Pharmacogenomics has become integral to personalised medicine in breast cancer, utilising genetic insights to customize treatment strategies and enhance patient outcomes. Understanding how genetic variations influence drug metabolism, response, and toxicity is crucial for guiding treatment selection and dosing regimens. Genetic polymorphisms in drug-metabolizing enzymes and transporters significantly impact pharmacokinetic variability, influencing the efficacy and safety of chemotherapy agents and targeted therapies. Biomarkers associated with the hormone receptor status of breast cancer and mutations serve as key determinants of treatment response, aiding in the selection of therapies. Despite substantial progress in understanding the pharmacogenomic landscape of breast cancer, efforts to identify novel genetic markers and refine treatment optimisation strategies are required. Genome-wide association studies and advanced sequencing technologies hold promise for uncovering genetic determinants of drug response variability and elucidating complex pharmacogenomic interactions. The future of pharmacogenomics in breast cancer lies in real-time treatment monitoring, the discovery of additional predictive markers, and the seamless integration of pharmacogenomic data into clinical decision-making processes. However, translating pharmacogenomic discoveries into routine clinical practice requires collaborative efforts among stakeholders to address implementation challenges and ensure equitable access to genetic testing. By embracing pharmacogenomics, clinicians can tailor treatment approaches to individual patients, maximizing therapeutic benefits while minimizing adverse effects. This review discusses the integration of pharmacogenomics in breast cancer treatment, highlighting the significance of understanding genetic influences on treatment response and toxicity, and the potential of advanced technologies in refining treatment strategies.

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.

UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: No funding received.

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.

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.

BACKGROUND: Effective disease surveillance, including that for COVID-19, is compromised without a standardised method for categorising the immunosuppressed as a clinical risk group.
METHODS: We conducted a systematic review and meta-analysis to evaluate whether excess COVID-associated mortality compared to the immunocompetent could meaningfully subdivide the immunosuppressed. Our study adhered to UK Immunisation against infectious disease (Green Book) criteria for defining and categorising immunosuppression. Using OVID (EMBASE, MEDLINE, Transplant Library, and Global Health), PubMed, and Google Scholar, we examined relevant literature between the entirety of 2020 and 2022. We selected for cohort studies that provided mortality data for immunosuppressed subgroups and immunocompetent comparators. Meta-analyses, grey literature and any original works that failed to provide comparator data or reported all-cause or paediatric outcomes were excluded. Odds Ratios (OR) and 95% confidence intervals (CI) of COVID-19 mortality were meta-analysed by immunosuppressed category and subcategory. Subgroup analyses differentiated estimates by effect measure, country income, study setting, level of adjustment, use of matching and publication year. Study screening, extraction and bias assessment were performed blinded and independently by two researchers; conflicts were resolved with the oversight of a third researcher. PROSPERO registration number is CRD42022360755.
RESULTS: We identified 99 unique studies, incorporating data from 1,542,097 and 56,248,181 unique immunosuppressed and immunocompetent patients with COVID-19 infection, respectively. Compared to immunocompetent people (pooled OR, 95%CI), solid organ transplants (2.12, 1.50-2.99) and malignancy (2.02, 1.69-2.42) patients had a very high risk of COVID-19 mortality. Patients with rheumatological conditions (1.28, 1.13-1.45) and HIV (1.20, 1.05-1.36) had just slightly higher risks than the immunocompetent baseline. Case type, setting income and mortality data matching and adjustment were significant modifiers of excess immunosuppressed mortality for some immunosuppressed subgroups.
CONCLUSIONS: Excess COVID-associated mortality among the immunosuppressed compared to the immunocompetent was seen to vary significantly across subgroups. This novel means of subdivision has prospective benefit for targeting patient triage, shielding and vaccination policies during periods of high disease transmission.

Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

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.

Gastric cancer remains one of the most commonly diagnosed cancers in the world. It carries a high mortality rate, with cases being more prevalent in the developing world, and has been linked to diet and Helicobacter pylori infection. It is a highly heterogeneous disease, with most cases being of a sporadic nature. Most patients present at an advanced stage due to the asymptomatic nature of the early stages of the disease. A multidisciplinary approach is often best implemented to help decide how to best manage individual cases. However, the overall clinical outcome and survival of patients with advanced gastric cancer remain poor. Recent therapeutic advancements focus on the identification of molecular biomarkers associated with gastric cancer that have predictive, diagnostic, and prognostic implications. This enables the development of specific targeted therapies that have shown efficacy in numerous trials, either as monotherapy or in combination with standard chemotherapy. Despite this, tumour heterogeneity and treatment resistance are still issues leading to poor survival outcomes. An emerging approach is focusing efforts on the bidirectional crosstalk between tumour cells and the microenvironment through targeting ion channels. A key player in this is human ether-á-go-go-related gene 1 (hERG1). This voltage-gated potassium ion channel has been shown to have predictive, diagnostic, and prognostic significance, enabling the stratification of high-risk individuals. In addition, targeting hERG1 in combination with chemotherapy has been shown to potentiate tumour regression. This comprehensive literature review will aim to consolidate our understanding of current biomarkers in gastric cancer. The relevance of hERG1 in gastric cancer as a useful novel biomarker and the potential therapeutic implications as targeted therapy will be explored. This offers a new and personalised approach to helping to manage patients with gastric cancer.