Electromagnetic Fields Literature Analysis for Precision Medicine.

During the last century technological advances have increased the number of anthropogenic electromagnetic fields (EMFs) and therefore human exposures. In this work we have mined from more than 30,000 EMF-related publications the genes, diseases and molecular mechanisms associated with the exposure to six different subsets of EMFs. Results show 3653 unique disease MeSH terms and 9966 unique genes identified of which only 4340 genes are human. Overall, our approach highlights the molecular aspects of the increasing exposure to EMFs.

Mapping Exposome Derived Phenotypes into SNOMED Codes.

Human phenotypes define the healthy or diseased status of an individual and they arise from the complex interactions between environmental and genetic factors. The whole set of human exposures constitute the human exposome. These exposures have multiple sources including physical and socioeconomic factors. In this manuscript we have used text mining techniques to retrieve 1295 and 1903 Human Phenotype Ontology terms associated with these exposome factors and we have subsequently mapped 83% and 90% of the HPO terms respectively) into SNOMED as a clinically actionable code. We have developed a proof-of-concept approach to facilitate the integration of exposomic and clinical data.

Clinical Validation of the Covariates Pharmacokinetic Model for Propofol in an Adult Population.

BACKGROUND AND OBJECTIVE: Pharmacokinetic or pharmacokinetic-pharmacodynamic models have been instrumental in facilitating the clinical use of propofol in target-controlled infusion systems in anaesthetic practice. There has been debate over which model should be recommended for practice. The covariates model is an updated pharmacokinetic model for propofol. The aim of this study was to prospectively validate this model in an adult population. METHODS: Twenty-nine patients were included, with a range of ages to assess model performance in younger and older individuals. Subjects received propofol through a target-controlled infusion device programmed with the covariates model. Subjects were randomised to one of two increasing/decreasing regimes of propofol plasma target concentrations between 2 and 5 µg.mL-1. After the start of the infusion, arterial and venous blood samples were drawn at pre-specified timepoints between 1.5 and 20 min and between 1.5 and 45 min, respectively. Predictive performance was assessed using established methodology. RESULTS: The model achieved a bias of 9 (- 45 to 82) and precision of 24 (9-82) for arterial samples and bias of - 8 (- 64 to 70) and precision of 23 (9-70) for venous samples. Predicted concentrations tended to be higher than the measured concentrations in female individuals but lower in male individuals. There was no clear systematic difference in the bias between younger and older patients. CONCLUSIONS: The covariates propofol pharmacokinetic model achieved an acceptable level of predictive performance, as assessed by both arterial and venous sampling, for use in target-controlled infusion in clinical practice. CLINICAL TRIAL REGISTRATION: NCT01492712 (15 December, 2011).

Pharmacokinetic models can estimate the changes in the concentration of a drug in the body over time. These have been instrumental in facilitating the clinical use of anaesthetic agents such as propofol in target-controlled infusions, which aim to achieve a set concentration in either plasma or the brain to achieve anaesthesia. The covariates model is a previously described pharmacokinetic model for propofol. The aim of the described study was to validate the performance of the model in an independent adult population. Participants received anaesthesia with propofol through a target-controlled infusion device programmed with the covariates model. The concentration of propofol in the blood was measured at various timepoints and compared to the target concentration specified by the target-controlled infusion device. The analysis showed that overall, the covariates model performed to a level acceptable for use in clinical practice and compared favourably to other pharmacokinetic models.

A Comparative Analysis of Phenotypes Derived from Genes or Biomedical Literature in COVID-19.

Since the emergence of SARS-CoV-2 in November 2019, there has been an exponential production of literature due to worldwide efforts to understand the interactions between the virus and the human body. Using an "in-house" developed script we retrieved gene annotations and identified phenotype enrichments. Human Phenotype Ontology terms were retrieved from the literature using the Onassis R package. This produced both disease-gene and disease-phenotype data as well as data for gene-phenotype interactions. Overall, we retrieved 181 human phenotypes that were identified by both approaches. Further in-depth analysis of these relationships could provide further insights in the molecular mechanisms related with the observed phenotypes, answers and hypotheses for key concepts within COVID-19 research.

Exploring Molecular Mechanisms Within Biomedical Literature.

Text mining of the biomedical literature enables vast quantities of information to be extracted and summarised. Here we describe an updated and improved version of previous methodology for the analysis of gene and protein biomarkers that enables the use of the newer Pubtator Central annotations, based in full text, improving the performance using a local SQLite database, that reduces the running time and resources required to perform the analyses facilitating its use in any computer, and expands its capabilities to enable the retrieval and analysis of chemical and metabolic biomarkers.

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.

Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. Interpretable machine learning methods have the potential to provide the means to overcome some of these issues but are largely unexplored within the neurocritical care domain. This article examines existing models used in neurocritical care from the perspective of interpretability. Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.

Integration of Annotated Phenotype, Gene and Chemical Text Data to Advance Exposome Informatics.

The field of phenomics has a range of biomedical informatics tools such as the Human Phenotype Ontology, providing a structured vocabulary with relationships between abnormal phenotype terms. Artificial intelligence has been widely used for entity extraction and tagging large corpora of text from PubMed and is reflected in applications such as PheneBank and PubTator. Phexpo is a tool for predicting chemical - phenotype relationships and vice-versa, although lacks the ability to decipher known relationships from unknown. Integration of these three resources can provide new meaningful relationships between phenotypes, genes and chemicals and has yet to be fully leveraged. Here we present a methodology to construct two new datasets for phenotype - gene and phenotype - chemical relationships and showcase how these datasets can be used to enhance exposome informatics.

Construction of a Physical Factor Resource for Exposome Informatics Research.

Exposome research is focused on all the exposures individuals experience during their lifetime and how it shapes their health and development of disease. The chemical and biological aspects of the exposome are readily available in data formats. In comparison there is a lack of data frameworks available for physical factors (e.g. noise, lighting, electromagnetic fields) and their biological relationships which would allow a greater understanding of the contribution of the physical environment on disease development and burden. We present the construction of a prototype that captures knowledge on physical factors and their interactions with genes and diseases derived from the biomedical literature to reflect the physical exposome.

Use of Direct Intracranial Pressure and Brain Tissue Oxygen Monitoring in Perioperative Management of Patients with Moyamoya Disease.

Intracranial pressure monitoring and brain tissue oxygen monitoring are commonly used in head injury for goal-directed therapies, but there may be more indications for its use. Moyamoya disease involves progressive stenosis of the arterial circulation and formation of collateral vessels that are at risk of hemorrhage. The risk of ischemic events during revascularization surgery and postoperatively is high. Impaired cerebral autoregulation may be one of the factors that are implicated. We present our experience with monitoring of cerebral oxygenation and autoregulation in the pathological hemisphere during the perioperative period in four patients with moyamoya disease.

Influence of Patient Demographics on Optimal Cerebral Perfusion Pressure Following Traumatic Brain Injury.

The relationship between optimal cerebral perfusion pressure (CPPopt) and patient characteristics has yet to be defined but could have significant implications for future guidelines recommending cerebral perfusion pressure (CPP) targets.Data from 36 traumatic brain injured patients admitted to neurological intensive care were analysed retrospectively. Linear mixed effects (LME) analysis was performed using an unadjusted-adjusted approach.Clinical characteristics with p < 0.10 were included in the adjusted model. A second adjusted model which included all variables of interest was created. Model fit was assessed using the root-mean-square error (RMSE).The adjusted model included time from initiation of intracranial pressure (ICP) monitoring (estimate = 0.00292, p < 0.001), age (estimate = -0.211, p = 0.0750) and the presence of diffuse axonal injury (DAI) (estimate = -35.5, p < 0.001). The RMSE of this model was 8.11 mmHg. The RMSE of the model containing all variables was 8.09 mmHg.Time, age and the presence of DAI may be important predictors of CPPopt. The models were too inaccurate at predicting CPPopt for employment in clinical practice but warrant further investigation. CPPopt is a dynamic measurement influenced by many factors, supporting the utility of investigating the feasibility of CPPopt-guided therapy.

Evaluation of Software for Automated Measurement of Adherence to ICP-Monitoring Threshold Guideline.

Challenges inherent in clinical guideline development include a long time lag between the key results and incorporation into best practice and the qualitative nature of adherence measurement, meaning it will have no directly measurable impact. To address these issues, a framework has been developed to automatically measure adherence by clinicians in neurological intensive care units to the Brain Trauma Foundation's intracranial pressure (ICP)-monitoring guidelines for severe traumatic brain injury (TBI).The framework processes physiological and treatment data taken from the bedside, standardises the data as a set of process models, then compares these models against similar process models constructed from published guidelines. A similarity metric (i.e. adherence measure) between the two models is calculated, composed of duration and scale of non-adherence.In a pilot clinical validation test, the framework was applied to physiological/treatment data from three TBI patients exhibiting ICP secondary insults at a local neuro-centre where clinical experts coded key clinical interventions/decisions about patient management.The framework identified non-adherence with respect to drug administration in one patient, with a spike in non-adherence due to an inappropriately high dosage; a second patient showed a high severity of guideline non-adherence; and a third patient showed non-adherence due to a low number of associated events and treatment annotations.

Telemetric Intracranial Pressure: A Snapshot Does not Give the Full Story.

Telemetric intracranial pressure (ICP) monitors are useful tools in the management of complex hydrocephalus and idiopathic intracranial hypertension (IIH). Clinicians may use them as a "snapshot" screening tool to assess shunt function or ICP. We compared "snapshot" telemetric ICP recordings with extended, in-patient periods of monitoring to determine whether this practice is safe and useful for clinical decision making.

Phexpo: a package for bidirectional enrichment analysis of phenotypes and chemicals.

Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed "phexpo," a tool to perform and explore bidirectional chemical and phenotype interactions using enrichment analyses. Phexpo utilizes gene annotations from 2 curated public repositories, the Comparative Toxicogenomics Database and the Human Phenotype Ontology. We have applied phexpo in 3 case studies linking: (1) individual chemicals (a drug, warfarin, and an industrial chemical, chloroform) with phenotypes, (2) individual phenotypes (left ventricular dysfunction) with chemicals, and (3) multiple phenotypes (covering polycystic ovary syndrome) with chemicals. The results of these analyses demonstrated successful identification of relevant chemicals or phenotypes supported by bibliographic references. The phexpo R package (https://github.com/GHLCLab/phexpo) provides a new bidirectional analyses approach covering relationships from chemicals to phenotypes and from phenotypes to chemicals.

An "in silico" Bench to Bedside Approach to Investigating Sepsis Biomarkers.

Sepsis results in various patient complications and is due to a heightened inflammatory response against infection. This condition requires further exploration of biomarkers. We employed an "in silico" method comprised of text-mining and additionally clinical validation through the Medical Information Mart for Intensive Care. We highlight that Factor VIII shows potential as a pertinent septic shock biomarker.

Linking Genome and Exposome: Computational Analysis of Human Variation in Chemical-Target Interactions.

The growing amount of available public data repositories containing a plethora of rich chemical and biomedical information is enabling new in silico research avenues. In this project we aim to link human genome variations and the exposome applying in silico biomedical informatics approaches to analyse the potential effects of those variants in the interactions with different chemicals.

Characterizing the Scope of Exposome Research Through Topic Modeling and Ontology Analysis.

Exposomics is a field of research which is receiving growing attention. In this work, we characterize the exposome research landscape and update our previous study of formal knowledge representation approaches to this field. We applied a deductive analysis using the National Center for Biomedical Ontology Recommender for comparability of the results generated from a literature dataset and newly available ontologies with our previously published work. We highlight the changes in ontology recommendations.

Transcranial Bioimpedance Measurement as a Non-invasive Estimate of Intracranial Pressure.

OBJECTIVES: We have previously demonstrated a relationship between transcranial bioimpedance (TCB) measurements and intracranial pressure (ICP) in an animal model of raised ICP. The primary objective of this study was to explore the relationship between non-invasive bioelectrical impedance measurements of the brain and skull and ICP in traumatic brain injury (TBI) patients. MATERIALS AND METHODS: Included patients were adults admitted to the Neurological Intensive Care Unit with TBI and undergoing invasive ICP monitoring as part of their routine clinical care. Multi-frequency TCB measurements were performed hourly through bi-temporal electrodes. The bioimpedance parameters of Z c (impedance at the characteristic frequency) and R 0 (resistance to a direct current) were then modelled against ICP using unadjusted and adjusted linear models. RESULTS: One hundred and sixty-eight TCB measurements were available from ten study participants. Using an unadjusted linear modelling approach, there was no significant relationship between measured ICP and Zc or R0. The most significant relationship between ICP and TCB parameters was found by adjusting for multiple patient specific variables and using Zc and R0 normalised per patient (p < 0.0001, r 2 = 0.32). CONCLUSIONS: These pilot results confirm some degree of relationship between TCB parameters and invasively measured ICP. The magnitude of this relationship is small and, on the basis of the current study, TCB is unlikely to provide a clinically useful estimate of ICP in patients admitted with TBI.

Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data.

OBJECTIVE: Technology in neurointensive care units can collect and store vast amounts of complex patient data. The CHART-ADAPT project is aimed at developing technology that will allow for the collection, analysis and use of these big data at the patient's bedside in neurointensive care units. A requirement of this project is to automatically extract and transfer high-frequency waveform data (e.g. ICP) from monitoring equipment to high performance computing infrastructure for analysis. Currently, no agreed data standard exists in neurointensive care for the description of this type of data. In this pilot study, we investigated the use of Medical Waveform Format Encoding Rules (MFER- www.mfer.org-ISO 11073-92001) as a possible data standard for neurointensive care waveform data. MATERIALS AND METHODS: Several waveform formats were explored (e.g. XML, DICOM waveform) and evaluated for suitability given existing computing infrastructure constraints, e.g. NHS network capacity and the processing capabilities of existing integration software. Key requirements of the format included a compact data size and the use of a recognised standard. The MFER waveform format (ISO/TS 11073-92001) met both requirements. To evaluate the practicality of the MFER waveform format, seven waveform signals (ICP, ECG, ART, CVP, EtCO2, Pleth, Resp) collected over a period of 8 h from a patient at the Institute of Neurological Sciences in Glasgow were converted into MFER waveform format. RESULTS: The MFER waveform format has two main components: sampling information and frame information. Sampling information describes the frequency of the data sampling and the resolution of the data. Frame information describes the data itself; it consists of three elements: data block (the actual data), channel (each type of waveform data occupies a channel) and sequence (the repetition of the data). All seven waveform signals were automatically and successfully converted into the MFER waveform format. One MFER file was created for each minute of data (total of 479 files, 181 KB each). CONCLUSIONS: The MFER waveform format has potential as a lightweight standard for representing high-frequency neurointensive care waveform data. Further work will include a comparison with other waveform data formats and a live trial of using the MFER waveform format to stream patient data over a longer period.

Multi-resolution Convolution Methodology for ICP Waveform Morphology Analysis.

Intracranial pressure (ICP) monitoring is a key clinical tool in the assessment and treatment of patients in neurointensive care. ICP morphology analysis can be useful in the classification of waveform features.A methodology for the decomposition of an ICP signal into clinically relevant dimensions has been devised that allows the identification of important ICP waveform types. It has three main components. First, multi-resolution convolution analysis is used for the main signal decomposition. Then, an impulse function is created, with multiple parameters, that can represent any form in the signal under analysis. Finally, a simple, localised optimisation technique is used to find morphologies of interest in the decomposed data.A pilot application of this methodology using a simple signal has been performed. This has shown that the technique works with performance receiver operator characteristic area under the curve values for each of the waveform types: plateau wave, B wave and high and low compliance states of 0.936, 0.694, 0.676 and 0.698, respectively.This is a novel technique that showed some promise during the pilot analysis. However, it requires further optimisation to become a usable clinical tool for the automated analysis of ICP signals.

Identification of Clinically Relevant Groups of Patients Through the Application of Cluster Analysis to a Complex Traumatic Brain Injury Data Set.

In neurological intensive care units (NICUs) we are collecting an ever increasing quantity of data. These range from patient demographics and physiological monitoring to treatment strategies and outcomes. The BrainIT database is an example of this type of rich data source. It contains validated data on 264 patients who suffered traumatic brain injury (TBI) admitted to 22 NICUs in 11 European countries between March 2003 and July 2005 [1, 6].