Operational complexity versus design efficiency: challenges of implementing a phase IIa multiple parallel cohort targeted treatment platform trial in advanced breast cancer

BACKGROUND

Platform trial designs are used increasingly in cancer clinical research and are considered an efficient model for evaluating multiple compounds within a single disease or disease subtype. However, these trial designs can be challenging to operationalise. The use of platform trials in oncology clinical research has increased considerably in recent years as advances in molecular biology enable molecularly defined stratification of patient populations and targeted therapy evaluation. Whereas multiple separate trials may be deemed infeasible, platform designs allow efficient, parallel evaluation of multiple targeted therapies in relatively small biologically defined patient sub-populations with the promise of increased molecular screening efficiency and reduced time for drug evaluation. Whilst the theoretical efficiencies are widely reported, the operational challenges associated with these designs (complexity, cost, regulatory, resource) are not always well understood. MAIN: In this commentary, we describe our practical experience of the implementation and delivery of the UK plasmaMATCH trial, a platform trial in advanced breast cancer, comprising an integrated screening component and multiple parallel downstream mutation-directed therapeutic cohorts. plasmaMATCH reported its primary results within 3 years of opening to recruitment. We reflect on the operational challenges encountered and share lessons learnt to inform the successful conduct of future trials. Key to the success of the plasmaMATCH trial was well co-ordinated stakeholder engagement by an experienced clinical trials unit with expert methodology and trial management expertise, a federated model of clinical leadership, a well-written protocol integrating screening and treatment components and including justification for the chosen structure and intentions for future adaptions, and an integrated funding model with streamlined contractual arrangements across multiple partners. Findings based on our practical experience include the importance of early engagement with the regulators and consideration of a flexible resource infrastructure to allow adequate resource allocation to support concurrent trial activities as adaptions are implemented in parallel to the continued management of patient safety and data quality of the ongoing trial cohorts.

CONCLUSION

Platform trial designs allow the efficient reporting of multiple treatment cohorts. Operational challenges can be overcome through multidisciplinary engagement, streamlined contracting processes, rationalised protocol and database design and appropriate resourcing.

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

Crossover based technique for data augmentation

BACKGROUND AND OBJECTIVE

Medical image classification problems are frequently constrained by the availability of datasets. "Data augmentation" has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results.

METHODS

This paper introduces "Crossover technique", a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures.

RESULTS

The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture.

CONCLUSION

The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation.

Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review

Suicide and self-harm clusters exist in various forms, including point, mass, and echo clusters. The early identification of clusters is important to mitigate contagion and allocate timely interventions. A systematic review was conducted to synthesize existing evidence of quantitative analyses of suicide and self-harm clusters. Electronic databases including Medline, Embase, Web of Science, and Scopus were searched from date of inception to December 2020 for studies that statistically analyzed the presence of suicide or self-harm clusters. Extracted data were narratively synthesized due to heterogeneity among the statistical methods applied. Of 7268 identified studies, 79 were eligible for narrative synthesis. Most studies quantitatively verified the presence of suicide and self-harm clusters based on the scale of the data and type of cluster. A Poisson-based scan statistical model was found to be effective in accurately detecting point and echo clusters. Mass clusters are typically detected by a time-series regression model, although limitations exist. Recently, the statistical analysis of suicide and self-harm clusters has progressed due to advances in quantitative methods and geospatial analytical techniques, most notably spatial scanning software. The application of such techniques to real-time surveillance data could effectively detect emerging clusters and provide timely intervention.

Current Status and Trends in Research on Caries Diagnosis: A Bibliometric Analysis

There are a wide variety of devices for the detection and diagnosis of caries from the initial stages. The purpose of this study was to perform a bibliometric study on research regarding caries diagnosis by identifying the contributing researchers, organizations, countries or regions, journals, and to provide an analysis of keyword co-occurrence and co-authorship networks. An advanced search was performed in Web of Science (WOS) Core Collection database, using the terms “caries diagno*” and “caries detect*” in the “topic” field, from 2013 to 2021. Bibliometric parameters were extracted using WOS’s analyze results tools and VOSviewer software. A total of 816 documents were identified. Most of them, (61.3%) are included in “Dentistry Oral Surgery & Medicine” category within WOS. The largest scientific production on the subject is observed between 2018 and 2021, with a total of 344 records. The most productive author is Mendes FM, followed by Braga MM. The journal with the most articles published on caries diagnosis is Caries Research, with 55 articles (6.74%). The terms with the highest co-occurrence refer to the validity of diagnostic methods, tools or principles used in diagnosis or general aspects related to caries detection and diagnosis.

Risk Perception towards COVID-19: A Systematic Review and Qualitative Synthesis

Several studies have attempted to identify how people's risk perceptions differ in regard to containing COVID-19 infections. The aim of the present review was to illustrate how risk awareness towards COVID-19 predicts people's preventive behaviors and to understand which features are associated with it. For the review, 77 articles found in six different databases (ProQuest, PsycInfo, PubMed, Science Direct, SCOPUS, and Web of Science) were considered, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was followed, and data synthesis was conducted using a mixed-methods approach. The results indicate that a high-risk perception towards COVID-19 predicts, in general, compliance with preventive behaviors and social distancing measures. Additionally, risk awareness was found to be associated with four other key themes: demographic factors, individual factors, geographical factors, and timing. Therefore, gaining a greater understanding of individual and cultural differences as well as how people behave could be the basis of an effective strategy for raising public risk awareness and for countering COVID-19.

Linking norms, ratings, and relations of words and concepts across multiple language varieties

Psychologists and linguists collect various data on word and concept properties. In psychology, scholars have accumulated norms and ratings for a large number of words in languages with many speakers. In linguistics, scholars have accumulated cross-linguistic information about the relations between words and concepts. Until now, however, there have been no efforts to combine information from the two fields, which would allow comparison of psychological and linguistic properties across different languages. The Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts (NoRaRe) is the first attempt to close this gap. Building on a reference catalog that offers standardization of concepts used in historical and typological language comparison, it integrates data from psychology and linguistics, collected from 98 data sets, covering 65 unique properties for 40 languages. The database is curated with the help of manual, automated, semi-automated workflows and uses a software API to control and access the data. The database is accessible via a web application, the software API, or using scripting languages. In this study, we present how the database is structured, how it can be extended, and how we control the quality of the data curation process. To illustrate its application, we present three case studies that test the validity of our approach, the accuracy of our workflows, and the integrative potential of the database. Due to regular version updates, the NoRaRe database has the potential to advance research in psychology and linguistics by offering researchers an integrated perspective on both fields.

Uptake of the multi-arm multi-stage (MAMS) adaptive platform approach: a trial-registry review of late-phase randomised clinical trials

BACKGROUND

For medical conditions with numerous interventions worthy of investigation, there are many advantages of a multi-arm multi-stage (MAMS) platform trial approach. However, there is currently limited knowledge on uptake of the MAMS design, especially in the late-phase setting. We sought to examine uptake and characteristics of late-phase MAMS platform trials, to enable better planning for teams considering future use of this approach.

DESIGN

We examined uptake of registered, late-phase MAMS platforms in the EU clinical trials register, Australian New Zealand Clinical Trials Registry, International Standard Randomised Controlled Trial Number registry, Pan African Clinical Trials Registry, WHO International Clinical Trial Registry Platform and databases: PubMed, Medline, Cochrane Library, Global Health Library and EMBASE. Searching was performed and review data frozen on 1 April 2021. MAMS platforms were defined as requiring two or more comparison arms, with two or more trial stages, with an interim analysis allowing for stopping of recruitment to arms and typically the ability to add new intervention arms.

RESULTS

62 late-phase clinical trials using an MAMS approach were included. Overall, the number of late-phase trials using the MAMS design has been increasing since 2001 and been accelerated by COVID-19. The majority of current MAMS platforms were either targeting infectious diseases (52%) or cancers (29%) and all identified trials were for treatment interventions. 89% (55/62) of MAMS platforms were evaluating medications, with 45% (28/62) of the MAMS platforms having at least one or more repurposed medication as a comparison arm.

CONCLUSIONS

Historically, late-phase trials have adhered to long-established standard (two-arm) designs. However, the number of late-phase MAMS platform trials is increasing, across a range of different disease areas. This study highlights the potential scope of MAMS platform trials and may assist research teams considering use of this approach in the late-phase randomised clinical trial setting.

PROSPERO REGISTRATION NUMBER

CRD42019153910.

Sim2Ls: FAIR simulation workflows and data

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.

Telerehabilitation for lower extremity recovery poststroke: a systematic review and meta-analysis protocol

INTRODUCTION

Approximately 30% of individuals with stroke report unmet lower extremity recovery needs after formal hospital-based rehabilitation programmes have ended. Telerehabilitation can mitigate issues surrounding accessibility of rehabilitation services by providing ongoing support to promote recovery, however, no review exists that is specific to telerehabilitation for lower extremity recovery. This paper describes the protocol of a systematic review and meta-analysis that aims to describe and evaluate the effectiveness of lower extremity-focused telerehabilitation interventions on clinical outcomes poststroke.

METHODS AND ANALYSIS

A systematic review of relevant electronic databases (MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, Google Scholar, PEDro, PubMed and Cochrane Library) between inception and February 2022 will be undertaken to identify eligible interventional studies published in English that compared telerehabilitation focusing on lower extremity recovery to another intervention or usual care for individuals living in the community with stroke. Clinical outcomes examined will include those related to physical function and impairment, activities and participation that are typically assessed in clinical practice and research. Two reviewers will independently screen results, identify studies to be included for review, extract data and assess risk of bias. Meta-analyses will be performed if sufficient data exist. Sensitivity analyses will be performed by removing studies with low methodological quality, and subgroup analyses will be performed if data allow by stratifying papers based on salient demographic or stroke factors and comparing results. The reporting of the review will follow the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The quality of evidence regarding various outcomes for telerehabilitation for lower extremity recovery poststroke will be assessed according to the Grading of Recommendation, Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION

No ethical approval or informed consent is needed for this systematic review. The findings of this review will be disseminated via peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42021246886.

[The repeated disability because of thyroid cancer in adult population]

The article presents the results of analysis of repeated disability because of thyroid cancer in adult population of Moscow in 2015-2019. The study established negative dynamics of increasing of the number of individuals with repeated disability because of thyroid cancer in all essential age contingents (young, middle and elderly age) of adult population. The similar trend was revealed in gender contingents of individuals with repeated disability because of thyroid cancer (in males, increase was made up to 112.5% and in females up to 196.3%). In the gender and age structure of repeated disability because of thyroid cancer, predominated females and the elderly. The study established dynamics of decreasing of percentage of individuals with repeated disability because of thyroid cancer of the disability groups I and III and corresponding increasing in disability group II. The percentage of individuals with repeated disability because of thyroid cancer in disability groups I and II was higher in males as compared with females. Most individuals with disabilities because of thyroid cancer don't work. The study results make available spectrum of disorders of main functions of organism, their degree and types and intensity of limitations in vital activity of individuals with repeated disability because of thyroid cancer. The organized database of characteristics of repeated disability because of thyroid cancer in adult population can be used in implementation of personalized approach to medical social rehabilitation of increasing number of disabled individuals with malignant damage of the most important organ of endocrine system.

Characteristics and quality of clinical practice guidelines addressing acupuncture interventions: a systematic survey of 133 guidelines and 433 acupuncture recommendations

OBJECTIVE

To systematically summarise acupuncture-related Clinical Practice Guidelines (CPGs)'s clinical and methodological characteristics and critically appraise their methodology quality.

DESIGN

We summarised the characteristics of the guidelines and recommendations and evaluated their methodological quality using the Appraisal of Guidelines Research and Evaluation II (AGREE II) instrument.

DATA SOURCES

Nine databases were searched from 1 January 2010 to 20 September 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

We included the latest version of acupuncture CPGs, which must have used at least one systematic review addressing the benefits and harms of alternative care options to inform acupuncture recommendations.

DATA EXTRACTION AND SYNTHESIS

Reviewers, working in pairs, independently screened and extracted data. When there are statistical differences among types of CPGs, we reported the data by type in the text, but when not, we reported the overall data.

RESULTS

Of the 133 eligible guidelines, musculoskeletal and connective tissue diseases proved the most commonly addressed therapeutic areas. According to the AGREE II instrument, the CPG was moderate quality in the domain of clarity of scope and purpose, clarity of presentation, the rigour of development, stakeholder involvement and low quality in editorial independence, and applicability. The study identified 433 acupuncture-related recommendations; 380 recommended the use of acupuncture, 28 recommended against the use of acupuncture and 25 considered acupuncture but did not make recommendations. Of the 303 recommendations that used Grading of Recommendations Assessment, Development and Evaluation to determine the strength of recommendations, 152 were weak recommendations, 131 were strong recommendations, of which 104 were supported by low or very low certainty evidence (discordant recommendations).

CONCLUSION

In the past 10 years, a large number of CPGs addressing acupuncture interventions exist. Although these guidelines may be as or more rigorous than many others, considerable room for improvement remains.

Redundancy cancellation of compressed measurements by QRS complex alignment

The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram(ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS.

Traditional East Asian Herbal Medicine for Post-Stroke Insomnia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Post-stroke insomnia (PSI) is a highly prevalent complication after stroke. Current evidence of psychotropic drug use for PSI management is scarce and indicates harmful adverse events (AEs). Traditional East Asian herbal medicine is a widely used traditional remedy for insomnia. However, so far, no study has systematically reviewed the efficacy and safety of traditional east asian herbal medicine (HM) for PSI. Therefore, we perform meta-analysis to evaluate the effectiveness and safety of HM for PSI. After a comprehensive electronic search of 15 databases, we review the randomized controlled trials (RCTs) of HM use as monotherapy for PSI. Our outcomes were the Pittsburgh sleep quality index and total effective rate. In total, 24 RCTs were conducted with 1942 participants. HM showed statistically significant benefits in sleep quality. It also appeared to be safer than psychotropic drugs in terms of AEs, except when the treatment period was two weeks. The methods used for RCTs were poor, and the quality of evidence assessed was graded “low” or “moderate.” The findings of this review indicate that the use of HM as a monotherapy may have potential benefits in PSI treatment when administered as an alternative to conventional medications. However, considering the methodological quality of the included RCTs, we were uncertain of the clinical evidence. Further, well-designed RCTs are required to confirm these findings.

Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards

Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many people who supervise CCTV in real time. In this paper, we present an alternative approach to automatic image analysis using a variety of artificial intelligence techniques. Based on collaborative learning, these are punished if they make an incorrect classification. The main advantage is the possibility of continually increasing the amount of knowledge during system operation. However, overtraining is possible, so each time, the best classifier is chosen. Another advantage for practical use is the small database, which allows for the quick and practical implementation of such a system. To verify its effectiveness, this ship classification system was tested on data obtained in a Polish city, Szczecin, as part of a bigger project for classifying inland ships and publicly available databases (for more general ship problems).

The role of electronic health record developers in hospital patient sharing

OBJECTIVE

To determine whether hospital adoption of a new electronic health record (EHR) developer increases patient sharing with hospitals using the same developer.

MATERIALS AND METHODS

We extracted data on patients shared with other hospitals for 3076 US nonfederal acute care hospitals from the 2011 to 2016 Centers for Medicare & Medicaid Services Physician Shared Patient Patterns database. We calculated the ratio of patients shared with hospitals outside of the focal hospital's network that use the same EHR developer as the focal hospital, and estimated difference-in-differences models to compare same-developer patient sharing among hospitals that switched to a new developer with those that did not switch developer.

RESULTS

Switching to a new EHR developer increased the ratio of patients shared with other hospitals having the same EHR developer by 4.1-19.3%, depending on model specification. The magnitude of this effect varied by EHR developer and was increasing in developer market share.

DISCUSSION

Consolidation in the EHR industry has led to higher patient sharing among hospitals with the same EHR developer. Contributing factors could include the growth of developer-based health information exchanges, customizable referral management systems, and provider preferences for easy and reliable data exchange. However, hospital transfers that are significantly influenced by EHR developer could lead to poor patient-provider matches.

CONCLUSION

Hospitals' choice of EHR developer impacts the flow of patients across hospitals, which could have both desirable and undesirable effects on patient care. Future research should investigate whether health outcomes decline with greater same-developer patient sharing.

Patient-driven innovations reported in peer-reviewed journals: a scoping review

BACKGROUND

Awareness of patients' innovative capabilities is increasing, but there is limited knowledge regarding the extent and nature of patient-driven innovations in the peer-reviewed literature.

OBJECTIVES

The objective of the review was to answer the question: what is the nature and extent of patient-driven innovations published in peer-reviewed scientific journals?

ELIGIBILITY CRITERIA

We used a broad definition of innovation to allow for a comprehensive review of different types of innovations and a narrow definition of 'patient driven' to focus on the role of patients and/or family caregivers. The search was limited to years 2008-2020.

SOURCES OF EVIDENCE

Four electronic databases (Medline (Ovid), Web of Science Core Collection, PsycINFO (Ovid) and Cinahl (Ebsco)) were searched in December 2020 for publications describing patient-driven innovations and complemented with snowball strategies.

CHARTING METHODS

Data from the included articles were extracted and categorised inductively.

RESULTS

A total of 96 articles on 20 patient-driven innovations were included. The number of publications increased over time, with 69% of the articles published between 2016 and 2020. Author affiliations were exclusively in high income countries with 56% of first authors in North America and 36% in European countries. Among the 20 innovations reported, 'Do-It-Yourself Artificial Pancreas System' and the online health network 'PatientsLikeMe', were the subject of half of the articles.

CONCLUSIONS

Peer-reviewed publications on patient-driven innovations are increasing and we see an important opportunity for researchers and clinicians to support patient innovators' research while being mindful of taking over the work of the innovators themselves.

The top 50 most cited articles in carpal tunnel syndrome research: A bibliometrics study

BACKGROUND

Citation analysis was applied to identify the influential studies in the specific field. More and more literature related to carpal tunnel syndrome (CTS) have been published in recent years. To our knowledge, no one has performed a citation analysis of CTS. Thus, our study identified the top 50 influential articles pertaining to CTS and conduct an analysis of their characteristics.

METHODS

The Web of Science database was used to identify all the articles from 1900 to 2020. We obtained the top 50 articles ranked by citation times, and articles were included and excluded based on the relevance to CTS. Also, we collected the information about journal name, level of evidence, source country and institution, and research type for further analysis.

RESULTS

The top 50 articles were published between 1959 and 2012. The number of citations ranged from 151 to 1083. The citation density was between 3.23 and 40.27 per year. Muscle Nerve published most articles in CTS research, followed by Journal of Bone and Joint Surgery American Volume. The USA was the leading country, and all the top 5 institutions were from the USA. Katz JN with the highest h-index published most articles. Level III was the most common evidence level.

CONCLUSIONS

We identified the top 50 cited articles related to CTS. These influential articles might provide researchers with a comprehensive list of the major contribution related to CTS research.

Data Mining, Quality and Management in the Life Sciences

With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends with a scientific publication. The benefits of data sharing and reproducibility of results have taken the centerpiece within the life science research supported by FAIR principles that firmly underline the importance of open data. The current data-intensive multidisciplinary research has also highlighted the significance of how data is mined and managed. Here we describe some of the features adopted by EMBL-EBI data resources to support data mining, data quality, and data management. We also highlight how EMBL-EBI has responded to the current pandemic through its data resources.

An Academic-Health Department Community Partnership to Expand Disease Investigation and Contact Tracing Capacity and Efficiency During the COVID-19 Pandemic

Disease investigation and contact tracing are long-standing public health strategies used to control the spread of infectious disease. Throughout the COVID-19 pandemic, health departments across the country have lacked the internal workforce capacity and technology needed to efficiently isolate positive cases and quarantine close contacts to slow the spread of SARS-CoV-2. This article describes an innovative disease investigation and contact tracing program developed through a formalized community partnership between a local county health department and local university. This innovative new program added 108 contact tracers to the county's public health workforce, as well as enabled these contact tracers to work remotely using a call center app and secure cloud-based platform to manage the county's caseload of cases and contacts. An overview of the requirements needed to develop this program (eg, hiring, health data security protocols, data source management), as well as lessons learned is discussed.

Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research

Background: Cgmanalysis is an open-source software based on the R programming language for data management and descriptive analysis of data from continuous glucose monitoring (CGM). We sought to validate the summary measures calculated by cgmanalysis against the results from proprietary software associated with four CGM commercially available models. Methods: Two weeks of data from 188 patients with type 1 diabetes using commercially available CGMs. Freestyle Libre Gen 1 (n = 53), Medtronic Guardian 3 (n = 52), Dexcom G6 reported by Dexcom Clarity (n = 48), and Dexcom G6 reported by Tandem (n = 35) were analyzed using proprietary software and cgmanalysis. Agreement was assessed using scatterplots, Bland-Altman plots, and equivalence tests. Results: Good agreement was obtained for all glycemic summary measures for all CGMs assessed. None of the differences between the cgmanalysis package and the manufacturers' software were outside the prespecified bounds of equivalence. Conclusions: Cgmanalysis is a validated open-source software to analyze commercially available CGM data and can be used to standardize diabetes technology research.

Epitope-Specific T Cell Receptor Data and Tools in the Immune Epitope Database

T cells play a critical role in the response of the immune system to non-self antigens. The Immune Epitope Database (IEDB) continuously curates published T cell data, among other immune-related data, and allows users to create custom queries based on their research interests and aims. The IEDB's companion site, the IEDB Analysis Resource, features a number of tools for analysis and prediction of immune epitopes; these include TCRMatch, a new tool used to predict candidate epitopes for T cell receptors with unknown specificity. Together these resources enable users to browse and filter large amounts of published data as well as generate hypotheses to guide future experiments.

THE INFLUENCE OF THE REGULATORY SYSTEM ON THE STUDY DESIGN AND DATA MANAGEMENT PRACTICES IN CLINICAL TRIALS

OBJECTIVE

The aim: To review real-life regulatory-dependent study design and data management practices of post marketing multicenter studies of medical devices conducted in 2021 in Ukraine and Poland.

PATIENTS AND METHODS

Materials and methods: This article presents the case study of 4 post marketing multicenter studies of medical devices conducted in 2021 in Ukraine and European Union.

RESULTS

Results: The case study presented effective cross-border cooperation between Ukrainian and European actors. Despite the gaps in Ukrainian legislative framework on medical devices, complex solutions on employment of the most stringent regulatory provisions led to appropriate study design. Usage of the highly compliant electronic data capture led to fast-track study start-up and solid clinical data collection.

CONCLUSION

Conclusions: Publications on real-life regulatory-dependent clinical trials conduct might be essential to innovate the regulatory system in Ukraine. The cross-border cooperation might assist the advancement of clinical trials industry in Ukraine. Gaps in medical devices regulations in Ukraine impede the context-specific clinical trials solutions for biotech industry in Ukraine. The regulatory framework and practice in Ukraine may be perceived as externally driven due to gaps in medical devices regulations, lack of capacities of domestic notified bodies and business interests of Sponsors.