Use Of Precision Medicine Research Articles

Prognostic Significance of DNA Topoisomerase II Alpha (TOP2A) in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a malignant tumor with an increasing incidence worldwide. Although radiation therapy has improved the therapeutic efficiency of CCA treatment, differential expression of genes among cholangiocarcinoma subtypes has been revealed through precise sequencing. However, no specific molecular therapeutic targets or biomarkers have been figured out for use in precision medicine, and the exact mechanism by which antitumorigenic effects occur is still unclear. Therefore, it is necessary to conduct further studies on the development and mechanisms associated with CCA. We examined the clinical data and pathological features of patients with cholangiocarcinomas. We investigated the associations between DNA Topoisomerase II Alpha (TOP2A) expression and patient outcomes, such as metastasis-free survival (MFS) and disease-specific survival (DSS), as well as clinical characteristics and pathological results. TOP2A expression was shown to be upregulated in CCA tissue sections by immunohistochemistry staining and data mining. Moreover, we observed that the TOP2A expression correlated with clinical features, such as the primary tumor stage, histological variants, and patients with hepatitis. Furthermore, high expression of TOP2A was associated with worse survival outcomes in terms of the overall survival (p < 0.0001), disease-specific survival (p < 0.0001), and metastasis-free survival (p < 0.0001) compared with patients in the low TOP2A expression group. This indicates that a high level of TOP2A expression is related to an unfavorable prognosis. Our results show that TOP2A is highly expressed in CCA tissues, and its upregulation is correlated with the primary disease stage and poor prognosis significantly. Consequently, TOP2A is a prognostic biomarker and a novel therapeutic target for the treatment of CCA.

Personalized antiseizure medication therapy in critically ill adult patients.

Precision medicine has the potential to have a significant impact on both drug development and patient care. It is crucial to not only provide prompt effective antiseizure treatment for critically ill patients after seizures start but also have a proactive mindset and concentrate on epileptogenesis and the underlying cause of the seizures or seizure disorders. Critical illness presents different treatment issues compared with the ambulatory population, which makes it challenging to choose the best antiseizure medications and to administer them at the right time and at the right dose. Since there is a paucity of information available on antiseizure medication dosing in critically ill patients, therapeutic drug monitoring is a useful tool for defining each patient's personal therapeutic range and assisting clinicians in decision-making. Use of pharmacogenomic information relating to pharmacokinetics, hepatic metabolism, and seizure etiology may improve safety and efficacy by individualizing therapy. Studies evaluating the clinical implementation of pharmacogenomic information at the point-of-care and identification of biomarkers are also needed. These studies may make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and optimize medications for each individual patient. This review will discuss the available literature and provide future insights on precision medicine use with antiseizure therapy in critically ill adult patients.

Detection of LPS-induced macrophage activation with single-cell resolution through DC insulator-based electrokinetic devices.

Macrophages are considered critical in the initiation, maintenance and resolution of inflammation. LPS-induced inflammation is often used as a model to understand the cell inflammation responses. Current techniques identifying the LPS-induced inflammation are experiencing cell destruction or cell labeling or are based on the whole cell population information with low identification degree. This limits the detection process with time-consuming cytokine selection, low resolution of population heterogeneity, and unavailability for their next use. Direct current insulator-based electrokinetics (DC-iEK) is introduced to achieve an easier and non-invasive identification of inflamed cells with high resolution. A biophysical scale is also established first time for screening medicine in the treatment of inflammation. The new microfluidic design concentrates cells with applied voltages forming streamline providing more stable cell capture conditions and unique biophysical factors at different capture positions. The average electric field of the cell capture positions is recorded to characterize each cell population. The characterization value of macrophage decreases from to 1.61 × 104 V/m when treated with 0.1 mM LPS and to 1.42 × 104 V/m when treated with 1 mM LPS. By treating the inflamed macrophages with representative effective medicines, healing signals could also be detected by a newly established inflammation scale. The cells showed proliferation and functional activity after extraction. DC-iEK has provided an easy and non-invasive approach to identify inflammation for further fundamental and clinical precision medicine use. This article is protected by copyright. All rights reserved.

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer.

The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

Cohort profile: The Clinical and Multi-omic (CAMO) cohort, part of the Norwegian Women and Cancer (NOWAC) study.

Breast cancer is the most common cancer worldwide and the leading cause of cancer related deaths among women. The high incidence and mortality of breast cancer calls for improved prevention, diagnostics, and treatment, including identification of new prognostic and predictive biomarkers for use in precision medicine. With the aim of compiling a cohort amenable to integrative study designs, we collected detailed epidemiological and clinical data, blood samples, and tumor tissue from a subset of participants from the prospective, population-based Norwegian Women and Cancer (NOWAC) study. These study participants were diagnosed with invasive breast cancer in North Norway before 2013 according to the Cancer Registry of Norway and constitute the Clinical and Multi-omic (CAMO) cohort. Prospectively collected questionnaire data on lifestyle and reproductive factors and blood samples were extracted from the NOWAC study, clinical and histopathological data were manually curated from medical records, and archived tumor tissue collected. The lifestyle and reproductive characteristics of the study participants in the CAMO cohort (n = 388) were largely similar to those of the breast cancer patients in NOWAC (n = 10 356). The majority of the cancers in the CAMO cohort were tumor grade 2 and of the luminal A subtype. Approx. 80% were estrogen receptor positive, 13% were HER2 positive, and 12% were triple negative breast cancers. Lymph node metastases were present in 31% at diagnosis. The epidemiological dataset in the CAMO cohort is complemented by mRNA, miRNA, and metabolomics analyses in plasma, as well as miRNA profiling in tumor tissue. Additionally, histological analyses at the level of proteins and miRNAs in tumor tissue are currently ongoing. The CAMO cohort provides data suitable for epidemiological, clinical, molecular, and multi-omics investigations, thereby enabling a systems epidemiology approach to translational breast cancer research.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review: Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review. Sources of information: Original research articles were identified from MEDLINE and Google Scholar using the search terms “diabetes,” “diabetic kidney disease,” “diabetic nephropathy,” “chronic kidney disease,” “kidney failure,” “dialysis,” “nephrology,” “genomics,” “metabolomics,” and “proteomics.” Methods: A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner’s/caregiver’s lived experience. Key findings: Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy. Limitations: There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory. Implications: Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

Current Testing Practices for t(11;14) Rearrangements in Patients with Newly Diagnosed Multiple Myeloma in the United States

Background: Despite an improvement in the awareness and use of precision medicine and molecular testing in hematological cancers, there are still significant disparities in biomarker testing. This ultimately leads to inequality in molecular testing access and can lead to suboptimal medical care for some patients, who do not receive the most appropriate and effective therapy. Venetoclax is a selective BCL-2 inhibitor and has shown promising antimyeloma activity in patients with t(11;14) multiple myeloma (MM). t(11;14) is the most frequent chromosomal translocation in MM reported in approximately 15-20% of newly diagnosed MM patients (NDMM) (Kumar et al. Blood 2017). In this study, we assessed current t(11;14)(q13;q32) testing practices in patients with NDMM in the United States (US) to gain insights on challenges for biomarker testing and equitable access to precision medicine. Methods: A retrospective, real-world evidence study was conducted between January and December 2021 using the Diaceutics DXRX Data Repository database. DXRX is a patient-level claims- and laboratory-linked dataset covering more than 4000 laboratories and 360 million patient lives between 2011 and 2022 in the US. Eligible patients were identified using International Classification of Disease (ICD) codes for NDMM (ICD-10 code C90.0). A 2-year lookback was utilized to ensure patients were not in remission or relapse (ICD-10 codes C90.01, C90.02). Percentages were used to describe study population characteristics. t(11;14) and plasma cell enrichment (PCE) testing rates were estimated using descriptive statistics for the entire cohort as well as for race/ethnicity subgroups. PCE testing was considered as it contributes to increasing sensitivity of testing. Results: We identified 27,586 eligible patients with NDMM. The median age at diagnosis was 70 years (interquartile range: 65-75) and 45% were female. Among the eligible patients, 69% (n=19,034) specifically received t(11;14) testing, within 1 month from diagnosis to testing. Fluorescence in situ hybridization (FISH; dual color, dual fusion probe and immunoglobin [IG] break apart) was the most commonly utilized technology (89%; n=16,940) for detecting t(11;14); next-generation sequencing panels were limited (<1%) in utilization. Confirmatory reflex testing with a t(11;14) dual probe following positive IG break apart was not performed in 17% (n=3235) of patients. In patients who were tested with the t(11;14) dual probe FISH assay, less than half (40.9%) were tested for t(11;14) with PCE. Results of the FISH assay were available for 4941 patients. A lower positivity rate was observed with non-PCE testing (13.1%; n=647) compared with those utilizing PCE (16.8%; n=830), p=0.494. Testing rates of t(11;14) in an academic setting were 72% (n=10,659) vs 64% (n=8374) in the community setting, p<0.0001. PCE was performed on only 24.9% (n=2093) of patients in a community setting vs 61% (n=6502) of samples in an academic setting, p<0.0001. For the subgroup analysis, race/ethnicity data were available for 9245 patients with NDMM; 75.7% were White, 15.9% were Black, 2.6% were Asian, 2% were Hispanic, and 3.2% were classified as other. t(11;14) testing rates were higher for patients who were White (72.8%) compared with the minority groups (66.9% for patients who were Black, 68.8% for patients who were Asian, and 65.7% for patients who were Hispanic). The t(11;14) testing setting for minority group patients was community (62%) vs academic (38%). Conclusions: Notable variations in t(11;14) testing patterns were observed among patients with NDMM in different care settings and racial/ethnic groups. There is a clear unmet need for standardization across academic and community settings to mitigate suboptimal testing methodologies that could impact treatment decision-making and patient outcomes. From our study, there is a clear need to improve t(11;14) testing rates as the clinical utility changes from a prognostic to predictive biomarker with the availability of personalized healthcare options. Education of PCE utilization for physicians, especially community-based pathologists and laboratories, will be essential to reduce false negatives and inequalities in optimal testing, especially PCE in ethnic minorities in the US.

IL-32 and IL-34 in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a major challenge to clinicians due to its unacceptably high mortality and morbidity. The etiology of HCC is multi-faceted, including viral infection, alcoholism and non-alcoholic fatty liver disease. Dysregulated host immunity contributes to tumorigenesis among these susceptible individuals with pre-existing condition(s). IL-32 and IL-34 are key cytokines driving the development of chronic inflammatory conditions such as rheumatoid arthritis, systemic lupus erythematosus, as well as chronic liver diseases. IL-32 and IL-34 play an important role augmenting the development of HCC, due to their direct influence over host inflammation, however, new roles for these cytokines in HCC are emerging. Here we comprehensively review the latest research for IL-32 and IL-34 in HCC, identifying a subset of potential therapeutic targets for use in precision medicine.

AACR Cancer Progress Report 2022: Decoding Cancer Complexity, Integrating Science, and Transforming Patient Outcomes.

AACR Cancer Progress Report 2022: Decoding Cancer Complexity, Integrating Science, and Transforming Patient Outcomes.

Multi-Omics Integrative Analysis of Lung Adenocarcinoma: An in silico Profiling for Precise Medicine.

Lung adenocarcinoma (LUAD) is one of the most common histological subtypes of lung cancer. The aim of this study was to construct consensus clusters based on multi-omics data and multiple algorithms. In order to identify specific molecular characteristics and facilitate the use of precision medicine on patients we used gene expression, DNA methylation, gene mutations, copy number variation data, and clinical data of LUAD patients for clustering. Consensus clusters were obtained using a consensus ensemble of five multi-omics integrative algorithms. Four molecular subtypes were identified. The CS1 and CS2 subtypes had better prognosis. Based on the immune and drug sensitivity predictions, we inferred that CS1 may be less responsive to immunotherapy and less sensitive to chemotherapeutic drugs. The high immune infiltration of CS2 cells may respond well to immunotherapy. Additionally, the CS2 subtype may also respond to EGFR molecular targeted therapy. The CS3 and CS4 subtypes were associated with poor prognosis. These two subtypes had more mutations, especially TP53 ones, as well as higher sensitivity to chemotherapeutics for lung cancer. However, CS3 was enriched in immune-related pathways and may respond to anti-PD1 immunotherapy. In addition, CS1 and CS4 were less sensitive to ferroptosis inhibitors. We performed a comprehensive analysis of the five types of omics data using five clustering algorithms to reveal the molecular characteristics of LUAD patients. These findings provide new insights into LUAD subtypes and potential clinical treatment strategies to guide personalized management and treatment.

Impact of oncology clinical pharmacist intervention on clinical trial enrollment in The U.S. Oncology Network’s MYLUNG Consortium.

1503 Background: The Molecularly Informed Lung Cancer Treatment in a Community Cancer Network: A Pragmatic Consortium (MYLUNG) clinical trial platform aims to advance the use of precision medicine in non-small cell lung cancer patients through a series of prospective and iterative clinic trials. “Protocol 2” is evaluating the patient and tissue journey of newly diagnosed lung cancer patients presenting for care. Timely patient accrual to oncology clinical trials is a known practice challenge. The US Oncology Network recently implemented a clinical pharmacist (ClinReview) to provide remote clinical services to support Protocol 2 enrollment. Methods: An oncology-trained clinical pharmacist remotely reviewed chemotherapy regimen orders and a weekly custom recruitment report within six community network practices (n = 149 physicians). The ClinReview pharmacist identified, screened, and assisted with recruitment of eligible patients for enrollment in the MYLUNG study. Relevant and concise patient data were provided to the on-site research team to facilitate ease of enrollment. Enrollments and intervention data were tracked to monitor the impact of the pharmacist intervention. The primary outcome of monthly enrollment was evaluated using a paired t-test. Results: Over a 6-month period, the ClinReview pharmacist screened 367 potentially eligible patients, 325 patients were recommended for enrollment, and 103 patients (32%) were consented and enrolled. Enrollment due to this ClinReview intervention increased monthly and ranged from 5 in first month to 33 enrollments in month 6. Average monthly enrollment was significantly greater after ClinReview intervention (3.4 patients/month vs. 6.8 patients/month; p = 0.008). Of the 154 patients recommended for enrollment that were not enrolled, 104 (68%) exceeded their eligibility window allowed by the trial, 15 (10%) were deceased or enrolled into hospice care, 10 (6%) declined trial participation, and 25 (16%) transferred care or were treated at outside facilities. Conclusions: We demonstrate that incorporation of an oncology clinical pharmacist in clinical research teams significantly enhanced clinical trial enrollment. The remote pharmacist easily adapted into clinic workflows in community practices. Validation across a broader spectrum of differentially resourced oncology practices will be conducted as the MYLUNG clinical trials platform is executed. [Table: see text]

DNA methylation may mediate psychotropic drug-induced metabolic side effects: results from a 1-month observational study

IntroductionMetabolic side effects of psychotropic medications are a major drawback to patients’ effective treatment. Among the mechanisms underlying their development, DNA methylation may be involved.ObjectivesThe aim of this study was to estimate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and baseline DNA methylation or 1-month DNA methylation changes, using an epigenome-wide approach.MethodsSeventy-nine psychiatric patients recruited as part of PsyMetab study, who started a treatment with either an antipsychotic, a mood stabilizer or mirtazapine were selected. Epigenome-wide DNA methylation was measured using the Illumina Methylation EPIC BeadChip at baseline and after one month of treatment.ResultsA global methylation increase was observed after 1 month of treatment, which was more pronounced in patients whose weight remained stable (i.e., &lt;2.5% weight increase). Epigenome-wide significant methylation changes were observed at 52 loci in the whole cohort and at one site, namely cg12209987, located in an intergenic region within an enhancer, specifically in patients who underwent important early weight gain (i.e., ≥5% weight increase) during the same period of treatment (p&lt;5*10-8). Multivariable analysis confirmed an association between an increase in methylation at this locus and weight gain in the whole cohort (p=0.004). Epigenome-wide association analyses failed to identify any significant link between other metabolic changes (e.g. glucose or lipid levels) and methylation data.ConclusionsThese findings give new insight into the mechanisms of psychotropic drug-induced weight gain. With improved understanding of the metabolic side effects, the use of precision medicine with epigenetics may become possibleDisclosureNo significant relationships.

IUMRG: multi-layered network-guided propagation modeling for the inference of susceptibility genes and potential drugs against uveal melanoma

Uveal melanoma (UM) is the most common primary malignant intraocular tumor. The use of precision medicine for UM to enable personalized diagnosis, prognosis, and treatment require the development of computer-aided strategies and predictive tools that can identify novel high-confidence susceptibility genes (HSGs) and potential therapeutic drugs. In the present study, a computational framework via propagation modeling on integrated multi-layered molecular networks (abbreviated as iUMRG) was proposed for the systematic inference of HSGs in UM. Under the leave-one-out cross-validation experiments, the iUMRG achieved superior predictive performance and yielded a higher area under the receiver operating characteristic curve value (0.8825) for experimentally verified SGs. In addition, using the experimentally verified SGs as seeds, genome-wide screening was performed to detect candidate HSGs using the iUMRG. Multi-perspective validation analysis indicated that most of the top 50 candidate HSGs were indeed markedly associated with UM carcinogenesis, progression, and outcome. Finally, drug repositioning experiments performed on the HSGs revealed 17 potential targets and 10 potential drugs, of which six have been approved for UM treatment. In conclusion, the proposed iUMRG is an effective supplementary tool in UM precision medicine, which may assist the development of new medical therapies and discover new SGs.

Using Precision Medicine for the Diagnosis and Treatment of Viral Pneumonia.

The COVID-19 pandemic has drawn considerable attention to viral pneumonia from clinicians, public health authorities, and the general public. With dozens of viruses able to cause pneumonia in humans, differentiating viral from bacterial pneumonia can be very challenging in clinical practice using traditional diagnostic methods. Precision medicine is a medical model in which decisions, practices, interventions, and therapies are adapted to the individual patient on the basis of their predicted response or risk of disease. Precision medicine approaches hold promise as a way to improve outcomes for patients with viral pneumonia. This review describes the latest advances in the use of precision medicine for diagnosing and treating viral pneumonia in adults and discusses areas where further research is warranted.

HESC derived cardiomyocyte biosensor to detect the different types of arrhythmogenic properties of drugs

In the present work, we introduce a new cell-based biosensor for detecting arrhythmias based on a novel utilization of the combination of the Atomic Force Microscope (AFM) lateral force measurement as a nanosensor with a dual 3D cardiomyocyte syncytium. Two spontaneously coupled clusters of cardiomyocytes form this.The syncytium's functional contraction behavior was assessed using video sequences analyzed with Musclemotion ImageJ/Fiji software, and immunocytochemistry evaluated phenotype composition.The application of caffeine solution induced arrhythmia as a model drug, and its spontaneous resolution was monitored by AFM lateral force recording and interpretation and calcium fluorescence imaging as a reference method describing non-synchronized contractions of cardiomyocytes.The phenotypic analysis revealed the syncytium as a functional contractile and conduction cardiac behavior model. Calcium fluorescence imaging was used to validate that AFM fully enabled to discriminate cardiac arrhythmias in this in vitro cellular model. The described novel 3D hESCs-based cellular biosensor is suitable to detect arrhythmic events on the level of cardiac contractile and conduction tissue cellular model. The resulting biosensor allows for screening of arrhythmogenic properties of tailored drugs enabling its use in precision medicine.

DNA Methylation-Based Prediction of Post-operative Atrial Fibrillation.

BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF.MethodsWe enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort.ResultsA total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort.ConclusionsWe have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

Swarming Magnetic Photoactive Microrobots for Dental Implant Biofilm Eradication.

Titanium dental implants are a multibillion dollar market in the United States alone. The growth of a bacterial biofilm on a dental implant can cause gingivitis, implant loss, and expensive subsequent care. Herein, we demonstrate the efficient eradication of dental biofilm on titanium dental implants via swarming magnetic microrobots based on ferromagnetic (Fe3O4) and photoactive (BiVO4) materials through polyethylenimine micelles. The ferromagnetic component serves as a propulsion force using a transversal rotating magnetic field while BiVO4 is the photoactive generator of reactive oxygen species to eradicate the biofilm colonies. Such photoactive magnetically powered, precisely navigated microrobots are able to destroy biofilm colonies on titanium implants, demonstrating their use in precision medicine.

Management of Postoperative Pain in Patients Following Spine Surgery: A Narrative Review.

Perioperative pain management is a unique challenge in patients undergoing spine surgery due to the increased incidence of both pre-existing chronic pain conditions and chronic postsurgical pain. Peri-operative planning and counseling in spine surgery should involve an interdisciplinary approach that includes consideration of patient-level risk factors, as well as pharmacologic and non-pharmacologic pain management techniques. Consideration of psychological factors and patient focused education as an adjunct to these measures is paramount in developing a personalized perioperative pain management plan. Understanding the currently available body of knowledge surrounding perioperative opioid management, management of opioid use disorder, regional/neuraxial anesthetic techniques, ketamine/lidocaine infusions, non-opioid oral analgesics, and behavioral interventions can be useful in developing a comprehensive, multi-modal treatment plan among patients undergoing spine surgery. Although many of these techniques have proved efficacious in the immediate postoperative period, long-term follow-up is needed to define the impact of such approaches on persistent pain and opioid use. Future techniques involving the use of precision medicine may help identify phenotypic and physiologic characteristics that can identify patients that are most at risk of developing persistent postoperative pain after spine surgery.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self.

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Current and future challenges of the use of precision medicine in cancer diagnosis and treatment in Brazil

This study seeks to culturally and ethically reflect on the current and future challenges of incorporating new biotechnologies to diagnose and treat cancer in Brazil and assess their impact on cancer control. To this end, this study begins by problematizing the literature on the subject and the results of a study that conducted a web survey with medical specialists from the Brazilian public and private sectors; the latter associated with the ten most representative cancer societies in the country, working in clinical practice, surgery, radiotherapy, pathology, and diagnosis. We discussed this study around three thematic axes: knowledge and expectations on the advent of new technologies to diagnose and treat cancer; structural and ethical considerations in the current and future use of new technologies; and possible scenarios associated with the use and application of new technologies to diagnose and treat cancer. We have raised some questions: will new technological paradigms benefit all Brazilian Unified National Health System (SUS) users, or will they be the privileges of the few? Will they reduce discrepancies regarding the distribution of technological services and resources and cancer diagnosis and treatment? How can different segments of society participate and influence this process? What scenarios can still compose this picture? Aiming to contribute to planning the future of cancer care in Brazil, this study proposes the development of future actions from five strategic dimensions: economy; science, innovation and technology; structure; culture; and regulation.