Clinical Research Environment Research Articles

Photobiomodulation effects on neuronal transdifferentiation of immortalized adipose-derived mesenchymal stem cells

Adipose-derived mesenchymal stem cells (ADMSCs) possess the ability to transform into various cell types, including neurons. It has been proposed that the optimization of this transformation can be achieved by using photobiomodulation (PBM). The objective of this laboratory-based investigation was to induce the transformation of immortalized ADMSCs (iADMSCs) into neurons with chemical triggers and then evaluate the supportive effects of PBM at two different wavelengths, 525 nm and 825 nm, each administered at a dose of 5 J/cm2, as well as the combined application of these wavelengths. The results revealed that the treated cells retained their stem cell characteristics, although the cells exposed to the green laser exhibited a reduction in the CD44 marker. Furthermore, early, and late neuronal markers were identified using flow cytometry analysis. The biochemical analysis included the assessment of cell morphology, viability, cell proliferation, potential cytotoxicity, and the generation of reactive oxygen species (ROS). The findings of this study indicate that PBM does not harm the differentiation process and may even enhance it, but it necessitates a longer incubation period in the induction medium. These research findings contribute to the validation of stem cell technology for potential applications in in vivo, pre-clinical, and clinical research environments.

Opportunities and challenges to the development and deployment of vaccines for pregnant women in Germany

Maternal immunisation is a powerful tool to protect both pregnant women and their children. A new maternal RSV vaccine holds promise to protect newborns from RSV-associated illness in the first few months of life, but no official recommendation has been made in Germany. Since RSV causes a significant burden of paediatric hospital admissions, we consider it a pertinent opportunity to review barriers to maternal vaccination in Germany, which might also apply to other settings. Access to vaccination for pregnant women in Germany is shaped by an interplay of legal, regulatory, institutional, and sociocultural factors, with a less permissive clinical research environment, delays in recommendation and roll-out, and lower acceptance by healthcare professionals and the population. Actionable recommendations to improve availability and uptake include coordination with other national regulatory bodies to reduce delays, awareness and literacy campaigns for health professionals and the general public, and capacity building for vaccine clinical research.

Sources of variability in Luminex bead-based cytokine assays: Evidence from twelve years of multi-site proficiency testing

Bead array assays, such as those sold by Luminex, BD Biosciences, Sartorius, Abcam and other companies, are a well-established platform for multiplexed quantification of cytokines and other biomarkers in both clinical and discovery research environments. In 2011, the National Institute of Allergy and Infectious Diseases (NIAID)-funded External Quality Assurance Program Oversight Laboratory (EQAPOL) established a proficiency assessment program to monitor participating laboratories performing multiplex cytokine measurements using Luminex bead array technology. During every assessment cycle, each site was sent an assay kit, a protocol, and blinded samples of human sera spiked with recombinant cytokines. Site results were then evaluated for performance relative to peer laboratories. After over a decade of biannual assessments, the cumulative dataset contained over 15,500 bead array observations collected at more than forty laboratories in twelve countries. These data were evaluated alongside post-assessment survey results to empirically test factors that may contribute to variability and accuracy in Luminex bead-based cytokine assays. Bead material, individual technical ability, analyte, analyte concentration, and assay kit vendor were identified as significant contributors to assay performance. In contrast, the bead reader instrument model and the use of automated plate washers were found not to contribute to variability or accuracy, and sample results were found to be highly-consistent between assay kit-manufacturing lots and over time. In addition to these statistical analyses, subjective evaluations identified technical ability, instrument failure, protocol adherence, and data transcription errors as the most common causes of poor performance in the proficiency program. The findings from the EQAPOL multiplex program were then used to develop recommended best practices for bead array monitoring of human cytokines. These included collecting samples to assay as a single batch, centralizing analysis, participating in a quality assurance program, and testing samples using paramagnetic-bead kits from a single manufacturer using a standardized protocol.

How can we help doctors stay in rural areas of Japan? A comparison in medical students' attitudes toward general practice and clinical research of a medical school in Japan and in Sweden.

In Sweden, primary healthcare centers play an important role in the performance of general practice, education, and clinical research. In Japan, general physicians or general practitioners are expected to be more active in the small-scale hospitals and clinics in rural areas. This study aimed to explore the differences in attitudes toward general practice and clinical research among medical students in Japan and Sweden to present solutions to help doctors stay in rural areas of Japan. This cross-sectional study was conducted at two medical schools in Japan and Sweden in 2018, using an anonymous and self-administered questionnaire survey that comprised 16 items including 9 items on clinical research. Participants were 154 medical students (response rate: 69.4% for 222 students) in Japan and 56 (27.1% for 201 students) in Sweden. The proportion of medical students who wanted to become general physicians was greater in Japan than in Sweden (Japan:Sweden=36.4%:17.9%; P=0.012). Although fewer Japanese students wanted to conduct research in rural areas than Swedish students (43.5%:57.1%; P<0.001), the positive proportion of Japanese students working in clinical research and/or taking an academic degree in rural areas was greater than that of Swedish students (52.0%:23.2%; P=0.032). As Swedish medical students and young doctors learn considerably from primary healthcare centers, their attitudes toward clinical research are more developed than those of their Japanese counterparts. However, more Japanese medical students than Swedish students wish to become general practitioners, and they are likely to strive to conduct clinical research at small-scale hospitals/clinics in rural areas. Therefore, the improvement of the clinical research environment in small-scale hospitals and clinics in rural areas is needed at the earliest in Japan.

Clinical research stakeholders’ experiences of clinical research during COVID-19: a qualitative study

BackgroundThe COVID-19 pandemic created a complex high-risk clinical research environment with clinical research activities significantly impacted. Clinical research stakeholders adapted rapidly to new clinical practices; PPE, infection control policies, all while engaging with a more unwell patient demographic. The aim of this study is to explore the experiences of conducting clinical research during COVID-19 with clinical research stakeholders.MethodsThis qualitative study of semi-structured interviews conducted with clinical research stakeholders in an acute Hospital setting across a variety of disciplines; Consultant Geriatrician, Clinical Research Nurse, Occupational Therapy, Physiotherapy. Interviews were fully transcribed prior to reflexive thematic analysis. NVivo software was used to support data management and analysis.ResultsThree main themes were produced; (1) The challenging COVID-19 clinical research landscape, (2) COVID-19 clinical research communication barriers, and (3) Adaptations and learnings from clinical research during COVID-19.ConclusionsThis study explored the experiences of conducting clinical research during COVID-19 with clinical research stakeholders examining challenges faced and adaptations required. The findings inform, equip and support clinical research stakeholders in the event of future adverse public health events.

Ultrasound Elastography in Children.

Ultrasound elastography is a novel ultrasound technique, being extensively researched in children in the last decade. It measures tissue elasticity with the observation of tissue response after an external stimulus. From research to clinical practice, ultrasound elastography has evolved significantly in liver fibrosis evaluation in children; however, several other applications of the technique are available in both clinical practice and research environments. Practically, in children any organ can be assessed, including the brain in early ages, along with muscle and connective tissue elasticity evaluation, spleen, kidney, skin, lymphatic tissue, etc. The elastography method, age, body mass index and technical points should be considered when performing ultrasound elastography. This review highlights its vast potential as a diagnostic tool in the pediatric population, where ultrasound is a dominant imaging tool; however, the indications and exam protocol for its clinical use in several fields in pediatrics have yet to be elucidated.

FHIR-PYrate: a data science friendly Python package to query FHIR servers

BackgroundWe present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient’s history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks.MethodsThe package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant.ResultsAs an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases.ConclusionsFHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

Complete sequence verification of plasmid DNA using the Oxford Nanopore Technologies’ MinION device

BackgroundSequence verification is essential for plasmids used as critical reagents or therapeutic products. Typically, high-quality plasmid sequence is achieved through capillary-based Sanger sequencing, requiring customized sets of primers for each plasmid. This process can become expensive, particularly for applications where the validated sequence needs to be produced within a regulated and quality-controlled environment for downstream clinical research applications.ResultsHere, we describe a cost-effective and accurate plasmid sequencing and consensus generation procedure using the Oxford Nanopore Technologies’ MinION device as an alternative to capillary-based plasmid sequencing options. This procedure can verify the identity of a pure population of plasmid, either confirming it matches the known and expected sequence, or identifying mutations present in the plasmid if any exist. We use a full MinION flow cell per plasmid, maximizing available data and allowing for stringent quality filters. Pseudopairing reads for consensus base calling reduces read error rates from 5.3 to 0.53%, and our pileup consensus approach provides per-base counts and confidence scores, allowing for interpretation of the certainty of the resulting consensus sequences. For pure plasmid samples, we demonstrate 100% accuracy in the resulting consensus sequence, and the sensitivity to detect small mutations such as insertions, deletions, and single nucleotide variants. In test cases where the sequenced pool of plasmids contains subclonal templates, detection sensitivity is similar to that of traditional capillary sequencing.ConclusionsOur pipeline can provide significant cost savings compared to outsourcing clinical-grade sequencing of plasmids, making generation of high-quality plasmid sequence for clinical sequence verification more accessible. While other long-read-based methods offer higher-throughput and less cost, our pipeline produces complete and accurate sequence verification for cases where absolute sequence accuracy is required.

Abstract B024: Pivot to SPARCC diversity in the clinical cancer research workforce; implementation and evaluation of an academic enrichment pathway before, during, and after the COVID-19 global pandemic

Abstract Academic pipeline programs are designed to mitigate systemic barriers that have historically excluded individuals due to deep-rooted, structural inequities. The Student-centered Pipeline to Advance Research in Cancer Careers (SPARCC) was developed in 2018 to diversify the clinical research health professions workforce and has graduated three cohorts of scholars from 2019-2021. Due to COVID-19 and safety concerns, SPARCC evolved, pivoting to establish effective learning environments virtually and in-person to ensure the original curriculum of academic, research, and professional identity development were upheld. The program aims to a.) immerse students in the clinical cancer research environment, supporting immediate employment as a clinical research professional (CRP) and b.) to provide structured support and guidance for individuals intending to pursue advanced professional degrees. SPARCC programmatic evaluations included a multifactorial strategy utilizing the same three evaluative instruments each year. These included a 17-item knowledge, attitudes, and practices (KAP) survey specific to clinical cancer research, daily workshop evaluations, and clinical practicum rotation evaluations. The SPARCC KAP was distributed immediately prior to the start of the program, upon graduation from the program, and six months later. After exploratory factor analysis, items were grouped into three factors: knowledge of clinical research practice, knowledge of research practice, and knowledge of culturally responsive cancer care. Mixed ANOVAS were used to assess changes in factors based on time (pre-, post-, and 6-month follow up; within) and cohort (year of participation; between). Over three years 156 students applied, 65% of whom identified belonging to an underrepresented ace or ethnicity. Thirty-six scholars matriculated through the program: 14(39%) White; 13(36%) Black; 3(8%) Asian; 3(8%) Native American; 1 3%) Native American/Black; 1(3%) Black/Asian; 1(3%) Pacific Islander. Forty-four percent (16 scholars) identified as Hispanic/Latino/a. Six scholars (17%) entered the clinical research workforce within six months of graduating from the SPARCC program, all who identified as belonging to underrepresented groups. The KAP evaluations revealed that scholars’ knowledge increased significantly after participation in the program in each of the three factors: knowledge of clinical research practice (p &amp;lt; .007); knowledge of research practice (p &amp;lt; .007); and knowledge of culturally responsive cancer care (p &amp;lt; .007). Within six months of graduation from SPARCC, 17% of scholars entered the clinical research workforce, and nearly 60% were enrolled in graduate-research degree programs or medical school. SPARCC demonstrated curricular flexibility and ingenuity when pivoting from entirely in-person to entirely virtual, and finally to a hybrid program over the first three years of the program. Through didactic, clinical, research, and career professional identity exploration, robust learning experiences engaged scholars despite limitations on in-person contact. Citation Format: Kristina Kaljo, Lindsey McAlarnen, Michael Braun, Emmanuel Ngui, Janet S. Rader. Pivot to SPARCC diversity in the clinical cancer research workforce; implementation and evaluation of an academic enrichment pathway before, during, and after the COVID-19 global pandemic [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr B024.

Biobanking in LMIC settings for infectious diseases: Challenges and enablers

Biobanking facilities are well established in high-income settings, where substantial funding has been invested in infrastructure. In contrast, such facilities are much less developed in resource-restricted settings. However, low-and middle-income countries (LMICs) still face a disproportionately high infectious diseases burden. Thus, the further development of infrastructure facilities, including biobanks is warranted as an important component of this unfolding clinical research environment. This perspective manuscript summarises the challenges and enablers for biobanking in LMICs, with a particular focus on infectious diseases, incorporating some of the lessons learned from the recent coronavirus disease 2019 (COVID-19) pandemic.

Rapidly adapting the clinical research environment at an NCI-designated comprehensive cancer center to the COVID-19 pandemic.

e13534 Background: In March 2020, responding to COVID-19, the Columbia University Institutional Review Board (IRB) mandated all research pause in-person activities except studies “offering the potential for direct benefit” to patients. New enrollment was also paused. Columbia University Irving Medical Center concurrently mandated all non-essential employees work remotely, and all feasible patient visits transition to telehealth. The clinical research infrastructure at the Herbert Irving Comprehensive Cancer Center (HICCC) was required to adapt its operations in response. Methods: The HICCC convened an executive committee (EC), consisting of multi-disciplinary leadership, canvassed peer institutions, and engaged the IRB Executive Director (ED), Chief of Infectious Disease, a bioethicist, and others ad hoc. Results: The IRB ED agreed with the EC that all approved interventional therapeutic clinical trials for cancer (n = 554) offered the potential for direct benefit to patients. The EC established an “Emergency Preparedness” standard operating procedure (SOP) with input from the IRB and HICCC Protocol Review &amp; Monitoring and Data &amp; Safety Monitoring Committees. This SOP required assessment of each protocol for risks/benefit, transition to remote processes when possible, and referenced the Code of Federal Regulations allowing changes to a research plan without prospective IRB-approval if “necessary to eliminate apparent immediate hazard”. For new subjects, the EC created a process for enrollment as exceptions to the research pause requiring written justification by the PI that included risk-mitigation strategies and approval by the EC. 116 patients were accrued under this process. All external monitoring and auditing was transitioned to remote. Finally, 25% of research nurses and 10% of coordinators were redeployed to other short-staffed areas of the medical center. After 3 months, the IRB lifted the research pause in tranches, and the EC charged its Disease Based Teams with prioritizing trials to reopen. Accrual nadired at 30% of pre-pandemic levels in June 2020, which fully recovered by March 2021. Conclusions: A centralized research infrastructure working closely with established institutional committees was critical for the unprecedented yet effective and rapid ramp down/up of clinical trials. After 1-year, accrual recovered to pre-pandemic levels, leveraging new processes such as electronic consent, telehealth, and remote work for staff.

SOIN - MI Data Lab: Personalized Ophthalmology Through Collaborative Data Collection and Dynamic Patient Consent.

The Swiss Ophthalmic Image Network (SOIN) is part of the Swiss Personalized Health Network (SPHN). SOIN contains a collaborative, clinical research environment, MI Data Lab, which allows privacy-preserving, data-driven, research. Personalized care of chronic ocular disease, based on Machine Learning (ML) and medical imaging, can dramatically improve quality of life and reduce the burden on health and social care systems. MI Data Lab allows research partners to consolidate their data in a space where doctors and data scientists cooperate to design novel ML algorithms, on curated datasets. To date, we have created several algorithms to detect ocular biomarkers automatically, and applied such tools to 100k+ retinal images. MI Data Lab enables the development of predictive models, the extraction novel traits to be explored in terms of -omic associations, treatment outcome, and priors for disease progression.

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation

Background and objectiveDespite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both annotating and training new experts for this same task. In scenarios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures and interictal events can occur on hours- or days-long recordings. Although other solutions have already been proposed, most of them are not integrated on clinical and basic science environments due to their complexity and required specialization. Here we present a pipeline that arises from coordinated efforts between life-science researchers, clinicians and data scientists to develop an interactive and iterative workflow to train machine-learning tools for the automatic detection of electroencephalographic events in a variety of scenarios. MethodsThe approach consists on a series of subsequent steps covering data loading and configuration, event annotation, model training/re-training and event detection. With slight modifications, the combination of these blocks can cope with a variety of scenarios. To illustrate the flexibility and robustness of the approach, three datasets from clinical (patients of Dravet Syndrome) and basic research environments (mice model of the same disease) were evaluated. From them, and in response to researchers’ daily needs, four real world examples of interictal event detection and seizure classification tasks were selected and processed. ResultsResults show that the current approach was of great aid for event annotation and model development. It was capable of creating custom machine-learning solutions for each scenario with slight adjustments on the analysis protocol, easily accessible to users without programming skills. Final annotator similarity metrics reached values above 80% on all cases of use, reaching 92.3% on interictal event detection on human recordings. ConclusionsThe presented framework is easily adaptable to multiple real world scenarios and the interactive and ease-to-use approach makes it manageable to clinical and basic researches without programming skills. Nevertheless, it is conceived so data scientists can optimize it for specific scenarios, improving the knowledge transfer between these fields.

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.

Abstract PO-116: Identifying disparities across race, ethnicity, and gender in pediatric neuro-oncology clinical research - from patient to provider

Abstract Background: Literature has shown that racial, ethnic, and gender disparities exist among clinical research participants and within the ranks of academic institutions. To assess this and develop solutions to potential disparities in pediatric neuro-oncology, the Pacific Pediatric Neuro-Oncology Consortium (PNOC) and Children's Brain Tumor Network (CBTN) Diversity, Equity, and Inclusion (DEI) working group distributed a survey to all members of the consortia regarding the state of DEI in our research environments. In parallel, the working group collated clinical data from a public CBTN dataset and a PNOC clinical trial patient cohort to assess racial, ethnic, and gender differences in clinical research participation and outcomes. Methods: This study consisted of two components: an electronically distributed REDCap survey of all PNOC and CBTN consortia members (distributed Fall 2020) and analysis of pediatric neuro-oncology patient cohorts from CBTN and PNOC. The survey collected demographic information of respondents and Likert opinions on DEI at the consortia- and institution level (e.g. “All team members are treated fairly”). Responses were then stratified based on self-identified race, ethnicity, and gender and by age and job title. Patient-level data from the CBTN and PNOC cohorts was collated to evaluate overall patient demographics and clinical outcomes stratified by race, ethnicity, and gender and including differences in survival and clinical research enrollment. Outcomes: Fifty-seven PNOC/CBTN members initiated survey responses, with 45 completing the entire survey (estimated 20-25% response rate). Responders were predominantly white, non-hispanic females with the most common age range of 35-44 years and with faculty or physician job titles, followed by clinical research staff. Statistically significant differences were identified in questions related to the DEI environment mainly at the institution-level. Regarding feelings of inclusion, acceptance, and fair treatment, distinctions were identified across self-reported race and gender with white race correlating with higher frequency of feeling included and respected and females most commonly reporting neutral or disagreement that all team members are treated fairly and that different cultures and backgrounds are valued and interact well. At the patient level, the CBTN and PNOC data pulls included 1711 and 463 patients, respectively, with analyses currently underway and to be aligned with data from the Central Brain Tumor Registry of the United States (CBTRUS) to assess for race, ethnicity, gender, and locoregional differences in our patient cohort and in the context of national registry data. Conclusions: The experiences and feelings of inclusion and treatment of clinical research members within the clinical research environment of pediatric neuro-oncology differ based on self-identified race and gender. Investigation is in process to extrapolate patient-level differences across race, ethnicity, and gender and in the context of larger registry data for pediatric neuro-oncology. Citation Format: Emily Marshall, Tom Belle Davidson, Jeffrey Stevens, Kristina Cole, Fatema Malbari, Tabitha Cooney, Lance Ballaster, Kaitlin Lehmann, Shannon Robins, Miguel Brown, Christopher Blackden, Christopher Friedman, Ammar Naqvi, Jonathan Waller, Ximena Cueller, Jennifer Mason, Jena Lilly, Phillip Jay B Storm, Adam Resnick, Michael Prados, Sabine Mueller, Angela Waanders, Cassie Kline. Identifying disparities across race, ethnicity, and gender in pediatric neuro-oncology clinical research - from patient to provider [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-116.

Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering

Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segment masses on dynamic contrast-enhanced (DCE) MRI of the breast. Our method was compared against existing approaches based on classic image processing, namely (i) Otsu’s method for thresholding-based segmentation, and (ii) the traditional FCM algorithm. A further comparison was performed against state-of-the-art Convolutional Neural Networks (CNNs) for medical image segmentation, namely SegNet and U-Net, in a 5-fold cross-validation scheme. The results showed the validity of the proposed approach, by significantly outperforming the competing methods in terms of the Dice similarity coefficient (84.47±4.75). Moreover, a Pearson’s coefficient of ρ=0.993 showed a high correlation between segmented volume and the gold standard provided by clinicians. Overall, the proposed method was confirmed to outperform the competing literature methods. The proposed computer-assisted approach could be deployed into clinical research environments by providing a reliable tool for volumetric and radiomics analyses.

PSY4-4 Clinical oncology research environment in Malaysia: now and future

The Malaysian government is committed to improve clinical research infrastructure. More than 7 years after Clinical Research Malaysia (CRM) formation, we have more than 150 Clinical Research Sites and 850 clinical research investigators registered for multiple discipline of medicine. The top 10 common cancers in Malaysia includes Breast, Colorectal, Lung, Lymphoma, Nasopharynx, Leukemia, Prostate, Cervical, Liver and Ovarian cancer. Every tear Malaysia conducted 40 new Oncology clinical research sponsored by multinational companies. Majority are in Clinical Trial Phase 2 and 3. In 2017, CRM launch Phase 1 Realisation Program to produce First in Human (FIH) Phase 1 centers. 2 years later the first FIH center was accredited for Phase FIH Clinical Research. The Oncology disease prevalence is indifferent with the developed nation couple with multiple variety of population, Malaysia is ideal for clinical research destination. In addition, the conduct of Clinical Research is governed by Good Clinical Practice with ISO 9001:2015 accredited Standard Operating Procedure. The conducive environments mean clinical research are delivered according to the speed, reliability and quality. The recent reported recruitment rate at 83% has prepared the clinical research sites in facing COVID-19 pandemic where we will describe the recovery of clinical research recruitment is accelerated. The speaker will share data on how the research environment in Malaysia transformed creating conducive environment for oncology clinical research. Consequently, achieving 2 out of 3 National Key Economic Area Key Performance Index, ahead by almost 3 years with new jobs created and more number industry sponsored research conducted in the country.

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

IntroductionIn-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. Methods21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. ResultsVideo-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. ConclusionsWe trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.

The state of neuro-oncology during the COVID-19 pandemic: a worldwide assessment.

BackgroundIt remains unknown how the COVID-19 pandemic has changed neuro-oncology clinical practice, training, and research efforts.MethodsWe performed an international survey of practitioners, scientists, and trainees from 21 neuro-oncology organizations across 6 continents, April 24–May 17, 2020. We assessed clinical practice and research environments, institutional preparedness and support, and perceived impact on patients.ResultsOf 582 respondents, 258 (45%) were US-based and 314 (55%) international. Ninety-four percent of participants reported changes in their clinical practice. Ninety-five percent of respondents converted at least some practice to telemedicine. Ten percent of practitioners felt the need to see patients in person, specifically because of billing concerns and pressure from their institutions. Sixty-seven percent of practitioners suspended enrollment for at least one clinical trial, including 62% suspending phase III trial enrollments. More than 50% believed neuro-oncology patients were at increased risk for COVID-19. Seventy-one percent of clinicians feared for their own personal safety or that of their families, specifically because of their clinical duties; 20% had inadequate personal protective equipment. While 69% reported increased stress, 44% received no psychosocial support from their institutions. Thirty-seven percent had salary reductions and 63% of researchers temporarily closed their laboratories. However, the pandemic created positive changes in perceived patient satisfaction, communication quality, and technology use to deliver care and mediate interactions with other practitioners.ConclusionsThe pandemic has changed treatment schedules and limited investigational treatment options. Institutional lack of support created clinician and researcher anxiety. Communication with patients was satisfactory. We make recommendations to guide clinical and scientific infrastructure moving forward and address the personal challenges of providers and researchers.