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Clinical trials of oncolytic virotherapy have shown low toxicity and encouraging signs of efficacy. However, it remains critically important to develop methods for systemic viral delivery if such therapies are to be clinically implemented to treat established tumors. In this respect, much effort is being focused on combining oncolytic viruses with standard treatment modalities such as inhibitors of [VEGF.sub.165] (an alternatively spliced isoform of VEGF-A) signaling, which are widely used to treat several different cancers. Here, we have demonstrated that combining [VEGF.sub.165] inhibitors with systemic delivery of oncolytic viruses leads to substantial regression and cure of established tumors in immunocompetent mice. We have shown that manipulating VEGF165-mediated signaling by administering [VEGF.sub.165] to mice harboring mouse melanoma cells that do not express [VEGF.sub.165]and by administering a VEGF inhibitor and then withdrawing treatment to allow VEGF levels to rebound in mice harboring mouse melanoma cells expressing [VEGF.sub.165] allows tumor-associated endothelial cells transiently to support viral replication. This approach led to direct tumor cell lysis and triggered innate immune-mediated attack on the tumor vasculature. It also resulted in long-term antitumor effects, even against tumors in which viral replication is poorly supported. Since this combinatorial approach targets the tumor endothelium, we believe these data have direct, wide-ranging, and immediate clinical applicability across a broad range of tumor types., Introduction Cancer gene/virus therapy will not achieve its potential until vectors can be delivered systemically to metastatic disease (1). Many barriers exist in immunocompetent hosts, including immune inactivation, mislocalization, specific [...]

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce new, intelligent x-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems have been constructed for medical imaging; specifically mammography and dental encephalography. However, the I-ImaS system concept could be applied to all areas of x-ray imaging, including homeland security and industrial QA. The I-ImaS system intelligence is implemented by the use of APS technology and FPGAs, allowing real-time analysis of data during image acquisition. This gives the system the capability to perform as an on-the-fly adaptive imaging system, with the potential to create images with maximum diagnostic information within given dose constraints. The I-ImaS system uses a scanning linear array of scintillator-coupled 1.5-D CMOS Active Pixel Sensors to create a full 2-D x-ray image of an object. This paper describes the parameters considered when choosing the scintillator elements of the detectors. A study of the positioning of the sensors to form a linear detector is also considered, along with a discussion of the potential losses in image quality associated with creating a linear sensor by tiling many smaller sensors. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographic and encephalographic I-ImaS systems that are currently under construction. Index Terms--CMOS, scintillators, x-ray imaging.

Radiation hardness is a critical design concern for present and future silicon detectors in high energy physics. Tracking systems at the CERN Large Hadron Collider (LHC) are expected to operate for ten years and to receive fast hadron fluences equivalent to [10.sup.15][cm.sup.2] 1-MeV neutrons. Recently, low temperature operating conditions have been suggested as a means of suppressing the negative effects of radiation damage on detector charge collection properties. To investigate this effect, simulations have been carried out using the ISE-TCAD DESSIS device simulator. The so-called 'three-level model' has been used. A comprehensive analysis of the influence of the [V.sub.2], [C.sub.i][O.sub.i] and [V.sub.2]O capture cross sections on the effective doping concentration ([N.sub.eff]) as a function of temperature and fluence has been carried out. The capture cross sections have been varied in the range [10.sup.-18] - [10.sup.-12] [cm.sup.2]. The simulated results are compared with charge collection spectra obtained with 1064-nm laser pulses on devices irradiated with 23-GeV protons as a function of detector bias voltage. To validate the model, a wide range of temperature and fluence has been studied using a one-dimensional (1-D) simplified structure. Thousands of simulation results have been cross checked with the experimental data. The data between 190 K (the lower limit for simulations due to computational difficulties) and 290 K are well reproduced for all of the fluences considered. We conclude that the three-level model can be successfully used to predict irradiated detector behavior down to a temperature of at least 190 K. Index Terms--Cryogenic temperatures, device simulation, particle physics, radiation damage effects.

The effect of particle irradiation on high-resistivity silicon detectors has been extensively studied with the goal of engineering devices able to survive the very challenging radiation environment at the CERN Large Hadron Collider (LHC). The main aspect under investigation has been the changes observed in detector effective doping concentration ([N.sub.eff]). We have previously proposed a mechanism to explain the evolution of [N.sub.eff], whereby charge is exchanged directly between closely-spaced defect centres in the dense terminal clusters formed by hadron irradiation. This model has been implemented in both a commercial finite-element device simulator (ISE-TCAD) and a purpose-built simulation of interdefect charge exchange. To control the risk of breakdown due to the high leakage currents foreseen during ten years of LHC operation, silicon detectors will be operated below room temperature (around -10[degrees]C. This, and more general current interest in the field of cryogenic operation, has led us to investigate the behavior of our model over a wide range of temperatures. We present charge collection spectra from 1064 nm laser pulses as a function of detector bias between temperatures of 115 K and 290 K, using devices irradiated with 23 GeV protons in the range [10.sup.13] -4 x [10.sup.14] protons * [cm.sup.-2]. These data allow a deeper investigation of the influence of defect capture cross sections on [N.sub.eff]. The model prediction for the reversion to n-type of heavily-irradiated detectors at low temperature is investigated and deviations from the model are explored. Index Terms--Cryogenic operation, particle physics, radiaiton effects, silicon radiation detectors.

New guidance issued by the National Institute for Clinical Excellence (NICE) is set to cut dramatically the number of hospital admissions resulting from head injury and improve patient care. It [...]

Introduction: More people are living with and beyond a cancer diagnosis. There is limited understanding of the long-term effects of cancer and cancer treatment on quality of life and personal and household finances when compared to people without cancer. In a separate protocol we have proposed to link de-identified data from electronic primary care and hospital records for a large population of cancer survivors and matched controls. In this current protocol, we propose the linkage of Patient Reported Outcomes Measures data to the above data for a subset of this population. The aim of this study is to investigate the full impact of living with and beyond a cancer diagnosis compared to age and gender matched controls. A secondary aim is to test the feasibility of the collection of Patient Reported Outcomes Measures (PROMS) data and the linkage procedures of the PROMs data to electronic health records data. Materials and methods: This is a cross-sectional study, aiming to recruit participants treated at the Leeds Teaching Hospitals National Health Service Trust. Eligible patients will be cancer survivors at around 5 years post-diagnosis (breast, colorectal and ovarian cancer) and non-cancer patient matched controls attending dermatology out-patient clinics. They will be identified by running a query on the Leeds Teaching Hospitals Trust patient records system. Approximately 6000 patients (2000 cases and 4000 controls) will be invited to participate via post. Participants will be invited to complete PROMs assessing factors such as quality of life and finances, which can be completed on paper or online (surveys includes established instruments, and bespoke instruments (demographics, financial costs). This PROMs data will then be linked to routinely collected de-identified data from patient's electronic primary care and hospital records. Discussion: This innovative work aims to create a truly 'comprehensive patient record' to provide a broad picture of what happens to cancer patients across their cancer pathway, and the long-term impact of cancer treatment. Comparisons can be made between the cases and controls, to identify the aspects of life that has had the greatest impact following a cancer diagnosis. The feasibility of linking PROMs data to electronic health records can also be assessed. This work can inform future support offered to people living with and beyond a cancer diagnosis, clinical practice, and future research methodologies. [ABSTRACT FROM AUTHOR]

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifying chest X-rays into one of three classes: COVID-19, non-COVID pneumonia and no-pneumonia. Classification performance of the models investigated is evaluated through ROC curves, confusion matrices and standard classification metrics. Explainability modules are implemented to explore the image features most important to classification. Data analysis and model evalutions show that the popular open-source dataset COVIDx is not representative of the real clinical problem and that results from testing on this are inflated. Dependence on open-source data can leave models vulnerable to bias and confounding variables, requiring careful analysis to develop clinically useful/viable AI tools for COVID-19 detection in chest X-rays. [ABSTRACT FROM AUTHOR]

Most data collected by hospitals as a consequence of the delivery of routine care is not utilised for analytics or organisational intelligence. This project aims to develop tools to enhance the utilisation of routinely collected cancer data within hospitals across England. This was achieved by developing a web application using open source tools to provide health care professionals and hospital managers with easy to use, interactive analytics for cancer data. The application uses data items hospitals in England are mandated to collect as part of the Cancer Outcomes and Services Dataset (COSD), to provide clinical insight into survival outcomes, population distributions, service demands, waiting times, geographical case distributions and treatment information in real-time or near real-time. Development was guided by end user needs through the use of panels of clinical and non-clinical end users. [ABSTRACT FROM AUTHOR]

Background: The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. Methods: We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. Results: We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. Conclusions: Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research. [ABSTRACT FROM AUTHOR]