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Telemedicine in heart failure (HF) management may positively impact health outcomes, but varied effects in studies hinder guidance in HF guidelines. Evidence on the effectiveness of telemedicine in HF subpopulations is limited. We conducted a scoping review to evaluate and synthesise evidence on the effectiveness of telemedicine across HF subpopulations that could guide telemedicine strategies in routine practice. Meta-analyses concerning randomised controlled trials (RCTs) with subgroup analyses on telemedicine effectives were identified in PubMed. We identified 15 RCTs, encompassing 21 different subgroups based on characteristics of HF patients. Findings varied across studies and no definite evidence was found about which patients benefit most from telemedicine. Subgroup definitions were inconsistent, not always a priori defined and subgroups contained few patients. Some studies found heterogeneous effects of telemedicine on mortality and hospitalisation across subgroups defined by: New York Heart Association (NYHA) classification, previous HF decompensation, implantable device, concurrent depression, time since hospital discharge and duration of HF. Patients represented in the RCTs were mostly male, aged 65–75 years, with HF with reduced ejection fraction and NYHA class II/III. Traditional RCTs have not been able to provide clinicians with guidance; continuous real-world evidence generation could enhance monitoring and identify who benefits from telemedicine. [ABSTRACT FROM AUTHOR]

The KidGen Collaborative's Policy Implementation Workshop 2023 celebrated the 10th anniversary of Australia's first kidney genetics clinic in Brisbane. This event marked the establishment of a national network now comprising 19 kidney genetics clinics across Australia, all dedicated to providing equitable access to genomic testing for families affected by genetic kidney diseases. The workshop reflected on past progress and outlined future objectives for kidney genetics in Australia, recognising the collaborative efforts of clinical teams, researchers, and patients. Key insights from the workshop are documented in the proceedings. [ABSTRACT FROM AUTHOR]

Parkinson's Disease is the second most prevalent neurological disorder globally, and its cause is still largely unknown. Likewise, there is no cure, and existing treatments do little more than subdue symptoms before becoming ineffective. It is increasingly important to understand the factors contributing to Parkinson's Disease aetiology so that new and more effective pharmacotherapies can be established. In recent years, there has been an emergence of research linking gut dysbiosis to Parkinson's Disease via the gut–brain axis. Advancements in microbial profiling have led to characterisation of a Parkinson's-specific microbial signature, where novel treatments that leverage and correct gut dysbiosis are beginning to emerge for the safe and effective treatment of Parkinson's Disease. Preliminary clinical studies investigating microbiome-targeted therapeutics for Parkinson's Disease have revealed promising outcomes, and as such, the aim of this review is to provide a timely and comprehensive update of the most recent advances in this field. Faecal microbiota transplantation has emerged as a novel and potential frontrunner for microbial-based therapies due to their efficacy in alleviating Parkinson's Disease symptomology through modulation of the gut–brain axis. However, more rigorous clinical investigation, along with technological advancements in diagnostic and in vitro testing tools, are critically required to facilitate the widespread clinical translation of microbiome-targeting Parkinson's Disease therapeutics. [ABSTRACT FROM AUTHOR]

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/. Scientific contribution This work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application. [ABSTRACT FROM AUTHOR]

Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches. [ABSTRACT FROM AUTHOR]

Introduction: n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials. Methods: The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained. Results: A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis. Conclusions: The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this narrative review is to explore the advantages and limitations of VHs in delivering healthcare, including access to specialized professionals, streamlined communication, efficient scheduling, integration of electronic health records, ongoing monitoring, and support, transcending geographical boundaries, and resource optimization. Methods: Review of literature. Results: The national healthcare systems are facing an alarming rise in pressure due to global shifts. Virtual hospitals (VH) offer a practical solution to numerous systemic challenges, including rising costs and increased workloads for healthcare providers. VH also facilitate the delivery of personalized services and enable the monitoring of patients beyond the conventional confines of healthcare settings, reducing the reliance on waiting medicine carried out in doctors' offices or hospitals. Conclusion: VH can mirror the conventional healthcare referral system. [ABSTRACT FROM AUTHOR]

Abstract The KidGen Collaborative's Policy Implementation Workshop 2023 celebrated the 10th anniversary of Australia's first kidney genetics clinic in Brisbane. This event marked the establishment of a national network now comprising 19 kidney genetics clinics across Australia, all dedicated to providing equitable access to genomic testing for families affected by genetic kidney diseases. The workshop reflected on past progress and outlined future objectives for kidney genetics in Australia, recognising the collaborative efforts of clinical teams, researchers, and patients. Key insights from the workshop are documented in the proceedings.

Abstract Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches.

Abstract Introduction n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials. Methods The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained. Results A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis. Conclusions The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design.

INTRODUCTION. Small interfering RNAs (siRNAs) are among the most promising types of therapeutic nucleic acids aiming at the inhibition of pathogenetically relevant gene expression through the RNA interference mechanism. However, the limited bioavailability and immunogenicity of siRNAs and imperfect delivery systems hinder the clinical potential and applicability of siRNA medicinal products.AIM. This study aimed to summarise recent advances in the development of siRNA medicinal products and the corresponding delivery systems, review clinical trial results, and outline future development prospects for these medicinal products.DISCUSSION. This article covers the molecular mechanisms underlying RNA interference, the considerations for siRNA development, and the techniques for effective siRNA delivery. The article dwells upon various systems for nucleic acid delivery to targeted cells. The most promising delivery systems are non-viral systems, including liposomes, exosomes, nanoparticles, polymers, cell-penetrating peptides, and GalNAc ligands. Their main advantages include their ease of complexation with nucleic acids, modification and functionalization potential, favourable safety profile, ability to cross biological barriers, and tropism to target tissues. The article summarises the information that has accumulated over the past few years in clinical trials of siRNA medicinal products for a range of conditions, including metabolic disorders, infections, and cancers, as well as hereditary, ophthalmic, renal, and hepatic diseases. Special attention is paid to siRNA medicinal products undergoing clinical trials (over 10 products) and approved for clinical use (6 products, including MIR 19, the first authorised Russian siRNA medicinal product).CONCLUSION. Ultimately, siRNA medicinal products are a promising tool for personalised medicine, exhibiting therapeutic potential for a wide range of pathological conditions. Further studies of siRNA medicinal products should aim at improving siRNA production technology to increase their bioavailability and half-life period. In addition, these studies should aim at enhancing delivery systems for these products to mitigate toxicity risks and maximise efficacy.

INTRODUCTION. Pharmacogenetic (PGx) testing plays a significant role in predicting the risk of adverse drug reactions (ADRs) associated with valproic acid (VPA) products, which are among the most prescribed medicinal products in neurology and psychiatry. However, the sensitivity and specificity of PGx screening panels may be insufficient as individual valproate metabolism varies across ethnically/racially diverse patient populations.AIM. The study aimed to identify implementation areas for a personalised approach to the development of PGx panels for assessing the safety and risk of valproate therapy in various ethnic and racial groups residing in the Russian Federation.DISCUSSION. The authors reviewed the results of population studies concerning the frequency of non-functional and low-function alleles of genes encoding isoenzymes that play key roles in VPA P-oxidation in the liver. This review focused on studies published in eLIBRARY.RU, PubMed, Scopus, and Google Scholar in 2012–2022. The inclusion criteria were full-text original articles, systematic reviews, meta-analyses, Cochrane reviews, and clinical cases in Russian or English. The analysis revealed that the need for personalised assessment of the risk and safety of VPA may depend on the frequency of risk alleles for slowing down VPA P-oxidation in the liver across racial and ethnic groups worldwide, and particularly in Russia. The authors identified new areas to implement the personalised approach to the development of PGx panels for assessing the safety and risk of valproate therapy with consideration of the rates of hepatic VPA P-oxidation in patients of different ethnic and racial backgrounds. However, the review of population-based associative genetic research from around the world demonstrated the current lack of clarity in the prospects of translating international findings directly into Russian clinical practice through the development of PGx panels due to Russia’s ethnic/racial diversity and vast territory.CONCLUSIONS. To increase the sensitivity and specificity of Russian PGx panels, bridging studies are required to extrapolate the associations established between the most common risk alleles and VPA P-oxidation disorders in other ethnic groups to a specific population of a specific Russian region.

Specific molecular and inflammatory endotypes have been identified for chronic respiratory disorders, including asthma and COPD (chronic obstructive pulmonary disease). These endotypes correspond with clinical aspects of disease, enabling targeted medicines to address certain pathophysiologic pathways, often referred to as “precision medicine”. With respect to bronchiectasis, many comorbidities and underlying causes have been identified. Inflammatory endotypes have also been widely studied and reported. Additionally, several genes have been shown to affect disease progression. However, the lack of a clear classification has also hampered our understanding of the disease’s natural course. The aim of this review is, thus, to summarize the current knowledge on biomarkers and actionable targets of this complex pathologic condition and to point out unmet needs, which are required in the design of effective diagnostic and therapeutic trials.

Abstract Background Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. Results We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. Conclusions The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Background: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. Results: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. Conclusions: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security. [ABSTRACT FROM AUTHOR]

The current evidence on whether annual diabetic retinopathy (DR) screening intervals can be extended was reviewed. A systematic review protocol was followed (PROSPERO ID: CRD42022359590). Original longitudinal articles that specifically assessed DR screening intervals were in English and collected data after 2000 were included. Two reviewers independently conducted the search and reviewed the articles for quality and relevant information. The heterogeneity of the data meant that a meta‐analysis was not appropriate. Twelve publications were included. Studies were of good quality and many used data from DR screening programs. Studies fit into three categories; those that assessed specific DR screening intervals, those that determined optimal DR screening intervals and those that developed/assessed DR screening risk equations. For those with type 2 diabetes, extending screening intervals to 3‐ to 4‐yearly in those with no baseline DR appeared safe. DR risk equations considered clinical factors and allocated those at lower risk of DR progression screening intervals of up to five years. Those with baseline DR or type 1 diabetes appeared to have a higher risk of progression to STDR and needed more frequent screening. DR screening intervals can be extended to 3–5 yearly in certain circumstances. These include patients with type 2 diabetes and no current DR, and those who have optimal management of other risk factors such as glucose and blood pressure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cefotaxime administration is recommended in doses of 3–12 g/day in adults with a Glomerular Filtration Rate (GFR) > 5 mL/min. This study aimed to assess the impact of renal function and obesity on cefotaxime concentrations in intensive care unit (ICU) patients. A retrospective cohort study was conducted on consecutive ICU patients receiving continuous cefotaxime infusion between 2020 and 2022 [IRBN992021/CHUSTE]. Doses were not constant; consequently, a concentration-to-dose ratio (C/D) was considered. Statistical analysis was performed to assess the relationship between cefotaxime concentrations, renal function, and obesity. A total of 70 patients, median age 61 years, were included, with no significant difference in cefotaxime concentrations between obese and non-obese patients. However, concentrations varied significantly by GFR, with underdosing prevalent in patients with normal to increased renal function and overdosing in those with severely impaired renal function. Adjustment of cefotaxime dosing according to GFR was associated with improved target attainment. Cefotaxime dosing in critically ill patients should consider renal function, with higher initial doses required in patients with normal to increased GFR and lower doses in those with severely impaired renal function. Therapeutic drug monitoring may aid in optimising dosing regimens. Prospective studies are warranted to validate these findings and inform clinical practice. [ABSTRACT FROM AUTHOR]

IntroductionPersonalised prevention using genomic information requires active involvement from patients and the public, who should be well-informed and empowered to make healthcare decisions that reflect their personal values. We aimed to map engagement practises, and assess the extent and types of engagement methods used in the field of personalised prevention of common chronic conditions using genomic information.MethodsA scoping review on selected literature (in Medline, Embase, Scopus, Web of Science, APA PsycINFO, and IBSS) from 2015 to 2023 was performed. Articles included described practises of patient and public engagement in personalised prevention and genomics conducted in Europe focusing on cancer, cardiovascular diseases and neurodegenerative disorders. Engagement was explored based on grouping practises across the domains of care, research, education, and governance.ResultsA total of 23 articles describing 23 engagement practises were selected. Analysis revealed diverse engagement levels, the majority falling into the low to medium engagement category, and showing mainly unidirectional methods of engagement, especially consultation. Most engagement activities related to cancer, and none to neurodegenerative disorders. Most publications appeared in the care domain, followed by the research domain, a combination of research and care, and a combination of governance and education.ConclusionThese results suggest that most practises to engage patients and public in personalised prevention using genomic information appear to have lower levels of engagement. Elaborating on and implementing practises that engage and empower patients and the public at all levels of the engagement spectrum and for all chronic diseases is needed, fostering a more inclusive and participatory approach to personalised prevention.

Psychedelics have emerged as promising therapeutics for several psychiatric disorders. Hypotheses around their mechanisms have revolved around their partial agonism at the serotonin 2 A receptor, leading to enhanced neuroplasticity and brain connectivity changes that underlie positive mindset shifts. However, these accounts fail to recognise that the gut microbiota, acting via the gut-brain axis, may also have a role in mediating the positive effects of psychedelics on behaviour. In this review, we present existing evidence that the composition of the gut microbiota may be responsive to psychedelic drugs, and in turn, that the effect of psychedelics could be modulated by microbial metabolism. We discuss various alternative mechanistic models and emphasize the importance of incorporating hypotheses that address the contributions of the microbiome in future research. Awareness of the microbial contribution to psychedelic action has the potential to significantly shape clinical practice, for example, by allowing personalised psychedelic therapies based on the heterogeneity of the gut microbiota.

Summary: The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies (“multiomic” strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Summary: Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Pregnant and postnatal women are a high-risk population particularly prone to rapid progression to sepsis with significant morbidity and mortality worldwide. Moreover, severe maternal infections can have a serious detrimental impact on neonates with almost 1 million neonatal deaths annually attributed to maternal infection or sepsis. In this review we discuss the susceptibility of pregnant women and their specific physiological and immunological adaptations that contribute to their vulnerability to sepsis, the implications for the neonate, as well as the issues with antimicrobial stewardship and the challenges this poses when attempting to reach a balance between clinical care and urgent treatment. Finally, we review advancements in the development of pregnancy-specific diagnostic and therapeutic approaches and how these can be used to optimize the care of pregnant women and neonates.

Introduction: Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Methods: Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Results: Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. Discussion: The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing. [ABSTRACT FROM AUTHOR]

Bacteriophage therapy is a promising approach to treating bacterial infections. Research and development of bacteriophage therapy is intensifying due to the increase in antibiotic resistance and the faltering development of new antibiotics. Bacteriophage therapy uses bacteriophages (phages), i.e., prokaryotic viruses, to specifically target and kill pathogenic bacteria. The legal handling of this type of therapy raises several questions. These include whether phage therapeutics belong to a specially regulated class of medicinal products, and which legal framework should be followed with regard to the various technical ways in which phage therapeutics can be manufactured and administered. The article shows to which class of medicinal products phage therapeutics from wild type phages and from genetically modified (designer) phages do or do not belong. Furthermore, the article explains which legal framework is relevant for the manufacture and administration of phage therapeutics, which are manufactured in advance in a uniform, patient-independent manner, and for tailor-made patient-specific phage therapeutics. For the systematically coherent, successful translation of phage therapy, the article considers pharmaceutical law and related legal areas, such as genetic engineering law. Finally, the article shows how the planned legislative revisions of Directive 2001/83/EC and Regulation (EC) No 726/2004 may affect the legal future of phage therapy. [ABSTRACT FROM AUTHOR]

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration‑guided drug selection, single‑patient gene therapy design and synergy‑based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice‑changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine‑enabled N‑of‑1 medicine, which individualises combination regimen and dosing using a patient’s own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital‑based platforms. [ABSTRACT FROM AUTHOR]

Background: The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. Methods: A Delphi survey was conducted to gather expert's consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. Results: The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. Conclusions: The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Treatment with targeted agents is an emerging concept for cholangiocarcinoma, a rare cancer with a lack of effective therapies. However, there is limited real-world evidence regarding the use of targeted agents using molecular profiling data from liquid biopsies in clinical practice. This single-centre retrospective study aimed to characterise treatment efficacy and predictors of clinical benefit in cholangiocarcinoma following targeted therapies. Our findings suggest that patients with exposure to targeted agents and response to such treatment are the greatest beneficiaries of this personalized treatment approach; those with previous localised approaches such as surgery had further benefit, suggesting that localised treatment options can be considered in appropriate cases. Further research is needed to identify patients who are most likely to benefit from targeted agents using the liquid biopsy approach and evaluate the optimal sequence of such therapies to maximise treatment response and survival outcomes in this challenging clinical population. Background: Cholangiocarcinoma (CCA) is associated with poor outcomes and limited treatment options, leading to increased use of targeted therapies for its management. Here, we performed one of the largest single-centre reviews evaluating outcomes following personalised targeted agents in CCA patients. Methods: All consecutive CCA patients receiving systemic therapy between January 2010 and April 2023 at UCLH were included. The primary objective of this study was to evaluate treatment response, survival outcomes and predictors of clinical benefit in CCA patients treated with molecularly guided therapies. Patient demographic factors, disease characteristics and survival outcomes were evaluated using the Kaplan–Meier method and Cox proportional-hazards models. Results: Of the 227 consecutive CCA patients, 162 (71%) had molecular profiling, of whom 56 (35%) were eligible and 55 received molecular-targeted treatment. CCA histological classifications comprised intrahepatic (N = 32), extrahepatic (N = 11), hilar (N = 4) and unknown (N = 9) subtypes. Most patients received targeted agents based on genomic profiling in a second treatment line setting (N = 34). Frequently observed genomic alterations occurred in the FGFR2 (N = 21), IDH1 (N = 7) and BRCA2 (N = 6) genes. Median progression-free survival (PFS) following first-, second- and third-line systemic therapy and overall survival (OS) were 8.44 (95% CI, 7.49–12.78), 5.65 (95% CI, 3.71–7.13), 5.55 (2.79–12.58) and 29.01 (24.21–42.91) months, respectively. CCA subtype and FGFR/BRCA molecular aberration status were not associated with PFS or OS. However, a prior CCA-related surgical history was predictive of OS (p = 0.02). Stratification by best overall response to second-line targeted agents demonstrated an association with PFS (p = 0.002) and OS (p = 0.02). Duration of treatment with second-line targeted therapy was associated with OS (p < 0.001). Conclusions: Patients receiving targeted therapeutics achieved promising outcomes, especially those attaining a favourable treatment response and those receiving targeted agents for longer periods. Liquid biopsies can reliably provide information on extended molecular profiling to aid patient selection for personalised therapies. [ABSTRACT FROM AUTHOR]

Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this narrative review is to explore the advantages and limitations of VHs in delivering healthcare, including access to specialized professionals, streamlined communication, efficient scheduling, integration of electronic health records, ongoing monitoring, and support, transcending geographical boundaries, and resource optimization.Methods: Review of literature.Results: The national healthcare systems are facing an alarming rise in pressure due to global shifts. Virtual hospitals (VH) offer a practical solution to numerous systemic challenges, including rising costs and increased workloads for healthcare providers. VH also facilitate the delivery of personalized services and enable the monitoring of patients beyond the conventional confines of healthcare settings, reducing the reliance on waiting medicine carried out in doctors' offices or hospitals.Conclusion: VH can mirror the conventional healthcare referral system.

We employed a Sendai virus-based reprogramming method to transform human lymphoblastoid cell lines (LCL) derived from two individuals diagnosed with phenylketonuria (PKU) into induced pluripotent stem cells (iPSC). This reprogramming process involved the expression of the four Yamanaka factors: KLF4, OCT4, SOX2, and C-MYC. The resulting patient-specific iPSCs exhibited a normal karyotype and expressed endogenous pluripotent markers NANOG and OCT-4. Notably, these iPSCs demonstrated strong differentiation capabilities, giving rise to cell populations representing the ectoderm, endoderm, and mesoderm germ layers.

Scientific relevance. Valproic acid (VPA) is a psychotropic medicinal product, which may be associated with serious adverse drug reactions (ADRs). While pharmacogenetics and pharmacometabolomics can significantly affect the safety of valproates, there are no unified approaches to predicting, preventing, and correcting VPA-induced ADRs.Aim. This study aimed to collate the results of national and international studies on toxic VPA metabolites and to develop a novel personalised approach to assessing the safety and risks of valproate therapy in real-world clinical practice.Discussion. This study analysed national and international publications reflecting the results of preclinical and clinical studies on toxic VPA metabolites submitted to e-Library, PubMed, Scopus, and Google Scholar in 2012–2022. The inclusion criteria were full-text original articles, systematic reviews, meta-analyses, Cochrane reviews, and clinical cases in Russian or English. According to the analysis results, VPA has 20 studied toxic metabolites, which result from hepatic VPA metabolism involving P-oxidation, acetylation (β-oxidation), and glucuronidation enzymes. The functional activity of these enzymes is genetically determined and associated with heterozygous or homozygous carriage of non-functional/low-function single-nucleotide variant alleles in genes encoding these enzymes. The safety of VPA and its compounds can be improved by transferring the results of preclinical and clinical studies into real-world clinical practice using pharmacogenetics-informed pharmacometabolomics. Pharmacogenetics-informed pharmacometabolomics is a novel and personalised approach that helps, based on pharmacogenetic profiling, identify patients at high risk of VPA-induced ADRs, individually select starting and target doses of VPA and its compounds, determine the timing and frequency for therapeutic drug monitoring and monitoring toxic VPA metabolites in biological fluids (blood, saliva, and urine), and select a strategy for the prevention and correction of VPA-induced ADRs, taking into account patients’ individual pharmacometabolic profiles.Conclusions. The quality of medical care for patients with neurological diseases and mental disorders will improve with proper monitoring of VPA-induced ADRs by all entities involved in the medicinal product life cycle; active involvement of neurologists and psychiatrists in the prediction, prevention, and monitoring of the safety of valproate treatment; and inclusion of specific sections on practical pharmacogenetics-informed pharmacometabolomics and pharmacovigilance in the professional training curricula for neurologists and psychiatrists.

Effective cancer diagnosis, treatment and control depend on interactions among numerous distinct factors, from technology to data to skills to sociology. But a crucial influence is the extent to which the health system takes account of the distinct perspectives of the many different groups of interdependent stakeholders concerned with cancer, including patients, practitioners and planners. This paper provides some elucidation as to how far and how efficiently these interactions currently take place in Europe. It also makes some tentative suggestions as to how conscious systematic interventions could improve cancer outcomes. It is based on a series of expert panels and surveys conducted by the European Alliance for Personalised Medicine (EAPM) that provided information at the national level on three selected parameters: implementation of next-generation sequencing (NGS) and liquid biopsy (LB), attitudes of patients to prevention and practices of sharing genomic data among healthcare professionals (HCPs). The varying data infrastructure highlights the urgent need for substantial improvements to accommodate the increasing importance of genomics data in cancer diagnosis and care. Additionally, we identify disparities in age-specific approaches to cancer prevention, emphasising the necessity for tailored strategies to address unique age group perspectives. Moreover, distinct regional prioritizations in cancer treatment underscore the importance of considering regional variations when shaping future cancer care strategies. This study advocates for collaborative data sharing supported by technological innovation to overcome these challenges, ultimately fostering a holistic and equitable provision of cancer care in Europe.

Metabolomics involves the identification and quantitative analysis of all small metabolites present in cells, tissues, and bodily fluids that are formed as a result of biochemical reactions within the cell. These metabolites form a large pool of substrates and can be modified by serving as a substrate for enzymes involved in other metabolic pathways. Therefore, the metabolome in an organism is so dynamic that there is variation in their quantity and chemical composition over time. Nuclear Magnetic Resonance (NMR), Gas Chromatography Mass Spectrometry (GC-MS), and Liquid Chromatography Mass Spectrometry (LC-MS) are the most commonly used technologies in metabolomics. The metabolites are first isolated based on their polarity, chemical composition, and structural resemblances, after which they undergo specialised processes and are then analysed. Metabolomics, coupled with MS, has advanced rapidly and found widespread use in periodontal research. The presence of distinct metabolic and microbiological profiles in different types of periodontitis, as well as their link to clinical indicators of periodontal inflammation, has demonstrated the usefulness of metabolomics in screening, preventing, and monitoring prognosis. Conventional diagnostics fail to detect periodontitis in its early stages, cannot discriminate between past and present disease activity, and are incapable of analysing the entire repertoire of biomarkers in the biological system. Therefore, metabolomics, in conjunction with other omics technologies, can provide tailored periodontal disease therapy. The present review aimed to explore metabolomics, its applications in periodontics, and the potential for personalised treatment.

Parkinson’s Disease is the second most prevalent neurological disorder globally, and its cause is still largely unknown. Likewise, there is no cure, and existing treatments do little more than subdue symptoms before becoming ineffective. It is increasingly important to understand the factors contributing to Parkinson’s Disease aetiology so that new and more effective pharmacotherapies can be established. In recent years, there has been an emergence of research linking gut dysbiosis to Parkinson’s Disease via the gut–brain axis. Advancements in microbial profiling have led to characterisation of a Parkinson’s-specific microbial signature, where novel treatments that leverage and correct gut dysbiosis are beginning to emerge for the safe and effective treatment of Parkinson’s Disease. Preliminary clinical studies investigating microbiome-targeted therapeutics for Parkinson’s Disease have revealed promising outcomes, and as such, the aim of this review is to provide a timely and comprehensive update of the most recent advances in this field. Faecal microbiota transplantation has emerged as a novel and potential frontrunner for microbial-based therapies due to their efficacy in alleviating Parkinson’s Disease symptomology through modulation of the gut–brain axis. However, more rigorous clinical investigation, along with technological advancements in diagnostic and in vitro testing tools, are critically required to facilitate the widespread clinical translation of microbiome-targeting Parkinson’s Disease therapeutics.

Effective cancer diagnosis, treatment and control depend on interactions among numerous distinct factors, from technology to data to skills to sociology. But a crucial influence is the extent to which the health system takes account of the distinct perspectives of the many different groups of interdependent stakeholders concerned with cancer, including patients, practitioners and planners. This paper provides some elucidation as to how far and how efficiently these interactions currently take place in Europe. It also makes some tentative suggestions as to how conscious systematic interventions could improve cancer outcomes. It is based on a series of expert panels and surveys conducted by the European Alliance for Personalised Medicine (EAPM) that provided information at the national level on three selected parameters: implementation of next-generation sequencing (NGS) and liquid biopsy (LB), attitudes of patients to prevention and practices of sharing genomic data among healthcare professionals (HCPs). The varying data infrastructure highlights the urgent need for substantial improvements to accommodate the increasing importance of genomics data in cancer diagnosis and care. Additionally, we identify disparities in age-specific approaches to cancer prevention, emphasising the necessity for tailored strategies to address unique age group perspectives. Moreover, distinct regional prioritizations in cancer treatment underscore the importance of considering regional variations when shaping future cancer care strategies. This study advocates for collaborative data sharing supported by technological innovation to overcome these challenges, ultimately fostering a holistic and equitable provision of cancer care in Europe. [ABSTRACT FROM AUTHOR]

Awareness related to the risk/benefit profile of therapies used in paediatric and elderly patients is limited. We carried out a study, called the MEAP 3.0 study, to collect and analyse evidence of adverse drug reactions (ADRs) and drug–drug interactions (DDIs) that occurred in frail populations under polypharmacy in a real-world setting. Data were retrieved from reports of ADRs and pharmacological counselling from patients treated in hospitals and territorial health services. We collected 2977 ADRs reports and identified 'anti-infectives for systemic use' and 'cardiovascular system' as the most frequently implicated pharmacological classes in under-18 and over-65 patients, respectively. We detected 2179 DDIs, of which 10.7% were related to at least one ADR: 22 were classified as 'contraindicated' (7 in the paediatric group and 15 in the elderly one), and 61 as 'major' (6 in the paediatric patients and 55 in the geriatric ones), while 151 DDIs were classified as 'moderate' (10 referred to paediatric population, and 109 to elderly patient) and as 'minor' (1 in paediatric patients, and 31 in the elderly ones). The MEAP 3.0 project demonstrates that pharmacovigilance surveillance and therapeutic reconciliation are valid strategies to avoid potential DDIs and the occurrence of ADRs, allowing for personalised medicine. [ABSTRACT FROM AUTHOR]

For decades, the Wind Chill Temperature Index (WCT) and its various iterations have been used to assess the risk of frostbite on unclothed body parts. This paper presents an innovative knowledge-based Cold Weather Ensemble Decision Aid (CoWEDA) that can be used to guide the selection of the most appropriate cold weather ensemble(s) relative to anticipated mission physical activities and environmental conditions. CoWEDA consists of a validated six-cylinder thermoregulatory model, a database of clothing properties, algorithms for calculating the whole ensemble properties from individual garments and a graphical user interface. The user-friendly CoWEDA allows users to select from an inventory of clothing items to build an ensemble suitable for their needs. CoWEDA predicts the risks of both frostbite and hypothermia and ensures that a selected clothing ensemble will provide adequate protection to prevent cold injury. CoWEDA predictions provide not only estimates of frostbite risk similar to WCT tables but also hypothermia times and clothing required to prevent cold injuries. In addition, a CoWEDA model variant can predict survivability and clothing requirements during cold water immersion. Thus, CoWEDA represents a significant enhancement of the WCT-based guidance for cold weather safety and survival by providing greater individual fidelity in cold injury predictions. [ABSTRACT FROM AUTHOR]

With the trend towards promoting personalised medicine (PM), the application of pharmacogenetics and pharmacogenomics (PGx) is of growing importance. For the purposes of clinical trials, the inclusion of PGx is an additional tool that should be considered for improving our knowledge about the effectiveness and safety of new drugs. A search of available clinical trials containing pharmacogenetic and PGx information was conducted on ClinicalTrials.gov. The results show there has been an increase in the number of trials containing PGx information since the 2000 s, with particular relevance in the areas of Oncology (28.43%) and Mental Health (10.66%). Most of the clinical trials focus on treatment as their primary purpose. In those clinical trials entries where the specific genes considered for study are detailed, the most frequently explored genes are CYP2D6 (especially in Mental Health and Pain), CYP2C9 (in Hematology), CYP2C19 (in Cardiology and Mental Health) and ABCB1 and CYP3A5 (particularly prominent in Transplantation and Cardiology), among others. Researchers and clinicans should be trained in pharmacogenetics and PGx in order to be able to make a proper interpretation of this data, contributing to better prescribing decisions and an improvement in patients' care, which would lead to the performance of PM. [ABSTRACT FROM AUTHOR]

This study focuses on the application of nurse‐led multidisciplinary collaborative therapy (MDT) management model for calciphylaxis prevention of patients with terminal renal disease. Through the establishment of a multidisciplinary management team spanning nephrology department, blood purification center, dermatology department, burn and plastic surgery department, infection department, stem cell platform, nutrition department, pain department, cardiology department, hydrotherapy group, dermatology group, and outpatient treatment room, the distribution of duties among team members were clarified to bring out the best advantages of a multidisciplinary teamwork during treatment and nursing. For patients with calciphylaxis symptoms in terminal renal disease, a case‐by‐case management model was carried out with the focus on personalised problem. We emphasised on personalised wound care, precise medication care, active pain management, psychological intervention and palliative care, the amelioration of calcium and phosphorus metabolism disorder, nutritional supplementation, and the therapeutic intervention based on human amniotic mesenchymal stem cell regeneration. The MDT model effectively compensates for traditional nursing mode and could serve as a novel clinical management modality for calciphylaxis prevention in patients with terminal renal disease. [ABSTRACT FROM AUTHOR]

Introduction: Retrospective exploratory analysis to identify biomarker pairs in the AVAGAST Phase III study. Aim: The main hypothesis of this retrospective exploratory biomarker analysis is the identification of dichotomization levels based on optimal selection driven by the predictive value of single biomarkers. The outcome of interest optimization might unveil additional treatment benefits. Furthermore, testing the biomarker pairs at their optimal cut-off selection might provide the predictive score candidates. Material and methods: 712 plasma 92% and 727 plasma 94% tumor samples of all patients were using Cox model by identifying optimal dichotomization to maximize treatment benefit. A quadrant analysis grouped biomarker pairs into subsets yielded the best clinical benefit. Candidate biomarker score using the nested 2-fold cross-validation method was used to adjust the optimal cut-off selection. Results: Patients with lower VEGF-R1 at optimal cut-off with low IHER2G showed significant improvement in PFS - first line (HR = 0.62; 95% CI: 0.50 to 0.78). The interaction p-value of the biomarker pair was adjusted at 0.0147094. Conclusions: The predictive biomarker is a potential candidate for PFS - first line in patients with advanced gastric cancer treated with Bevacizumab. [ABSTRACT FROM AUTHOR]

Pharmacogenetics (PGx) is the study and application of how interindividual differences in our genomes can influence drug responses. By evaluating individuals’ genetic variability in genes related to drug metabolism, PGx testing has the capabilities to individualise primary care and build a safer drug prescription model than the current “one-size-fits-all” approach. In particular, the use of PGx testing in psychiatry has shown promising evidence in improving drug efficacy as well as reducing toxicity and adverse drug reactions. Despite randomised controlled trials demonstrating an evidence base for its use, there are still numerous barriers impeding its implementation. This review paper will discuss the management of mental health conditions with PGx-guided treatment with a strong focus on youth mental illness. PGx testing in clinical practice, the concerns for its implementation in youth psychiatry, and some of the barriers inhibiting its integration in clinical healthcare will also be discussed. Overall, this paper provides a comprehensive review of the current state of knowledge and application for PGx in psychiatry and summarises the capabilities of genetic information to personalising medicine for the treatment of mental ill-health in youth. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) is a transformative force in clinical decision support (CDS) systems within healthcare. Its emergence, fuelled by the growing volume and diversity of healthcare data, offers significant potential in patient care, diagnosis, treatment, and health management. This study systematically reviews AI's role in enhancing CDS across six domains, underscoring its impact on patient outcomes and healthcare efficiency. Methods: A four-step systematic review was conducted, involving a comprehensive literature search, application of inclusion and exclusion criteria, data extraction and synthesis, and analysis. Sources included PubMed, Embase, and Google Scholar, with papers published in English since 2019. Selected studies focused on AI's application in CDS, with 32 papers ultimately reviewed. Results: The review identified six AI CDS domains: Data-Driven Insights and Analytics, Diagnostic and Predictive Modelling, Treatment Optimisation and Personalised Medicine, Patient Monitoring and Telehealth Integration, Workflow and Administrative Efficiency, and Knowledge Management and Decision Support. Each domain is crucial in improving various aspects of CDS, from enhancing diagnostic accuracy to optimising resource management. AI's capabilities in EHR analysis, predictive analytics, personalised treatment, and telehealth demonstrate its critical role in advancing healthcare. Discussion: AI significantly enhances healthcare by improving diagnostic precision, predictive capabilities, and administrative efficiency. It facilitates personalised medicine, remote monitoring, and evidence-based decision-making. However, challenges such as data privacy, ethical considerations, and integration with existing systems persist. This requires collaboration among technologists, healthcare professionals, and policymakers. Conclusion: AI is revolutionising healthcare by enhancing CDS in several domains, contributing to more efficient, effective, and patient-centric care. However, it should complement, not replace, human expertise. Future directions include ethical AI development, continuous professional development for healthcare personnel, and collaborative efforts to address challenges. This approach ensures AI's potential is fully harnessed, leading to a synergistic blend of technology and human care.

Next-generation sequencing (NGS) and liquid biopsy (LB) showed positive results in the fight against different cancer types. This paper aims to assess the uptake of advanced molecular diagnostics/NGS for quick and efficient genetic profiles of tumour cells. For that purpose, the European Alliance for Personalised Medicine conducted a series of expert interviews to ascertain the current status across member states. One stakeholder meeting was additionally conducted to prioritize relevant factors by stakeholders. Seven common pillars were identified, and twenty-five measures were defined based on these pillars. Results showed that a multi-faceted approach is necessary for successful NGS implementation and that regional differences may be influenced by healthcare policies, resources, and infrastructure. It is important to consider different correlations when interpreting the results and to use them as a starting point for further discussion.