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Article ; Online: Identifying developments over a decade in the digital health and telemedicine landscape in the UK using quantitative text mining

Nophar Geifman / Jo Armes / Anthony D. Whetton

Frontiers in Digital Health, Vol

2023 Volume 5

Abstract: The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine ... ...

Abstract	The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine and health analytics has, in recent years, seen significant growth in the United Kingdom and worldwide. It is clearly acknowledged by multiple stakeholders that digital health innovations are necessary for the future of improved and more economic healthcare service delivery. Here we consider digital health-related research and applications by using an informatics tool to objectively survey the field. We have used a quantitative text-mining technique, applied to published works in the field of digital health, to capture and analyse key approaches taken and the diseases areas where these have been applied. Key areas of research and application are shown to be cardiovascular, stroke, and hypertension; although the range seen is wide. We consider advances in digital health and telemedicine in light of the COVID-19 pandemic.
Keywords	digital health ; telemedicine ; United Kingdom ; trends ; text mining ; Medicine ; R ; Public aspects of medicine ; RA1-1270 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	028
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases

Nophar Geifman / Anthony D. Whetton

Journal of Translational Medicine, Vol 18, Iss 1, Pp 1-

2020 Volume 11

Abstract: Abstract Background The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively ...

Abstract	Abstract Background The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high rates of infection and mortality. A third virus, the H5N1, responsible for avian influenza, has caused infection with some clinical similarities to those in COVID-19 infections. Cytokines, small proteins that modulate immune responses, have been directly implicated in some of the severe responses seen in COVID-19 patients, e.g. cytokine storms. Understanding the immune processes related to COVID-19, and other similar infections, could help identify diagnostic markers and therapeutic targets. Methods Here we examine data of cytokine, immune cell types, and disease associations captured from biomedical literature associated with COVID-19, Coronavirus in general, SARS, and H5N1 influenza, with the objective of identifying potentially useful relationships and areas for future research. Results Cytokine and cell-type associations captured from Medical Subject Heading (MeSH) terms linked to thousands of PubMed records, has identified differing patterns of associations between the four corpuses of publications (COVID-19, Coronavirus, SARS, or H5N1 influenza). Clustering of cytokine-disease co-occurrences in the context of Coronavirus has identified compelling clusters of co-morbidities and symptoms, some of which already known to be linked to COVID-19. Finally, network analysis identified sub-networks of cytokines and immune cell types associated with different manifestations, co-morbidities and symptoms of Coronavirus, SARS, and H5N1. Conclusion Systematic review of research in medicine is essential to facilitate evidence-based choices about health interventions. In a fast moving pandemic the approach taken here will identify trends and enable rapid comparison to the literature of related diseases.
Keywords	COVID-19 ; Coronavirus ; SARS ; H5N1 influenza ; Cytokines ; Haematopoietic cells ; Medicine ; R ; covid19
Subject code	610
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Clinical trial data reuse – overcoming complexities in trial design and data sharing

Toby Wilkinson / Siddharth Sinha / Niels Peek / Nophar Geifman

Trials, Vol 20, Iss 1, Pp 1-

2019 Volume 4

Abstract: Abstract There are many acknowledged benefits for the reuse of clinical trial data; from independent verification of published results to the evaluation of new hypotheses. However, the reuse of shared clinical trial data is not without obstacles. Here we ...

Abstract	Abstract There are many acknowledged benefits for the reuse of clinical trial data; from independent verification of published results to the evaluation of new hypotheses. However, the reuse of shared clinical trial data is not without obstacles. Here we present some of the issues and lessons learned from our own experiences in accessing and analyzing trial data; specifically, where we aim to combine and pool data from multiple different trials. In addition to issues around missing annotation and incomplete datasets, we identify trial-design complexity as a potential hurdle that may complicate downstream analyses. We address potential solutions and emphasize the need for benefits of transparent sharing and analysis of participant-level clinical trial data with appropriate risk mitigation, a matter important to efficient clinical research.
Keywords	Data sharing ; Clinical trials ; Pooled analysis ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2019-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: New drug candidates for treatment of atypical meningiomas

Zsolt Zador / Andrew T King / Nophar Geifman

PLoS ONE, Vol 13, Iss 3, p e

An integrated approach using gene expression signatures for drug repurposing.

2018 Volume 0194701

Abstract: Atypical meningiomas are common central nervous system neoplasms with high recurrence rate and poorer prognosis compared to their grade I counterparts. Surgical excision and radiotherapy remains the mainstay therapy but medical treatments are limited. We ...

Abstract	Atypical meningiomas are common central nervous system neoplasms with high recurrence rate and poorer prognosis compared to their grade I counterparts. Surgical excision and radiotherapy remains the mainstay therapy but medical treatments are limited. We explore new drug candidates using computational drug repurposing based on the gene expression signature of atypical meningioma tissue with subsequent analysis of drug-generated expression profiles. We further explore possible mechanisms of action for the identified drug candidates using ingenuity pathway analysis (IPA).We extracted gene expression profiles for atypical meningiomas (12 samples) and normal meningeal tissue (4 samples) from the Gene Expression Omnibus, which were then used to generate a gene signature comprising of 281 differentially expressed genes. Drug candidates were explored using both the Board Institute Connectivity Map (cmap) and Library of Integrated Network-Based Cellular Signatures (LINCS). Functional analysis of significant differential gene expression for drug candidates was performed with IPA.Using our integrated approach, we identified multiple, already licensed, drug candidates such as emetine, verteporfin, phenoxybenzamine and trazodone. Analysis with IPA revealed that these drugs target signal cascades potentially relevant in pathogenesis of meningiomas, particular examples are the effect on ERK by trazodone, MAP kinases by emetine, and YAP-1 protein by verteporfin.Gene expression profiling and use of drug expression profiles have yielded several plausible drug candidates for treating atypical meningioma, some of which have already been suggested by preceding studies. Although our analyses suggested multiple anti-tumour mechanisms for these drugs, further in vivo studies are required for validation.To our knowledge this is the first study which combines relatively new, yet established computational techniques to identify additional treatments for a difficult to manage cerebral neoplasm. Beyond proposing already approved drug ...
Keywords	Medicine ; R ; Science ; Q
Subject code	570
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The challenges in data integration – heterogeneity and complexity in clinical trials and patient registries of Systemic Lupus Erythematosus

Helen Le Sueur / Ian N. Bruce / Nophar Geifman / on behalf of the MASTERPLANS Consortium

BMC Medical Research Methodology, Vol 20, Iss 1, Pp 1-

2020 Volume 5

Abstract: Abstract Background Individual clinical trials and cohort studies are a useful source of data, often under-utilised once a study has ended. Pooling data from multiple sources could increase sample sizes and allow for further investigation of treatment ... ...

Abstract	Abstract Background Individual clinical trials and cohort studies are a useful source of data, often under-utilised once a study has ended. Pooling data from multiple sources could increase sample sizes and allow for further investigation of treatment effects; even if the original trial did not meet its primary goals. Through the MASTERPLANS (MAximizing Sle ThERapeutic PotentiaL by Application of Novel and Stratified approaches) national consortium, focused on Systemic Lupus Erythematosus (SLE), we have gained valuable real-world experiences in aligning, harmonising and combining data from multiple studies and trials, specifically where standards for data capture, representation and documentation, were not used or were unavailable. This was not without challenges arising both from the inherent complexity of the disease and from differences in the way data were captured and represented across different studies. Main body Data were, unavoidably, aligned by hand, matching up equivalent or similar patient variables across the different studies. Heterogeneity-related issues were tackled and data were cleaned, organised and combined, resulting in a single large dataset ready for analysis. Overcoming these hurdles, often seen in large-scale data harmonization and integration endeavours of legacy datasets, was made possible within a realistic timescale and limited resource by focusing on specific research questions driven by the aims of MASTERPLANS. Here we describe our experiences tackling the complexities in the integration of large, diverse datasets, and the lessons learned. Conclusions Harmonising data across studies can be complex, and time and resource consuming. The work carried out here highlights the importance of using standards for data capture, recording, and representation, to facilitate both the integration of large datasets and comparison between studies. Where standards are not implemented at the source harmonisation is still possible by taking a flexible approach, with systematic preparation, and a ...
Keywords	Data integration ; Data harmonisation ; Clinical trials ; Lupus ; Pooled analysis ; Medicine (General) ; R5-920
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia

Matt Spick / Ammara Muazzam / Hardev Pandha / Agnieszka Michael / Lee A. Gethings / Christopher J. Hughes / Nyasha Munjoma / Robert S. Plumb / Ian D. Wilson / Anthony D. Whetton / Paul A. Townsend / Nophar Geifman

Heliyon, Vol 9, Iss 12, Pp e22604- (2023)

2023

Abstract: There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by ...

Abstract	There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
Keywords	Prostate cancer ; Tumor progression ; Biomarkers ; LC-MS ; Proteomics ; Lipidomics ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The mouse age phenome knowledgebase and disease-specific inter-species age mapping.

Nophar Geifman / Eitan Rubin

PLoS ONE, Vol 8, Iss 12, p e

2013 Volume 81114

Abstract: Background Similarities between mice and humans lead to generation of many mouse models of human disease. However, differences between the species often result in mice being unreliable as preclinical models for human disease. One difference that might ... ...

Abstract	Background Similarities between mice and humans lead to generation of many mouse models of human disease. However, differences between the species often result in mice being unreliable as preclinical models for human disease. One difference that might play a role in lowering the predictivity of mice models to human diseases is age. Despite the important role age plays in medicine, it is too often considered only casually when considering mouse models. Methods We developed the mouse-Age Phenotype Knowledgebase, which holds knowledge about age-related phenotypic patterns in mice. The knowledgebase was extensively populated with literature-derived data using text mining techniques. We then mapped between ages in humans and mice by comparing the age distribution pattern for 887 diseases in both species. Results The knowledgebase was populated with over 9800 instances generated by a text-mining pipeline. The quality of the data was manually evaluated, and was found to be of high accuracy (estimated precision >86%). Furthermore, grouping together diseases that share similar age patterns in mice resulted in clusters that mirror actual biomedical knowledge. Using these data, we matched age distribution patterns in mice and in humans, allowing for age differences by shifting either of the patterns. High correlation (r(2)>0.5) was found for 223 diseases. The results clearly indicate a difference in the age mapping between different diseases: age 30 years in human is mapped to 120 days in mice for Leukemia, but to 295 days for Anemia. Based on these results we generated a mice-to-human age map which is publicly available. Conclusions We present here the development of the mouse-APK, its population with literature-derived data and its use to map ages in mice and human for 223 diseases. These results present a further step made to bridging the gap between humans and mice in biomedical research.
Keywords	Medicine ; R ; Science ; Q
Subject code	616
Language	English
Publishing date	2013-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: OptiMissP

Angelica Arioli / Arianna Dagliati / Bethany Geary / Niels Peek / Philip A Kalra / Anthony D Whetton / Nophar Geifman

PLoS ONE, Vol 16, Iss 4, p e

A dashboard to assess missingness in proteomic data-independent acquisition mass spectrometry.

2021 Volume 0249771

Abstract: Background Missing values are a key issue in the statistical analysis of proteomic data. Defining the strategy to address missing values is a complex task in each study, potentially affecting the quality of statistical analyses. Results We have developed ...

Abstract	Background Missing values are a key issue in the statistical analysis of proteomic data. Defining the strategy to address missing values is a complex task in each study, potentially affecting the quality of statistical analyses. Results We have developed OptiMissP, a dashboard to visually and qualitatively evaluate missingness and guide decision making in the handling of missing values in proteomics studies that use data-independent acquisition mass spectrometry. It provides a set of visual tools to retrieve information about missingness through protein densities and topology-based approaches, and facilitates exploration of different imputation methods and missingness thresholds. Conclusions OptiMissP provides support for researchers' and clinicians' qualitative assessment of missingness in proteomic datasets in order to define study-specific strategies for the handling of missing values. OptiMissP considers biases in protein distributions related to the choice of imputation method and helps analysts to balance the information loss caused by low missingness thresholds and the noise introduced by selecting high missingness thresholds. This is complemented by topological data analysis which provides additional insight to the structure of the data and their missingness. We use an example in Chronic Kidney Disease to illustrate the main functionalities of OptiMissP.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases

Georgina Torrandell‐Haro / Gregory L. Branigan / Francesca Vitali / Nophar Geifman / Julie M. Zissimopoulos / Roberta Diaz Brinton

Alzheimer’s & Dementia: Translational Research & Clinical Interventions, Vol 6, Iss 1, Pp n/a-n/a (2020)

2020

Abstract: Abstract Introduction Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age‐related neurodegenerative diseases (NDD). Methods This retrospective cohort study surveyed US‐based Humana ... ...

Abstract	Abstract Introduction Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age‐related neurodegenerative diseases (NDD). Methods This retrospective cohort study surveyed US‐based Humana claims, which includes prescription and patient records from private‐payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin. Results Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow‐up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44–0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54–0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41–0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48–0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30–0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65‐1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy. Discussion Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at‐risk NDD populations. Common statin activated pathways indicate overarching systems required for risk ...
Keywords	age ; Alzheimer's disease ; amyotrophic lateral sclerosis ; bioinformatics ; biology pathway analysis ; cholesterol ; Neurology. Diseases of the nervous system ; RC346-429 ; Geriatrics ; RC952-954.6
Subject code	610
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Data-driven identification of endophenotypes of Alzheimer’s disease progression

Nophar Geifman / Richard E. Kennedy / Lon S. Schneider / Iain Buchan / Roberta Diaz Brinton

Alzheimer’s Research & Therapy, Vol 10, Iss 1, Pp 1-

implications for clinical trials and therapeutic interventions

2018 Volume 7

Abstract: Abstract Background Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype ... ...

Abstract	Abstract Background Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer’s Disease Assessment Scale—cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups’ distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions Our results, ‘learned’ from clinical data, indicate the existence of at least three subgroups of Alzheimer’s patients, each demonstrating a different trajectory of disease progression. This ...
Keywords	Alzheimer’s disease ; Precision medicine ; Endophenotypes ; Machine learning ; Statistical learning ; Latent class mixed models ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Neurology. Diseases of the nervous system ; RC346-429
Subject code	610
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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