Precision Health Symptom Science in Oncology Nursing.

Precision health is an emerging approach to predicting, preventing, treating, and managing disease. A goal of precision health symptom science research is the reliable prediction of patients' symptom burden to optimize robu.

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Efficacy of polygenic risk scores and digital technologies for INNOvative personalized cardiovascular disease PREVention in high-risk adults: protocol of a randomized controlled trial.

Background: Cardiovascular diseases (CVDs) pose a significant global health challenge, necessitating innovative approaches for primary prevention. Personalized prevention, based on genetic risk scores (PRS) and digital technologies, holds promise in revolutionizing CVD preventive strategies. However, the clinical efficacy of these interventions requires further investigation. This study presents the protocol of the INNOPREV randomized controlled trial, aiming to evaluate the clinical efficacy of PRS and digital technologies in personalized cardiovascular disease prevention. Methods: The INNOPREV trial is a four-arm RCT conducted in Italy. A total of 1,020 participants, aged 40-69 with high 10-year CVD risk based on SCORE 2 charts, will be randomly assigned to traditional CVD risk assessment, genetic testing (CVD PRS), digital intervention (app and smart band), or a combination of genetic testing and digital intervention. The primary objective is to evaluate the efficacy of providing CVD PRS information, measured at baseline, either alone or in combination with the use of an app and a smart band, on two endpoints: changes in lifestyle patterns, and modification in CVD risk profiles. Participants will undergo a comprehensive assessment and cardiovascular evaluation at baseline, with follow-up visits at one, five, and 12 months. Lifestyle changes and CVD risk profiles will be assessed at different time points beyond the initial assessment, using the Life's Essential 8 and SCORE 2, respectively. Blood samples will be collected at baseline and at study completion to evaluate changes in lipid profiles. The analysis will employ adjusted mixed-effect models for repeated measures to assess significant differences in the data collected over time. Additionally, potential moderators and mediators will be examined to understand the underlying mechanisms of behavior change. Discussion: As the largest trial in this context, the INNOPREV trial will contribute to the advancement of personalized cardiovascular disease prevention, with the potential to positively impact public health and reduce the burden of CVDs on healthcare systems. By systematically examining the clinical efficacy of PRS and digital interventions, this trial aims to provide valuable evidence to guide future preventive strategies and enhance population health outcomes.

[Cancer genomic panels for lymphoid malignancies].

Cancer precision medicine (genome-based individualized treatment for cancer patients) has already been introduced for solid tumors, and involves identifying driver genes in the development and progression of tumors and suggesting optimal treatments targeting those genes. So far, many patients have received this style of treatment. Meanwhile, preparations for cancer genomic medicine based on cancer gene panel testing are also underway for hematopoietic tumors. In this article, I would like to share fundamental information about the main genetic mutations in malignant lymphomas and their clinical significance, and discuss how this information should be utilized in cancer genomic medicine in the future.

[i-Leviathan: towards precision public health?] / i-Léviathan : vers une santé publique de précision ?

Hobbes' Leviathan symbolizes state sovereignty. In public health, this concept now extends to the prevention and promotion of health and the fight against non-communicable diseases (NCDs). This article explores the evolution towards an i-Leviathan, utilizing health data for more effective health surveillance. Precision public health, grounded in a personalized law approach, relies on the collection, availability, and use of these health data. This article analyzes the legal challenges of this precision, such as stigmatization, discrimination, and repression. It addresses the balance between public interests and individual freedoms, outlining state measures to monitor, control, and discipline healthy individuals.

Le Léviathan de Hobbes symbolise la souveraineté étatique. En santé publique, ce concept s'étend aujourd'hui à la prévention et la promotion de la santé et à la lutte contre les maladies non transmissibles. Cet article explore l'évolution vers un i-Léviathan, recourant à nos données de santé en vue d'une surveillance sanitaire plus efficace. Une santé publique de précision, ancrée dans une approche de droit personnalisé, dépend de la collecte, de la disponibilité et de l'utilisation de ces données de santé. Cet article analyse les enjeux juridiques de cette précision, tels que la stigmatisation, la discrimination et la répression. Il fait état de la balance entre intérêts publics et libertés individuelles, traçant les démarches étatiques de surveiller, contrôler et discipliner l'individu en bonne santé.

Insights into treatment-specific prognostic somatic mutations in NSCLC from the AACR NSCLC GENIE BPC cohort analysis.

BACKGROUND: Treatment of non-small lung cancer (NSCLC) has evolved in recent years, benefiting from advances in immunotherapy and targeted therapy. However, limited biomarkers exist to assist clinicians and patients in selecting the most effective, personalized treatment strategies. Targeted next-generation sequencing-based genomic profiling has become routine in cancer treatment and generated crucial clinicogenomic data over the last decade. This has made the development of mutational biomarkers for drug response possible. METHODS: To investigate the association between a patient's responses to a specific somatic mutation treatment, we analyzed the NSCLC GENIE BPC cohort, which includes 2,004 tumor samples from 1,846 patients. RESULTS: We identified somatic mutation signatures associated with response to immunotherapy and chemotherapy, including carboplatin-, cisplatin-, pemetrexed- or docetaxel-based chemotherapy. The prediction power of the chemotherapy-associated signature was significantly affected by epidermal growth factor receptor (EGFR) mutation status. Therefore, we developed an EGFR wild-type-specific mutation signature for chemotherapy selection. CONCLUSION: Our treatment-specific gene signatures will assist clinicians and patients in selecting from multiple treatment options.

Biomarkers in bronchiectasis.

Bronchiectasis is a heterogeneous disease with multiple aetiologies and diverse clinical features. There is a general consensus that optimal treatment requires precision medicine approaches focused on specific treatable disease characteristics, known as treatable traits. Identifying subtypes of conditions with distinct underlying biology (endotypes) depends on the identification of biomarkers that are associated with disease features, prognosis or treatment response and which can be applied in clinical practice. Bronchiectasis is a disease characterised by inflammation, infection, structural lung damage and impaired mucociliary clearance. Increasingly there are available methods to measure each of these components of the disease, revealing heterogeneous inflammatory profiles, microbiota, radiology and mucus and epithelial biology in patients with bronchiectasis. Using emerging biomarkers and omics technologies to guide treatment in bronchiectasis is a promising field of research. Here we review the most recent data on biomarkers in bronchiectasis.

Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalized therapy.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here, we show how a personalized organotypic "avatar" created from resected tissue allows spatial and temporal reporting on a complete in situ tumor microenvironment and mirrors clinical responses. Our perfusion culture method extends tumor slice viability, maintaining stable tumor content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighborhoods and potential for immunotherapy. We used avatars to assess the impact of a preclinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumor redifferentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identify a patient avatar-predictable response from clinical follow-up. Thus, avatars provide valuable information for syngeneic testing of therapeutics and a truly personalized therapeutic assessment platform for patients.

Pharmacogenetics in Italy: current landscape and future prospects.

Pharmacogenetics investigates sequence of genes that affect drug response, enabling personalized medication. This approach reduces drug-induced adverse reactions and improves clinical effectiveness, making it a crucial consideration for personalized medical care. Numerous guidelines, drawn by global consortia and scientific organizations, codify genotype-driven administration for over 120 active substances. As the scientific community acknowledges the benefits of genotype-tailored therapy over traditionally agnostic drug administration, the push for its implementation into Italian healthcare system is gaining momentum. This evolution is influenced by several factors, including the improved access to patient genotypes, the sequencing costs decrease, the growing of large-scale genetic studies, the rising popularity of direct-to-consumer pharmacogenetic tests, and the continuous improvement of pharmacogenetic guidelines. Since EMA (European Medicines Agency) and AIFA (Italian Medicines Agency) provide genotype information on drug leaflet without clear and explicit clinical indications for gene testing, the regulation of pharmacogenetic testing is a pressing matter in Italy. In this manuscript, we have reviewed how to overcome the obstacles in implementing pharmacogenetic testing in the clinical practice of the Italian healthcare system. Our particular emphasis has been on germline testing, given the absence of well-defined national directives in contrast to somatic pharmacogenetics.

[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information Mart for Intensive Care- IV (MIMIC- IV). METHODS: Utilizing the MIMIC- IV database, the case data of 2 096 patients with DKA admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center from 2008 to 2019 were analyzed. Machine learning models were developed, and receiver operator characteristic curve (ROC curve) and precision-recall curve (PR curve) were plotted to evaluate the model's effectiveness in predicting four common adverse outcomes: hypoglycemia, hypokalemia, reductions in Glasgow coma scale (GCS), and extended hospital stays. The risk of adverse outcomes was analyzed in relation to the rate of blood glucose decrease. Univariate and multivariate Logistic regression analyses were conducted to examine the relationship between relevant factors and the risk of hypokalemia. Personalized risk interpretation methods and predictive technologies were applied to individualize the analysis of optimal glucose control ranges for patients. RESULTS: The machine learning models demonstrated excellent performance in predicting adverse outcomes in patients with DKA, with areas under the ROC curve (AUROC) and 95% confidence interval (95%CI) for predicting hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stays being 0.826 (0.803-0.849), 0.850 (0.828-0.870), 0.925 (0.903-0.946), and 0.901 (0.883-0.920), respectively. Analysis of the relationship between the rate of blood glucose reduction and the risk of four adverse outcomes showed that a maximum glucose reduction rate > 6.26 mmol×L-1×h-1 significantly increased the risk of hypoglycemia (P < 0.001); a rate > 2.72 mmol×L-1×h-1 significantly elevated the risk of hypokalemia (P < 0.001); a rate > 5.53 mmol×L-1×h-1 significantly reduced the risk of GCS score reduction (P < 0.001); and a rate > 8.03 mmol×L-1×h-1 significantly shortened the length of hospital stay (P < 0.001). Multivariate Logistic regression analysis indicated significant correlations between maximum bicarbonate levels, blood urea nitrogen levels, and total insulin doses with the risk of hypokalemia (all P < 0.01). In terms of establishing personalized optimal treatment thresholds, assuming optimal glucose reduction thresholds for hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stay were x1, x2, x3, x4, respectively, the recommended glucose reduction rates to minimize the risks of hypokalemia and hypoglycemia should be ≤min{x1, x2}, while those to reduce GCS score decline and extended hospital stay should be ≥ max{x3, x4}. When these ranges overlap, i.e., max{x3, x4} ≤ min{x1, x2}, this interval was the recommended optimal glucose reduction range. If there was no overlap between these ranges, i.e., max{x3, x4} > min{x1, x2}, the treatment strategy should be dynamically adjusted considering individual differences in the risk of various adverse outcomes. CONCLUSIONS: The machine learning models shows good performance in predicting adverse outcomes in patients with DKA, assisting in personalized blood glucose management and holding important clinical application prospects.

Sample size calculation for comparing two ROC curves.

Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger AUC than the other. We propose a simple non-parametric statistical test for comparing the performance of two biomarkers. We also present a simple sample size calculation method for this test statistic. Our sample size formula requires specification of AUC values (or the standardized effect size of each biomarker between cases and controls together with the correlation coefficient between two biomarkers), prevalence of cases in the study population, type I error rate, and power. Through simulations, we show that the testing on two biomarkers controls type I error rate accurately and the proposed sample size closely maintains specified statistical power.

A Comprehensive Review: Pharmacogenomics and Personalized Medicine Customizing Drug Therapy Based on Individual Genetics Profiles.

Numerous factors, such as genetics, environmental factors, and illness determinants, might contribute to an unpleasant pharmaceutical response. In an effort to increase efficacy and safety, as well as to gain a better understanding of drug disposition and clinical consequences, researchers in the two quickly emerging fields of pharmacogenetics (which focuses on single genes) and pharmacogenomics (which focuses on many genes) have studied the genetic personalization of drug response. This is due to the fact that a large number of pharmacological responses seem to be genetically based, and the relationship between medication response and genotype may be important for diagnosis. We now have a better understanding of the genetic basis of individual medication responses because to research on pharmaceuticals and genes. Pharmacogenomics aims to improve patient outcomes by developing personalized medicine by using the diversity of the human genome and how it affects medication response. Translational in nature, pharmacogenomics research encompasses everything from the discovery of genotype-phenotype associations to clinical investigations that might show therapeutic relevance. Though the conversion of pharmacogenomics research findings into clinical practice has been sluggish, advances in the field offer considerable potential for future therapeutic applications in specific people.

High throughput platform technology for rapid target identification in personalized phage therapy.

As bacteriophages continue to gain regulatory approval for personalized human therapy against antibiotic-resistant infections, there is a need for transformative technologies for rapid target identification through multiple, large, decentralized therapeutic phages biobanks. Here, we design a high throughput phage screening platform comprised of a portable library of individual shelf-stable, ready-to-use phages, in all-inclusive solid tablets. Each tablet encapsulates one phage along with luciferin and luciferase enzyme stabilized in a sugar matrix comprised of pullulan and trehalose capable of directly detecting phage-mediated adenosine triphosphate (ATP) release through ATP bioluminescence reaction upon bacterial cell burst. The tablet composition also enhances desiccation tolerance of all components, which should allow easier and cheaper international transportation of phages and as a result, increased accessibility to therapeutic phages. We demonstrate high throughput screening by identifying target phages for select multidrug-resistant clinical isolates of Pseudomonas aeruginosa, Salmonella enterica, Escherichia coli, and Staphylococcus aureus with targets identified within 30-120 min.

Fusion Challenges in Solid Tumors: Shaping the Landscape of Cancer Care in Precision Medicine.

Targeting actionable fusions has emerged as a promising approach to cancer treatment. Next-generation sequencing (NGS)-based techniques have unveiled the landscape of actionable fusions in cancer. However, these approaches remain insufficient to provide optimal treatment options for patients with cancer. This article provides a comprehensive overview of the actionability and clinical development of targeted agents aimed at driver fusions. It also highlights the challenges associated with fusion testing, including the evaluation of patients with cancer who could potentially benefit from testing and devising an effective strategy. The implementation of DNA NGS for all tumor types, combined with RNA sequencing, has the potential to maximize detection while considering cost effectiveness. Herein, we also present a fusion testing strategy aimed at improving outcomes in patients with cancer.

[Building a comprehensive acupuncture clinical intervention system from individualized diagnosis and treatment to systematic design: reflections based on the modern rehabilitation treatment model].

The acupuncture clinical practice system is presenting a diversity of intervention methods and intervention concepts as modern technological civilization progresses. Therefore, it is of practical significance to construct a comprehensive acupuncture clinical intervention system and to scientifically design and implement the intervention system. This article aims to combine the current development status of rehabilitation treatment models, starting from the perspective of systems theory, to explore the importance of constructing a comprehensive acupuncture intervention system and its implementation strategies, providing new ideas and directions for the modern development of acupuncture clinical practice.