Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta-regression analysis of 120 randomized controlled trials

AIMS

To assess the potential for precision medicine in type 2 diabetes by quantifying the variability of body weight as response to pharmacological treatment and to identify predictors which could explain this variability.

METHODS

We used randomized clinical trials (RCTs) comparing glucose-lowering drugs (including but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo from four recent systematic reviews. RCTs reporting on body weight after treatment to allow for calculation of its logarithmic standard deviation (log[SD], i.e., treatment response heterogeneity) in verum (i.e., treatment) and placebo groups were included. Meta-regression analyses were performed with respect to variability of body weight after treatment and potential predictors.

RESULTS

A total of 120 RCTs with a total of 43 663 participants were analysed. A slightly larger treatment response heterogeneity was shown in the verum groups, with a median log(SD) of 2.83 compared to 2.79 from placebo. After full adjustment in the meta-regression model, the difference in body weight log(SD) was -0.026 (95% confidence interval -0.044; 0.008), with greater variability in the placebo groups. Scatterplots did not show any slope divergence (i.e., interaction) between clinical predictors and the respective treatment (verum or placebo).

CONCLUSIONS

We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs for body weight reduction in type 2 diabetes. The precision medicine approach may thus be of limited value in this setting.

A glycosylation-related signature predicts survival in pancreatic cancer

BACKGROUND

Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models.

MATERIALS AND METHODS

The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan-Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature.

RESULTS

Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines.

CONCLUSION

This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.

The time is ripe for the renaissance of autism treatments: evidence from clinical practitioners

Introduction

Recent changes in diagnostics criteria have contributed to the broadening of the autism spectrum disorders and left clinicians ill-equipped to treat the highly heterogeneous spectrum that now includes toddlers and children with sensory and motor issues.

Methods

To uncover the clinicians' critical needs in the autism space, we conducted surveys designed collaboratively with the clinicians themselves. Board Certified Behavioral Analysts (BCBAs) and developmental model (DM) clinicians obtained permission from their accrediting boards and designed surveys to assess needs and preferences in their corresponding fields.

Results

92.6% of BCBAs are open to diversified treatment combining aspects of multiple disciplines; 82.7% of DMs also favor this diversification with 21.8% valuing BCBA-input and 40.6% neurologists-input; 85.9% of BCBAs and 85.3% of DMs advocate the use of wearables to objectively track nuanced behaviors in social exchange; 76.9% of BCBAs and 57.0% DMs feel they would benefit from augmenting their knowledge about the nervous systems of Autism (neuroscience research) to enhance treatment and planning programs; 50.0% of BCBAs feel they can benefit for more training to teach parents.

Discussion

Two complementary philosophies are converging to a more collaborative, integrative approach favoring scalable digital technologies and neuroscience. Autism practitioners seem ready to embrace the Digital-Neuroscience Revolutions under a new cooperative model.

Utilizing Amniotic Fluid Metabolomics to Monitor Fetal Well-Being: A Narrative Review of the Literature

Fetal and perinatal periods are critical phases for long-term development. Early diagnosis of maternal complications is challenging due to the great complexity of these conditions. In recent years, amniotic fluid has risen in a prominent position in the latest efforts to describe and characterize prenatal development. Amniotic fluid may provide real-time information on fetal development and metabolism throughout pregnancy as substances from the placenta, fetal skin, lungs, gastric fluid, and urine are transferred between the mother and the fetus. Applying metabolomics to monitor fetal well-being, in such a context, could help in the understanding, diagnosis, and treatment of these conditions and is a promising area of research. This review shines a spotlight on recent amniotic fluid metabolomics studies and their methods as an interesting tool for the assessment of many conditions and the identification of biomarkers. Platforms in use, such as proton nuclear magnetic resonance (¹H NMR) and ultra-high-performance liquid chromatography (UHPLC), have different merits, and a combinatorial approach could be valuable. Metabolomics may also be used in the quest for habitual diet-induced metabolic signals in amniotic fluid. Finally, analysis of amniotic fluid can provide information on exposure to exogenous substances by detecting the exact levels of metabolites carried to the fetus and associated metabolic effects.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Performance and long-term consistency of five Galliform 68Ge/68Ga generators used for clinical Ga-68 preparations over a 4 year period

BACKGROUND

Gallium-68 is a positron emitter for PET applications that can be produced without cyclotron by a germanium (Ge-68) chloride/gallium (Ga-68) chloride generator. Short half-life (67.71 min) of Ga-68, matching pharmacokinetic properties of small biomolecules, facilitates isotope utilization in compounding radiopharmaceuticals for PET imaging. The increasing cost of good manufacturing practice-compliant generators has strengthened the need for radionuclide efficient use by planning specific radiopharmaceutical sessions during the week, careful maintenance of the generator and achievement of high labeling yield and radiochemical purity (RCP) of the radiolabeled molecules.

METHODS

The aim of this study was to evaluate the annual performance of five consecutive 68Ge/68Ga generators used for small-scale preparations of 68Ga-radiopharmaceuticals. To assess the long-term efficiency of isotope production we measured the weekly elution yield. To assess process efficiency we measured elution yield, labeling yield and RCP of four radiopharmaceutical preparations (68Ga-DOTATOC, 68Ga-PSMA-HBED-CC, 68Ga-PENTIXAFOR and 68Ga-DOTATATE).

RESULTS

The annual mean elution yield of the generators was 74.7%, higher than that indicated by the manufacturer, and it never went below 65%. The Ge-68 level in the final products was under the detection limits in all the produced batches (mean value 0.0000048%). The RCP of radiopharmaceuticals determined by high-performance liquid chromatography was 98 ± 0.22%. The mean yield of radiolabelling was 64.68, 68.71, 57 and 63.68% for 68Ga-DOTATOC, 68Ga-PSMA-HBED-CC, 68GaPENTIXAFOR and 68Ga-DOTATATE.

CONCLUSION

The ability to prepare in the hospital radiopharmacy high-purity and pharmaceutically acceptable 68Ga-radiolabeled probes on a routine basis facilitates patient access to precision imaging for clinical and research aims.

Development of software enabling Chinese medicine-based precision treatment for osteoporosis at the gene and pathway levels

Background

Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels.

Methods

PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP database), and the Wanfang database were searched to identify studies treating osteoporosis with CMs. The TCMSP was used to identify bioactive ingredients and related genes for each CM. Gene expression omnibus (GEO) database and the limma package were used to identify differentially expressed genes in osteoporosis. Perl software was used to identify the shared genes between the bioactive components in CM and osteoporosis. R packages and bioconductor packages were used to define the target relationship between shared genes and their related pathways. Third-party Python libraries were used to write program codes. Pyinstaller library was used to create an executable program file.

Results

Data mining: a total of 164 CMs were included, but Drynariae Rhizoma (gusuibu) was used to present this process. We obtained 44 precise relationships among the bioactive ingredients of Drynariae Rhizoma, shared genes, and pathways. Python programming: we developed the software to show the precise relationship among bioactive ingredients, shared genes, and pathways for each CM, including Drynariae Rhizoma.

Conclusions

This study could increase the precision of CM, and could provide a valuable and convenient software for searching precise relationships among bioactive ingredients, shared genes, and pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-022-00596-6.

Using the Biopsychosocial Model to Guide Patient-Centered Neurological Treatments

The biopsychosocial model was defined by George L. Engel to propose a holistic approach to patient care. Through this model, physicians can understand patients in their context to aid the development of tailored, individualized treatment plans that consider relevant biological, psychological, and social-cultural-spiritual factors impacting health and longitudinal care. In this article, we advocate for the use of the biopsychosocial model in neurology practice across outpatient and inpatient clinical settings. To do so, we first present the history of the biopsychosocial model, and its relationships to precision medicine and deep phenotyping. Then, we bring the neurologist up-to-date information on the components of the biopsychosocial clinical formulation, including predisposing, precipitating, perpetuating, and protective factors. We conclude by detailing illustrative neurological case examples using the biopsychosocial model, emphasizing the importance of considering relevant psychological and social factors to aid the delivery of patient-centered clinical care in neurology.

Metabolomics, Microbiomics, Machine learning during the COVID-19 pandemic

COVID-19 pandemic has a significant impact worldwide, from the point of view of public health, social, and economic aspects. The correct strategies of diagnosis and global management are still under debate. In the next future, we firmly believe that combining the so-called 3 M's (metabolomics, microbiomics, and machine learning [artificial intelligence]) will be the optimal, accurate tool for the early diagnosis of COVID-19 subjects, risk assessment and stratification, patient management, and decision-making. If the currently available preliminary data obtain further confirms, through future studies on larger samples, simple biomarkers will provide predictive models for data analysis and interpretation, allowing a step toward personalized holistic medicine.

Precision Autism: Genomic Stratification of Disorders Making Up the Broad Spectrum May Demystify Its "Epidemic Rates"

In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNET portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes’ expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

Chronic patient remote monitoring through the application of big data and internet of things

Chronic patients could benefit from the technological advances, but the clinical approaches for this kind of patients are still limited. This paper describes a system for chronic patients monitoring both, in home and external environments. For this purpose, we used novel technologies as big data, cloud computing and internet of things (IoT). Additionally, the system has been validated for three use cases: cardiovascular disease (CVD), hypertension (HPN) and chronic obstructive pulmonary disease (COPD), which were selected for their incidence in the population. This system is innovative within e-health, mainly due to the use of a big data architecture based on open-source components, also it provides a scalable and distributed environment for storage and processing of biomedical sensor data. The proposed system enables the incorporation of non-medical data sources in order to improve the self-management of chronic diseases and to develop better strategies for health interventions for chronic and dependents patients.

Fostering reproducibility, reusability, and technology transfer in health informatics

Computational methods can transform healthcare. In particular, health informatics with artificial intelligence has shown tremendous potential when applied in various fields of medical research and has opened a new era for precision medicine. The development of reusable biomedical software for research or clinical practice is time-consuming and requires rigorous compliance with quality requirements as defined by international standards.

However, research projects rarely implement such measures, hindering smooth technology transfer into the research community or manufacturers as well as reproducibility and reusability.

Here, we present a guideline for quality management systems (QMS) for academic organizations incorporating the essential components while confining the requirements to an easily manageable effort. It provides a starting point to implement a QMS tailored to specific needs effortlessly and greatly facilitates technology transfer in a controlled manner, thereby supporting reproducibility and reusability.

Ultimately, the emerging standardized workflows can pave the way for an accelerated deployment in clinical practice.

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.

Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study

BACKGROUND

Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment.

OBJECTIVE

The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity.

METHODS

We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge.

RESULTS

Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer.

CONCLUSIONS

Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

Personalised Medicine: implication and perspectives in the field of occupational health

"Personalised medicine" relies on identifying and integrating individual variability in genomic, biological, and physiological parameters, as well as in environmental and lifestyle factors, to define "individually" targeted disease prevention and treatment. Although innovative "omic" technologies supported the application of personalised medicine in clinical, oncological, and pharmacological settings, its role in occupational health practice and research is still in a developing phase. Occupational personalised approaches have been currently applied in experimental settings and in conditions of unpredictable risks, e.g.. war missions and space flights, where it is essential to avoid disease manifestations and therapy failure. However, a debate is necessary as to whether personalized medicine may be even more important to support a redefinition of the risk assessment processes taking into consideration the complex interaction between occupational and individual factors. Indeed, "omic" techniques can be helpful to understand the hazardous properties of the xenobiotics, dose-response relationships through a deeper elucidation of the exposure-disease pathways and internal doses of exposure. Overall, this may guide the adoption/implementation of primary preventive measures protective for the vast majority of the population, including most susceptible subgroups. However, the application of personalised medicine into occupational health requires overcoming some practical, ethical, legal, economical, and socio-political issues, particularly concerning the protection of privacy, and the risk of discrimination that the workers may experience. In this scenario, the concerted action of academic, industry, governmental, and stakeholder representatives should be encouraged to improve research aimed to guide effective and sustainable implementation of personalised medicine in occupational health fields.

Diagnostic potential of peri-implant crevicular fluid microRNA-21-3p and microRNA-150-5p and extracellular vesicles in peri-implant diseases

BACKGROUND

To explore the diagnostic usefulness of extracellular vesicles (EVs), and their subpopulations (micro-vesicles and exosomes), and microRNAs (miRNA-21-3p, miRNA-150-5p, and miRNA-26a-5p) in peri-implant crevicular fluid (PICF) of subjects with healthy, peri-implant mucositis and peri-implantitis implants.

METHODS

A total of 54 patients were enrolled into healthy, peri-implant mucositis, and peri-implantitis groups. PICF samples were collected, EVs subpopulations (MVs and Exo) were isolated and characterized by nanoparticle tracking analysis and transmission electron microscopy. The expression of miRNA-21-3p, miRNA-150-5p and miRNA-26a-5p was quantified by qRT-PCR. Logistic regression models and accuracy performance tests were estimated.

RESULTS

PICF samples show the presence of EVs delimited by a bi-layered membrane, in accordance with the morphology and size (< 200 nm). The concentration of PICF-EVs, micro-vesicles and exosomes was significantly increased in peri-implantitis implants compared to healthy implants (P = 0.023, P = 0.002, P = 0.036, respectively). miRNA-21-3p and miRNA-150-5p expression were significantly downregulated in patients with peri-implantitis in comparison with peri-implant mucositis sites (P = 0.011, P = 0.020, respectively). The reduced expression of miRNA-21-3p and miRNA-150-5p was associated with peri-implantitis diagnosis (OR:0.23, CI 0.08-0.66, P = 0.007 and OR:0.07, CI 0.01-0.78, P = 0.031, respectively). The model which included the miRNA-21-3p and miRNA-150-5p expression had a sensitivity of 93.3%, a specificity of 76.5%, a positive predictive value of 77.8%, and a negative predictive value of 92.9%. The positive and negative likelihood ratios were 3.97 and 0.09, respectively. The area under the receiver operating characteristics curve for the model was 0.84.

CONCLUSIONS

An increase concentration of EVs with a downregulation expression of miRNA-21-3p and miRNA-150-5p could be related with the peri-implantitis development.

Reframing Psychiatry for Precision Medicine

The art of observing and describing behaviors has driven diagnosis and informed basic science in psychiatry. In recent times, studies of mental illness are focused on understanding the brain's neurobiology but there is a paucity of information on the potential contributions from peripheral activity to mental health. In precision medicine, this common practice leaves a gap between bodily behaviors and genomics that we here propose to address with a new layer of inquiry that includes gene expression on tissues inclusive of brain, heart, muscle-skeletal and organs for vital bodily functions. We interrogate gene expression on human tissue as a function of disease-associated genes. By removing genes linked to disease from the typical human set, and recomputing gene expression on the tissues, we can compare the outcomes across mental illnesses, well-known neurological conditions, and non-neurological conditions. We find that major neuropsychiatric conditions that are behaviorally defined today (e.g., autism, schizophrenia, and depression) through DSM-observation criteria have strong convergence with well-known neurological conditions (e.g., ataxias and Parkinson's disease), but less overlap with non-neurological conditions. Surprisingly, tissues majorly involved in the central control, coordination, adaptation and learning of movements, emotion and memory are maximally affected in psychiatric diagnoses along with peripheral heart and muscle-skeletal tissues. Our results underscore the importance of considering both the brain-body connection and the contributions of the peripheral nervous systems to mental health.

Just data? Solidarity and justice in data-driven medicine

This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could-especially considering current developments in big data and artificial intelligence-compromise the right by leading to injustices and affecting concrete modes of individual self-determination. In order to address this potential tension, two key elements for ethical reflection on data-driven medicine are proposed: the controllability of information flows, including technical infrastructures that are conducive towards controllability, and a paradigm shift towards output-orientation in governance and policy.

Potent Therapy and Transcriptional Profile of Combined Erythropoietin-Derived Peptide Cyclic Helix B Surface Peptide and Caspase-3 siRNA against Kidney Ischemia/Reperfusion Injury in Mice

Cause-specific treatment and timely diagnosis are still not available for acute kidney injury (AKI) apart from supportive therapy and serum creatinine measurement. A novel erythropoietin-derived cyclic helix B surface peptide (CHBP) protects kidneys against AKI with different causes, but the underlying mechanism is not fully defined. Herein, we investigated the transcriptional profile of renoprotection induced by CHBP and its potential synergistic effects with siRNA targeting caspase-3, an executing enzyme of apoptosis and inflammation (CASP3siRNA), on ischemia/reperfusion (IR)-induced AKI. Utilizing a mouse model with 30-minute renal bilateral ischemia and 48-hour reperfusion, the renoprotection of CHBP or CASP3siRNA was demonstrated in renal function and structure, active caspase-3 and HMGB1 expression. Combined treatment of CHBP and CASP3siRNA further preserved kidney structure and reduced active caspase-3 and HMGB1. Furthermore, differentially expressed genes (DEGs) were identified with fold change >1.414 and P < 0.05. In IR kidneys, 281 DEGs induced by CHBP were mainly involved in promoting cell division and improving cellular function and metabolism (upregulated signal transducer and activator of transcription 5B and solute carrier family 22 member 7). The additional administration of CASP3siRNA caused 504 and 418 DEGs in IR + CHBP kidneys with or without negative control small-interfering RNA, with 37 genes in common. These DEGs were associated with modulated apoptosis and inflammation (upregulated BCL6, SLPI, and SERPINA3M) as well as immunity, injury, and microvascular homeostasis (upregulated complement factor H and GREM1 and downregulated ANGPTL2). This proof-of-effect study indicated the potent renoprotection of CASP3siRNA upon CHBP at the early stage of IR-induced AKI. Underlying genes, BCL6, SLPI, SERPINA3M, GREM1, and ANGPTL2, might be potential new biomarkers for clinical applications. SIGNIFICANCE STATEMENT: It is imperative to explore new strategies of cause-specific treatment and timely diagnosis for acute kidney injury (AKI). CHBP and CASP3siRNA synergistically protected kidney structure after 48-hour ischemia/reperfusion-induced AKI with reduced injury mediators CASP3 and high mobility group box 1. CHBP upregulated cell division-, function-, and metabolism-related genes, whereas CASP3siRNA further regulated immune response- and tissue homeostasis-associated genes. Combined CHBP and CASP3siRNA might be a potent and specific treatment for AKI, and certain dysregulated genes secretory leukocyte peptidase inhibitor and SERPINA3M could facilitate timely diagnosis.

Designing m-Health interventions for precision mental health support

Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers.

Enabling Technologies for Personalized and Precision Medicine

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

How could metabolomics change pediatric health?

In the last years, 'omics' technologies, and especially metabolomics, emerged as expanding scientific disciplines and promising technologies in the characterization of several pathophysiological processes.In detail, metabolomics, able to detect in a dynamic way the whole set of molecules of low molecular weight in cells, tissues, organs, and biological fluids, can provide a detailed phenotypic portray, representing a metabolic "snapshot."Thanks to its numerous strength points, metabolomics could become a fundamental tool in human health, allowing the exact evaluation of individual metabolic responses to pathophysiological stimuli including drugs, environmental changes, lifestyle, a great number of diseases and other epigenetics factors.Moreover, if current metabolomics data will be confirmed on larger samples, such technology could become useful in the early diagnosis of diseases, maybe even before the clinical onset, allowing a clinical monitoring of disease progression and helping in performing the best therapeutic approach, potentially predicting the therapy response and avoiding overtreatments. Moreover, the application of metabolomics in nutrition could provide significant information on the best nutrition regimen, optimal infantile growth and even in the characterization and improvement of commercial products' composition.These are only some of the fields in which metabolomics was applied, in the perspective of a precision-based, personalized care of human health.In this review, we discuss the available literature on such topic and provide some evidence regarding clinical application of metabolomics in heart diseases, auditory disturbance, nephrouropathies, adult and pediatric cancer, obstetrics, perinatal conditions like asphyxia, neonatal nutrition, neonatal sepsis and even some neuropsychiatric disorders, including autism.Our research group has been interested in metabolomics since several years, performing a wide spectrum of experimental and clinical studies, including the first metabolomics analysis of human breast milk. In the future, it is reasonable to predict that the current knowledge could be applied in daily clinical practice, and that sensible metabolomics biomarkers could be easily detected through cheap and accurate sticks, evaluating biofluids at the patient's bed, improving diagnosis, management and prognosis of sick patients and allowing a personalized medicine. A dream? May be I am a dreamer, but I am not the only one.

International consortium for personalized medicine: an international survey about the future of personalized medicine

Aim: The ICPerMed, international initiative promoting personalized medicine, has realized a survey among a group of experts, to define a common vision for the deployment of personalized medicine across healthcare systems until 2030. Materials & methods: ICPerMed defined five perspectives (p.4) and addressed an online questionnaire to 97 international experts to collect their views. Results: Seventy (72%) of the 97 experts effectively answered the survey from which 69 answers were exploitable. Respondents from a variety of international profiles approved the five proposed perspectives and reported required actions and best practices. Conclusion: There is a large consensus among experts directly involved in shaping international strategies and policies, calling for voluntarist public policies, new IT platforms enabling data-driven approaches, large-scale educational programs and new financing models.

Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science

The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale or their appropriateness to define change and its rate, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1324 records in a cross-section of the population comprising participants with autism between five and 65 years of age. We find that these empirical distributions violate the theoretical requirements of normality and homogeneous variance, essential for independence between bias and sensitivity. Further, we assess a subset of 52 typical controls versus those with autism and find a lack of proper elements to characterize neurodevelopmental trajectories in a coping nervous system changing at nonuniform, nonlinear rates. Repeating the assessments over four visits in a subset of the participants with autism for whom verbal criteria retained the same appropriate ADOS modules over the time span of the four visits reveals that switching the clinician changes the cutoff scores and consequently influences the diagnosis, despite maintaining fidelity in the same test's modules, room conditions, and tasks' fluidity per visit. Given the changes in probability distribution shape and dispersion of these ADOS scores, the lack of appropriate metric spaces to define similarity measures to characterize change and the impact that these elements have on sensitivity-bias codependencies and on longitudinal tracking of autism, we invite a discussion on readjusting the use of this test for scientific purposes.

Big Data in Medicine, the Present and Hopefully the Future

The emergence of data coming from different venues, as several “omic” approaches, is providing already compelling evidence that the smart use of this information could provide invaluable information to prevent, diagnose and treat human diseases. However, the most daunting challenges remain ahead, as the explosive accumulation of data from additional perspectives, including social graphs, biosensors, and imaging, promise to deliver crucial information that could be exploited for the improvement of the entire human race, both in developed, and developing countries, optimizing health expenses and reaching also the less fortunate sections of the societies. And yet, formidable challenges remain, that pertain for the most part to the collection of the data, their organization, and most relevantly their integration. Here we provide few, pointed examples to the present relevance of these big data approaches in human health as well potential road maps toward the implementation of broader data collections and analyses.

Qualifying and quantifying the precision medicine rhetoric

BACKGROUND

With the rise of precision medicine efforts worldwide, our study objective was to describe and map the emerging precision medicine landscape. A Google search was conducted between June 19, 2017 to July 20, 2017 to examine how "precision medicine" and its analogous terminology were used to describe precision medicine efforts. Resulting web-pages were reviewed for geographic location, data type(s), program aim(s), sample size, duration, and the key search terms used and recorded in a database. Descriptive statistics were applied to quantify terminology used to describe specific precision medicine efforts. Qualitative data were analyzed for content and patterns.

RESULTS

Of the 108 programs identified through our search, 84% collected only biospecimen(s) and, of those that collected at least two data types, 42% mentioned both Electronic Health Records (EHR) and biospecimen. Given the majority of efforts limited to biospecimen(s) use, genetic research seems to be prioritized in association with precision medicine. Roughly, 54% were found to collect two or more data types, which limits the output of information that may contribute to understanding of the interplay of genetic, lifestyle, and environmental factors. Over half were government-funded with roughly a third being industry-funded. Most initiatives were concentrated in the United States, Europe, and Asia.

CONCLUSIONS

To our knowledge, this is the first study to map and qualify the global precision medicine landscape. Our findings reveal that precision medicine efforts range from large model cohort studies involving multidimensional, longitudinal data to biorepositories with a collection of blood samples. We present a spectrum where past, present, and future PM-like efforts can fall based on their scope and potential impact. If precision medicine is based on genes, lifestyle and environmental factors, we recommend programs claiming to be precision medicine initiatives to incorporate multidimensional data that can inform a holistic approach to healthcare.

Bacterial Population Control with Macroscopic HKUST Crystals

Macroscopic HKUST crystals were shown to release significant amounts of copper in saline medium at a slow rate, which was exploited to control the growth of a bacterial population. This was achieved in both liquid and solid media, the latter illustrating the local effect of the crystals. In addition, these nanostructured crystals of observable size were loaded with chloramphenicol to exert a joint metal-antibiotic action, going beyond the traditional oligodynamic effect.

Using Technology to Inform and Deliver Precise Personalized Care to Patients With End-Stage Kidney Disease

Consistent with the increase of precision medicine, the care of patients with end-stage kidney disease (ESKD) requiring maintenance dialysis therapy should evolve to become more personalized. Precise and personalized care is nuanced and informed by a number of factors including an individual's needs and preferences, disease progression, and response to and tolerance of treatments. Technology can support the delivery of more precise and personalized care through multiple mechanisms, including more accurate and real-time assessments of key care elements, enhanced treatment monitoring, and remote monitoring of home dialysis therapies. Data from health care and non-health care sources and advanced analytical methods such as machine learning can be used to create novel insights, and large volumes of data can be integrated to support clinical decisions. Health care models continue to evolve and the opportunities and need for novel care approaches supported by technology and health informatics continue to expand as the delivery and organization of health care changes. Ultimately, precise personalized care for ESKD, including dialysis therapy, will become more feasible as the biological, social, and environmental determinants of health are more broadly understood and as advances in science, engineering, and information management create the means to provide truly precise care for ESKD.

Working together to deliver stratified medicine research effectively

INTRODUCTION OR BACKGROUND

Stratified medicine is an important area of research across all clinical specialties, with far reaching impact in many spheres. Despite recently formulated global policy and research programmes, major challenges for delivering stratified medicine studies persist. Across the globe, clinical research infrastructures have been setup to facilitate high quality clinical research.

SOURCES OF DATA

This article reviews the literature and summarizes views collated from a workshop held by the UK Pharmacogenetics and Stratified Medicine Network and the NIHR Clinical Research Network in November 2016.

AREAS OF AGREEMENT

Stratified medicine is an important area of clinical research and health policy, benefitting from substantial international, cross-sector investment and has the potential to transform patient care. However there are significant challenges to the delivery of stratified medicine studies.

AREAS OF CONTROVERSY

Complex methodology and lack of consistency of definition and agreement on key approaches to the design, regulation and delivery of research contribute to these challenges and would benefit from greater focus.

GROWING POINTS

Effective partnership and development of consistent approaches to the key factors relating to stratified medicine research is required to help overcome these challenges.

AREAS TIMELY FOR DEVELOPING RESEARCH

This paper examines the critical contribution clinical research networks can make to the delivery of national (and international) initiatives in the field of stratified medicine. Importantly, it examines the position of clinical research in stratified medicine at a time when pressures on the clinical and social services are mounting.

Is there a classical role for the clinical laboratory in digital health?

The classical role of the clinical laboratory, seen as the central place where the samples converge and from where the results are distributed, will be challenged by the development of digital health, the application of information technology (big data) and genomics to health care. When the development of disruptive new technologies will allow the production of accurate results outside the laboratory, its role will dramatically change. However, several factors are slowing down these evolutions. The quality of the existing data is relatively poor: lack of standardization of results, different units, different reference intervals, etc. The lab-on-a-chip technology is still relatively far from broad range application and the costs are higher than the traditional methods. There is the need for regulations of direct to consumer approaches that are posing big ethical problems. In the future, the clinical laboratory will maintain part of the "classical" role in the areas of research education and services. The large production will continue, favored by consolidation and reduction of the number of laboratories. The specialists of laboratory medicine have the task of collaborating with the national scientific societies and with the industry for improving harmonization of all the production phases, thus allowing the production of meaningful big data. Clinical laboratories have the role of implementing translational medicine. The new point-of-care (POC) technologies still need validation, the clinical laboratory is the place to do it. The advisory role toward clinicians and patients has to be improved, and a role in validating laboratory data interpretation apps and in controlling and supervising the functionality and the quality of the POC devices has to be developed.

Difficulties and challenges in the development of precision medicine

The rapid development of precision medicine is introducing a new era of significance in medicine. However, attaining precision medicine is an ambitious goal that is bound to encounter some challenges. Here, we have put forward some difficulties or questions that should be addressed by the progress in this field. The proposed issues include the long road to precision medicine for all types of diseases as the unknown domains of the human genome hinder the development of precision medicine. The challenges in the acquisition and analysis of large amounts of omics data, including difficulties in the establishment of a library of biological samples and large-scale data analysis, as well as the challenges of informed consent and medical ethics in precision medicine, must be overcome to attain the goals of precision medicine. To date, precision medicine programs have accomplished many preliminary achievements and will help to drive a dramatic revolution in clinical practices for the medical community. Through these advances, the diagnosis and treatment of many diseases will achieve many breakthroughs. This project is just beginning and requires a great deal of time and money. Precision medicine also requires extensive collaboration. Ultimately, these difficulties can be overcome. We should realize that precision medicine is good for patients, but there is still a long way to go.

Comedication with interacting drugs predisposes amiodarone users in cardiac and surgical intensive care units to acute liver injury: A retrospective analysis

Risk factors and underlying mechanisms for liver injury associated with amiodarone remain elusive. This study aimed to investigate the drug-related covariates for acute liver injury by amiodarone-an intriguing compound of high lipophilicity, with a long half-life and notable efficacy.The medical, pharmacy, and laboratory records of new amiodarone users admitted to the cardiac or surgical intensive care units of a medical center were examined retrospectively. A Cox regression model with time-varying dose-related variables of amiodarone was utilized to estimate the hazard ratio (HR) of amiodarone-associated liver injury while adjusting for concomitant therapy and relevant covariates.Of the 131 eligible patients among 6,572 amiodarone users (46,402 prescriptions), 6 were identified as amiodarone-associated liver injury cases. In comparison to controls (n = 125), this liver injury cohort (n = 6) had significantly higher numbers of amiodarone-interacting (2.7 ± 2.0 vs 0.9 ± 0.9 drugs, P = .02) and hepatotoxic (3.8 ± 0.8 vs 2.5 ± 1.7 drugs, P = .03) comedications. The number of comedications with amiodarone-interacting potential (HR 2.07, 95% confidence interval [CI] 1.02-4.22, P = .04) and amiodarone cumulative doses standardized by body surface area (HR 6.82, 95% CI 1.72-27.04, P = .01) were independent risk factors for liver injury associated with amiodarone.Drug-related (amiodarone cumulative dose, interacting drugs) factors were significant predictors of amiodarone-associated acute liver injury. A prudent evaluation of each medication profile is warranted to attain precision medicine at the level of patient care, especially for those treated by medications with complex physicochemical and pharmacokinetic properties, such as amiodarone.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Assessing Genetic Literacy Awareness and Knowledge Gaps in the US Population: Results from the Health Information National Trends Survey

BACKGROUND/AIMS

Public understanding of the role of genetics in disease risk is key to appropriate disease prevention and detection. This study assessed the current extent of awareness and use of genetic testing in the US population. Additionally, the study identified characteristics of subgroups more likely to be at risk for low genetic literacy.

METHODS

The study used data from the National Cancer Institute's 2017 Health Information National Trends Survey, including measures of genetic testing awareness, genetic testing applications and genetic testing usage. Multivariable logistic regression models estimated associations between sociodemographics, genetic testing awareness, and genetic testing use.

RESULTS

Fifty-seven percent of respondents were aware of genetic tests. Testing awareness differed by age, household income, and race/ethnicity. Most participants had heard of using tests to determine personal disease risk (82.58%) or inherited disease risk in children (81.41%), but less were familiar with determining treatment (38.29%) or drug efficacy (40.76%). Among those with genetic testing awareness, actual testing uptake was low.

CONCLUSIONS

A large portion of the general public lacks genetic testing awareness and may benefit from educational campaigns. As precision medicine expands, increasing public awareness about genetic testing applications for disease prevention and treatment will be important to support population health.

Characterization of Sensory-Motor Behavior Under Cognitive Load Using a New Statistical Platform for Studies of Embodied Cognition

The field of enacted/embodied cognition has emerged as a contemporary attempt to connect the mind and body in the study of cognition. However, there has been a paucity of methods that enable a multi-layered approach tapping into different levels of functionality within the nervous systems (e.g., continuously capturing in tandem multi-modal biophysical signals in naturalistic settings). The present study introduces a new theoretical and statistical framework to characterize the influences of cognitive demands on biophysical rhythmic signals harnessed from deliberate, spontaneous and autonomic activities. In this study, nine participants performed a basic pointing task to communicate a decision while they were exposed to different levels of cognitive load. Within these decision-making contexts, we examined the moment-by-moment fluctuations in the peak amplitude and timing of the biophysical time series data (e.g., continuous waveforms extracted from hand kinematics and heart signals). These spike-trains data offered high statistical power for personalized empirical statistical estimation and were well-characterized by a Gamma process. Our approach enabled the identification of different empirically estimated families of probability distributions to facilitate inference regarding the continuous physiological phenomena underlying cognitively driven decision-making. We found that the same pointing task revealed shifts in the probability distribution functions (PDFs) of the hand kinematic signals under study and were accompanied by shifts in the signatures of the heart inter-beat-interval timings. Within the time scale of an experimental session, marked changes in skewness and dispersion of the distributions were tracked on the Gamma parameter plane with 95% confidence. The results suggest that traditional theoretical assumptions of stationarity and normality in biophysical data from the nervous systems are incongruent with the true statistical nature of empirical data. This work offers a unifying platform for personalized statistical inference that goes far beyond those used in conventional studies, often assuming a “one size fits all model” on data drawn from discrete events such as mouse clicks, and observations that leave out continuously co-occurring spontaneous activity taking place largely beneath awareness.

Statistical Platform for Individualized Behavioral Analyses Using Biophysical Micro-Movement Spikes

Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results. Indeed, there is a critical need for fully objective automated analytical methods that easily handle the deluge of data these sensors output, while providing standardized scales amenable to apply across large sections of the population, to help promote personalized-mobile medicine. Here we use an open-access data set from Kaggle.com to illustrate the use of a new statistical platform and standardized data types applied to smart phone accelerometer and gyroscope data from 30 participants, performing six different activities. We report full distinction without confusion of the activities from the Kaggle set using a single parameter (linear acceleration or angular speed). We further extend the use of our platform to characterize data from commercially available smart shoes, using gait patterns within a set of experiments that probe nervous systems functioning and levels of motor control.

A Biomarker Characterizing Neurodevelopment with applications in Autism

Despite great advances in neuroscience and genetic studies, our understanding of neurodevelopmental disorders is still quite limited. An important reason is not having objective psychiatric clinical tests. Here we propose a quantitative neurodevelopment assessment by studying natural movement outputs. Movement is central to behaviors: It involves complex coordination, temporal alterations, and precise dynamic controls. We carefully analyzed the continuous movement output data, collected with high definition electromagnetic sensors at millisecond time scales. We unraveled new metrics containing striking physiological information that was unseen neither by using traditional motion assessments nor by naked eye observations. Our putative biomarker leads to precise individualized classifications. It illustrates clear differences between Autism Spectrum Disorder (ASD) subjects from mature typical developing (TD) individuals. It provides an ASD complementary quantitative classification, which closely agrees with the clinicaly assessed functioning levels in the spectrum. It also illustrates TD potential age-related neurodevelopmental trajectories. Applying our movement biomarker to the parents of the ASD individuals studied in the cohort also shows a novel potential familial signature ASD tie. This paper proposes a putative behavioral biomarker to characterize the level of neurodevelopment with high predicting power, as illustrated in ASD subjects as an example.

When Personal Feels Invasive: Foreseeing Challenges in Precision Medicine Communication

Precision medicine (PM) draws upon individual biological and psychosocial factors to create a personalized approach to healthcare. To date, little is known about how healthcare consumers will respond to such highly personalized guidance and treatment. The assumption is that responses will generally be favorable; yet in the media and in online public discussions about PM, concerns have been raised about invasions of privacy and autonomy. Findings from the tailoring literature-relevant because PM is, in a sense, "hypertailoring"-similarly suggest a potential for provoking unintended consequences such as personalization reactance, wherein perceived threat to one's privacy or freedom can lead to rejection of the personalized message or its source. Here, we review extant tailoring and other relevant research to identify challenges that could arise in PM communication. We then draw upon a patient-centered communication perspective to highlight elements of the communication process wherein resistance could be mitigated. This review aims to provide preliminary guidance for practitioners when communicating with patients and healthcare consumers about PM, as well as point scholars toward fruitful topics for research in this emerging health communication area.

Biomedical Big Data: New Models of Control Over Access, Use and Governance

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.

MD-Miner: a network-based approach for personalized drug repositioning

BACKGROUND

Due to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.

RESULTS

In this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.

CONCLUSIONS

This work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.