model are potential solutions. Transitioning from an industry-driven pricing approach to a health-driven payment model can help align the cost of treatments with actual health outcomes, establishing a foundation for a healthcare system that addresses immediate challenges and fosters long-term well-being. Acknowledging the lack of a universally applicable solution, the practical implementation of interventions requires a reframing of the pricing and access system and adaption to the targeted therapeutic approaches. Balancing innovation with financial sustainability necessitates a collaborative, adaptive, and transparent approach, as well as transitioning toward health-driven payment models, moving the focus from the cost of medications to the well-being of populations worldwide.

Reviewing advances in nanophotonic biosensors

Biosensing, a promising branch of exploiting nanophotonic devices, enables meticulous detection of subwavelength light, which helps to analyze and manipulate light-matter interaction. The improved sensitivity of recent high-quality nanophotonic biosensors has enabled enhanced bioanalytical precision in detection. Considering the potential of nanophotonics in biosensing, this article summarizes recent advances in fabricating nanophotonic and optical biosensors, focusing on their sensing function and capacity. We typically classify these types of biosensors into five categories: phase-driven, resonant dielectric nanostructures, plasmonic nanostructures, surface-enhanced spectroscopies, and evanescent-field, and review the importance of enhancing sensor performance and efficacy by addressing some major concerns in nanophotonic biosensing, such as overcoming the difficulties in controlling biological specimens and lowering their costs for ease of access. We also address the possibility of updating these technologies for immediate implementation and their impact on enhancing safety and health.

New horizons in prediction modelling using machine learning in older people's healthcare research

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own.

A comprehensive review of the intersection between asthma and depression

OBJECTIVE

To emphasize the necessity for increased research in this field, incorporating depression into the preventative, diagnostic, and therapeutic considerations for asthma. Additionally, we seek to highlight upcoming advancements that can be applied to simultaneously address these comorbidities, ultimately improving the overall well-being and quality of life for individuals coping with these conditions.

METHODS

A rigorous search in PubMed using the MeSH terms "asthma" and "depression" was performed, and papers were screened by the authors in view of their eligibility to contribute to the study.

RESULTS

There exists a correlation between these two conditions, with specific biological mechanisms and genetic factors playing a crucial role in their concurrent occurrence. In this review, we present preclinical and clinical research data, shed light on the possible mechanisms contributing to the co-occurrence of symptoms associated with both asthma and depression, and explore the intricate relationship between both conditions.

CONCLUSION

The evidence presented here supports the existence of a correlation between asthma and depression. By acknowledging these shared biological mechanisms, genetic factors, and epidemiological trends, we can formulate more efficacious strategies for addressing the dual impact of asthma and depression.

Prediction of Response to FDA-Approved Targeted Therapy and Immunotherapy in Acute Lymphoblastic Leukemia (ALL)

OPINION STATEMENT

Acute lymphoblastic leukemia (ALL) represents the predominant cancer in pediatric populations, though its occurrence in adults is relatively rare. Pre-treatment risk stratification is crucial for predicting prognosis. Important factors for assessment include patient age, white blood cell (WBC) count at diagnosis, extramedullary involvement, immunophenotype, and cytogenetic aberrations. Minimal residual disease (MRD), primarily assessed by flow cytometry following remission, plays a substantial role in guiding management plans. Over the past decade, significant advancements in ALL outcomes have been witnessed. Conventional chemotherapy has remarkably reduced mortality rates; however, its intensive nature raises safety concerns and has led to the emergence of treatment-resistant cases with recurrence of relapses. Consequently, The U.S. Food and Drug Administration (FDA) has approved several novel treatments for relapsed/refractory ALL due to their demonstrated efficacy, as indicated by improved complete remission and survival rates. These treatments include tyrosine kinase inhibitors (TKIs), the anti-CD19 monoclonal antibody blinatumomab, anti-CD22 inotuzumab ozogamicin, anti-CD20 rituximab, and chimeric antigen receptor (CAR) T-cell therapy. Identifying the variables that influence treatment decisions is a pressing necessity for tailoring therapy based on heterogeneous patient characteristics. Key predictive factors identified in various observational studies and clinical trials include prelymphodepletion disease burden, complex genetic abnormalities, and MRD. Furthermore, the development of serious adverse events following treatment could be anticipated through predictive models, allowing for appropriate prophylactic measures to be considered. The ultimate aim is to incorporate the concept of precision medicine in the field of ALL through valid prediction platform to facilitate the selection of the most suitable treatment approach.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome

BACKGROUND

Multiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections.

METHODS

Buffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study ("Glue Grant") multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction.

RESULTS

The LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890-0.987] (training) and 0.833 [0.699-0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997-1.000] (training) and 0.907 [0.816-0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884-0.985] (training) and 0.836 [0.703-0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex-0.649 [0.537-0.762] (training) and 0.493 [0.301-0.685] (test); ISS + sex-0.630 [0.516-0.744] (training) and 0.482 [0.293-0.670] (test); NISS + sex-0.651 [0.540-0.763] (training) and 0.525 [0.335-0.714] (test)).

CONCLUSIONS

The accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.

Towards implementation of cognitive bias modification in mental health care: State of the science, best practices, and ways forward

Cognitive bias modification (CBM) has evolved from an experimental method testing cognitive mechanisms of psychopathology to a promising tool for accessible digital mental health care. While we are still discovering the conditions under which clinically relevant effects occur, the dire need for accessible, effective, and low-cost mental health tools underscores the need for implementation where such tools are available. Providing our expert opinion as Association for Cognitive Bias Modification members, we first discuss the readiness of different CBM approaches for clinical implementation, then discuss key considerations with regard to implementation. Evidence is robust for approach bias modification as an adjunctive intervention for alcohol use disorders and interpretation bias modification as a stand-alone intervention for anxiety disorders. Theoretical predictions regarding the mechanisms by which bias and symptom change occur await further testing. We propose that CBM interventions with demonstrated efficacy should be provided to the targeted populations. To facilitate this, we set a research agenda based on implementation frameworks, which includes feasibility and acceptability testing, co-creation with end-users, and collaboration with industry partners.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Chronic inflammation: Cross linking insights from Ayurvedic Sciences, a silver lining to systems biology and personalized medicine

Precision in personalized medicine is a crucial subject that needs comprehensive discussion and scientific validation. Traditional healthcare approaches like the Ayurvedic Sciences are often contextually linked with personalized medicine. However, it is unfortunate that this knowledge concerning Ayurveda and personalized medicine is restricted to applying systems biology techniques to 'prakriti' the phenotypic expression and characterization detailed in the literature. There are other significant constructs besides prakruti that interest an Ayurvedic physician, which accounts for crafting precision in evidence-based medicinal practices. There is this influential model of Ayurvedic healthcare practice wherein the physician maps specific personalized characters in addition to prakruti to deduce the host responses to endogenous and exposome conditions. Subsequently, tailored protocols are administered that bring about holistic, personalized outcomes. The review aimed to determine the effective methods for integrating Systems Biology, Ayurvedic Sciences, and Personalized Medicine (precision medicinebased). Ayurveda adopts a holistic approach, considering multiple variables and their interconnections, while the modern reductionist approach focuses on understanding complex details of smaller parts through rigorous experimentation. Despite seeming extremes, ongoing research on lifestyle, gut health, and spiritual well-being highlights the evolving intersection between traditional Ayurvedic practices and modern science. The current focus is on developing the fundamental concept of Ayurveda Biology by incorporating Systems Biology techniques. Challenges in this integration include understanding diverse data types, bridging interdisciplinary knowledge gaps, and addressing technological limitations and ethical concerns. Overcoming these challenges will require interdisciplinary collaboration, innovative methodologies, substantial investment in technology, and cultural sensitivity to preserve Ayurveda's core principles while leveraging modern scientific advancements. The focus of discussions and debates on such collaborations should be breakthrough clinical models, such as chronic inflammation, which can be objectively related to specific stages of disease manifestations described in Ayurveda. Validating patient characteristics with systems biology approaches, particularly in shared pathologies like chronic inflammation, is crucial for bringing prediction and precision to personalized medicine.

Astronaut omics and the impact of space on the human body at scale

Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives.

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website. We divided the evaluation indicators into four categories, including (i) medical service quality; (ii) medical service efficiency; (iii) profitability and sustainability; (iv) case grouping ability. Then we carried out descriptive analysis and comprehensive scoring on outcome measurements performance, improvement strategy and specialty performance. At last, the DRGKB finally contains 297 entries. It was found that DRGs generally have a considerable impact on hospital operations, including average length of stay, medical quality and use of medical resources. At the same time, the current DRGs also have many deficiencies, including insufficient reimbursement rates and the ability to classify complex cases. We analyzed these underperforming parts by domain. In conclusion, this research innovatively constructed a knowledgebase to quantify the practice effects of DRGs, analyzed and visualized the development trends and area performance from a comprehensive perspective. This study provides a data-driven research paradigm for following DRGs-related work along with a proposed DRGs evolution model. Availability and implementation: DRGKB is freely available at http://www.sysbio.org.cn/drgkb/. Database URL: http://www.sysbio.org.cn/drgkb/.

Prevalence and Factors Associated With Awareness of Precision Medicine Among Individuals With Depression and Anxiety

OBJECTIVE

This study investigates the prevalence and determinants of awareness of precision medicine among a nationally representative sample of individuals with self-reported depression and anxiety in the United States."

METHODS

Data were obtained from the Health Information National Trends Survey (HINTS) 5, Cycle 4, which is a study administered by the National Cancer Institute and is nationally representative. The survey, conducted between February and June 2020, targeted non-institutionalized, civilian US adults aged 18 years and older. Utilizing survey-weighted logistic regression, predictors of precision medicine awareness were assessed, encompassing sociodemographic, health-related, and technological factors.

RESULTS

Among 890 individuals with self-reported depression and/or anxiety, approximately 15.3% reported awareness of precision medicine. Participants who had a higher level of education and those who had increased health-linked social media usage were three times more likely to be aware of precision medicine compared to those who did not. Old age was also positively associated with increased awareness.

CONCLUSION

The present study's findings have disclosed an alarming lack of awareness of precision medicine, particularly among mentally ill persons with anxiety or depression, in which the targeted subgroups, including individuals with lower education levels and limited health-linked social media utilization, indicated lower levels of awareness. As such, it is recommended that such disparities be tackled using customized interventions along with educational initiatives, as this is likely to improve awareness levels while also ensuring equitable and increased access to precision medicine within the context of mental health.

The promise and challenges of genomics-informed periodontal disease diagnoses

Recent advances in human genomics and the advent of molecular medicine have catapulted our ability to characterize human and health and disease. Scientists and healthcare practitioners can now leverage information on genetic variation and gene expression at the tissue or even individual cell level, and an enormous potential exists to refine diagnostic categories, assess risk in unaffected individuals, and optimize disease management among those affected. This review investigates the progress made in the domains of molecular medicine and genomics as they relate to periodontology. The review summarizes the current evidence of association between genomics and periodontal diseases, including the current state of knowledge that approximately a third of the population variance of periodontitis may be attributable to genetic variation and the management of several monogenic forms of the disease can be augmented by knowledge of the underlying genetic cause. Finally, the paper discusses the potential utility of polygenic risk scores and genetic testing for periodontitis diagnosis now and in the future, in light of applications that currently exist in other areas of medicine and healthcare.

A mobile app for chronic disease self-management for individuals with low health literacy: A multisite randomized controlled clinical trial

The purpose of this study was to evaluate the effects of a mobile app designed to improve chronic disease self-management in patients 40 years and older with low health literacy and who had at least one chronic health condition, and to assess the impact of delivering information at different levels of reading difficulty. A randomized controlled trial was completed at two sites. Individuals aged 40 years and older screened for low health literacy who had at least one chronic health condition were randomly assigned to a tailored information multimedia app with text at one of three grade levels. Four primary outcomes were assessed: patient activation, chronic disease self-efficacy, health-related quality of life, and medication adherence. All groups showed overall increases in activation, self-efficacy, and health-related quality of life, but no change in medication adherence. No between-group differences were observed. The mobile app may have been effective in increasing participants' levels of several psychosocial variables, but this interpretation can only be advanced tentatively in light of lack of control-experimental group differences. Reading difficulty level was not significantly related to outcomes.

Unveiling the Future of Oncology and Precision Medicine through Data Science

Information generated via next-generation sequencing (NGS) technologies is often termed multi-omics data [...].

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Ethical frameworks of informed consent in the age of pediatric precision medicine

Precision medicine is an emergent medical paradigm that uses information technology to inform the use of targeted therapies and treatments. One of the first steps of precision medicine involves acquiring the patient's informed consent to protect their rights to autonomous medical decision-making. In pediatrics, there exists mixed recommendations and guidelines of consent-related practices designed to safeguard pediatric patient interests while protecting their autonomy. Here, we provide a high-level, clinical primer of (1) ethical informed consent frameworks widely used in clinical practice and (2) promising modern adaptations to improve informed consent practices in pediatric precision medicine. Given the rapid scientific advances and adoption of precision medicine, we highlight the dual need to both consider the clinical implementation of consent in pediatric precision medicine workflows as well as build rapport with pediatric patients and their substitute decision-makers working alongside interdisciplinary health teams.

Social Determinants of Health and Cancer Care: An ASCO Policy Statement

ASCO's new policy statement on SDOH supports practices that sustain and advance cancer health equity.

Building a Competency Framework to Integrate Inter-disciplinary Precision Medicine Capabilities into the Medical Technology and Pharmaceutical Industry

INTRODUCTION

Integration of precision medicine (PM) competencies across the Medical Technology and Pharmaceutical industry is critical to enable industry professionals to understand and develop the skills needed to navigate the opportunities arising from rapid scientific and technological innovation in PM. Our objective was to identify the key competency domains required by industry professionals to enable them to upskill themselves in PM-related aspects of their roles.

METHODS

A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to PM competencies that were consistent across multiple disciplines and competency frameworks. A survey was used to confirm the applicability of these domains to the cross-functional and multi-disciplinary work practices of industry professionals. Companies were requested to trial the domains to determine their relevance in practice and feedback was obtained.

RESULTS

Four PM-relevant domains were identified from the literature review: medical science and technology; translational and clinical application; governance and regulation and professional practice. Survey results refined these domains, and case studies within companies confirmed the potential for this framework to be used as an adjunct to current role specific competency frameworks to provide a specific focus on needed PM capabilities.

CONCLUSION

The framework was well accepted by local industry as a supplement to role specific competency frameworks to provide a structure on how to integrate new and evolving technologies into their current workforce development planning and build a continuous learning and cross-disciplinary mindset.

Towards voltammetric point of care detection of leucovorin

Current healthcare trends have seen an increased emphasis on the move towards personalised precision medicine to tailor treatments to the individual and their response to diseases and disease therapies. This highlighting a transition from traditional "one size fits all" to a more nuanced approach. Despite advancements in fundamental knowledge to facilitate personalised prevision approaches, lack of resources to implement such plans remains one of the largest hurdles to overcome. Monitoring of drug therapies is one key aspect that could aid in the evolution of precision medicine alongside the development of drugs and targeted treatment systems. This contribution illustrates the potential of square wave voltammetry (SWV) as a proof-of-concept for monitoring of circulating blood concentrations of treatment therapies within artificial urine, using leucovorin calcium (LV) as a model cancer therapy drug. A low cost, easy-to-use and portable sensor has been developed and successfully employed for the detection of LV over the linear range 0.5-30 μM which represents the therapeutically relevant concentrations for LV within artificial urine without any prior sample preparation required with a limit of detection of 2.63 μM and initial investigations into saliva and serum as biological matrices. The developed sensor describe herein exhibits a proof-of-concept for the engagement of such electrochemical sensors as point-of-care devices, where the sensors ease of use and removal of time-consuming and complex sample preparation methods will ultimately increase its usability by physicians, widening the avenues where electrochemical sensors could be employed.

Cancer symptom cluster research in pediatric oncology: a work in progress

In the 21st century, advances in basic research have provided new insights in the field of pediatric oncology. Pediatric patients tend to experience higher levels of distressing symptoms, which together form a symptom cluster. In clinical practice, these symptom clusters are reported daily by children and adolescents with cancer. Translational research has emerged as the translation of new knowledge from basic science into clinical practice. Understanding how neuroimmunoendocrine pathways regulate cancer development and the aspects underlying the specific therapies, such as chemotherapy and immunotherapy, is an important frontier for future research in pediatric oncology. The goal of translational research is to show how different variables in tumor and patient characteristics explain the differential effects of interventions, as translational research provides new insights into the management of cancer symptoms in children and adolescents with cancer. Together, this approach could lead to improvements in pediatric oncology care worldwide.

Ultra-secure storage and analysis of genetic data for the advancement of precision medicine

Cloud computing provides the opportunity to store the ever-growing genotype-phenotype data sets needed to achieve the full potential of precision medicine. However, due to the sensitive nature of this data and the patchwork of data privacy laws across states and countries, additional security protections are proving necessary to ensure data privacy and security. Here we present SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. With SQUiD, genotype-phenotype data can be stored in a low-security, low-cost public cloud in the encrypted form, which researchers can securely query without the public cloud ever being able to decrypt the data. We demonstrate the usability of SQUiD by replicating various commonly used calculations such as polygenic risk scores, cohort creation for GWAS, MAF filtering, and patient similarity analysis both on synthetic and UK Biobank data. Our work represents a new and scalable platform enabling the realization of precision medicine without security and privacy concerns.

Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities

The de novo construction of a living organism is a compelling vision. Despite the astonishing technologies developed to modify living cells, building a functioning cell "from scratch" has yet to be accomplished. The pursuit of this goal alone has─and will─yield scientific insights affecting fields as diverse as cell biology, biotechnology, medicine, and astrobiology. Multiple approaches have aimed to create biochemical systems manifesting common characteristics of life, such as compartmentalization, metabolism, and replication and the derived features, evolution, responsiveness to stimuli, and directed movement. Significant achievements in synthesizing each of these criteria have been made, individually and in limited combinations. Here, we review these efforts, distinguish different approaches, and highlight bottlenecks in the current research. We look ahead at what work remains to be accomplished and propose a "roadmap" with key milestones to achieve the vision of building cells from molecular parts.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Qualitative assessment of knowledge, attitude and practice of oncologists about precision medicine in cancer patients- study from Lahore, Pakistan

BACKGROUND

Precision medicine (PM) is in great progressive stages in the West and allows healthcare practitioners (HCPs) to give treatment according to the patient's genetic findings, physiological and environmental characteristics. PM is a relatively new treatment approach in Pakistan Therefore, it is important to investigate the level of awareness, attitude, and challenges faced by oncology physicians while practicing PM for various therapies, especially cancer treatment.

OBJECTIVES

The present study aims to explore the level of awareness, attitude, and practice of PM in Pakistan along with the challenges faced by the oncologists for the treatment of cancer using the PM approach.

METHODS

Phenomenology-based qualitative approach was used. Face-to-face in-depth interviews were conducted using the purposive sampling approach among oncologists in Lahore, Pakistan. The data were analyzed using thematic content analysis to identify themes and sub-themes.

RESULTS

Out of 14 physicians interviewed 11 were aware of PM. They were keen on training to hone their skills and agreed on providing PM. Oncologists believed PM was expensive and given to affluent patients only. Other impeding factors include cost, lack of knowledge, and drug unavailability.

CONCLUSIONS

Despite basic knowledge and will to practice, resource and cost constraints were marked as significant barriers. Additional training programs and inclusion into the curriculum may help to pave the way to PM implementation in the future. In addition, health authorities and policymakers need to ensure a cheaper PM treatment can be made available for all cancer patients.

Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study

BACKGROUND

The rehabilitation of a patient who had a stroke requires precise, personalized treatment plans. Natural language processing (NLP) offers the potential to extract valuable exercise information from clinical notes, aiding in the development of more effective rehabilitation strategies.

OBJECTIVE

This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of patients who had a stroke treated at the University of Pittsburgh Medical Center.

METHODS

A cohort of 13,605 patients diagnosed with stroke was identified, and their clinical notes containing rehabilitation therapy notes were retrieved. A comprehensive clinical ontology was created to represent various aspects of physical rehabilitation exercises. State-of-the-art NLP algorithms were then developed and compared, including rule-based, machine learning-based algorithms (support vector machine, logistic regression, gradient boosting, and AdaBoost) and large language model (LLM)-based algorithms (ChatGPT [OpenAI]). The study focused on key performance metrics, particularly F1-scores, to evaluate algorithm effectiveness.

RESULTS

The analysis was conducted on a data set comprising 23,724 notes with detailed demographic and clinical characteristics. The rule-based NLP algorithm demonstrated superior performance in most areas, particularly in detecting the "Right Side" location with an F1-score of 0.975, outperforming gradient boosting by 0.063. Gradient boosting excelled in "Lower Extremity" location detection (F1-score: 0.978), surpassing rule-based NLP by 0.023. It also showed notable performance in the "Passive Range of Motion" detection with an F1-score of 0.970, a 0.032 improvement over rule-based NLP. The rule-based algorithm efficiently handled "Duration," "Sets," and "Reps" with F1-scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot prompts, achieved high recall but generally lower precision and F1-scores. However, it notably excelled in "Backward Plane" motion detection, achieving an F1-score of 0.846, surpassing the rule-based algorithm's 0.720.

CONCLUSIONS

The study successfully developed and evaluated multiple NLP algorithms, revealing the strengths and weaknesses of each in extracting physical rehabilitation exercise information from clinical notes. The detailed ontology and the robust performance of the rule-based and gradient boosting algorithms demonstrate significant potential for enhancing precision rehabilitation. These findings contribute to the ongoing efforts to integrate advanced NLP techniques into health care, moving toward predictive models that can recommend personalized rehabilitation treatments for optimal patient outcomes.