Genomic and personalized medicine: foundations and applications

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Artificial intelligence in liver imaging: methods and applications

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Individuals being high in their sensitivity to the environment: Are sensitive period changes in play?

All individuals on planet earth are sensitive to the environment, but some more than others. These individual differences in sensitivity to environments are seen across many animal species including humans, and can influence personalities as well as vulnerability and resilience to mental disorders. Yet, little is known about the underlying brain mechanisms. Key genes that contribute to individual differences in environmental sensitivity are the serotonin transporter, dopamine D4 receptor and brain-derived neurotrophic factor genes. By synthesizing neurodevelopmental findings of these genetic factors, and discussing them through the lens of mechanisms related to sensitive periods, which are phases of heightened neuronal plasticity during which a certain network is being finetuned by experiences, we propose that these genetic factors delay but extend postnatal sensitive periods. This may explain why sensitive individuals show behavioral features that are characteristic of a young brain state at the level of sensory information processing, such as reduced filtering or blockade of irrelevant information, resulting in a sensory processing system that 'keeps all options open'.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

RUBICON: a framework for designing efficient deep learning-based genomic basecallers

Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base

BACKGROUND

Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

RESULTS

We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.

CONCLUSIONS

We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

Pharmacogenomics of Preeclampsia therapies: Current evidence and future challenges for clinical implementation

Preeclampsia is a pregnancy-specific disorder, and it is a leading cause of maternal and perinatal morbidity and mortality. The application of pharmacogenetics to antihypertensive agents and dose selection in women with preeclampsia is still in its infancy. No current prescribing guidelines from the clinical pharmacogenetics implementation consortium (CPIC) exist for preeclampsia. Although more studies on pharmacogenomics are underway, there is some evidence for the pharmacogenomics of preeclampsia therapies, considering both the pharmacokinetic (PK) and pharmacodynamic (PD) properties of drugs used in preeclampsia. It has been revealed that the CYP2D6*10 variant is significantly higher in women with preeclampsia who are non-responsive to labetalol compared to those who are in the responsive group. Various genetic variants of PD targets, i.e., NOS3, MMP9, MMP2, TIMP1, TIMP3, VEGF, and NAMPT, have been investigated to assess the responsiveness of antihypertensive therapies in preeclampsia management, and they indicated that certain genetic variants of MMP9, TIMP1, and NAMPT are more frequently observed in those who are non-responsive to anti-hypertensive therapies compared to those who are responsive. Further, gene-gene interactions have revealed that NAMPT, TIMP1, and MMP2 genotypes are associated with an increased risk of preeclampsia, and they are more frequently observed in the non-responsive subgroup of women with preeclampsia. The current evidence is not rigorous enough for clinical implementation; however, an institutional or regional-based retrospective analysis of audited data may help close the knowledge gap during the transitional period from a traditional approach (a "one-size-fits-all" strategy) to the pharmacogenomics of preeclampsia therapies.

A pilot retrospective study of a physician-directed and genomics-based model for precision lifestyle medicine

Precision lifestyle medicine is a relatively new field in primary care, based on the hypothesis that genetic predispositions influence an individual's response to specific interventions such as diet, exercise, and prescription medications. Despite the increase in commercially available genomic testing, few studies have investigated effects of a physician-directed program to optimize chronic disease using genomics-based precision medicine. We performed an pilot, observational cohort study to evaluate effects of the Wild Health program, a physician and health coach service offering genomics-based lifestyle and medical interventions, on biomarkers indicative of chronic disease. 871 patients underwent genomic testing, biomarker testing, and ongoing health coaching after initial medical consultation by a physician. Improvements in several clinically relevant out-of-range biomarkers at baseline were identified in a large proportion of patients treated through lifestyle intervention without the use of prescription medication. Notably, normalization of several biomarkers associated with chronic disease occurred in 47.5% (hemoglobin A1c [HbA1c]), 33.3% (low density lipoprotein particle number [LDL-P]), and 33.2% (C-reactive protein [CRP]). However, due to the inherent limitations of our observational study design and use of retrospective data, ongoing work will be crucial for continuing to shed light on the effectiveness of physician-led, genomics-based lifestyle coaching programs. Future studies would benefit from implementing a randomized controlled study design, tracking specific interventions, and evaluating physiological data, such as BMI.

Development of Simvastatin-Loaded Particles Using Spray Drying Method for Ex Tempore Preparation of Cartridges for 2D Printing Technology

In this work, a spray drying method was developed to produce drug/polymer (simvastatin/polycaprolactone) microparticles that have the potential to be used as a pre-formulation for ex tempore preparation of 2D printing cartridges. An experimental model was designed with the process parameters set to predict the smallest particle size required for successful 2D printing. Three different types of particles (lactose, nanocellulose/lactose, calcium silicate) were produced, and the average size of the dry particles varied depending on the sampling location (cyclone, collection vessel). The encapsulation efficiency of simvastatin was highest with nanocellulose/lactose from the collection vessel. The one-month stability of simvastatin in the particles showed low content, but the addition of ascorbic acid as an antioxidant increased the chemical stability of the drug. Interestingly, the addition of antioxidants decreased the stability of simvastatin in the calcium silicate particles from the collection vessel. Dispersion of the particles in three different propylene glycol and water mixtures (10/90, 50/50, and 90/10% (v/v)), representing a printable ink medium with three different viscosity and surface tension properties, showed that nanocellulose/lactose was the most suitable antiadhesive in terms of dispersed particle size (˂1 µm). After one month of storage, the dispersed particles remained in the same size range without undesirable particle agglomeration.

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant's class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1-score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross-movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross-movement analysis and the artificial generation of larger amounts of data.

The effectiveness of the behavioural components of cognitive behavioural therapy for insomnia in older adults: A systematic review

Insomnia is more prevalent in older adults (> 60 years) than in the general population. Cognitive behavioural therapy for insomnia is the gold-standard treatment; however, it may prove too cognitively taxing for some. This systematic review aimed to critically examine the literature exploring the effectiveness of explicitly behavioural interventions for insomnia in older adults, with secondary aims of investigating their effect on mood and daytime functioning. Four electronic databases (MEDLINE - Ovid, Embase - Ovid, CINAHL, and PsycINFO) were searched. All experimental, quasi-experimental and pre-experimental studies were included, provided they: (a) were published in English; (b) recruited older adults with insomnia; (c) used sleep restriction and/or stimulus control; (d) reported outcomes pre-and-post intervention. Database searches returned 1689 articles; 15 studies, summarising the results of 498 older adults, were included - three focused on stimulus control, four on sleep restriction, and eight adopted multicomponent treatments comprised of both interventions. All interventions brought about significant improvements in one or more subjectively measured facets of sleep although, overall, multicomponent therapies demonstrated larger effects (median Hedge's g = 0.55). Actigraphic or polysomnographic outcomes demonstrated smaller or no effects. Improvements in measures of depression were seen in multicomponent interventions, but no intervention demonstrated any statistically significant improvement in measures of anxiety. This corroborates with the existing consensus that multicomponent approaches confer the most benefit, and adds to the literature by demonstrating this to be the case in brief, explicitly behavioural interventions. This review guides future study of treatments for insomnia in populations where cognitive behavioural therapy for insomnia is not appropriate.

Advances in point-of-care genetic testing for personalized medicine applications

Breakthroughs within the fields of genomics and bioinformatics have enabled the identification of numerous genetic biomarkers that reflect an individual's disease susceptibility, disease progression, and therapy responsiveness. The personalized medicine paradigm capitalizes on these breakthroughs by utilizing an individual's genetic profile to guide treatment selection, dosing, and preventative care. However, integration of personalized medicine into routine clinical practice has been limited-in part-by a dearth of widely deployable, timely, and cost-effective genetic analysis tools. Fortunately, the last several decades have been characterized by tremendous progress with respect to the development of molecular point-of-care tests (POCTs). Advances in microfluidic technologies, accompanied by improvements and innovations in amplification methods, have opened new doors to health monitoring at the point-of-care. While many of these technologies were developed with rapid infectious disease diagnostics in mind, they are well-suited for deployment as genetic testing platforms for personalized medicine applications. In the coming years, we expect that these innovations in molecular POCT technology will play a critical role in enabling widespread adoption of personalized medicine methods. In this work, we review the current and emerging generations of point-of-care molecular testing platforms and assess their applicability toward accelerating the personalized medicine paradigm.

Recent Advances with Precision Medicine Treatment for Breast Cancer including Triple-Negative Sub-Type

Breast cancer is a heterogeneous disease with different molecular subtypes. Breast cancer is the second leading cause of mortality in woman due to rapid metastasis and disease recurrence. Precision medicine remains an essential source to lower the off-target toxicities of chemotherapeutic agents and maximize the patient benefits. This is a crucial approach for a more effective treatment and prevention of disease. Precision-medicine methods are based on the selection of suitable biomarkers to envision the effectiveness of targeted therapy in a specific group of patients. Several druggable mutations have been identified in breast cancer patients. Current improvements in omics technologies have focused on more precise strategies for precision therapy. The development of next-generation sequencing technologies has raised hopes for precision-medicine treatment strategies in breast cancer (BC) and triple-negative breast cancer (TNBC). Targeted therapies utilizing immune checkpoint inhibitors (ICIs), epidermal growth factor receptor inhibitor (EGFRi), poly(ADP-ribose) polymerase inhibitor (PARPi), antibody-drug conjugates (ADCs), oncolytic viruses (OVs), glucose transporter-1 inhibitor (GLUT1i), and targeting signaling pathways are potential treatment approaches for BC and TNBC. This review emphasizes the recent progress made with the precision-medicine therapy of metastatic breast cancer and TNBC.

An Update of Epigenetic Drugs for the Treatment of Cancers and Brain Diseases: A Comprehensive Review

Epigenetics has long been recognized as a significant field in biology and is defined as the investigation of any alteration in gene expression patterns that is not attributed to changes in the DNA sequences. Epigenetic marks, including histone modifications, non-coding RNAs, and DNA methylation, play crucial roles in gene regulation. Numerous studies in humans have been carried out on single-nucleotide resolution of DNA methylation, the CpG island, new histone modifications, and genome-wide nucleosome positioning. These studies indicate that epigenetic mutations and aberrant placement of these epigenetic marks play a critical role in causing the disease. Consequently, significant development has occurred in biomedical research in identifying epigenetic mechanisms, their interactions, and changes in health and disease conditions. The purpose of this review article is to provide comprehensive information about the different types of diseases caused by alterations in epigenetic factors such as DNA methylation and histone acetylation or methylation. Recent studies reported that epigenetics could influence the evolution of human cancer via aberrant methylation of gene promoter regions, which is associated with reduced gene function. Furthermore, DNA methyltransferases (DNMTs) in the DNA methylation process as well as histone acetyltransferases (HATs)/histone deacetylases (HDACs) and histone methyltransferases (HMTs)/demethylases (HDMs) in histone modifications play important roles both in the catalysis and inhibition of target gene transcription and in many other DNA processes such as repair, replication, and recombination. Dysfunction in these enzymes leads to epigenetic disorders and, as a result, various diseases such as cancers and brain diseases. Consequently, the knowledge of how to modify aberrant DNA methylation as well as aberrant histone acetylation or methylation via inhibitors by using epigenetic drugs can be a suitable therapeutic approach for a number of diseases. Using the synergistic effects of DNA methylation and histone modification inhibitors, it is hoped that many epigenetic defects will be treated in the future. Numerous studies have demonstrated a link between epigenetic marks and their effects on brain and cancer diseases. Designing appropriate drugs could provide novel strategies for the management of these diseases in the near future.

Personal Genomes in Practice: Exploring Citizen and Healthcare Professionals’ Perspectives on Personalized Genomic Medicine and Personal Health Data Spaces Using a Mixed-Methods Design

Ongoing health challenges, such as the increased global burden of chronic disease, are increasingly answered by calls for personalized approaches to healthcare. Genomic medicine, a vital component of these personalization strategies, is applied in risk assessment, prevention, prognostication, and therapeutic targeting. However, several practical, ethical, and technological challenges remain. Across Europe, Personal Health Data Space (PHDS) projects are under development aiming to establish patient-centered, interoperable data ecosystems balancing data access, control, and use for individual citizens to complement the research and commercial focus of the European Health Data Space provisions. The current study explores healthcare users’ and health care professionals’ perspectives on personalized genomic medicine and PHDS solutions, in casu the Personal Genetic Locker (PGL). A mixed-methods design was used, including surveys, interviews, and focus groups. Several meta-themes were generated from the data: (i) participants were interested in genomic information; (ii) participants valued data control, robust infrastructure, and sharing data with non-commercial stakeholders; (iii) autonomy was a central concern for all participants; (iv) institutional and interpersonal trust were highly significant for genomic medicine; and (v) participants encouraged the implementation of PHDSs since PHDSs were thought to promote the use of genomic data and enhance patients’ control over their data. To conclude, we formulated several facilitators to implement genomic medicine in healthcare based on the perspectives of a diverse set of stakeholders.

Mapping the Landscape of Indian Genomics Research: A Scientometric Analysis

This Scientometric study aimed to provide state-of-the-art information on research growth and trends, areas of potential growth and development in genomics in India, and identify the key players (organizations or institutions, and funding agencies). It was found that the number of publications and citations related to genomics research has been steadily increasing over the years, indicating a growing interest and investment in the field as the Indian Council of Agricultural Research was the leading contributor to the field. Among the 159 contributing countries from 2012 to 2021, India contributed 4.46 percent of publications. The Department of Biotechnology (Ministry of Science and Technology, India) provided the most funds for genomics research. In the last decade, research was primarily focused on "Genetic Diversity", "Polymorphism", "Comparative Genomics", "Phylogeny", " Random amplification of polymorphic DNA (RAPD)", "Single-nucleotide polymorphism (SNP)", "Polymerase chain reaction (PCR)", "Gene Expression", etc. The study's findings may shed light on the strengths and weaknesses of the country's research infrastructure, as well as the effectiveness of government policies and funding mechanisms.

A Preliminary Study on “Personalised Treatment” against Varroa destructor Infestations in Honey Bee (Apis mellifera) Colonies

The requirement for the protection of bee colonies against Varroa destructor invasions has been noted by many breeders and is included as an aspect of the development of beekeeping. This research aimed to check the effect of the development of a colony exposed to laying eggs (brood surface) by queen bees with similar chemical potential (sisters) on the effect of a preparation combating V. destructor depending on the number of mites found in a given colony. We chose this as a standard model of conduct that treats each bee colony as one organism subjected to individual parasite control. For this purpose, we created a bee colony with a mother-of-one breeding line and fertilised drones from one colony. Infection with V. destructor occurred naturally and uncontrollably. Without interfering with the colony’s development, the frame insulator helped each colony’s brood (mothers’ reproductive potential) and the initial and final individuals from the mites themselves. The study was carried out in four species (two control species and two species with up to 20 and over 21 mites, respectively). Treatments with amitraz to combat damage were divided into four treatment subgroups: two treatments every four days or four treatments every two days. We observed the number of individuals that were protected in all subgroups in the average brood area. The reproductive potential of the sisters’ mothers did not change after the treatments with amitraz, which indicated that amitraz did not affect the delegation of egg laying. The invasion rate was also tracked relative to the control group, which allowed us to conclude that a two-time treatment with amitraz reduced the frequency of mites and a four-time treatment checked the effectiveness. Tailoring the control of V. destructor in bee colonies may be an effective measure in the fight against this parasite.

Profiling the peripheral immune response to ex vivo TNF stimulation in untreated juvenile idiopathic arthritis using single cell RNA sequencing

BACKGROUND

Juvenile Idiopathic Arthritis (JIA) is an autoimmune disease with a heterogenous clinical presentation and unpredictable response to available therapies. This personalized transcriptomics study sought proof-of-concept for single-cell RNA sequencing to characterize patient-specific immune profiles.

METHODS

Whole blood samples from six untreated children, newly diagnosed with JIA, and two healthy controls were cultured for 24 h with or without ex vivo TNF stimulation and subjected to scRNAseq to examine cellular populations and transcript expression in PBMCs. A novel analytical pipeline, scPool, was developed wherein cells are first pooled into pseudocells prior to expression analysis, facilitating variance partitioning of the effects of TNF stimulus, JIA disease status, and individual donor.

RESULTS

Seventeen robust immune cell-types were identified, the abundance of which was significantly affected by TNF stimulus, which resulted in notable elevation of memory CD8 + T-cells and NK56 cells, but down-regulation of naïve B-cell proportions. Memory CD8 + and CD4 + T-cells were also both reduced in the JIA cases relative to two controls. Significant differential expression responses to TNF stimulus were also characterized, with monocytes showing more transcriptional shifts than T-lymphocyte subsets, while the B-cell response was more limited. We also show that donor variability exceeds the small degree of possible intrinsic differentiation between JIA and control profiles. An incidental finding of interest was association of HLA-DQA2 and HLA-DRB5 expression with JIA status.

CONCLUSIONS

These results support the development of personalized immune-profiling combined with ex-vivo immune stimulation for evaluation of patient-specific modes of immune cell activity in autoimmune rheumatic disease.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

Health technology assessment of whole genome sequencing in the diagnosis of genetic disorders: a scoping review of the literature

OBJECTIVE

The aim of this scoping review is to map the available evidence about the use of health technology assessment (HTA) in the assessment of whole genome sequencing (WGS).

METHODS

A scoping review methodology was adopted. The population, concept, and context framework was used to build up the research question and to establish the eligibility criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews was adopted to implement a comprehensive search strategy. Evidence was retrieved from scientific databases and HTA organizations Web sites. Reports were classified as full HTA, mini-HTA, rapid reviews or other.

RESULTS

The search strategy identified seven reports. Five HTA organizations from five countries elaborated the reports: one full HTA, four rapid reviews, and two classified as others. The reports were mainly focused on the evaluation of the clinical utility and cost-effectiveness of genome-wide sequencing as well as informing policy questions by providing analyses of organizational and ethical considerations.

CONCLUSIONS

Few HTA organizations are drafting reports for WGS. It is essential to stimulate a critical reflection during the elaboration of HTA reports for WGS to steer choices of decision makers in the establishment of priorities for research and policy and reimbursement rates.

Toward Precision Medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Insomnia disorder: State of the science and challenges for the future

Insomnia disorder comprises symptoms during night and day that strongly affect quality of life and wellbeing. Prolonged sleep latency, difficulties to maintain sleep and early morning wakening characterize sleep complaints, whereas fatigue, reduced attention, impaired cognitive functioning, irritability, anxiety and low mood are key daytime impairments. Insomnia disorder is well acknowledged in all relevant diagnostic systems: Diagnostic and Statistical Manual of the American Psychiatric Association, 5th revision, International Classification of Sleep Disorders, 3rd version, and International Classification of Diseases, 11th revision. Insomnia disorder as a chronic condition is frequent (up to 10% of the adult population, with a preponderance of females), and signifies an important and independent risk factor for physical and, especially, mental health. Insomnia disorder diagnosis primarily rests on self-report. Objective measures like actigraphy or polysomnography are not (yet) part of the routine diagnostic canon, but play an important role in research. Disease concepts of insomnia range from cognitive-behavioural models to (epi-) genetics and psychoneurobiological approaches. The latter is derived from knowledge about basic sleep-wake regulation and encompass theories like rapid eye movement sleep instability/restless rapid eye movement sleep. Cognitive-behavioural models of insomnia led to the conceptualization of cognitive-behavioural therapy for insomnia, which is now considered as first-line treatment for insomnia worldwide. Future research strategies will include the combination of experimental paradigms with neuroimaging and may benefit from more attention to dysfunctional overnight alleviation of distress in insomnia. With respect to therapy, cognitive-behavioural therapy for insomnia merits widespread implementation, and digital cognitive-behavioural therapy may assist delivery along treatment guidelines. However, given the still considerable proportion of patients responding insufficiently to cognitive-behavioural therapy for insomnia, fundamental studies are highly necessary to better understand the brain and behavioural mechanisms underlying insomnia. Mediators and moderators of treatment response/non-response and the associated development of tailored and novel interventions also require investigation. Recent studies suggest that treatment of insomnia may prove to add significantly as a preventive strategy to combat the global burden of mental disorders.

Advances in microfluidics devices and its applications in personalized medicines

Microfluidics is an exponentially growing area and is being used for numerous applications from basic science to advanced biotechnology and medicines. Microfluidics provides a platform to the research community for studying and building new strategies for the diagnosis and therapeutics applications. In the last decade, microfluidic have enriched the field of diagnostics by providing new solutions which was not possible with conventional detection and treatment methods. Microfluidics has the ability to precisely control and perform high-throughput functions. It has been proven as an efficient and rapid method for biological sample preparation, analysis and controlled drug delivery system. Microfluidics plays significant role in personalized medicine. These personalized medicines are used for medical decisions, practices and other interventions as well as for individual patients based on their predicted response or risk of disease. This chapter highlights microfluidics in developing personalized medical applications for its applications in diseases such as cancer, cardiovascular disease, diabetes, pulmonary disease and several others.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

Real-life survey of pitfalls and successes of precision medicine in genetic epilepsies

OBJECTIVE

The term 'precision medicine' describes a rational treatment strategy tailored to one person that reverses or modifies the disease pathophysiology. In epilepsy, single case and small cohort reports document nascent precision medicine strategies in specific genetic epilepsies. The aim of this multicentre observational study was to investigate the deeper complexity of precision medicine in epilepsy.

METHODS

A systematic survey of patients with epilepsy with a molecular genetic diagnosis was conducted in six tertiary epilepsy centres including children and adults. A standardised questionnaire was used for data collection, including genetic findings and impact on clinical and therapeutic management.

RESULTS

We included 293 patients with genetic epilepsies, 137 children and 156 adults, 162 females and 131 males. Treatment changes were undertaken because of the genetic findings in 94 patients (32%), including rational precision medicine treatment and/or a treatment change prompted by the genetic diagnosis, but not directly related to known pathophysiological mechanisms. There was a rational precision medicine treatment for 56 patients (19%), and this was tried in 33/56 (59%) and was successful (ie, >50% seizure reduction) in 10/33 (30%) patients. In 73/293 (25%) patients there was a treatment change prompted by the genetic diagnosis, but not directly related to known pathophysiological mechanisms, and this was successful in 24/73 (33%).

SIGNIFICANCE

Our survey of clinical practice in specialised epilepsy centres shows high variability of clinical outcomes following the identification of a genetic cause for an epilepsy. Meaningful change in the treatment paradigm after genetic testing is not yet possible for many people with epilepsy. This systematic survey provides an overview of the current application of precision medicine in the epilepsies, and suggests the adoption of a more considered approach.

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin-angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

Language matters: precision health as a cross-cutting care, research and policy agenda

The biomedical research and healthcare delivery communities have increasingly come to focus their attention on the role of data and computation in order to improve the quality, safety, costs, and outcomes of both wellness promotion and care delivery. Depending on the scale of such efforts, and the environments in which they are situated, they are referred to variably as personalized or precision medicine, population health, clinical transformation, value-driven care, or value-based transformation. Despite the original intent of many efforts and publications that have sought to define personalized, precision, or data-driven approaches to improving health and wellness, the use of such terminology in current practice often treats said activities as discrete areas of endeavor within minimal cross-linkage across or between scales of inquiry. We believe that this current state creates numerous barriers that are preventing the advancement of relevant science, practice, and policy. As such, we believe that it is necessary to amplify and reaffirm our collective understanding that these fields share common means of inquiry, differentiated only by the units of measure being utilized, their sources of data, and the manner in which they are executed. Therefore, in this perspective, we explore and focus attention on such commonalities and then present a conceptual framework that links constituent activities into an integrated model that we refer to as a precision healthcare system. The presentation of this framework is intended to provide the basis for the types of shared, broad-based, and descriptive language needed to reference and realize such a framework.

Association between Endothelial nitric oxide synthase and Hepatic lipase gene polymorphisms with the risk of coronary artery disease in Southern Iran population - A case control study

Coronary artery disease is a multifactorial genetic disease caused by the interaction between genetic and environmental factors. Angiography is the gold standard method for the diagnosis and determining the stage of cardiac disorder. The rs1800588 at the Hepatic Lipase gene and rs1799983 at the endothelial nitric oxide synthase (eNOS) gene are two candidate SNP that result in increased risk of this disease. The aim of this study was to find out the associations of the two mentioned polymorphisms with angiographically proven coronary artery patients in a southern Iranian population. In this study, this two polymorphisms in 287 patients and 229 matched controls were confirmed by angiography and analyzed. Genotype analysis was carried out by PCR and RFLP. Data showed that a significant difference for the eNOS gene polymorphism (p = 0.004) and a non-significant difference for the Hepatic lipase polymorphism (p = 0.261) and increasing severity of angiographic evidences of coronary artery disease were observed. Conclusively the significant association of the G894T with the narrowing of two or three coronary vessels of this patients in an Iranian population have been detected.

Precision Medicine in Hematology 2021: Definitions, Tools, Perspectives, and Open Questions

During the past few years, our understanding of molecular mechanisms and cellular interactions relevant to malignant blood cell disorders has improved substantially. New insights include a detailed knowledge about disease-initiating exogenous factors, endogenous (genetic, somatic, epigenetic) elicitors or facilitators of disease evolution, and drug actions and interactions that underlie efficacy and adverse event profiles in defined cohorts of patients. As a result, precision medicine and personalized medicine are rapidly growing new disciplines that support the clinician in making the correct diagnosis, in predicting outcomes, and in optimally selecting patients for interventional therapies. In addition, precision medicine tools are greatly facilitating the development of new drugs, therapeutic approaches, and new multiparametric prognostic scoring models. However, although the emerging roles of precision medicine and personalized medicine in hematology and oncology are clearly visible, several questions remain. For example, it remains unknown how precision medicine tools can be implemented in healthcare systems and whether all possible approaches are also affordable. In addition, there is a need to define terminologies and to relate these to specific and context-related tools and strategies in basic and applied science. To discuss these issues, a working conference was organized in September 2019. The outcomes of this conference are summarized herein and include a proposal for definitions, terminologies, and applications of precision and personalized medicine concepts and tools in hematologic neoplasms. We also provide proposals aimed at reducing costs, thereby making these applications affordable in daily practice.

An Innovative Personalised Management Program for Older Adults with Parkinson's Disease: New Concepts and Future Directions

Introduction: Parkinson’s disease is a heterogeneous clinical syndrome. Parkinson’s disease in older persons presents with a diverse array of clinical manifestations leading to unique care needs. This raises the need for the healthcare community to proactively address the care needs of older persons with Parkinson’s disease. Though it is tempting to categorise different phenotypes of Parkinson’s disease, a strong evidence based for the same is lacking. There is considerable literature describing the varying clinical manifestations in old age. This article aims to review the literature looking for strategies in personalising the management of an older person with Parkinson’s disease.

Social determinants of health, personalized medicine, and child maltreatment

This review begins with a brief summary of the importance of child maltreatment as a major public health problem, given its prevalence and the substantial human and economic costs involved. The focus then shifts to consideration of personalized medicine and child maltreatment, including genetic and genomics factors, as well as the role of social determinants of health. Research on epigenetics related to child abuse and neglect is presented, followed by that pertaining to a few specific social factors, such as poverty, parental depression and substance use, and domestic (or intimate partner) violence. The review ends with a discussion of interventions to help address social determinants of health with brief descriptions of several model programs, and thoughts concerning the role of personalized medicine in addressing child maltreatment in the foreseeable future. IMPACT: This paper synthesizes knowledge on social determinants of health and advances in genetics and genomics related to the prevention of child maltreatment. It provides examples of model approaches to addressing the prevention of child maltreatment in primary care practices.

Drug Sensitivity and Drug Repurposing Platform for Cancer Precision Medicine

One of the critical Global challenges in achieving the UN Sustainable Development Goals 3 Good Health and Well Being is optimizing drug discovery and translational research for unmet medical need in both communicable and non-communicable diseases. Recently, the WHO reports there has been a shift from communicable diseases to non-communicable diseases with respect to being the leading cause of death globally and particularly in low- and middle-income countries such as South Africa. Hence, there is current drive to establish functional precision medicine program that addresses the unmet medical need using high throughput drug sensitivity and drug repurposing platform. Here, this paper serves as a perspective to showcase the recent development in high throughput drug sensitivity screening platform for the cancer precision medicine. We also elaborate on the benefit and applications of high-throughput drug screening platform for Precision Medicine. From a future perspective, this paper aims to showcase the possibility to integrate existing high-throughput drug sensitivity screening platform with the newly developed platform technologies such as microfluidics based single cell drug screening.

Emerging Topics in the Behavioral Neuroscience of Tinnitus

This volume has highlighted the many recent advances in tinnitus theory, models, diagnostics, therapies, and therapeutics. But tinnitus knowledge is far from complete. In this chapter, contributors to the Behavioral Neuroscience of Tinnitus consider emerging topics and areas of research needed in light of recent findings. New research avenues and methods to explore are discussed. Issues pertaining to current assessment, treatment, and research methods are outlined, along with recommendations on new avenues to explore with research.

Integration of pharmacogenomics and theranostics with nanotechnology as quality by design (QbD) approach for formulation development of novel dosage forms for effective drug therapy

To cater to medication needs in the future healthcare system, we need to shift from the conventional system of drug delivery to modern molecular signature-based drug delivery systems. The current drug therapies are either less effective, ineffective, or produce numerous adverse reactions. One scientific principle or discipline cannot adequately address all the problems, so we need an innovative application of the current scientific principles. Here we are proposing a novel concept of nanoformulation based on pharmacogenomics and theranostics for personalized error-free and targeted therapeutic agent delivery. The addition of more knowledge about the human genome opens the new way to study disease-gene, gene-drug, and drug-effect interactions, which is the basis of future medicines. Pharmacogenomics provides information about the disease etiology, role in genes in disease pathophysiology, disease biomarkers, drug targets, drug effects, and the fate of drugs inside the body. Theranostics approach utilizes the above information in diagnosis, treatment, and monitoring of the disease on a real-time basis. Personalized dosage forms can be formulated into a nanoformulation that provides a better therapeutic effect and minimizes adverse drug reactions. The therapeutic system needs to be shifted from the principle of one drug fits all to one drug unique population. In the present manuscript, we tried to conceptualize a modern therapeutic system by combining the three approaches viz. pharmacogenomics, theranostics, and nanotechnology applied in the area of formulation development to produce a multifunctional single tiny entity.

Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review

BACKGROUND

Psychiatric genetics has had limited success in translational efforts. A thorough understanding of the present state of translation in this field will be useful in the facilitation and assessment of future translational progress.

PURPOSE

A narrative literature review was conducted. Combinations of 3 groups of terms were searched in EBSCOhost, Google Scholar, and PubMed. The review occurred in multiple steps, including abstract collection, inclusion/exclusion criteria review, coding, and analysis of included papers.

RESULTS

One hundred and fourteen articles were analyzed for the narrative review. Across those, 4 bottlenecks were noted that, if addressed, may provide insights and help improve and increase translation in the field of psychiatric genetics. These 4 bottlenecks are emphasizing linear translational frameworks, relying on molecular genomic findings, prioritizing certain psychiatric disorders, and publishing more reviews than experiments.

CONCLUSIONS

These entwined bottlenecks are examined with one another. Awareness of these bottlenecks can inform stakeholders who work to translate and/or utilize psychiatric genetic information. Potential solutions include utilizing nonlinear translational frameworks as well as a wider array of psychiatric genetic information (e.g., family history and gene-environment interplay) in this area of research, expanding which psychiatric disorders are considered for translation, and when possible, conducting original research. Researchers are urged to consider how their research is translational in the context of the frameworks, genetic information, and psychiatric disorders discussed in this review. At a broader level, these efforts should be supported with translational efforts in funding and policy shifts.

Personalized Medicine for the Infertile Male

Personalized medicine uses a patient's genotype, environment, and lifestyle choices to create a tailored diagnosis and therapy plan, with the goal of minimizing side effects, avoiding lost time with ineffective treatments, and guiding preventative strategies. Although most precision medicine strategies are still within the laboratory phase of development, this article reviews the promising technologies with the greatest potential to improve the diagnosis and treatment options for male infertility, including sperm cell transplantation, genomic editing, and new biomarker assays, based on the latest proteomic and epigenomic studies.

Roadmap for Establishing Large-Scale Genomic Medicine Initiatives in Low- and Middle-Income Countries

In the post-genomic era, genomic medicine interventions as a key component of personalized medicine and tailored-made health care are greatly anticipated following recent scientific and technological advances. Indeed, large-scale sequencing efforts that explore human genomic variation have been initiated in several, mostly developed, countries across the globe, such as the United States, the United Kingdom, and a few others. Here, we highlight the successful implementation of large-scale national genomic initiatives, namely the Genome of Greece (GoGreece) and the DNA do Brasil (DNABr), aiming to emphasize the importance of implementing such initiatives in developing countries. Based on this experience, we also provide a roadmap for replicating these projects in other low-resource settings, thereby bringing genomic medicine in these countries closer to clinical fruition.

Precision Medicine: Steps along the Road to Combat Human Cancer

The diagnosis and treatment of diseases such as cancer is becoming more accurate and specialized with the advent of precision medicine techniques, research and treatments. Reaching down to the cellular and even sub-cellular level, diagnostic tests can pinpoint specific, individual information from each patient, and guide providers to a more accurate plan of treatment. With this advanced knowledge, researchers and providers can better gauge the effectiveness of drugs, radiation, and other therapies, which is bound to lead to a more accurate, if not more positive, prognosis. As precision medicine becomes more established, new techniques, equipment, materials and testing methods will be required. Herein, we will examine the recent innovations in assays, devices and software, along with next generation sequencing in genomics diagnostics which are in use or are being developed for personalized medicine. So as to avoid duplication and produce the fullest possible benefit, all involved must be strongly encouraged to collaborate, across national borders, public and private sectors, science, medicine and academia alike. In this paper we will offer recommendations for tools, research and development, along with ideas for implementation. We plan to begin with discussion of the lessons learned to date, and the current research on pharmacogenomics. Given the steady stream of advances in imaging mass spectrometry and nanoLC-MS/MS, and use of genomic, proteomic and metabolomics biomarkers to distinguish healthy tissue from diseased cells, there is great potential to utilize pharmacogenomics to tailor a drug or drugs to a particular cohort of patients. Such efforts very well may bring increased hope for small groups of non-responders and those who have demonstrated adverse reactions to current treatments.

Prediction of drug response in multilayer networks based on fusion of multiomics data

Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (Drug Response prEdiction based on MultiOmics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly.

Characteristics of the Airway Microbiome of Cystic Fibrosis Patients

Microbiota as an integral component of human body is actively investigated, including by massively parallel sequencing. However, microbiomes of lungs and sinuses have become the object of scientific attention only in the last decade. For patients with cystic fibrosis, monitoring the state of respiratory tract microorganisms is essential for maintaining lung function. Here, we studied the role of sinuses and polyps in the formation of respiratory tract microbiome. We identified Proteobacteria in the sinuses and samples from the lower respiratory tract (even in childhood). In some cases, they were accompanied by potentially dangerous basidiomycetes. The presence of polyps did not affect formation of the sinus microbiome. Proteobacteria are decisive in reducing the biodiversity of lung and sinus microbiomes, which correlated with the worsening of the lung function indicators. Soft mutations in the CFTR gene contribute to the formation of safer microbiome even in heterozygotes with class I mutations.