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Background: The aging paradox highlights the coexistence of increased life expectancy with a persistent desire to maintain a youthful appearance, driven by biological, psychological, and social factors. Advances in healthcare have extended human lifespans, yet societal pressure to appear youthful remains strong. Understanding the genetic, epigenetic, and environmental contributors to skin aging is crucial for optimising aesthetic procedures. Methods: This literature review synthesises research on the aging paradox, genetic influences on skin aging, and the effectiveness of non-surgical aesthetic interventions. The review involved a comprehensive search across databases including PubMed, Google Scholar, Web of Science, and Scopus using keywords like "aging paradox", "genetic imprint", "non-surgical aesthetics", "epigenetics", and "anti-aging treatments". Studies were selected based on relevance, credibility, recency, and diversity of perspectives. Results: The review identified 64 studies relevant to the aging paradox and non-surgical interventions. These studies underscore the significant roles of genetic variations and epigenetic mechanisms, such as DNA methylation and microRNA regulation, in determining aging trajectories and treatment responses. The proposed "PERSONAL" (Personalised Epigenetic Regime for Skin Optimisation, Nurturing Aesthetic Longevity) paradigm advocates for a comprehensive, personalised approach, integrating genetic and epigenetic profiling to tailor treatments to individual needs. Conclusions: By leveraging the complex interplay of genetic, epigenetic, and environmental factors, practitioners can enhance the precision and efficacy of non-surgical aesthetic interventions, improving patient satisfaction and promoting long-term skin health. Continuous monitoring and iterative adjustments within this paradigm are essential for achieving optimal outcomes. Level of evidence: Level 4, therapeutic study [ABSTRACT FROM AUTHOR]

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]

Objective Methods Results Conclusion To review the available literature on variant genital gender‐affirming surgery (GGAS), including the reasons for performing it, the surgeries themselves and their outcomes.A systematic review on the performance of variant GGAS was conducted (International Prospective Register of Systematic Reviews [PROSPERO] identifier: CRD42022306684) researching PubMed, Embase, Web of Science and Cochrane databases from inception up to 31 December 2023. Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines were followed, and risk of bias was assessed for each study using the five‐criteria quality assessment checklist.In total 23 case series were included, of which 17 on masculinising and six on feminising surgeries. Patients mainly choose these surgical procedures out of personal desire to avoid risk of complication or because they do not have dysphoria about certain parts of their genitalia. Complications in masculinising surgeries primarily arose from the extended urethra, which could be mitigated through primary perineal urethrostomy. Both phalloplasty and metoidioplasty carried a higher risk of urethral complications when the vagina was preserved. In feminising surgeries, risk of visceral damage and requirement for lifelong self‐dilation could be avoided when vulvoplasty was performed without vaginal canal creation. All studies had a high risk of bias.This review highlights the importance of variant GGAS and acknowledges the preferences of transgender and gender‐diverse individuals. Patients should be informed about the risks and benefits of each step in these procedures. [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]

Background: Tumour budding (TB) is a marker of tumour aggressiveness which, when measured in rectal cancer resection specimens, predicts worse outcomes and response to neoadjuvant therapy. We investigated the utility of TB assessment in the setting of neoadjuvant treatment. Methods and results: A single‐centre, retrospective cohort study was conducted. TB was assessed using the hot‐spot International Tumour Budding Consortium (ITBCC) method and classified by the revised ITBCC criteria. Haematoxylin and eosin (H&E) and AE1/AE3 cytokeratin (CK) stains for ITB (intratumoural budding) in biopsies with PTB (peritumoural budding) and ITB (intratumoural budding) in resection specimens were compared. Logistic regression assessed budding as predictors of lymph node metastasis (LNM). Cox regression and Kaplan–Meier analyses investigated their utility as a predictor of disease‐free (DFS) and overall (OS) survival. A total of 146 patients were included; 91 were male (62.3%). Thirty‐seven cases (25.3%) had ITB on H&E and 79 (54.1%) had ITB on CK assessment of biopsy tissue. In univariable analysis, H&E ITB [odds (OR) = 2.709, 95% confidence interval (CI) = 1.261–5.822, P = 0.011] and CK ITB (OR = 2.165, 95% CI = 1.076–4.357, P = 0.030) predicted LNM. Biopsy‐assessed H&E ITB (OR = 2.749, 95% CI = 1.258–6.528, P = 0.022) was an independent predictor of LNM. In Kaplan–Meier analysis, ITB identified on biopsy was associated with worse OS (H&E, P = 0.003, CK: P = 0.009) and DFS (H&E, P = 0.012; CK, P = 0.045). In resection specimens, CK PTB was associated with worse OS (P = 0.047), and both CK PTB and ITB with worse DFS (PTB, P = 0.014; ITB: P = 0.019). In multivariable analysis H&E ITB predicted OS (HR = 2.930, 95% CI = 1.261–6.809) and DFS (HR = 2.072, 95% CI = 1.031–4.164). CK PTB grading on resection also independently predicted OS (HR = 3.417, 95% CI = 1.45–8.053, P = 0.005). Conclusion: Assessment of TB using H&E and CK may be feasible in rectal cancer biopsy and post‐neoadjuvant therapy‐treated resection specimens and is associated with LNM and worse survival outcomes. Future management strategies for rectal cancer might be tailored to incorporate these findings. [ABSTRACT FROM AUTHOR]

Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool. [ABSTRACT FROM AUTHOR]

Introduction Predictive biomarkers for treatment efficacy of ulcerative colitis [UC] treatments are lacking. Here, we performed a longitudinal study investigating the association and potential predictive power of genome-wide peripheral blood [PB] DNA methylation signatures and response to tofacitinib treatment in UC. Methods We recruited moderate-to-severe UC patients starting tofacitinib treatment, and measured PB DNA methylation profiles at baseline [T1], after 8 weeks [T2], and in a subset [ n = 8] after a median of 20 weeks [T3] using the Illumina Infinium HumanMethylation EPIC BeadChip. After 8 weeks, we distinguished responders [R] from non-responders [NR] based on a centrally read endoscopic response [decrease in endoscopic Mayo score ≥1 or Ulcerative Colitis Endoscopic Index of Severity ≥2] combined with corticosteroid-free clinical and/or biochemical response. T1 PB samples were used for biomarker identification, and T2 and publicly available intraclass correlation [ICC] data were used for stability analyses. RNA-sequencing was performed to understand the downstream effects of the predictor CpG loci. Results In total, 16 R and 15 NR patients, with a median disease duration of 7 [4–12] years and overall comparable patient characteristics at baseline, were analysed. We identified a panel of 53 differentially methylated positions [DMPs] associated with response to tofacitinib [AUROC 0.74]. Most DMPs [77%] demonstrated both short- and long-term hyperstability [ICC ≥0.90], irrespective of inflammatory status. Gene expression analysis showed lower FGFR2 [ p BH = 0.011] and LRPAP1 [ p BH = 0.020], and higher OR2L13 [pBH = 0.016] expression at T1 in R compared with NR. Conclusion Our observations demonstrate the utility of genome-wide PB DNA methylation signatures to predict response to tofacitinib. [ABSTRACT FROM AUTHOR]

Opinion Statement: Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques. [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]

This survey assessed psychiatry residents'/early-career psychiatrists' attitudes towards the utility of therapeutic drug monitoring (TDM) of antipsychotics. A previously developed questionnaire on attitudes on TDM utility during antipsychotic treatment was cross-sectionally disseminated by national coordinators between 01/01/2022–31/12/2023. The frequency of using TDM for antipsychotics other than clozapine was the main outcome in a linear regression analysis, including sex, clinical setting, caseload, and factors generated by an exploratory factor analysis. Comparisons between residents and early-career psychiatrists, respondents working in in- and outpatient settings, and low-/middle- and high–income countries were performed. Altogether, 1,237 respondents completed the survey, with 37.9% having never used TDM for antipsychotics. Seven factors explained 41% of response variance; six of them were associated with frequency of TDM use (p < 0.05). Items with highest loadings for factors included clinical benefits of TDM (factors A and E: 0.7), negative expectations for beliefs of patients towards TDM (factor B: 0.6–0.7), weak TDM scientific evidence (factor C: 0.8), and TDM availability (factor D: −0.8). Respondents from low-/middle-income countries were less likely to frequently/almost always use TDM compared to high-income countries (9.4% vs. 21.5%, p < 0.001). TDM use for antipsychotics was poor and associated with limited knowledge and insufficient availability. [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]

In recent years, thanks to significant advances in basic science and biotechnologies, nephrology has witnessed a deeper understanding of the mechanisms leading to various conditions associated with or causing kidney disease, opening new perspectives for developing specific treatments. These new possibilities have brought increased challenges to physicians, who face with a new complexity in disease characterization and selection the right treatment for individual patients. We chose four therapeutic situations: anaemia in chronic kidney disease (CKD), heart failure in CKD, IgA nephropathy (IgAN) and membranous nephropathy (MN). The literature search was made through PubMed. Anaemia management remains challenging in CKD; a personalized therapeutic approach is often needed. Identifying patients who could benefit from a specific therapy is also an important goal for patients with CKD and heart failure with reduced ejection fraction. Several new treatments are under clinical development for IgAN; interestingly, they target specifically the pathogenetic mechanisms of the disease. The understanding of MN pathogenesis as an autoimmune disease and the discovery of several autoantibodies allows a better characterization of patients. High-sensible techniques for lymphocyte counting open the possibility of more personalized use of anti CD20 therapies. [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]

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development. [Display omitted] [ABSTRACT FROM AUTHOR]

This article reformulates Stephan Helmreich´s the ¨microbiomisation of race¨ as the historiality of otherness in the foundations of human microbiome science. Through the lens of my ethnographic fieldwork of a transnational community of microbiome scientists that conducted a landmark human microbiome research on indigenous microbes and its affiliated and first personalised microbiome initiative, the American Gut Project, I follow and trace the key actors, experimental systems and onto-epistemic claims in the emergence of human microbiome science a decade ago. In doing so, I show the links between the reinscription of race, comparative research on the microbial genetic variation of human populations and the remining of bioprospected data for personalised medicine. In these unpredictable research movements, the microbiome of non-Western peoples and territories is much more than a side project or a specific approach within the field: it constitutes the nucleus of its experimental system, opening towards subsequent and cumulative research processes and knowledge production in human microbiome science. The article demonstrates that while human microbiome science is articulated upon the microbial 'makeup' of non-wester(nised) communities, societies, and locales, its results and therapeutics are only applicable to medical conditions affecting rich nations (i.e., inflammatory, autoimmune, and metabolic diseases). My reformulation of ¨microbiomisation of race¨ as the condition of possibility of human microbiome science reveals that its individual dimension is sustained by microbial DNA data from human populations through bioprospecting practices and gains meaning through personalised medicine initiatives, informal online networks of pseudoscientific and commodified microbial-related evidence. [ABSTRACT FROM AUTHOR]

The article focuses on the transformative impact of digital technologies in personalized medicine, highlighting their adoption in medical practice and the integration of AI-based solutions. It explores the benefits observed from the perspectives of physicians, data scientists, and patients, emphasizing improved diagnosis accuracy, continuous disease monitoring, and the legal challenges associated with AI use in clinical settings within the European Union.

Personalised medicine is seen as an exciting opportunity to improve the health outcomes of people with obesity. As research on phenotyping and personalised treatment for obesity rapidly advances, this study sought to understand patient preferences and perspectives on personalised medicine for obesity. A participatory world café methodology was used to garner the perspectives of people living with obesity on the potential opportunities and limitations associated with a personalised approach to obesity risk identification and treatment. Data were recorded by participants on tablemats and analysed thematically using thematic analysis. Patients expressed the hope that personalised medicine for obesity would reduce stigma, support understanding of obesity as a disease, and improve treatment outcomes and acceptance. They also expressed concern about the accuracy of personalised medicine for obesity, its implications for insurance and that further advances in individual, personalised medicine, would detract attention from social, environmental, economic and psychological drivers of obesity. This study highlights how patients are generally very optimistic about the potential for personalised obesity medicine but also raise a number of legitimate concerns that will be of interest to clinicians, industry, and policy makers. • Personalised medicine represents the future of obesity treatment and clinical practice and expectations are high for the promise of personalised medicine in preventing and treating subsets of obesity. • This study is the first known study of patient perspectives on personalised medicine for obesity. • Patients hoped personalised obesity medicine will reduce stigma, improve understanding of obesity as a disease, and lead to improved treatment acceptance, adherence and outcomes. • Patients expressed concerns about the accuracy and reliability of personalised medicine for obesity and the implications for life assurance, treatment access, mental health and other drivers of obesity. [ABSTRACT FROM AUTHOR]

Opinion statement: Leiomyosarcoma (LMS) is one of the more common subtypes of soft tissue sarcomas (STS), accounting for about 20% of cases. Differences in anatomical location, risk of recurrence and histomorphological variants contribute to the substantial clinical heterogeneity in survival outcomes and therapy responses observed in patients. There is therefore a need to move away from the current one-size-fits-all treatment approach towards a personalised strategy tailored for individual patients. Over the past decade, tissue profiling studies have revealed key genomic features and an additional layer of molecular heterogeneity among patients, with potential utility for optimal risk stratification and biomarker-matched therapies. Furthermore, recent studies investigating intratumour heterogeneity and tumour evolution patterns in LMS suggest some key features that may need to be taken into consideration when designing treatment strategies and clinical trials. Moving forward, national and international collaborative efforts to aggregate expertise, data, resources and tools are needed to achieve a step change in improving patient survival outcomes in this disease of unmet need. [ABSTRACT FROM AUTHOR]

Background: Prescribing drugs for psychosis (antipsychotics) is challenging due to high rates of poor treatment outcomes, which are in part explained by an individual's genetics. Pharmacogenomic (PGx) testing can help clinicians tailor the choice or dose of psychosis drugs to an individual's genetics, particularly psychosis drugs with known variable response due to CYP2D6 gene variants ('CYP2D6-PGx antipsychotics'). Aims: This study aims to investigate differences between demographic groups prescribed 'CYP2D6-PGx antipsychotics' and estimate the proportion of patients eligible for PGx testing based on current pharmacogenomics guidance. Methods: A cross-sectional study took place extracting data from 243 patients' medical records to explore psychosis drug prescribing, including drug transitions. Demographic data such as age, sex, ethnicity, and clinical sub-team were collected and summarised. Descriptive statistics explored the proportion of 'CYP2D6-PGx antipsychotic' prescribing and the nature of transitions. We used logistic regression analysis to investigate associations between demographic variables and prescription of 'CYP2D6-PGx antipsychotic' versus 'non-CYP2D6-PGx antipsychotic'. Results: Two-thirds (164) of patients had been prescribed a 'CYP2D6-PGx antipsychotic' (aripiprazole, risperidone, haloperidol or zuclopenthixol). Over a fifth (23%) of patients would have met the suggested criteria for PGx testing, following two psychosis drug trials. There were no statistically significant differences between age, sex, or ethnicity in the likelihood of being prescribed a 'CYP2D6-PGx antipsychotic'. Conclusions: This study demonstrated high rates of prescribing 'CYP2D6-PGx-antipsychotics' in an EIP cohort, providing a rationale for further exploration of how PGx testing can be implemented in EIP services to personalise the prescribing of drugs for psychosis. [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]

Morbidity and mortality during chemotherapy in older adults with haematological malignancy can be unpredictable. The Haemato‐Oncology Frailty (HOF) score was previously found to predict outcomes in a cohort of patients with plasma cell myeloma. In this study, we assess its utility in assessing frailty in patients with lymphoma, and compare its performance to that of two other frailty scores. The HOF score was able to predict progression‐free survival in this population, and was also shown to have potential in assessing the dynamism of frailty during chemotherapy. It performed well when compared to the Charlson Comorbidity Index (CCI) score and the Haematopoietic Cell Transplantation‐Specific Comorbidity Index (HCTCI), although the study was not powered to assess for non‐inferiority. The HOF score is a new score with the potential for application in different haematological malignancies. [ABSTRACT FROM AUTHOR]

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignancy worldwide, with most deaths caused by locally advanced and metastatic disease. Treatment of resectable metastases is typically limited to invasive surgery with adjuvant radiotherapy; however, many patients fail to respond and there is minimal data to predict response or propose effective alternatives. Precision medicine could improve this, though genomic biomarkers remain elusive in the high mutational background and genomic complexity of cSCC. A phenotypic approach to precision medicine using patient‐derived ex vivo tumour models is gaining favour for its capacity to directly assess biological responses to therapeutics as a functional, predictive biomarker. However, the use of ex vivo models for guiding therapeutic selection has yet to be employed for metastatic cSCC. This review will therefore evaluate the existing experimental models of metastatic cSCC and discuss how ex vivo methods could overcome the shortcomings of these existing models. Disease‐specific considerations for a prospective methodological pipeline will also be discussed in the context of precision medicine. [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]