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

Awareness of genomic testing among patients with breast cancer in Europe

PURPOSE

Genomic testing, involving expression profiling of tumour tissue, is a powerful tool for determining appropriate treatments for certain cancer patients. This study aimed to evaluate awareness of genomic testing in breast cancer patients in five European countries.

METHODS

The survey was initiated by Cancer Patients Europe and developed with patient associations, oncologists, and a psycho-oncologist. Participants were recruited via email and social media and completed a 42-question internet survey.

RESULTS

Of 1383 participants in eligible countries completing the survey, 566 women with current or previous HR+/HER2- breast cancer, potentially eligible for genomic testing, were analysed. 245 (43.3 %) were aged 50-59 years and 381 (67.3 %) had received higher education. 238 participants (42.1 %) had heard about genomic testing; 122 (21.6 %) were informed of their eligibility for testing, and 104 (18.4 %) were given reasons for the test. The majority (N = 479; 84.6 %) felt they lacked sufficient information to decide, and only 139 (24.6 %) opted for testing. Overall, 246 (43.5 %) wanted more information on additional testing and 234 (41.3 %) wanted more information on treatment options. The main information sources were medical professionals (N = 363; 64.1 %) and the internet (N = 351; 62.0 %). However, 398 participants (70.3 %) indicated that their healthcare professionals did not advise them on where to find more information.

CONCLUSIONS

This study highlights insufficient awareness of, and access to, genomic testing in breast cancer. Healthcare professionals need to improve communication with patients regarding genomic testing and involve them in shared decision-making. Likewise, patient associations have a role in providing clear information to patients.

To Implement or Not to Implement? A Commentary on the Pitfalls of Judging the Value and Risks of Personalized Prognostic Statistical Models

Unlabelled

Prognostic models in medicine have garnered significant attention, with established guidelines governing their development. However, there remains a lack of clarity regarding the appropriate circumstances for (1) creating and (2) implementing tools based on models with limited performance. This commentary addresses this gap by analyzing the pros and cons of tool development and providing a structured outline that includes critical questions to consider in the decision-making process, based on an example of patients with osteoarthritis. We propose three general justifications for the implementation of survey-based models: (1) mitigation of expectation bias among patients and clinicians, (2) advancement of personalized medicine, and (3) enhancement of existing predictive information sources. Nevertheless, it is crucial to acknowledge that implementing such models is always context-dependent and may harm certain patients, necessitating careful consideration of the withdrawal of tool development and implementation in specific cases. To facilitate the identification of these scenarios, we delineate 16 possibilities following the implementation of a personalized prognostic model and compare the consequences to a current one-size-fits-all treatment recommendation at a population level. Our analysis encompasses the possible patient benefits and harms resulting from implementing or not implementing personalized prognostic models and summarizes them. These findings, together with context-related factors, are important to consider when deciding if, how, and for whom a personalized prognostic tool should be created and implemented. We present a checklist of questions and an Excel sheet calculation table, allowing researchers to weigh the benefits and harms of creating and implementing a personalized prognostic model at a population level against one-size-fits-all standard care in a structured and standardized manner. We condense this into a single value using a uniform Benefit-Risk Score formula. Together with context-related factors, the calculation table and formula are designed to aid researchers in their decision-making process on providing a personalized prognostic tool and deciding for or against its complete or partial implementation. This work serves as a foundation for further discourse and refinement of tool development decisions for prognostic models in health care.

Integrating pharmacogenomics in three Middle Eastern countries' healthcare (Lebanon, Qatar, and Saudi Arabia): Current insights, challenges, and strategic directions

BACKGROUND AND OBJECTIVES

Pharmacogenomics (PGx) leverages genomic information to tailor drug therapies, enhancing precision medicine. Despite global advancements, its implementation in Lebanon, Qatar, and Saudi Arabia faces unique challenges in clinical integration. This study aimed to investigate PGx attitudes, knowledge implementation, associated challenges, forecast future educational needs, and compare findings across the three countries.

METHODS

This cross-sectional study utilized an anonymous, self-administered online survey distributed to healthcare professionals, academics, and clinicians in Lebanon, Qatar, and Saudi Arabia. The survey comprised 18 questions to assess participants' familiarity with PGx, current implementation practices, perceived obstacles, potential integration strategies, and future educational needs.

RESULTS

The survey yielded 337 responses from healthcare professionals across the three countries. Data revealed significant variations in PGx familiarity and educational involvement. Qatar and Saudi Arabia participants were more familiar with PGx compared to Lebanon (83%, 75%, and 67%, respectively). Participation in PGx-related talks was most prevalent in Saudi Arabia (96%), followed by Qatar (53%) and Lebanon (35%). Key challenges identified included test cost and reimbursement, insufficient physician knowledge, and lack of infrastructure. Lebanon reported the highest concern for test costs (16%), compared to the lowest in Saudi Arabia (5%). Despite these challenges, a strong consensus emerged on PGx's potential to improve patient outcomes, with over 86% of respondents in all three countries expressing this belief. Educational interest areas varied by country, with strong interest in PGx for cancer chemotherapy in Saudi Arabia and Lebanon and for diabetes mellitus in Qatar.

CONCLUSION

This study highlights the significant influence of varied educational backgrounds and infrastructural limitations on PGx implementation across Lebanon, Qatar, and Saudi Arabia. The findings emphasize the need for targeted strategies in each country to address these distinct barriers. Integrating PGx education into healthcare training programs and clinical workflows could unlock PGx's potential to optimize patient care.

Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China

BACKGROUND

Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate information for the treatment, prevention, and control of allergic rhinitis.

METHODS

We developed a Long Short-Term Memory model for effectively predicting the daily outpatient visits of allergic rhinitis patients based on air pollution and meteorological data. We collected the outpatient data from the departments of otolaryngology, emergency medicine, pediatrics, and respiratory medicine at the Affiliated Hospital of Hangzhou Normal University, from January 2022 to August 2024. The data were stratified by gender and age and were separately input into the model for evaluation. A total of 25,425 outpatient data samples were assessed in this study.

RESULTS

Based on the data obtained from males (n = 13,943), females (n = 11,482), adults (n = 17,473), and minors (n = 7,952), the normalized mean squared errors of the Long Short-Term Memory model were 0.4674976, 0.3812502, 0.418301, and 0.4322124, respectively. By comparing the NMSE prediction results of ARIMA and LSTM models on this dataset, the LSTM model was found to outperform the ARIMA model in terms of stability and accuracy.

CONCLUSIONS

The model presented here could effectively predict the daily outpatient visits for allergic rhinitis patients based on air pollution and meteorological data, thereby offering valuable data-driven support for hospital management and for potentially improving societal management and prevention of allergic rhinitis.

Omics in Keratoconus: From Molecular to Clinical Practice

Keratoconus (KC) is a progressive ocular disorder marked by structural and functional alterations of the cornea, leading to significant visual impairment. Recent studies indicate that these corneal changes are linked to molecular and cellular mechanisms that disrupt and degrade the extracellular matrix. This degradation is influenced by proteinases that contribute to a loss of homeostasis and an imbalance in the antioxidant/oxidative state within the cornea, fostering oxidative stress, inflammation, and apoptosis. Although these biological processes have been identified primarily through molecular biology research, omics technologies have significantly advanced our understanding of the physiological and pathological phenomena associated with KC. Omics studies encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics, have emerged as critical tools in elucidating the complex biological landscape of various diseases, including ocular conditions. The integrative application of these studies has demonstrated their potential in personalizing medicine across diverse fields such as oncology, neurology, and ophthalmology. This review aims to describe findings from omics research applied to keratoconus, highlighting the genomic, transcriptomic, proteomic, epigenomic, and metabolomic aspects derived from ocular and other biological samples. Notably, the molecular insights gained from these studies hold promise for identifying biomarkers of keratoconus, which could enhance diagnostic accuracy and therapeutic strategies. The exploration of these biomarkers may facilitate improved management and treatment options for patients, contributing to personalized care in keratoconus management.

The Gut-Brain Axis in Irritable Bowel Syndrome: Implementing the Role of Microbiota and Neuroimmune Interaction in Personalized Prevention-A Narrative Review

Background and Purpose

Irritable bowel syndrome (IBS) is a disorder characterized by microbiota-neuroimmune interaction resulting in disturbance to the gut-brain axis (GBA). The purpose of this review is to garner an overview of the different pathophysiological mechanisms indicated in the development of IBS and the associated sequalae on gut microbiota alongside its role in the GBA. Moreover, we aim to provide an insight into the possibility of utilizing personalized medicine when managing said affected populations.

Methods

A comprehensive review was performed of the relevant literature pertaining to the current state of GBA alteration implicated in IBS, comprising microbiota-neuroimmune interaction alongside disturbance and activation, respectively. Different search databases were utilized, including PubMed/MEDLINE and ScienceDirect.

Results

The review demonstrated the most evident etiologies of IBS being the imbalance of microbiota and the alteration to the GBA. Furthermore, the interrelation between microbiota and neuroimmunity was discussed. Promising avenues for IBS prevention and management are offered through emerging research on the pathophysiological mechanisms indicated in IBS-associated GBA alteration. This entails a role for the involved interactions between microbiota modification and neuroimmunity activation.

Conclusion

Promising prospects for symptom prevention and management are signaled by the possibility of personalized therapy specifically designed to address the GBA dysfunction indicated in IBS. Policymakers and developers should encourage further study and allocate available resources to aid researchers in the implementation and identification of novel preventive therapeutics. Furthermore, physicians should advocate and integrate the use of personalized medical approaches of IBS to help ensure a better quality of life.

Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing

BACKGROUND

There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS

This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS

The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION

Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

Engineering Useful Microbial Species for Pharmaceutical Applications

Microbial engineering has made a significant breakthrough in pharmaceutical biotechnology, greatly expanding the production of biologically active compounds, therapeutic proteins, and novel drug candidates. Recent advancements in genetic engineering, synthetic biology, and adaptive evolution have contributed to the optimization of microbial strains for pharmaceutical applications, playing a crucial role in enhancing their productivity and stability. The CRISPR-Cas system is widely utilized as a precise genome modification tool, enabling the enhancement of metabolite biosynthesis and the activation of synthetic biological pathways. Additionally, synthetic biology approaches allow for the targeted design of microorganisms with improved metabolic efficiency and therapeutic potential, thereby accelerating the development of new pharmaceutical products. The integration of artificial intelligence (AI) and machine learning (ML) plays a vital role in further advancing microbial engineering by predicting metabolic network interactions, optimizing bioprocesses, and accelerating the drug discovery process. However, challenges such as the efficient optimization of metabolic pathways, ensuring sustainable industrial-scale production, and meeting international regulatory requirements remain critical barriers in the field. Furthermore, to mitigate potential risks, it is essential to develop stringent biocontainment strategies and implement appropriate regulatory oversight. This review comprehensively examines recent innovations in microbial engineering, analyzing key technological advancements, regulatory challenges, and future development perspectives.

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Genetically Engineered Bacteria as a Promising Therapeutic Strategy Against Cancer: A Comprehensive Review

As a significant cause of global mortality, the cancer has also economic impacts. In the era of cancer therapy, mitigating side effects and costs and overcoming drug resistance is crucial. Microbial species can grow inside the tumor microenvironment and inhibit cancer growth through direct killing of tumor cells and immunoregulatory effects. Although microbiota or their products have demonstrated anticancer effects, the possibility of acting as pathogens and exerting side effects in certain individuals is a risk. Hence, several genetically modified/engineered bacteria (GEB) have been developed to this aim with ability of diagnosing and selective targeting and destruction of cancers. Additionally, GEB are expected to be considerably more efficient, safer, more permeable, less costly, and less invasive theranostic approaches compared to wild types. Potential GEB strains such as Escherichia coli (Nissle 1917, and MG1655), Salmonella typhimurium YB1 SL7207 (aroA gene deletion), VNP20009 (∆msbB/∆purI) and ΔppGpp (PTet and PBAD), and Listeria monocytogenes Lmat-LLO have been developed to combat cancer cells. When used in tandem with conventional treatments, GEB substantially improve the efficacy of anticancer therapy outcomes. In addition, public acceptance, optimal timing (s), duration (s), dose (s), and strains identification, interactions with other strains and the host cells, efficacy, safety and quality, and potential risks and ethical dilemmas include major challenges.

Review of Precision Medicine and Diagnosis of Neonatal Illness

Background/Objectives: Precision medicine is a state-of-the-art medicine tactic that tailors information about people's genes, environment, and lifestyle to aid the prevention, diagnosis, and treatment of various diseases to provide an overview of the currently available knowledge and applicability of precision medicine in the diagnosis of different cases admitted to the NICU, such as encephalopathies, respiratory distress syndrome of prematurity, hemodynamic instability, acute kidney injury, sepsis, and hyperbilirubinemia. Methods: The authors searched databases, such as PubMed and PubMed Central, for the terms neonatal "precision medicine", "personalized medicine", "genomics", and "metabolomics", all related to precision medicine in the diagnosis of neonatal illness. The related studies were collected. Results: The review highlights the diagnostic approach that serves to implement precision medicine in the NICU and provide precision diagnosis, monitoring, and treatment. Conclusions: In this review, we projected several diagnostic approaches that provide precision identification of health problems among sick neonates with complex illnesses in the NICU; some are noninvasive and available in ordinary healthcare settings, while others are invasive or not feasible or still in ongoing research as machine learning algorithms. Future studies are needed for the wide implementation of artificial intelligence tools in the diagnosis of neonatal illnesses.

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.

Knowledge and attitudes towards genomic medicine and pharmacogenomics of medical undergraduate students in Sri Lanka: a cross-sectional study

Genomic medicine and pharmacogenomics (PGX) are emerging practices in medicine that play a vital role in providing personalized and efficient treatments for patients. While many countries have integrated these novel concepts into their undergraduate medical curricula to enhance the quality of healthcare, Sri Lanka remains relatively new to these advancements. Herein, we accessed the knowledge and attitude of Sri Lankan medical undergraduates on genomic medicine and PGX and explored the readiness of introducing genomic insights to Sri Lankan undergraduate medical education. The study sample was the undergraduate students of Medical Faculty of Wayamba University in Sri Lanka, being a newly developed and diverse institution seeking research findings to enhance the curriculum and teaching-learning activities aiming to produce competent graduates. A descriptive cross-sectional study was conducted by distributing a questionnaire to all five student batches at Faculty of Medicine, Wayamba University of Sri Lanka. The data of 232 respondents (55% response rate), demonstrated a good level of knowledge on genomic medicine and PGX, with no significant variation of the level of knowledge across the five academic years. A nuanced range of attitudes, encompassing both negative and positive perspectives towards genomic medicine and PGX was observed varying according to the specific questions posed. However, heavy concerns regarding data privacy, insurance implications, and the timing of implementation appeared. The results of the study highlight a need for curriculum enhancement, acknowledging the level of knowledge while emphasizing areas for improvement in students' perspectives on genomic medicine and PGX for better advancements in future healthcare of Sri Lanka.

Bridging the gap: understanding the perspective of healthcare professional students towards precision medicine in a Nigerian tertiary institution (a cross-sectional study)

BACKGROUND

Individuals often respond differently to medications, giving rise to the field of precision medicine (PM), which focuses on tailoring treatments to individual genetic, environmental, and lifestyle factors. This study examined the level of comfort healthcare professional students have with their knowledge of precision medicine, alongside their attitudes and perceptions toward precision medicine, at a tertiary institution in Nigeria.

METHODS

A cross-sectional questionnaire-based study was conducted among healthcare professional students (400-600 level) at the University of Nigeria Nsukka between January and March 2024. The data were analyzed via IBM Statistical Product and Service Solutions (SPSS) for Windows version 27. Descriptive analyses (frequency, percentage, mean, and standard deviation) and chi-square tests were used to summarize and compare the variables. Statistical significance was set at p < 0.05.

RESULTS

A total of 431 healthcare professional students participated in this study. Fewer than half (n = 200, 46.4%) were pharmacy students, and the majority were within the age range of 21-25 years (n = 288, 66.8%). Nearly half (n = 206, 47.8%) reported having information about precision medicine from the internet, and the majority (n = 341, 79.1%) expressed having an interest in a career involving research in precision medicine. More than half of the students (n = 240, 55.7%) were comfortable with their knowledge of precision medicine and had favourable attitudes (n = 236, 54.8%). Additionally, more than half had positive perceptions of ethical concerns (n = 216, 50.1%) and education in precision medicine (n = 239, 55.5%). Gender, age, department, level of study, awareness of PM, and interest in a career involving research were significantly associated with students' knowledge, attitudes, and perceptions of precision medicine (p < 0.001).

CONCLUSION

Healthcare professional students were comfortable with their knowledge of PM and, in addition, had favourable attitudes and positive perceptions toward the use of precision medicine.

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.

Bridging silicon and carbon worlds with digital twins and on-chip systems in drug discovery

This perspective discusses the convergence of digital twin (DT) technology and on-the-chip systems as pivotal innovations in precision medicine, substantially advancing drug discovery. DT leverages extensive health data to create dynamic virtual patient models, enabling predictive insights and optimized treatment strategies. Concurrently, on-the-chip systems from the Carbon world replicate human biological processes on microfluidic platforms, providing detailed insights into disease mechanisms and pharmacological interactions. The convergence of these technologies promises to revolutionize drug development by enhancing therapeutic precision, accelerating discovery timelines, and reducing costs. Specifically, it assesses their role in drug development, from refining therapeutic precision to expediting discovery timelines and reducing the final price. Nevertheless, integrating these technologies faces challenges, including data collection and privacy concerns, technical intricacies, and clinical adoption barriers. This manuscript argues for interdisciplinary cooperation to navigate these challenges, positing DTs and on-the-chip technologies as foundational elements in personalized healthcare and drug discovery.

Pixels to precision: Neuroradiology's leap into 3D printing for personalized medicine

The realm of precision medicine, particularly its application within various sectors, shines notably in neuroradiology, where it leverages the advancements of three-dimensional (3D) printing technology. This synergy has significantly enhanced surgical planning, fostered the creation of tailor-made medical apparatus, bolstered medical pedagogy, and refined targeted therapeutic delivery. This review delves into the contemporary advancements and applications of 3D printing in neuroradiology, underscoring its pivotal role in refining surgical strategies, augmenting patient outcomes, and diminishing procedural risks. It further articulates the utility of 3D-printed anatomical models for enriched comprehension, simulation, and educational endeavors. In addition, it illuminates the horizon of bespoke medical devices and prosthetics, illustrating their utility in addressing specific cranial and spinal anomalies. This narrative extends to scrutinize how 3D printing underpins precision medicine by offering customized drug delivery mechanisms and therapies tailored to the patient's unique medical blueprint. It navigates through the inherent challenges of 3D printing, including the financial implications, the need for procedural standardization, and the assurance of quality. Prospective trajectories and burgeoning avenues, such as material and technological innovations, the confluence with artificial intelligence, and the broadening scope of 3D printing in neurosurgical applications, are explored. Despite existing hurdles, the fusion of 3D printing with neuroradiology heralds a transformative era in precision medicine, poised to elevate patient care standards and pioneer novel surgical paradigms.

Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2-3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization-a major competitor of chromatography-mass spectrometry-decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

Genetic Diversity Landscape in African Population: A Review of Implications for Personalized and Precision Medicine

Introduction

Africa, a continent considered to be the cradle of human beings has the largest genetic diversity among its population than other continents. This review discusses the implications of this high African genetic diversity to the development of personalized and precision medicine.

Methodology

A comprehensive search across PubMed, Google Scholar, Science Direct, DOAJ, AJOL, and the Cochrane Library electronic databases and manual Google searches was conducted using key terms "genetics", "genetic diversity", "Africa", "precision medicine", and "personalized medicine". Updated original and review studies focusing on the implications of African high genetic diversity on personalized and precision medicine were included. Included studies were thematically synthesized to elucidate their positive or negative implications for personalized healthcare, aiming to foster informed clinical practice and scientific inquiry.

Results

African populations' high genetic diversity presents opportunities for personalized and precision medicine including improving pharmacogenomics, understanding gene interactions, discovering new variants, mapping disease genes, creating updated genomic reference panels, and validating biomarkers. However, challenges include underrepresentation in studies, scarcity of reference genomes, inaccuracy of genetic testing and interpretation, and ancestry misclassification. Addressing these requires the establishment of genomic research centers, increasing funding, creating biobanks and repositories, education, infrastructure, and international cooperation to enhance healthcare equity and outcomes through personalized and precision medicine.

Conclusion

High African genetic diversity presents both positive and negative implications for personalized and precision medicine. Deep further research is recommended to harness the challenges and use the opportunities to develop customized treatments.

Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review

Background and Aims

Cardiovascular diseases are responsible for a high mortality rate globally. Precision medicine has emerged as an essential tool for improving cardiovascular disease outcomes. In this context, using advanced imaging exams is fundamental in cardiovascular precision medicine, enabling more accurate diagnoses and customized treatments. This review aims to provide a concise review on how advanced cardiovascular imaging supports precision medicine, highlighting its benefits, challenges, and future directions.

Methods

A literature review was carried out using the Pubmed and Google Scholar databases, using search strategies that combined terms such as precision medicine, cardiovascular diseases, and imaging tests.

Results

More advanced analysis aimed at diagnosing and describing cardiovascular diseases in greater detail is made possible by tests such as cardiac computed tomography, cardiac magnetic resonance imaging, and cardiac positron emission tomography. In addition, the aggregation of imaging data with other omics data allows for more personalized treatment and a better description of patient profiles.

Conclusion

The use of advanced imaging tests is essential in cardiovascular precision medicine. Although there are still technical and ethical obstacles, it is essential that there is collaboration between health professionals, as well as investments in technology and education to better disseminate cardiovascular precision medicine and consequently promote improved patient outcomes.

Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease

Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.

A brief clinical genetics review: stepwise diagnostic processes of a monogenic disorder-hypertriglyceridemia

The completion of the Human Genome Project and tremendous advances in automated high-throughput genetic analysis technologies have enabled explosive progress in the field of genetics, which resulted in countless discoveries of novel genes and pathways. Many phenotype- or disease-associated single nucleotide polymorphisms (SNPs) with a high statistical significance have been identified through numerous genome-wide association studies (GWAS), and various polygenic risk scoring (PRS) schemes have been proposed to identify individuals with a high risk for a certain trait or disorder. Meanwhile, medical education in genetics has lagged far behind, leaving many physicians and healthcare providers unprepared in the genomic era. Thus, there is an urgent need to educate physicians and healthcare providers with basic knowledge and skills in genetics. To facilitate this, some basic terminologies and concepts are discussed in this review. In addition, some important considerations in delineating and incorporating clinical genetic testing in the diagnosis and management of a monogenic disorder are illustrated in a stepwise fashion. Furthermore, the effects of disease-associated SNPs represented by a PRS scheme clearly demonstrated that even the phenotypes of a monogenic disorder due to the same pathogenic variant in family members are modulated by the polygenic background. In human genetics, despite these explosive advancements, we are still far from clearly deciphering the interplay of gene variants to effect unique characteristics in an individual. In addition, sophisticated genome or gene directed therapies are being investigated for numerous disorders. Therefore, evolution in the field of genetics is likely to continue into the foreseeable future. In the meantime, much emphasis should be placed on educating physicians and healthcare professionals to be well-versed and skillful in the clinical use of genetics so that they can fully embrace the new era of precision medicine.

Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

Introduction

Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health.

Methods

Firstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health.

Results

After screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a "one-size-fits-all approach" with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation).

Discussion

Although there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

Rewriting the textbook for pharma: how to adapt and thrive in a digital, personalized and collaborative world

The pharmaceutical industry is undergoing a sweeping transformation, driven by technological innovations, demographic shifts, regulatory changes and consumer expectations. For adaptive players in pharma to excel in this rapidly changing landscape, which will be markedly different from today by 2030 and beyond, they will require a different set of skills, capabilities and mindsets, as well as a willingness to collaborate and co-create value with multiple stakeholders. The industry needs to rewrite the textbook for pharma by embracing and implementing four key dimensions of change: digitalization, personalization, collaboration and innovation. In this article, we will examine how these dimensions of change are reshaping the industry, and provide practical and strategic guidance based on best practices and examples. Specifically, adaptive pharma companies should embrace the use of advanced digital technologies, such as artificial intelligence and machine learning, to streamline processes and solve challenges rapidly. Personalization, both in medicine and patient engagement, will also be key to success in the 'digital revolution', and a collaborative approach involving partnerships with tech start-ups, health-care providers and regulatory bodies will also be essential to create an integrated and responsive health-care ecosystem. Using these ideas for a rewritten textbook for pharma, adaptive players in pharma will evolve to be personalized and digitized health-focused organizations that provide comprehensive solutions which go beyond drugs and devices.

The future of medicine: an outline attempt using state-of-the-art business and scientific trends

Introduction

Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future.

Methods

The current literature about the latest business and scientific evolutions and trends was reviewed.

Results

In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient.

Conclusion

Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

Epigenetics, Microbiome and Personalized Medicine: Focus on Kidney Disease

Personalized medicine, which involves modifying treatment strategies/drug dosages based on massive laboratory/imaging data, faces large statistical and study design problems. The authors believe that the use of continuous multidimensional data, such as those regarding gut microbiota, or binary multidimensional systems properly transformed into a continuous variable, such as the epigenetic clock, offer an advantageous scenario for the design of trials of personalized medicine. We will discuss examples focusing on kidney diseases, specifically on IgA nephropathy. While gut dysbiosis can provide a treatment strategy to restore the standard gut microbiota using probiotics, transforming epigenetic omics data into epigenetic clocks offers a promising tool for personalized acute and chronic kidney disease care. Epigenetic clocks involve a complex transformation of DNA methylome data into estimated biological age. These clocks can identify people at high risk of developing kidney problems even before symptoms appear. Some of the effects of both the epigenetic clock and microbiota on kidney diseases seem to be mediated by endothelial dysfunction. These "big data" (epigenetic clocks and microbiota) can help tailor treatment plans by pinpointing patients likely to experience rapid declines or those who might not need overly aggressive therapies.

Telehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies

INTRODUCTION

Precision prevention has shown an upsurge in popularity among epidemiologists in both developed and developing countries in the past decade.

OBJECTIVES

Initially practiced in oncology, this approach is increasingly adopted in public health to guard against other common non-communicable diseases (NCDs), such as diabetes and cardiovascular diseases. It aims to tailor preventive measures according to each individual's unique characteristics, such as genomic data, socio-demographic features, environmental factors, and cultural background.

METHODS

Healthcare information technologies, including telehealth and artificial intelligence (AI), have served as a vital catalyst in the expansion of this field in the past decade. Under this framework, real-time contemporaneous clinical data is collected via a wide range of digital health devices, such as telehealth monitors, wearables, etc., and then analyzed by AI or non-AI prediction models, which then generate preventive recommendations.

RESULTS

The utilization of telehealth technologies in the precision prevention of cardiovascular diseases (CVDs) is a very illustrative application. This paper explores these topics as well as certain limitations and unintended consequences (UICs) and outlines telehealth as a core enabler of precision prevention as well as public health.

A Precision Medicine Guided Approach to the Utilization of Biomarkers in MASLD

The new nomenclature of metabolic dysfunction-associated steatotic liver disease (MASLD) emphasizes a positive diagnosis based on cardiometabolic risk factors. This definition is not only less stigmatizing but also allows for subclassification and stratification, thereby addressing the heterogeneity of what was historically referred to as nonalcoholic fatty liver disease. The heterogeneity within this spectrum is influenced by several factors which include but are not limited to demographic/dietary factors, the amount of alcohol use and drinking patterns, metabolic status, gut microbiome, genetic predisposition together with epigenetic factors. The net effect of this dynamic and intricate system-level interaction is reflected in the phenotypic presentation of MASLD. Therefore, the application of precision medicine in this scenario aims at complex phenotyping with consequent individual risk prediction, development of individualized preventive strategies, and improvements in the clinical trial designs. In this review, we aim to highlight the importance of precision medicine approaches in MASLD, including the use of novel biomarkers of disease, and its subsequent utilization in future study designs.

Identification of Time-Series Pattern Marker in Its Application to Mortality Analysis of Pneumonia Patients in Intensive Care Unit

Electronic Health Records (EHRs) are a significant source of big data used to track health variables over time. The analysis of EHR data can uncover medical markers or risk factors, aiding in the diagnosis and monitoring of diseases. We introduce a novel method for identifying markers with various temporal trend patterns, including monotonic and fluctuating trends, using machine learning models such as Long Short-Term Memory (LSTM). By applying our method to pneumonia patients in the intensive care unit using the MIMIC-III dataset, we identified markers exhibiting both monotonic and fluctuating trends. Specifically, monotonic markers such as red cell distribution width, urea nitrogen, creatinine, calcium, morphine sulfate, bicarbonate, sodium, troponin T, albumin, and prothrombin time were more frequently observed in the mortality group compared to the recovery group throughout the 10-day period before discharge. Conversely, fluctuating trend markers such as dextrose in sterile water, polystyrene sulfonate, free calcium, and glucose were more frequently observed in the mortality group as the discharge date approached. Our study presents a method for detecting time-series pattern markers in EHR data that respond differently according to disease progression. These markers can contribute to monitoring disease progression and enable stage-specific treatment, thereby advancing precision medicine.

Social determinants of health, the microbiome, and surgical injury

ABSTRACT

Postinjury infection continues to plague trauma and emergency surgery patients fortunate enough to survive the initial injury. Rapid response systems, massive transfusion protocols, and the development of level 1 trauma centers, among others, have improved the outcome for millions of patients worldwide. Nonetheless, despite this excellent initial care, patients still remain vulnerable to postinjury infections that can result in organ failure, prolonged critical illness, and even death. While risk factors have been identified (degree of injury, blood loss, time to definitive care, immunocompromise, etc.), they remain probabilistic, not deterministic, and do not explain outcome variability at the individual case level. Here, we assert that analysis of the social determinants of health, as reflected in the patient's microbiome composition (i.e., community structure, membership) and function (metabolomic output), may offer a "window" with which to define individual variability following traumatic injury. Given emerging knowledge in the field, a more comprehensive evaluation of biomarkers within the patient's microbiome, from stool-based microbial metabolites to those in plasma and those present in exhaled breath, when coupled with clinical metadata and machine learning, could lead to a more deterministic assessment of an individual's risk for a poor outcome and those factors that are modifiable. The aim of this piece is to examine how measurable elements of the social determinants of health and the life history of the patient may be buried within the ecologic memory of the gut microbiome. Here we posit that interrogation of the gut microbiome in this manner may be used to inform novel approaches to drive recovery following a surgical injury.

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

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

Precision prevention in worksite health-A scoping review on research trends and gaps

OBJECTIVES

To map the current state of precision prevention research in the workplace setting, specifically to study contexts and characteristics, and to analyze the precision prevention approach in the stages of risk assessment/data monitoring, data analytics, and the health promotion interventions implemented.

METHODS

Six international databases were searched for studies published between January 2010 and May 2023, using the term "precision prevention" or its synonyms in the context of worksite health promotion.

RESULTS

After screening 3,249 articles, 129 studies were reviewed. Around three-quarters of the studies addressed an intervention (95/129, 74%). Only 14% (18/129) of the articles primarily focused on risk assessment and data monitoring, and 12% of the articles (16/129) mainly included data analytics studies. Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. The main data analyses of all studies were regression analyses (44% with analyses of variance or linear mixed models), whereas machine learning methods (e.g., Algorithms, Markov Models) were conducted only in 8% of the articles.

DISCUSSION

Although there is a growing number of precision prevention studies in the workplace, there are still research gaps in applying new data analysis methods (e.g., machine learning) and implementing innovative study designs. In the future, it is desirable to take a holistic approach to precision prevention in the workplace that encompasses all the stages of precision prevention (risk assessment/data monitoring, data analytics and interventions) and links them together as a cycle.

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

The effectiveness of digital twins in promoting precision health across the entire population: a systematic review

Digital twins represent a promising technology within the domain of precision healthcare, offering significant prospects for individualized medical interventions. Existing systematic reviews, however, mainly focus on the technological dimensions of digital twins, with a limited exploration of their impact on health-related outcomes. Therefore, this systematic review aims to explore the efficacy of digital twins in improving precision healthcare at the population level. The literature search for this study encompassed PubMed, Embase, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, and Wanfang Database to retrieve potentially relevant records. Patient health-related outcomes were synthesized employing quantitative content analysis, whereas the Joanna Briggs Institute (JBI) scales were used to evaluate the quality and potential bias inherent in each selected study. Following established inclusion and exclusion criteria, 12 studies were screened from an initial 1321 records for further analysis. These studies included patients with various conditions, including cancers, type 2 diabetes, multiple sclerosis, heart failure, qi deficiency, post-hepatectomy liver failure, and dental issues. The review coded three types of interventions: personalized health management, precision individual therapy effects, and predicting individual risk, leading to a total of 45 outcomes being measured. The collective effectiveness of these outcomes at the population level was calculated at 80% (36 out of 45). No studies exhibited unacceptable differences in quality. Overall, employing digital twins in precision health demonstrates practical advantages, warranting its expanded use to facilitate the transition from the development phase to broad application.PROSPERO registry: CRD42024507256.

Precision medicine to personalize medicine in allergic airway disease

PURPOSE OF REVIEW

The purpose of this study is to understand the approach to precision medicine and personalized medicine in the management of allergic airway disease.

RECENT FINDINGS

Identification of biomarkers as key tools used in precision medicine has led to the development of multiple biologic drugs being used as new treatments for allergic airway disease.

SUMMARY

In addition to these advances, there is still much needed effort to improve the feasibility and utility of integrating biologic omics data of precision medicine with physicochemical, behavioral, psychological, and social data to deliver optimized treatments that is personalized for each individual.

Understanding the impact of ABCG2 polymorphisms on drug pharmacokinetics: focus on rosuvastatin and allopurinol

INTRODUCTION

In addition to the well-established understanding of the pharmacogenetics of drug-metabolizing enzymes, there is growing data on the effects of genetic variation in drug transporters, particularly ATP-binding cassette (ABC) transporters. However, the evidence that these genetic variants can be used to predict drug effects and to adjust individual dosing to avoid adverse events is still limited.

AREAS COVERED

This review presents a summary of the current literature from the PubMed database as of February 2024 regarding the impact of genetic variants on ABCG2 function and their relevance to the clinical use of the HMG-CoA reductase inhibitor rosuvastatin and the xanthine oxidase inhibitor allopurinol.

EXPERT OPINION

Although there are pharmacogenetic guidelines for the ABCG2 missense variant Q141K, there is still some conflicting data regarding the clinical benefits of these recommendations. Some caution appears to be warranted in homozygous ABCG2 Q141K carriers when rosuvastatin is administered at higher doses and such information is already included in the drug label. The benefit of dose adaption to lower possible side effects needs to be evaluated in prospective clinical studies.

Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology

Nanotechnology has emerged as a transformative force in oncology, facilitating advancements in site-specific cancer therapy and personalized oncomedicine. The development of nanomedicines explicitly targeted to cancer cells represents a pivotal breakthrough, allowing the development of precise interventions. These cancer-cell-targeted nanomedicines operate within the intricate milieu of the tumour microenvironment, further enhancing their therapeutic efficacy. This comprehensive review provides a contemporary perspective on precision cancer medicine and underscores the critical role of nanotechnology in advancing site-specific cancer therapy and personalized oncomedicine. It explores the categorization of nanoparticle types, distinguishing between organic and inorganic variants, and examines their significance in the targeted delivery of anticancer drugs. Current insights into the strategies for developing actively targeted nanomedicines across various cancer types are also provided, thus addressing relevant challenges associated with drug delivery barriers. Promising future directions in personalized cancer nanomedicine approaches are delivered, emphasising the imperative for continued optimization of nanocarriers in precision cancer medicine. The discussion underscores translational research's need to enhance cancer patients' outcomes by refining nanocarrier technologies in nanotechnology-driven, site-specific cancer therapy.

Combining Double-Dose and High-Dose Pulsed Dapsone Combination Therapy for Chronic Lyme Disease/Post-Treatment Lyme Disease Syndrome and Co-Infections, Including Bartonella: A Report of 3 Cases and a Literature Review

Three patients with relapsing and remitting borreliosis, babesiosis, and bartonellosis, despite extended anti-infective therapy, were prescribed double-dose dapsone combination therapy (DDDCT) for 8 weeks, followed by one or several two-week courses of pulsed high-dose dapsone combination therapy (HDDCT). We discuss these patients' cases to illustrate three important variables required for long-term remission. First, diagnosing and treating active co-infections, including Babesia and Bartonella were important. Babesia required rotations of multiple anti-malarial drug combinations and herbal therapies, and Bartonella required one or several 6-day HDDCT pulses to achieve clinical remission. Second, all prior oral, intramuscular (IM), and/or intravenous (IV) antibiotics used for chronic Lyme disease (CLD)/post-treatment Lyme disease syndrome (PTLDS), irrespective of the length of administration, were inferior in efficacy to short-term pulsed biofilm/persister drug combination therapy i.e., dapsone, rifampin, methylene blue, and pyrazinamide, which improved resistant fatigue, pain, headaches, insomnia, and neuropsychiatric symptoms. Lastly, addressing multiple factors on the 16-point multiple systemic infectious disease syndrome (MSIDS) model was important in achieving remission. In conclusion, DDDCT with one or several 6-7-day pulses of HDDCT, while addressing abnormalities on the 16-point MSIDS map, could represent a novel effective clinical and anti-infective strategy in CLD/PTLDS and associated co-infections including Bartonella.

Measurement of Glomerular Filtration Rate in Patients Undergoing Renal Surgery for Cancer: Estimated Glomerular Filtration Rate versus Measured Glomerular Filtration Rate in the Era of Precision Medicine

BACKGROUND

In the era of precision medicine, determining reliable renal function assessment remains a critical and debatable issue, especially in nephrology and oncology.

SUMMARY

This paper delves into the significance of accurately measured glomerular filtration rate (mGFR) in clinical practice, highlighting its essential role in guiding medical decisions and managing kidney health, particularly in the context of renal cancer (RC) patients undergoing nephrotoxic anti-cancer drugs. The limitations and advantages of traditional glomerular filtration rate (GFR) estimation methods, primarily using serum biomarkers like creatinine and cystatin C, are discussed, emphasizing their possible inadequacy in cancer patients. Specifically, newer formulae designed for GFR estimation in cancer patients may not perform at best in RC patients. The paper explores various methods for direct GFR measurement, including the gold standard inulin clearance and alternatives like iohexol plasma clearance.

KEY MESSAGE

Despite the logistical challenges of these methods, their implementation is crucial for accurate renal function assessment. The paper concludes by emphasizing the need for continued research and innovation in GFR measurement methodologies to improve patient outcomes, particularly in populations with complex medical needs.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Targeting Cellular Senescence in Aging and Age-Related Diseases: Challenges, Considerations, and the Emerging Role of Senolytic and Senomorphic Therapies

Cellular senescence is characterized by the permanent arrest of cell proliferation and is a response to endogenous and exogenous stress. The continuous accumulation of senescent cells (SnCs) in the body leads to the development of aging and age-related diseases (such as neurodegenerative diseases, cancer, metabolic diseases, cardiovascular diseases, and osteoarthritis). In the face of the growing challenge of aging and age-related diseases, several compounds have received widespread attention for their potential to target SnCs. As a result, senolytics (compounds that selectively eliminate SnCs) and senomorphics (compounds that alter intercellular communication and modulate the behavior of SnCs) have become hot research topics in the field of anti-aging. In addition, strategies such as combination therapies and immune-based approaches have also made significant progress in the field of anti-aging therapy. In this article, we discuss the latest research on anti-aging targeting SnCs and gain a deeper understanding of the mechanism of action and impact of different anti-aging strategies on aging and age-related diseases, with the aim of providing more effective references and therapeutic ideas for clinical anti-aging treatment in the face of the ever-grave challenges of aging and age-related diseases.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Pharmacogenomics of Preeclampsia therapies: Current evidence and future challenges for clinical implementation

Preeclampsia is a pregnancy-specific disorder, and it is a leading cause of maternal and perinatal morbidity and mortality. The application of pharmacogenetics to antihypertensive agents and dose selection in women with preeclampsia is still in its infancy. No current prescribing guidelines from the clinical pharmacogenetics implementation consortium (CPIC) exist for preeclampsia. Although more studies on pharmacogenomics are underway, there is some evidence for the pharmacogenomics of preeclampsia therapies, considering both the pharmacokinetic (PK) and pharmacodynamic (PD) properties of drugs used in preeclampsia. It has been revealed that the CYP2D6*10 variant is significantly higher in women with preeclampsia who are non-responsive to labetalol compared to those who are in the responsive group. Various genetic variants of PD targets, i.e., NOS3, MMP9, MMP2, TIMP1, TIMP3, VEGF, and NAMPT, have been investigated to assess the responsiveness of antihypertensive therapies in preeclampsia management, and they indicated that certain genetic variants of MMP9, TIMP1, and NAMPT are more frequently observed in those who are non-responsive to anti-hypertensive therapies compared to those who are responsive. Further, gene-gene interactions have revealed that NAMPT, TIMP1, and MMP2 genotypes are associated with an increased risk of preeclampsia, and they are more frequently observed in the non-responsive subgroup of women with preeclampsia. The current evidence is not rigorous enough for clinical implementation; however, an institutional or regional-based retrospective analysis of audited data may help close the knowledge gap during the transitional period from a traditional approach (a "one-size-fits-all" strategy) to the pharmacogenomics of preeclampsia therapies.

Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence

Stem/progenitor cells have been widely evaluated as a promising therapeutic option for heart failure (HF). Numerous clinical trials with stem/progenitor cell-based therapy (SCT) for HF have demonstrated encouraging results, but not without limitations or discrepancies. Recent technological advancements in multiomics, bioinformatics, precision medicine, artificial intelligence (AI), and machine learning (ML) provide new approaches and insights for stem cell research and therapeutic development. Integration of these new technologies into stem/progenitor cell therapy for HF may help address: 1) the technical challenges to obtain reliable and high-quality therapeutic precursor cells, 2) the discrepancies between preclinical and clinical studies, and 3) the personalized selection of optimal therapeutic cell types/populations for individual patients in the context of precision medicine. This review summarizes the current status of SCT for HF in clinics and provides new perspectives on the development of computation-aided SCT in the era of precision medicine and AI/ML.