TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use.

BACKGROUND: The study of cancer genomics continually matures as the number of patient samples sequenced increases. As more data is generated, oncogenic drivers for specific cancer types are discovered along with their associated risks. This in turn leads to potential treatment strategies that pave the way to precision medicine. However, significant financial and analytical barriers make it infeasible to sequence the entire genome of every patient. In contrast, targeted sequencing panels give reliable information on relevant portions of the genome at a fiscally responsible cost. Therefore, we have created the Targeted Panel (TarPan) Viewer, a software tool, to investigate this type of data. RESULTS: TarPan Viewer helps investigators understand data from targeted sequencing data by displaying the information through a web browser interface. Through this interface, investigators can easily observe copy number changes, mutations, and structural events in cancer samples. The viewer runs in R Shiny with a robust SQLite backend and its input is generated from bioinformatic algorithms reliably described in the literature. Here we show the results from using TarPan Viewer on publicly available follicular lymphoma, breast cancer, and multiple myeloma data. In addition, we have tested and utilized the viewer internally, and this data has been used in high-impact peer-reviewed publications. CONCLUSIONS: We have designed a flexible, simple to setup viewer that is easily adaptable to any type of cancer targeted sequencing, and has already proven its use in a research laboratory environment. Further, we believe with deeper sequencing and/or more targeted application it could be of use in the clinic in conjunction with an appropriate targeted sequencing panel as a cost-effective diagnostic test, especially in cancers such as acute leukemia or diffuse large B-cell lymphoma that require rapid interventions.

Randomised clinical trial assessing migration of uncemented primary total hip replacement stems, with and without autologous impaction bone grafting.

PURPOSE: Uncemented stems in primary total hip replacement (THR) are concerning in the elderly due to ectatic femoral canals and cortical thinning resulting in higher incidence of fracture and subsidence in this population. To obviate this concern, the authors developed a technique using autologous impaction bone grafting to achieve a better fitting femoral stem. The aim of this randomised clinical trial was to assess the efficacy of the technique. METHODS: From 2013 to 2015, a total of 98 consecutive participants (100 primary THR procedures) were inducted into a single-institution, single-blinded, randomised clinical trial assessing, with radiostereometric analysis (RSA), the efficacy of autologous impaction bone grafting in uncemented primary THR compared with traditional uncemented primary THR technique. The primary outcome measure was femoral component migration using RSA. Secondary outcomes were post-operative proximal femoral bone density (using DEXA), hip function and quality of life using Oxford Hip Score (OHS) and Short Form-12 Health Survey (SF-12), hip pain and patient satisfaction. RESULTS: There was no difference in femoral component stability (p > 0.5) or calcar resorption between the Graft and No Graft Groups at two years. There was also no difference in OHS, SF-12, pain or satisfaction between the Graft and No Graft Groups at two years (p > 0.39). CONCLUSIONS: Autologous impaction bone grafting in uncemented primary THR has shown its short-term post-operative outcomes to be equivalent to standard uncemented technique, whilst offering a better fit in patients who are between femoral stem sizes. AUSTRALIAN CLINICAL TRIAL REGISTRATION NUMBER: ACTRN12618000652279.

Ultra-high-speed indirect x-ray imaging system with versatile spatiotemporal sampling capabilities.

A new generation of cameras has made ultra-high-speed x-ray imaging at synchrotron light sources a reality, revealing never-before-seen details of sub-surface transient phenomena. We introduce a versatile indirect imaging system capable of capturing-for the first time-hundreds of sequential x-ray pulses in 16-bunch mode at the European Synchrotron Radiation Facility, recording at 5.68 Mfps over dozens of microseconds, with an effective exposure of 100 ps. The versatile multiplex camera construction of the system allows for various arrangements, including different scintillator configurations, and simultaneous imaging with different resolutions and regions of interest. Image results from a gas gun impact experiment, in which an additive manufactured aluminum lattice was dynamically compressed, is presented as a demonstration of the system's capabilities.

Evaluating scintillator performance in time-resolved hard X-ray studies at synchrotron light sources.

The short pulse duration, small effective source size and high flux of synchrotron radiation is ideally suited for probing a wide range of transient deformation processes in materials under extreme conditions. In this paper, the challenges of high-resolution time-resolved indirect X-ray detection are reviewed in the context of dynamic synchrotron experiments. In particular, the discussion is targeted at two-dimensional integrating detector methods, such as those focused on dynamic radiography and diffraction experiments. The response of a scintillator to periodic synchrotron X-ray excitation is modelled and validated against experimental data collected at the Diamond Light Source (DLS) and European Synchrotron Radiation Facility (ESRF). An upper bound on the dynamic range accessible in a time-resolved experiment for a given bunch separation is calculated for a range of scintillators. New bunch structures are suggested for DLS and ESRF using the highest-performing commercially available crystal LYSO:Ce, allowing time-resolved experiments with an interframe time of 189 ns and a maximum dynamic range of 98 (6.6 bits).

The Preschool Confusion Assessment Method for the ICU: Valid and Reliable Delirium Monitoring for Critically Ill Infants and Children.

OBJECTIVES: Delirium assessments in critically ill infants and young children pose unique challenges due to evolution of cognitive and language skills. The objectives of this study were to determine the validity and reliability of a fundamentally objective and developmentally appropriate delirium assessment tool for critically ill infants and preschool-aged children and to determine delirium prevalence. DESIGN AND SETTING: Prospective, observational cohort validation study of the PreSchool Confusion Assessment Method for the ICU in a tertiary medical center PICU. PATIENTS: Participants aged 6 months to 5 years and admitted to the PICU regardless of admission diagnosis were enrolled. MEASUREMENTS AND MAIN RESULTS: An interdisciplinary team created the PreSchool Confusion Assessment Method for the ICU for pediatric delirium monitoring. To assess validity, patients were independently assessed for delirium daily by the research team using the PreSchool Confusion Assessment Method for the ICU and by a child psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders criteria. Reliability was assessed using blinded, concurrent PreSchool Confusion Assessment Method for the ICU evaluations by research staff. A total of 530-paired delirium assessments were completed among 300 patients, with a median age of 20 months (interquartile range, 11-37) and 43% requiring mechanical ventilation. The PreSchool Confusion Assessment Method for the ICU demonstrated a specificity of 91% (95% CI, 90-93), sensitivity of 75% (95% CI, 72-78), negative predictive value of 86% (95% CI, 84-88), positive predictive value of 84% (95% CI, 81-87), and a reliability κ-statistic of 0.79 (0.76-0.83). Delirium prevalence was 44% using the PreSchool Confusion Assessment Method for the ICU and 47% by the reference rater. The rates of delirium were 53% versus 56% in patients younger than 2 years old and 33% versus 35% in patients 2-5 years old using the PreSchool Confusion Assessment Method for the ICU and reference rater, respectively. The short-form PreSchool Confusion Assessment Method for the ICU maintained a high specificity (87%) and sensitivity (78%) in post hoc analysis. CONCLUSIONS: The PreSchool Confusion Assessment Method for the ICU is a highly valid and reliable delirium instrument for critically ill infants and preschool-aged children, in whom delirium is extremely prevalent.

Personalized medicine: CCO's vision, accomplishments and future plans.

Personalized medicine is a rapidly expanding field, with the potential to improve patient care. Its benefits include increasing efficiency in cancer screening, diagnosis and treatment through early detection, targeted therapy and identifying individuals with an underlying genetic risk for cancer or adverse outcomes. Through the work of Cancer Care Ontario (CCO)'s Pathology and Laboratory Medicine Program, a number of initiatives have been undertaken to support developments in personalized medicine. In keeping with the momentum of recent accomplishments, CCO has led the formation of the Personalized Medicine Steering Committee to develop a comprehensive provincial genetics strategy for the future of cancer care.

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification.

De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.

Discovery and preclinical development of a therapeutically active nanobody-based chimeric antigen receptor targeting human CD22.

Chimeric antigen receptor (CAR) T cell therapies targeting B cell-restricted antigens CD19, CD20, or CD22 can produce potent clinical responses for some B cell malignancies, but relapse remains common. Camelid single-domain antibodies (sdAbs or nanobodies) are smaller, simpler, and easier to recombine than single-chain variable fragments (scFvs) used in most CARs, but fewer sdAb-CARs have been reported. Thus, we sought to identify a therapeutically active sdAb-CAR targeting human CD22. Immunization of an adult Llama glama with CD22 protein, sdAb-cDNA library construction, and phage panning yielded >20 sdAbs with diverse epitope and binding properties. Expressing CD22-sdAb-CAR in Jurkat cells drove varying CD22-specific reactivity not correlated with antibody affinity. Changing CD28- to CD8-transmembrane design increased CAR persistence and expression in vitro. CD22-sdAb-CAR candidates showed similar CD22-dependent CAR-T expansion in vitro, although only membrane-proximal epitope targeting CD22-sdAb-CARs activated direct cytolytic killing and extended survival in a lymphoma xenograft model. Based on enhanced survival in blinded xenograft studies, a lead CD22sdCAR-T was selected, achieving comparable complete responses to a benchmark short linker m971-scFv CAR-T in high-dose experiments. Finally, immunohistochemistry and flow cytometry confirm tissue and cellular-level specificity of the lead CD22-sdAb. This presents a complete report on preclinical development of a novel CD22sdCAR therapeutic.

medigan: a Python library of pretrained generative models for medical image synthesis.

Purpose: Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. Approach: We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. Results: The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. Conclusion: medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.

Analysis of Caregiver Burden Expressed in Social Media Discussions.

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus. An expert panel summarized the forums' themes into ten categories. The largest theme extracted from Reddit's forums discussed the personal emotional toll of being a caregiver. This was followed by logistic issues while caregiving and caring for parents who have cancer. Smaller themes included approaches to end-of-life care, physical equipment needs when caregiving, and the use of wearables or technology to help monitor care recipients. The platform often discusses caregiving for parents which may reflect the age of Reddit's users. This study confirms that Reddit forums are used for caregivers to discuss the burdens associated with their role and the types of stress that can result from informal caregiving.

Use of electroconvulsive therapy in a patient with chorea neuroacanthocytosis and prominent delusions.

A middle-aged white man with a diagnosis of chorea neuroacanthocytosis developed progressive and distressing persecutory delusions with an obsessional component. Pharmacotherapy was ineffective in controlling the symptoms or halting their progression. Electroconvulsive therapy was attempted with very limited success but had to be discontinued owing to mild elation and increased irritability. The patient's distress was only improved after his transfer to a nursing home specializing in Huntington disease.

The BIOTA Biodiversity Observatories in Africa--a standardized framework for large-scale environmental monitoring.

The international, interdisciplinary biodiversity research project BIOTA AFRICA initiated a standardized biodiversity monitoring network along climatic gradients across the African continent. Due to an identified lack of adequate monitoring designs, BIOTA AFRICA developed and implemented the standardized BIOTA Biodiversity Observatories, that meet the following criteria (a) enable long-term monitoring of biodiversity, potential driving factors, and relevant indicators with adequate spatial and temporal resolution, (b) facilitate comparability of data generated within different ecosystems, (c) allow integration of many disciplines, (d) allow spatial up-scaling, and (e) be applicable within a network approach. A BIOTA Observatory encompasses an area of 1 km(2) and is subdivided into 100 1-ha plots. For meeting the needs of sampling of different organism groups, the hectare plot is again subdivided into standardized subplots, whose sizes follow a geometric series. To allow for different sampling intensities but at the same time to characterize the whole square kilometer, the number of hectare plots to be sampled depends on the requirements of the respective discipline. A hierarchical ranking of the hectare plots ensures that all disciplines monitor as many hectare plots jointly as possible. The BIOTA Observatory design assures repeated, multidisciplinary standardized inventories of biodiversity and its environmental drivers, including options for spatial up- and downscaling and different sampling intensities. BIOTA Observatories have been installed along climatic and landscape gradients in Morocco, West Africa, and southern Africa. In regions with varying land use, several BIOTA Observatories are situated close to each other to analyze management effects.

Patient survey examining the experience of care of a hospital-based opt-out tobacco dependency treatment service (the CURE Project).

INTRODUCTION: Treating tobacco dependency in patients admitted to hospital is a key priority in the National Health Service long-term plan. This service evaluation assessed the perception, needs and experience of care within an opt-out hospital-based tobacco dependency treatment service (the Conversation, Understand, Replace, Experts and Evidence Base (CURE) team) in North-West England. METHODS: A survey was offered to all eligible patients between 1 July 2020 and 30 September 2020. Eligibility criteria were adult patients identified as an active smoker being approached by the CURE team as part of the standard opt-out service model, on a non-covid ward without a high suspicion of COVID-19 infection and able to read and write in English. RESULTS: 106 completed surveys were evaluated. Participants demonstrated high levels of tobacco dependency with an average of 37 years smoking history and 66% describing the onset of cravings within 30 min of hospital admission. The average number quit attempts in the previous 12 months was 1.3 but only 9% had used the most effective National Institute for Health and Care Excellence (NICE) recommended treatments. 100% felt the opt-out service model was appropriate and 96% stated the treatment and support they had received had prompted them to consider a further quit attempt. 82% of participants rated their experience of care as 9/10 or 10/10. Participants wanted a broad range of support post discharge with the most popular option being with their general practitioner. 66% and 65% of participants would have been interested in a vaping kit as stop smoking intervention and support vaping-friendly hospital grounds respectively. CONCLUSION: These results suggest this hospital-based, opt-out tobacco dependency treatment service delivers high-quality experience of care and meets the needs of the patients it serves. It also highlights the opportunity to enhance outcomes by providing access to NICE recommended most-effective interventions (varenicline, vaping and combination nicotine replacement therapy) and providing flexible, individualised discharge pathways.

Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups.

Sequencing studies have shed some light on the pathogenesis of progression from smouldering multiple myeloma (SMM) and symptomatic multiple myeloma (MM). Given the scarcity of smouldering samples, little data are available to determine which translational programmes are dysregulated and whether the mechanisms of progression are uniform across the main molecular subgroups. In this work, we investigated 223 SMM and 1348 MM samples from the University of Arkansas for Medical Sciences (UAMS) for which we had gene expression profiling (GEP). Patients were analysed by TC-7 subgroup for gene expression changes between SMM and MM. Among the commonly dysregulated genes in each subgroup, PHF19 and EZH2 highlight the importance of the PRC2.1 complex. We show that subgroup specific differences exist even at the SMM stage of disease with different biological features driving progression within each TC molecular subgroup. These data suggest that MMSET SMM has already transformed, but that the other precursor diseases are distinct clinical entities from their symptomatic counterpart.

Increased biogas production in a wastewater treatment plant by anaerobic co-digestion of fruit and vegetable waste and sewer sludge - a full scale study.

Anaerobic digestion is a well established technology for the reduction of organic matter and stabilization of wastewater. Biogas, a mixture of methane and carbon dioxide, is produced as a useful by-product of the process. Current solid waste management at the city of Prince George is focused on disposal of waste and not on energy recovery. Co-digestion of fresh fruit and vegetable waste with sewer sludge can improve biogas yield by increasing the load of biodegradable material. A six week full-scale project co-digesting almost 15,000 kg of supermarket waste was completed. Average daily biogas production was found to be significantly higher than in previous years. Digester operation remained stable over the course of the study as indicated by the consistently low volatile acids-to-alkalinity ratio. Undigested organic material was visible in centrifuged sludge suggesting that the waste should have been added to the primary digester to prevent short circuiting and to increase the hydraulic retention time of the freshly added waste.

Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research.

Direct extraction and use of electronic health record (EHR) data is a long-term and multifaceted endeavor that includes design, development, implementation and evaluation of methods and tools for semi-automating tasks in the research data collection process, including, but not limited to, medical record abstraction (MRA). A systematic mapping of study data elements was used to measure the coverage of the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard for a federally sponsored, pragmatic cardiovascular randomized controlled trial (RCT) targeting adults. We evaluated site-level implementations of the HL7® FHIR® standard to investigate study- and site-level differences that could affect coverage and offer insight into the feasibility of a FHIR-based eSource solution for multicenter clinical research.

A DICOM dataset for evaluation of medical image de-identification.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

Health economic analysis for the 'CURE Project' pilot: a hospital-based tobacco dependency treatment service in Greater Manchester.

INTRODUCTION: Treating tobacco dependency in patients admitted to acute care National Health Service (NHS) trusts is a key priority in the NHS 10-year plan. This paper sets out the results of a health economic analysis for 'The CURE Project' pilot; a new hospital-based tobacco dependency service. METHODS: A health economic analysis to understand the costs of the intervention (both for the inpatient service and postdischarge costs), the return on investment (ROI) and the cost per quality-adjusted life year (QALY) of the CURE Project pilot in Greater Manchester. ROI and cost per QALY were calculated using the European Study on Quantifying Utility of Investment in Protection from Tobacco and Greater Manchester Cost Benefit Analysis Tools. RESULTS: The total intervention costs for the inpatient service in the 6-month CURE pilot were £96 224 with a cost per patient who smokes of £40.21. The estimated average cost per patient who was discharged on pharmacotherapy was £97.40. The cost per quit (22% quit rate for smokers at 12 weeks post discharge) was £475. The gross financial ROI ratio was £2.12 return per £1 invested with a payback period of 4 years. The cashable financial ROI ratio was £1.06 return per £1 invested with a payback period of 10 years. The public value ROI ratio was £30.49 per £1 invested. The cost per QALY for this programme was £487. DISCUSSION: The CURE Project pilot has been shown to be exceptionally cost-effective with highly significant ROI in this health economic analysis. This supports the NHS priority to embed high-quality tobacco addiction treatment services in acute NHS trusts, and the CURE Project provides a blueprint and framework to achieve this.