Precision Medicine: From Science To Value

Comparison of targeted vs. expanded pharmacogenomic testing: What are we missing?

BACKGROUND

Pharmacogenomics (PGx) is used as a medication management strategy by a small but growing number of institutions. PGx allows prescribers to individually treat patients concordant with their genes. Recent litigation for preventable PGx-mediated adverse events highlights the need to accelerate PGx implementation for patient safety. Genetic variations cause drug metabolism, transport, and target changes, affecting medication response and tolerability. PGx testing often consists of targeted testing aimed at specific gene-drug pairs or disease states. Conversely, expanded panel testing can evaluate all known actionable gene-drug interactions, enhancing proactive clarity regarding patient response.

OBJECTIVES

Evaluate the divergence of targeted PGx testing with a single gene-drug pair test (cardiac), a two-gene panel, and a focused psychiatric panel compared to expanded PGx testing.

METHODS

An expanded PGx panel (≥25 genes) was compared to a single gene-drug pair test of CYP2C19/clopidogrel, a dual gene test of CYP2C19/CYP2D6, a 7-gene psychiatric list, and a 14-gene psychiatric panel to inform specific depression and pain management drugs. The expanded panel provided a baseline to evaluate total PGx variations compared to those possibly missed by targeted testing.

RESULTS

Targeted testing did not identify up to 95% of total PGx gene-drug interactions discovered. The expanded panel reported all gene-drug interactions for any medication with Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance or U.S. Food and Drug Administration (FDA) labeling for that gene. Single gene CYP2C19/clopidogrel testing missed or did not report on ∼95% of total interactions, CYP2C19/CYP2D6 testing missed or did not report ∼89%, and the 14-gene panel missed or did not report on ∼73%. The 7-gene list missed ∼20% of discovered potential PGx interactions but was not designed to identify gene-drug interactions.

CONCLUSIONS

Targeted PGx testing for limited genes or by specialty may miss or not report significant portions of PGx gene-drug interactions. This can lead to potential patient harm from the missed interactions and subsequent failed therapies and/or adverse reactions.

Application of Preoperative Multimodal Image Fusion Technique in Microvascular Decompression Surgery via Suboccipital Retrosigmoid Approach

OBJECTIVE

To explore the application value of preoperative multimodal image fusion technique in microvascular decompression (MVD) surgery via the suboccipital retrosigmoid approach.

METHODS

Comprehensive data of 13 patients with primary trigeminal neuralgia (TN) and 13 patients with hemifacial spasm (HFS) treated by MVD surgery via the suboccipital retrosigmoid approach at the Department of Neurosurgery in Zhuhai People's Hospital from January 2021 to December 2021 were retrospectively analyzed. Preoperatively, all patients underwent cranial thin-section computed tomography and magnetic resonance examinations. Three-dimensional (3D) digital images of the skull, brainstem, nerves, and blood vessels were constructed by the 3D-slicer software or RadiAnt DICOM Viewer, which were then applied to design the surgical approach and surgical plan. The multimodal image fusion results, clinical characteristics, intraoperative data, surgical outcomes, and complications of all patients were summarized.

RESULTS

The 3D digital images after fusion reconstruction can vividly show the anatomical relationship between the skull, brainstem, nerves, and blood vessels and was helpful to tailor the surgical strategy. All 26 patients underwent a smooth surgery. During the surgery, the key points were accurately located, the corners of the transverse sinus and sigmoid sinus were completely exposed, and no venous sinus injury occurred in all 26 patients. The key point was approximately located at the top point of the digastric groove, 12.3 ± 0.46 mm vertically above and 6.3 ± 0.6 mm laterally to the Frankfurt horizontal plane. The average cranial opening time was 30.4 (±3.6) min, and the mean operating time was 104.7 (±12.1) min. The diameter of the bone window was about 2.0 cm-3.0 cm, and the bone flap was restored. Among the 13 patients with primary TN, 12 (92.3%) exhibited complete relief of pain and 1 had significant relief. Complications of surgery included facial sensory numbness in 1 case, vertigo in 2 cases, and herpes at the corners of the mouth in 1 case. Of the 13 patients with HFS, 12 (92.3%) had complete relief of facial twitching symptoms and 1 had significant relief, and the complications included mild facial palsy in 2 (15.4%) cases and facial sensory numbness in another 2 (15.4%) cases. The mean follow-up time after surgery ranged from 6-16 months, and 1 of 26 patients experienced recurrence of HFS during the follow-up period.

CONCLUSIONS

Preoperative multimodal image fusion technology can provide adequate preoperative assessment for patients and assistance in designing surgical approaches, which is an important guideline for MVD surgery via the suboccipital retrosigmoid approach for primary TN and facial muscle spasm.

Managing re-identification risks while providing access to the All of Us research program

OBJECTIVE

The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels.

METHODS

At the time of the study, the resource consisted of 329 084 participants. Systematic amendments were applied to the data to mitigate re-identification risk (eg, generalization of geographic regions, suppression of public events, and randomization of dates). We computed the re-identification risk for each participant using a state-of-the-art adversarial model specifically assuming that it is known that someone is a participant in the program. We confirmed the expected risk is no greater than 0.09, a threshold that is consistent with guidelines from various US state and federal agencies. We further investigated how risk varied as a function of participant demographics.

RESULTS

The results indicated that 95th percentile of the re-identification risk of all the participants is below current thresholds. At the same time, we observed that risk levels were higher for certain race, ethnic, and genders.

CONCLUSIONS

While the re-identification risk was sufficiently low, this does not imply that the system is devoid of risk. Rather, All of Us uses a multipronged data protection strategy that includes strong authentication practices, active monitoring of data misuse, and penalization mechanisms for users who violate terms of service.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records

In this article, we propose a general framework to learn optimal treatment rules for type 2 diabetes (T2D) patients using electronic health records (EHRs). We first propose a joint modeling approach to characterize patient's pretreatment conditions using longitudinal markers from EHRs. The estimation accounts for informative measurement times using inverse-intensity weighting methods. The predicted latent processes in the joint model are used to divide patients into a finite of subgroups and, within each group, patients share similar health profiles in EHRs. Within each patient group, we estimate optimal individualized treatment rules by extending a matched learning method to handle multicategory treatments using a one-versus-one approach. Each matched learning for two treatments is implemented by a weighted support vector machine with matched pairs of patients. We apply our method to estimate optimal treatment rules for T2D patients in a large sample of EHRs from the Ohio State University Wexner Medical Center. We demonstrate the utility of our method to select the optimal treatments from four classes of drugs and achieve a better control of glycated hemoglobin than any one-size-fits-all rules.

Harnessing the Power of Precision Medicine and Novel Biomarkers to Treat Crohn’s Disease

Crohn’s disease (CD) is a chronic inflammatory condition that affects the gastrointestinal tract. It is part of a spectrum of inflammatory Bowel Diseases (IBD). The disease is complex, characterized by significant inter and intra-individual heterogeneity, which contributes to a diverse and multifaceted portrayal of the disease. Consequently, applying specific and accurate treatment is challenging, and therapeutic success rates remain disappointing and insufficient. In recent years, significant advances in the therapeutic potential of CD have been made. Hope has been provided by these developments in the form of an expanding treatment toolkit. However, even with these beneficial adjustments, patients are frequently treated using an ineffective “one size fits all” treatment protocol, ultimately leading to a plateau in drug effectiveness and a decline in overall treatment success rates. Furthermore, with the advancement in the genome-wide association study, in combination with significant bioinformatic developments, the world of medicine has moved in the direction of personalized, tailored-treatment medicine, and this trend has not escaped the world of IBDs. Prediction models, novel biomarkers, and complex algorithms are emerging and inspiring optimism that CD patients will be treated with “precision medicine” in the near future, meaning that their treatments will be selected based on the patient’s various unique features. In this review, we will outline the current diagnostic and therapeutic limitations that lead to a glass ceiling effect and thus send us in pursuit of discovering novel biomarkers. We will illustrate the challenges and difficulties in discovering relevant and innovative biomarkers and implementing them into everyday clinical practice. We will also heighten the progress made in practicing personalized medicine for CD patients and shed light on future directions and horizons.

A mobile app for chronic disease self-management for individuals with low health literacy: A multisite randomized controlled clinical trial

Objective

The purpose of this study was to evaluate the effects of a mobile app designed to improve chronic disease self-management in older adult patients with low health literacy and who had at least one chronic health condition, and to assess the impact of delivering information at different levels of reading difficulty.

Methods

A randomized controlled trial was completed at two sites. Individuals 40 years of age and older screened for low health literacy who had at least one chronic health condition were randomly assigned to a tailored information multimedia app with text at one of three grade levels. Four primary outcomes were assessed: patient activation, chronic disease self-efficacy, health-related quality of life, and medication adherence.

Results

All groups showed overall increases in activation, self-efficacy, and health-related quality of life, but no change in medication adherence. No between-group differences were observed.

Conclusions

The mobile app was effective in increasing participants' levels of several psychosocial variables, but reading difficulty level was not significantly related to outcomes.Registered at clinicaltrials.gov NCT02922439.

Targeted Therapy for Orofacial Pain: A Novel Perspective for Precision Medicine

Orofacial pain (OFP) is a dental specialty that includes the diagnosis, management and treatment of disorders of the jaw, mouth, face, head and neck. Evidence-based understanding is critical in effectively treating OFPs as the pathophysiology of these conditions is multifactorial. Since OFP impacts the quality of life of the affected individuals, treating patients successfully is of the utmost significance. Despite the therapeutic choices available, treating OFP is still quite challenging, owing to inter-patient variations. The emerging trends in precision medicine could probably lead us to a paradigm shift in effectively managing the untreatable long-standing pain conditions. Precision medicine is designed based on the patient's genetic profile to meet their needs. Several significant relationships have been discovered based on the genetics and genomics of pain in the past, and some of the notable targets are discussed in this review. The scope of this review is to discuss preclinical and clinical trials that include approaches used in targeted therapy for orofacial pain. Future developments in pain medicine should benefit from current trends in research into novel therapeutic approaches.

Reinventing mental health care in youth through mobile approaches: Current status and future steps

In this perspective, we aim to bring together research on mobile assessments and interventions in the context of mental health care in youth. After the COVID-19 pandemic, one out of five young people is experiencing mental health problems worldwide. New ways to face this burden are now needed. Young people search for low-burden services in terms of costs and time, paired with high flexibility and easy accessibility. Mobile applications meet these principles by providing new ways to inform, monitor, educate, and enable self-help, thus reinventing mental health care in youth. In this perspective, we explore the existing literature reviews on mobile assessments and interventions in youth through data collected passively (e.g., digital phenotyping) and actively (e.g., using Ecological Momentary Assessments-EMAs). The richness of such approaches relies on assessing mental health dynamically by extending beyond the confines of traditional methods and diagnostic criteria, and the integration of sensor data from multiple channels, thus allowing the cross-validation of symptoms through multiple information. However, we also acknowledge the promises and pitfalls of such approaches, including the problem of interpreting small effects combined with different data sources and the real benefits in terms of outcome prediction when compared to gold-standard methods. We also explore a new promising and complementary approach, using chatbots and conversational agents, that encourages interaction while tracing health and providing interventions. Finally, we suggest that it is important to continue to move beyond the ill-being framework by giving more importance to intervention fostering well-being, e.g., using positive psychology.

Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity

INTRODUCTION

Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes.

OBJECTIVE

The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management.

RESULTS

The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity.

CONCLUSION

Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.

Introduction to Radiomics and Artificial Intelligence: A Primer for Radiologists

Health informatics and artificial intelligence (AI) are expected to transform the healthcare enterprise and the future practice of radiology. There is an increasing body of literature on radiomics and deep learning/AI applications in medical imaging. There are also a steadily increasing number of FDA cleared AI applications in radiology. It is therefore essential for radiologists to have a basic understanding of these approaches, whether in academia or private practice. In this article, we will provide an overview of the field and familiarize the readers with the fundamental concepts behind these approaches.

The implementation of large-scale genomic screening or diagnostic programmes: A rapid evidence review

Genomic healthcare programmes, both in a research and clinical context, have demonstrated a pivotal opportunity to prevent, diagnose, and treat rare diseases. However, implementation factors could increase overall costs and affect uptake. As well, uncertainties remain regarding effective training, guidelines and legislation. The purpose of this rapid evidence review was to draw together the available global evidence on the implementation of genomic testing programmes, particularly on population-based screening and diagnostic programmes implemented at the national level, to understand the range of factors influencing implementation. This review involved a search of terms related to genomics, implementation and health care. The search was limited to peer-reviewed articles published between 2017-2022 and found in five databases. The review included thirty articles drawing on sixteen countries. A wide range of factors was cited as critical to the successful implementation of genomics programmes. These included having policy frameworks, regulations, guidelines; clinical decision support tools; access to genetic counselling; and education and training for healthcare staff. The high costs of implementing and integrating genomics into healthcare were also often barriers to stakeholders. National genomics programmes are complex and require the generation of evidence and addressing implementation challenges. The findings from this review highlight that there is a strong emphasis on addressing genomic education and engagement among varied stakeholders, including the general public, policymakers, and governments. Articles also emphasised the development of appropriate policies and regulatory frameworks to govern genomic healthcare, with a focus on legislation that regulates the collection, storage, and sharing of personal genomic data.

The impact of receiving polygenic risk scores for alcohol use disorder on psychological distress, risk perception, and intentions to reduce drinking

For the return of polygenic risk scores to become an acceptable clinical practice in psychiatry, receipt of polygenic risk scores must be associated with minimal harm and changes in behavior that decrease one's risk for developing a psychiatric outcome. Data from a randomized controlled trial was used to assess the impact of different levels of hypothetical polygenic risk scores for alcohol use disorder on psychological distress, risk perception, and intentions to change drinking behaviors. The analytic sample consisted of 325 participants recruited from an urban, public university. Results demonstrated that there were significant increases in psychological distress as the level of genetic risk for alcohol use disorder increased. In addition, the perceived chance of developing alcohol use disorder significantly increased as the level of genetic risk increased. Promisingly, a greater proportion of participants indicated that they would intend to engage in follow-up behaviors, such as seeking additional information, talking to a healthcare provider about risk, and reducing drinking behaviors, as the level of genetic risk increased. Returning polygenic risk scores for alcohol use disorder in a clinical setting has the potential to promote risk-reducing behavior change, especially with increasing levels of genetic risk. The study was registered on ClinicalTrials.gov (Identifier: NCT05143073).

Complement C3 Facilitates Stratification of Stages of Chronic Hepatitis B and Signifies Development of Acute-on-Chronic Liver Failure in Acute Decompensated Cirrhosis

INTRODUCTION

Patients with chronic hepatitis B (CHB) have a dynamic disease process and risk of end-stage liver disease. It is critical to unambiguously differentiate the stages of the disease and focus on therapy prior to onset of an irreversible clinical endpoint.

METHODS

We retrospectively analyzed a wide range of CHB patients at different stages. The predictive power of serum complement component 3 (C3) levels for the development of acute-on-chronic liver failure (ACLF) in patients with decompensated cirrhosis was established and validated.

RESULTS

The decrease in serum C3 levels paralleled the severity of diseases related to hepatitis B virus. Patients with decompensated cirrhosis who developed ACLF had significantly lower serum C3 levels than others on admission (0.50 vs. 0.80 g/L, P < 0.001). Data analysis also revealed that low serum C3 was a significant risk factor for developing ACLF (hazard ratio = 0.32, P < 0.01). The area under the receiver operating characteristic curve (auROC) for serum C3 levels that predicted the development of ACLF in patients with decompensated cirrhosis was 0.90, which had sensitivity and specificity of 88.2% and 88.7%, respectively. A similar result was observed in the validation set (auROC = 0.86 for predicting development of ACLF in patients with decompensated cirrhosis).

CONCLUSIONS

Serum C3 levels are valuable in assessing the severity of CHB-related stages. Low C3 levels signifies the development of ACLF in patients with decompensated cirrhosis.

Returning complex genetic risk information to promote better health-related behaviors: a commentary of the literature and suggested next steps

Genome-wide association studies aim to identify genetic variants that are associated with a disease phenotype in order to enhance precision medicine efforts. Despite the excitement surrounding the promise of precision medicine and interest among the public in accessing personalized genetic information, there has been little effort dedicated to understanding how complex genetic risk information could be incorporated into clinical practice to inform prevention, screening, and treatment. In this article, we briefly summarize the literature on the impact of receiving genetic risk information on health-related behavior, discuss the limitations of these studies, and outline the challenges that will need to be overcome, along with suggested next steps for future studies, to understand the true promise of precision medicine. The current literature demonstrates that there is no consistent or strong evidence that receiving complex genetic risk information, such as polygenic risk scores, has an impact on behavior; however, there are a number of limitations that may impact the failure to find significant effects associated with receiving genetic feedback. Behavior change is a complex process and simply providing genetic risk information without incorporating a theoretical perspective on behavior change diminishes the potential impact of receiving genetic risk information on actual behavior change. Future studies and interventions which return genetic feedback should be designed using theoretical frameworks of behavior change models to improve the impact of receiving personalized genetic information.

How the VALID Act could affect patient access to laboratory developed testing for therapeutic drug monitoring

•LDTs are important to lab medicine as they allow flexibility for laboratories to provide testing that patients need.•Many TDMs are LCMS-based and are considered LDTs.•Some TDMs currently have little to no options for obtaining FDA cleared testing.•The VALID Act may threaten the offering of LDTs in the United States.•The current VALID Act has flaws in its legislations which need to be addressed.

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Objective

This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization.

Methods

Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem.

Results

With the concept of "Internet + medical integration," the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements.

Conclusion

The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.

Personalized Care for Patients with Cancer in the Precision-Medicine Era

Important advances in cancer management have been made in the beginning of the 21st century [...].

Past Myocardial Infarctions and Gender Predict the LVEF Regardless of the Status of Coronary Collaterals: An AI-Informed Research

BACKGROUND: The degree of the development of coronary collaterals is long considered an alternate – that is, a collateral – source of blood supply to an area of the myocardium threatened with vascular ischemia or insufficiency. Hence, the coronary collaterals are beneficial but can also promote harmful (adverse) effects. For instance, the coronary steal effect during the myocardial hyperemia phase and that of restenosis following coronary angioplasty. OBJECTIVES: Our study explores the contribution of coronary collaterals – if any exist – while considering other potential predictors, including demographics and medical history, toward the left ventricular (LV) dysfunction measured through the LV ejection fraction (LVEF). METHODS: Our cross-sectional design study used convenience sampling of 100 patients (n = 100; a male-to-female ratio of 4:1). We conducted frequentist inference statistics using IBM-SPSS version 24 and Microsoft Office Excel 2016 with the analysis ToolPak plugin; we ran parallel neural networks (supervised machine learning (ML)) and a two-step clustering (non-supervised ML) for robust conjoint inference with frequentist statistics. RESULTS: The past incidents of myocardial infarction (p = 0.036) and gender (p = 0.072) influenced the LVEF; both are significant predictors at a 90% confidence interval. We found that gender and past incidents of MI influenced the LVEF regardless of the status of coronary collaterals. Our study did not yield any positive or significant findings concerning the status of coronary collaterals or the coronary circulation dominance patterns. CONCLUSION: Regardless of the status of coronary collaterals, we verified that the female gender is protective of the LV function, contrary to the past infarction incidents that predispose to a deteriorated LV function. Our study’s innovation relates to its status as the first study from India to explore the coronary collaterals and the ejection fraction while incorporating frequentist statistics and narrow artificial intelligence to infer reliable results.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Musculoskeletal research in human space flight - unmet needs for the success of crewed deep space exploration

Based on the European Space Agency (ESA) Science in Space Environment (SciSpacE) community White Paper "Human Physiology - Musculoskeletal system", this perspective highlights unmet needs and suggests new avenues for future studies in musculoskeletal research to enable crewed exploration missions. The musculoskeletal system is essential for sustaining physical function and energy metabolism, and the maintenance of health during exploration missions, and consequently mission success, will be tightly linked to musculoskeletal function. Data collection from current space missions from pre-, during-, and post-flight periods would provide important information to understand and ultimately offset musculoskeletal alterations during long-term spaceflight. In addition, understanding the kinetics of the different components of the musculoskeletal system in parallel with a detailed description of the molecular mechanisms driving these alterations appears to be the best approach to address potential musculoskeletal problems that future exploratory-mission crew will face. These research efforts should be accompanied by technical advances in molecular and phenotypic monitoring tools to provide in-flight real-time feedback.

The Future of Precision Medicine in the Cure of Alzheimer's Disease

This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.

Pharmacogenomics and health disparities, are we helping?

Pharmacogenomics has been at the forefront of precision medicine during the last few decades. Precision medicine carries the potential of improving health outcomes at both the individual as well as population levels. To harness the benefits of its initiatives, careful dissection of existing health disparities as they relate to precision medicine is of paramount importance. Attempting to address the existing disparities at the early stages of design and implementation of these efforts is the only guarantee of a successful just outcome. In this review, we glance at a few determinants of existing health disparities as they intersect with pharmacogenomics research and implementation. In our opinion, highlighting these disparities is imperative for the purpose of researching meaningful solutions. Failing to identify, and hence address, these disparities in the context of the current and future precision medicine initiatives would leave an already strained health system, even more inundated with inequality.

Patient Perspectives on a Targeted Text Messaging Campaign to Encourage Screening for Diabetes: Qualitative Study

BACKGROUND

A sizeable proportion of prediabetes and diabetes cases among adults in the United States remain undiagnosed. Patient-facing clinical decision support (CDS) tools that leverage electronic health records (EHRs) have the potential to increase diabetes screening. Given the widespread mobile phone ownership across diverse groups, text messages present a viable mode for delivering alerts directly to patients. The use of unsolicited text messages to offer hemoglobin A1c (HbA1c) screening has not yet been studied. It is imperative to gauge perceptions of "cold texts" to ensure that information and language are optimized to promote engagement with text messages that affect follow-through with health behaviors.

OBJECTIVE

This study aims to gauge the perceptions of and receptiveness to text messages to inform content that would facilitate engagement with text messages intended to initiate a mobile health (mHealth) intervention for targeted screening. Messages were designed to invite those not already diagnosed with diabetes to make a decision to take part in HbA1c screening and walk them through the steps required to perform the behavior based solely on an automated text exchange.

METHODS

In total, 6 focus groups were conducted at Wake Forest Baptist Health (WFBH) between September 2019 and February 2020. The participants were adult patients without diabetes who had completed an in-person visit at the Family and Community Medicine Clinic within the previous year. We displayed a series of text messages and asked the participants to react to the message content and suggest improvements. Content was deductively coded with respect to the Health Belief Model (HBM) and inductively coded to identify other emergent themes that could potentially impact engagement with text messages.

RESULTS

Participants (N=36) were generally receptive to the idea of receiving a text-based alert for HbA1c screening. Plain language, personalization, and content, which highlighted perceived benefits over perceived susceptibility and perceived severity, were important to participants' understanding of and receptiveness to messages. The patient-physician relationship emerged as a recurring theme in which patients either had a desire or held an assumption that their provider would be working behind the scenes throughout each step of the process. Participants needed further clarification to understand the steps involved in following through with HbA1c screening and receiving results.

CONCLUSIONS

Our findings suggest that patients may be receptive to text messages that alert them to a risk of having an elevated HbA1c in direct-to-patient alerts that use cold texting. Using plain and positive language, integrating elements of personalization, and defining new processes clearly were identified by participants as modifiable content elements that could act as facilitators that would help overcome barriers to engagement with these messages. A patient's relationship with their provider and the financial costs associated with texts and screening may affect receptiveness and engagement in this process.

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis

Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

Theranostics for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor prognosis. Current endocrine therapy or anti HER-2 therapy is not available for these patients. Chemotherapeutic treatment response varies among patients due to the disease heterogeneity. To overcome these challenges, theranostics for treating TNBC have been widely investigated. Anticancer material conjugated nanoparticles with target-binding ligand and tracer agents enable simultaneous drug delivery and visualization of the lesion with minimal off-target toxicity. In this review, we summarize recently FDA-approved targeted therapies for TNBC, such as poly-ADP-ribose polymerase (PARP) inhibitors, check point inhibitors, and antibody-drug conjugates. Particularly, novel theranostic approaches including lipid-based, polymer-based, and carbon-based nanocarriers are discussed, which can provide basic overview of nano-therapeutic modalities in TNBC diagnosis and treatment.

Future directions in regulatory affairs

The field of regulatory affairs deals with the regulatory requirements for marketing authorization of therapeutic products. This field is facing a myriad of forces impacting all aspects of the development, regulation and value proposition of new therapeutic products. Changes in global megatrends, such as geopolitical shifts and the rise of the green economy, have emphasized the importance of manufacturing and supply chain security, and reducing the environmental impacts of product development. Rapid changes due to advances in science, digital disruption, a renewed focus on the centrality of the patient in all stages of therapeutic product development and greater collaboration between national regulatory authorities have been accelerated by the COVID-19 pandemic. This article will discuss the various trends that are impacting the development of new therapies for alleviating disease and how these trends therefore impact on the role of the regulatory affairs professional. We discuss some of the challenges and provide insights for the regulatory professional to remain at the forefront of these trends and prepare for their impacts on their work.

The impact of changing the funding model for genetic diagnostics and improved access to personalized medicine in oncology

AIM

In January 2017, a new funding model for diagnostic genetic testing in cancer was introduced in Poland.

OBJECTIVES

The aim of this study was to assess the impact of changing the funding model for genetic diagnosis in oncology on improving access to personalized medicine in Poland between 2017 and 2019.

METHODS

The analysis included data on settlements with the National Health Fund for genetic tests in cancer under a contract of the hospital treatment type and under the contract in the type of separately contracted services between 2017 and 2019.

RESULTS

The 150,647 diagnostic genetic tests were reported, which were billed to 111,872 patients. The average number of tests per patient was 1.35. One test was billed to 83.5% of patients, 11.2% of patients had two tests billed, and 5.3% had at least three tests billed. The number of services provided under the hospital treatment contract in 2018 doubled compared to the previous year. For separately contracted services, more than threefold increase in genetic testing performed in 2019 compared to 2018 was observed.

CONCLUSION

our data show that the novel funding model for genetic services introduced in Poland has positive impact on the availability of genetic testing for patients.

RAPID framework for improved access to precision oncology for lethal disease: Results from a modified multi-round delphi study

Introduction

Predictive oncology, germline technologies, and adaptive seamless trials are promising advances in the treatment of lethal cancers. Yet, access to these therapies is stymied by costly research, regulatory barriers, and structural inequalities worsened by the COVID-19 pandemic.

Methods

To address the need for a comprehensive strategy for rapid and more equitable access to breakthrough therapies for lethal cancers, we conducted a modified multi-round Delphi study with 70 experts in oncology, clinical trials, legal and regulatory processes, patient advocacy, ethics, drug development, and health policy in Canada, Europe, and the US. Semi-structured ethnographic interviews (n = 33) were used to identify issues and solutions that participants subsequently evaluated in a survey (n = 47). Survey and interview data were co-analyzed to refine topics for an in-person roundtable where recommendations for system change were deliberated and drafted by 26 participants.

Results

Participants emphasized major issues in patient access to novel therapeutics including burdens of time, cost, and transportation required to complete eligibility requirements or to participate in trials. Only 12% of respondents reported satisfaction with current research systems, with "patient access to trials" and "delays in study approval" the topmost concerns.

Conclusion

Experts agree that an equity-centered precision oncology communication model should be developed to improve access to adaptive seamless trials, eligibility reforms, and just-in-time trial activation. International advocacy groups are a key mobilizer of patient trust and should be involved at every stage of research and therapy approval. Our results also show that governments can promote better and faster access to life-saving therapeutics by engaging researchers and payors in an ecosystem approach that responds to the unique clinical, structural, temporal, and risk-benefit situations that patients with life-threatening cancers confront.

A review of the cost-effectiveness of genetic testing for germline variants in familial cancer

BACKGROUND

Targeted germline testing is recommended for those with or at risk of breast, ovarian, or colorectal cancer. The affordability of genetic sequencing has improved over the past decade, therefore the cost-effectiveness of testing for these cancers is worthy of reassessment.

OBJECTIVE

To systematically review economic evaluations on cost-effectiveness of germline testing in breast, ovarian, or colorectal cancer.

METHODS

A search of PubMed and Embase databases for cost-effectiveness studies on germline testing in breast, ovarian, or colorectal cancer, published between 1999 and May 2022. Synthesis of methodology, cost-effectiveness, and reporting (CHEERS checklist) was performed.

RESULTS

The incremental cost-effectiveness ratios (ICERs; in 2021-adjusted US$) for germline testing versus the standard care option in hereditary breast or ovarian cancer (HBOC) across target settings were as follows: (1) population-wide testing: 344-2.5 million/QALY; (2) women with high-risk: dominant = 78,118/QALY, 8,337-59,708/LYG; (3) existing breast or ovarian cancer: 3,012-72,566/QALY, 39,835/LYG; and (4) metastatic breast cancer: 158,630/QALY. Likewise, ICERs of germline testing for colorectal cancer across settings were: (1) population-wide testing: 132,200/QALY, 1.1 million/LYG; (2) people with high-risk: 32,322-76,750/QALY, dominant = 353/LYG; and (3) patients with existing colorectal cancer: dominant = 54,122/QALY, 98,790-6.3 million/LYG. Key areas of underreporting were the inclusion of a health economic analysis plan (100% of HBOC and colorectal studies), engagement of patients and stakeholders (95.4% of HBOC, 100% of colorectal studies) and measurement of outcomes (18.2% HBOC, 38.9% of colorectal studies).

CONCLUSION

Germline testing for HBOC was likely to be cost-effective across most settings, except when used as a co-dependent technology with the PARP inhibitor, olaparib in metastatic breast cancer. In colorectal cancer studies, testing was cost-effective in those with high-risk, but inconclusive in other settings. Cost-effectiveness was sensitive to the prevalence of tested variants, cost of testing, uptake, and benefits of prophylactic measures. Policy advice on germline testing should emphasize the importance of these factors in their recommendations.

COMMUTE: Communication-efficient transfer learning for multi-site risk prediction

OBJECTIVES

We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.

METHODS

We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity.

RESULTS

Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70.

CONCLUSION

COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Microfluidic Strategies for Extracellular Vesicle Isolation: Towards Clinical Applications

Extracellular vesicles (EVs) are double-layered lipid membrane vesicles released by cells. Currently, EVs are attracting a lot of attention in the biological and medical fields due to their role as natural carriers of proteins, lipids, and nucleic acids. Thus, they can transport useful genomic information from their parental cell through body fluids, promoting cell-to-cell communication even between different organs. Due to their functionality as cargo carriers and their protein expression, they can play an important role as possible diagnostic and prognostic biomarkers in various types of diseases, e.g., cancers, neurodegenerative, and autoimmune diseases. Today, given the invaluable importance of EVs, there are some pivotal challenges to overcome in terms of their isolation. Conventional methods have some limitations: they are influenced by the starting sample, might present low throughput and low purity, and sometimes a lack of reproducibility, being operator dependent. During the past few years, several microfluidic approaches have been proposed to address these issues. In this review, we summarize the most important microfluidic-based devices for EV isolation, highlighting their advantages and disadvantages compared to existing technology, as well as the current state of the art from the perspective of the use of these devices in clinical applications.

Heath policy guiding the identification, analysis and management of secondary findings for individuals undergoing genomic sequencing: a systematic review protocol

INTRODUCTION

Genomic sequencing is increasingly enabling precision care across medical specialties; however, the discovery of genomic 'secondary findings' (SFs) unrelated to the patient's primary indication remains a profuse, unintended consequence. Existing practices within the continuum of SF identification, analysis and management are numerous, inconsistent and sometimes contradictory across health conditions and regions. Final decisions are often at the discretion of the genomic sequencing laboratory, bioinformatician or treating physician. This difference in healthcare delivery causes inconsistent information, disclosure and downstream impacts required to manage SFs and patient outcomes. Improving our understanding of the SF health policy landscape can determine components of the SF policy continuum spanning generation through to management that are in conflict, limitations of current guidance and existing needs across clinical settings.

METHODS AND ANALYSIS

We will carry out a systematic review to catalogue and appraise current guidance directing the identification, analysis and management of SFs for participants receiving genomic sequencing globally. We will conduct a comprehensive search of Medline (Medline R, Medline Epub Ahead of Print and Medline-In-Process & In-Data-Review Citations), Embase and Cochrane databases (n=5, inception to Feb 2022) and a grey literature search of international genomics websites (n=64; inception to May 2022). Key inclusion criteria include: guidance produced by health organisations, bioethics committees and professional associations, outlining recommendations for: (1) SF identification, (2) SF analysis or (3) SF management. Non-English language articles and conference abstracts will be excluded. Guidance will be critically appraised with the Appraisal of Guidelines for Research & Evaluation Instrument (AGREE) II tool. We will interpret our findings by process and across populations using a qualitative descriptive approach.

ETHICS AND DISSEMINATION

Our systematic review evaluates published data and does not require ethics review. Our findings will be disseminated through peer-reviewed publications, conference presentations and workshops with precision medicine stakeholders.

PROSPERO REGISTRATION NUMBER

CRD42022316079.

Impact of health literacy and its interventions on health outcomes in those with atrial fibrillation: a systematic review protocol

INTRODUCTION

Atrial fibrillation (AF) is associated with increased risk of stroke, heart failure and death. Health literacy, an aspect that falls within precision health, has been recognised as an important factor. We will be focusing on the impact of these interventions specifically to AF and its health outcomes.

METHODS AND ANALYSIS

This protocol is informed by the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. The results will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to determine the impacts of health literacy interventions on AF outcomes. Searches will be carried out on databases including MEDLINE, EMBASE, Web of Science, CINAHL, Emcare, Cochrane Library and Google Scholar. Citations will be collected via Endnote 20, then into Covidence for duplicate removal, and article screening. Extraction will occur using a standardised extraction tool and studies will be synthesised using best evidence synthesis. Downs and Black's checklist will be used for risk of bias and assessment of overall quality of evidence will use the Grading of Recommendations, Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION

Approval from human research ethics committee is not required. Dissemination will occur in peer-reviewed journals and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42022304835.

Toward a More Just System of Care in Molecular Pathology

Policy Points American health care policy must be critically assessed to establish the role it plays in sustaining and alleviating the health disparities that currently exist in molecular genetic testing. It is critical to understand the economic and sociocultural influences that drive patients to undergo or forgo molecular testing, especially in marginalized patient populations. A multipronged solution with actions necessary from multiple stakeholders is required to reduce the cost of health care, rebalance regional disparities, encourage physician engagement, reduce data bias, and earn patients' trust.

CONTEXT

The health status of a population is greatly influenced by both biological processes and external factors. For years, minority and low socioeconomic patient populations have faced worse outcomes and poorer health in the United States. Experts have worked extensively to understand the issues and find solutions to alleviate this disproportionate burden of disease. As a result, there have been some improvements and successes, but wide gaps still exist. Diagnostic molecular genetic testing and so-called personalized medicine are just now being integrated into the current American health care system. The way in which these tests are integrated can either exacerbate or reduce health disparities.

METHODS

We provide case scenarios-loosely based on real-life patients-so that nonexperts can see the impacts of complex policy decisions and unintentional biases in technology without needing to understand all the intricacies. We use data to explain these findings from an extensive literature search examining both peer-reviewed and gray literature.

FINDINGS

Access to diagnostic molecular genetic testing is not equitable or sufficient, owing to at least five major factors: (1) cost to the patient, (2) location, (3) lack of provider buy-in, (4) data-set bias, and (5) lack of public trust.

CONCLUSIONS

Molecular genetic pathology can be made more equitable with the concerted efforts of multiple stakeholders. Confronting the five major factors identified here may help us usher in a new era of precision medicine without its discriminatory counterpart.

Genetic diagnosis for adult patients at a genetic clinic

Clinical utility of genetic testing has rapidly increased in the past decade to identify the definitive diagnosis, etiology, and specific management. The majority of patients receiving testing are children. There are several barriers for genetic tests in adult patients; barriers may arise from either patients or clinicians. Our study aims to realize the detection rate and the benefits of genetic tests in adults. We conducted a prospective study of 10 adult patients who were referred to a genetic clinic. Exome sequencing (ES) was pursued in all cases, and chromosomal microarray (CMA) was performed for six cases. Our result is impressive; six cases (60%) received likely pathogenic and pathogenic variants. Four definitive diagnosis cases had known pathogenic variants in KCNJ2, TGFBR1, SCN1A, and FBN1, whereas another two cases revealed novel likely pathogenic and pathogenic variants in GNB1 and DNAH9. Our study demonstrates the success in genetic diagnosis in adult patients: four cases with definitive, two cases with possible, and one case with partial diagnosis. The advantage of diagnosis is beyond obtaining the diagnosis itself, but also relieving any doubt for the patient regarding any previous questionable diagnosis, guide for management, and recurrence risk in their children or family members. Therefore, this supports the value of genetic testing in adult patients.

Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS

BACKGROUND

Transcranial magnetic stimulation (TMS) is a widely used technique for the noninvasive assessment and manipulation of brain activity and behavior. Although extensively used for research and clinical purposes, recent studies have questioned the reliability of TMS findings because of the high inter-individual variability that has been observed.

OBJECTIVE

In this study, we compared the efficacy and reliability of different targeting scenarios on the TMS-evoked response.

METHODS

24 subjects underwent a single pulse stimulation protocol over two parietal nodes belonging to the Dorsal Attention (DAN) and Default Mode (DMN) Networks respectively. Across visits, the stimulated target for both networks was chosen either based on group-derived networks' maps or personalized network topography based on individual anatomy and functional profile. All stimulation visits were conducted twice, one month apart, during concomitant electroencephalography recording.

RESULTS

At the network level, we did not observe significant differences in the TMS-evoked response between targeting conditions. However, reliable patterns of activity were observed- for both networks tested- following the individualized targeting approach. When the same analyses were carried out at the electrode space level, evidence of reliable patterns was observed following the individualized stimulation of the DAN, but not of the DMN.

CONCLUSIONS

Our findings suggest that individualization of stimulation sites might ensure reliability of the evoked TMS-response across visits. Furthermore, individualized stimulation sites appear to be of foremost importance in highly variable, high order task-positive networks, such as the DAN.

Variations in the Frequencies of Polymorphisms in the CYP450s Genes in Eight Major Ethnicities of Iran: A Review of the Human Data

Genetic polymorphisms in cytochrome P450 genes can cause variation in metabolism. Thus, single nucleotide variants significantly impact drug pharmacokinetics, toxicity factors, and efficacy and safety of medicines. The distribution of CYP450 alleles varies drastically across ethnicities, with significant implications for personalized medicine and the healthcare system. We combined whole-genome and exome sequencing data to provide a review of CYP450 allele polymorphisms with clinical importance. Data were collected from 800 unrelated Iranians (100 subjects from 8 major ethnicities of Iran), more than 32,000 unrelated Europeans (other than Caucasian), and four Middle Eastern countries. We analyzed the frequencies and similarities of 17 CYP450 frequent alleles related to nine important CYP450 isoenzymes and homozygous and heterozygous genotypes based on these alleles in eight major Iranian ethnics by integrating these data with population-specific linkage information and compared these datasets with mentioned populations.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Predictors of response to anti-CGRP monoclonal antibodies: a 24-week, multicenter, prospective study on 864 migraine patients

Background and objectives

The identification of predictors of response to antiCGRP mAbs could favor tailored therapies and personalized treatment plans. This study is aimed at investigating predictors of ≥ 50%, ≥ 75% and 100% response at 24 weeks in patients with high-frequency episodic (HFEM: 8–14 days/month) or chronic migraine (CM).

Methods

This is a large, multicenter, cohort, real-life study. We considered all consecutive adult patients affected by HFEM or CM who were prescribed antiCGRP mAbs for ≥ 24 weeks in 20 headache centers. Patients were interviewed face-to-face using a shared semi-structured questionnaire carefully exploring socio-demographic and clinical characteristics. Patients received subcutaneous erenumab (70 mg or140 mg, monthly), galcanezumab (120 mg monthly, following a 240 mg loading dose), or fremanezumab (225 mg, monthly or 675 mg, quarterly) according to drug market availability, physician’s choice, or patient’s preference. The primary endpoint of the study was the assessment of ≥ 50% response predictors at 24 weeks. Secondary endpoints included ≥ 75% and 100% response predictors at 24 weeks.

Results

Eight hundred sixty-four migraine patients had been treated with antiCGRP mAbs for ≥ 24 weeks (erenumab: 639 pts; galcanezumab: 173 pts; fremanezumab: 55 pts). The ≥50% response (primary endpoint) in HFEM was positively associated with unilateral pain (UP) + unilateral cranial autonomic symptoms (UAs) (OR:4.23, 95%CI:1.57–11.4; p = 0.004), while in CM was positively associated with UAs (OR:1.49, 95%CI:1.05–2.11; p = 0.026), UP + UAs (OR:1.90, 95%CI:1.15–3.16; p = 0.012), UP + allodynia (OR:1.71, 95%CI:1.04–2.83; p = 0.034), and negatively associated with obesity (OR:0.21, 95%CI:0.07–0.64; p = 0.006). The 75% response (secondary endpoint) was positively associated with UP + UAs in HFEM (OR:3.44, 95%CI:1.42–8.31; p = 0.006) and with UP + UAs (OR:1.78, 95%CI:1.14–2.80; p = 0.012) and UP + allodynia (OR:1.92, 95%CI:1.22–3.06; p = 0.005) in CM. No predictor of 100% response emerged in patients with HFEM or CM.

Conclusions

A critical evaluation of headache characteristics indicating peripheral or central sensitization may help in predicting responsiveness to antiCGRP mAbs in HFEM and CM. A more precise pain profiling may represent a steppingstone for a mechanism-based approach and personalized treatment of migraine with compounds targeting specific molecular mechanisms.

Patient attrition in Molecular Tumour Boards: a systematic review

BACKGROUND

Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision-making within precision medicine. Though in use globally, reporting on these meetings often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment.

METHODS

A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 51 papers were reviewed spanning a 6-year period from 11 different countries.

RESULTS

In total, 20% of patients received treatment through the MTB process. Of those that did not receive treatment, the main reasons were no mutations identified (27%), no actionable mutations (22%) and clinical deterioration (15%). However, data were often incomplete due to inconsistent reporting of MTBs with only 55% reporting on patients having no mutations, 55% reporting on the presence of actionable mutations with no treatment options and 59% reporting on clinical deterioration.

DISCUSSION

As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.

Preferences for pharmacogenomic testing in polypharmacy patients: a discrete choice experiment

Aim: To elicit preferences for pharmacogenomic (PGx) testing in polypharmacy patients. Materials & methods: A face-to-face discrete choice experiment survey was designed and administered to adult polypharmacy patients recruited at a local retail pharmacy in Albuquerque (NM, USA). Results: A total of 128 eligible polypharmacy patients completed the discrete choice experiment survey and significantly preferred a PGx test with lower cost, better confidentiality and higher certainty of identifying best medication/dose and side effects and one that can be used to advocate for their treatment needs (all p < 0.01). Conclusion: This is the first study eliciting preferences for PGx testing among polypharmacy patients. The study found most polypharmacy patients were willing to take a PGx test and their preferences were mostly influenced by test cost.

Still Searching for Understanding: The Importance of Diverse Research Designs, Methods, and Perspectives

BACKGROUND

Evidence-based medicine and evidence hierarchies have been widely adopted and have strongly influenced decision making across many fields, including clinical aphasiology. However, questions remain about the creation, usefulness, and validity of current evidence hierarchies.

AIMS

This article builds on ideas about scientific approaches and evidence originally shared by Elman (1995, 1998, 2006). This article reviews the history of evidence hierarchies and argues that improving the diversity of research designs, methods, and perspectives will improve understanding of the numerous and complex variables associated with aphasia intervention. Researchers and clinicians are encouraged to synthesize diverse types of scientific evidence. It is hoped that this article will stimulate thought and foster discussion in order to encourage high-caliber research of all types.

MAIN CONTRIBUTION

Concepts from a wide variety of fields including philosophy of science, research design and methodology, and precision medicine are brought together in an attempt to focus research on the scientific understanding of aphasia treatment effects.

CONCLUSION

It is hoped that by incorporating diverse research designs, methods, and perspectives, clinical aphasiologists will become better able to provide effective, personalized treatments, ensuring that each person with aphasia is able to improve their communication ability and quality of life.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Individual Variabilities in Adipose Stem Cell Proliferation, Gene Expression and Responses to Lipopolysaccharide Stimulation

Adipose stem cells (ASCs) are reported to play a role in normal physiology as well as in inflammation and disease. The objective of this work was to elucidate inter-individual differences in growth, gene expression and response to inflammatory stimuli in ASCs from different donors. Human ASC1 (male donor) and ASC2 (female donor) were purchased from Lonza (Walkersville, MD). Cell proliferation was determined by the sulforhodamine B assay. After time-dependent treatment of ASCs with or without bacterial lipopolysaccharide (LPS), marker gene mRNAs for proliferation, steroid hormones, and xenobiotic and immune pathways were determined using RT-PCR, and secreted cytokine levels in media were measured using the Bio-Plex cytokine assay kit. ASCs from both donors expressed androgen receptors but not estrogen receptors. ASC2 had a 2-fold higher proliferation rate and a 6-fold higher level of proliferation marker Ki67 mRNA than ASC1. ASC2 exhibited significantly greater fold induction of TNF-α and CCL2 by LPS compared to ASC1. TNF-α and GM-CSF protein levels were also significantly higher in the LPS-induced ASC2 media, but IL-6 secretion was higher in the LPS-induced ASC1 media. Our findings suggest that inter-individual variability and/or possible sex differences exist in ASCs, which may serve as a key determinant to inflammatory responses of ASCs.

Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model

Background

Early stage hepatocellular carcinoma (HCC) has a high recurrence rate after surgery and lacks reliable predictive tools. We explored the potential of combining enhanced CT with gut microbiome to develop a predictive model for recurrence after early HCC surgery.

Methods

A total of 112 patients with early HCC who underwent hepatectomy from September 2018 to December 2020 were included in this study, and the machine learning method was divided into a training group (N = 71) and a test group (N = 41) with the observed endpoint of recurrence-free survival (RFS). Features were extracted from the arterial and portal phases of enhanced computed tomography (CT) images and gut microbiome, and features with minimum absolute contraction and selection operator regression were created, and the extracted features were scored to create a preoperative prediction model by using the multivariate Cox regression analysis with risk stratification analysis.

Results

In the study cohort, the model constructed by combining radiological and gut flora features provided good predictive performance (C index, 0.811 (0.650-0.972)). The combined radiology and gut flora-based model constructed risk strata with high, intermediate, or low risk of recurrence and different characteristics of recurrent tumor imaging and gut flora. Recurrence of early stage hepatocellular carcinoma may be associated with oxidative stress in the intestinal flora.

Conclusions

This study successfully constructs a risk model integrating enhanced CT and gut microbiome characteristics that can be used for the risk of postoperative recurrence in patients with early HCC. In addition, intestinal flora associated with HCC recurrence may be involved in oxidative stress.