a b s t r a c t: Objective: To study the impact of target volume changes in brain metastases during fractionated stereotactic radiosurgery (fSRS) and identify patients that benefit from MRI guidance. Material and methods: For 15 patients (18 lesions) receiving fSRS only (fSRSonly) and 19 patients (20 lesions) receiving fSRS postoperatively (fSRSpostop), a treatment planning MRI (MR0) and repeated MRI during treatment (MR1) were acquired. The impact of target volume changes on the target coverage was analyzed by evaluating the planned dose distribution (based on MR0) on the planning target volume (PTV) during treatment as defined on MR1. The predictive value of target volume changes before treatment (using the diagnostic MRI (MRD)) was studied to identify patients that experienced the largest changes during treatment. Results: Target volume changes during fSRS did result in large declines of the PTV dose coverage up to 34.8% (median = 3.2%) for fSRSonly patients. For fSRSpostop the variation and declines were smaller (median PTV dose coverage change = 0.5% (4.5% to 1.9%)). Target volumes changes did also impact the minimum dose in the PTV (fSRSonly; 2.7 Gy (16.5 to 2.3 Gy), fSRSpostop; 0.4 Gy (4.2 to 2.5 Gy)). Changes in target volume before treatment (i.e. seen between the MRD and MR0) predicted which patients experienced the largest dose coverage declines during treatment. Conclusion: Target volume changes in brain metastases during fSRS can result in worsening of the target dose coverage. Patients benefiting the most from a repeated MRI during treatment could be identified before treatment.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs.
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Purpose/Objective: Most dose-escalation trials in glioblastoma patients integrate the escalated dose throughout the standard course by targeting a specific subvolume. We hypothesize that anatomical changes during irradiation may affect the dose coverage of this subvolume for both proton- and photon-based radiotherapy. Material and Methods: For 24 glioblastoma patients a photon- and proton-based dose escalation treatment plan (of 75 Gy/30 fr) was simulated on the dedicated radiotherapy planning MRI obtained before treatment. The escalated dose was planned to cover the resection cavity and/or contrast enhancing lesion on the T1w post-gadolinium MRI sequence. To analyze the effect of anatomical changes during treatment, we evaluated on an additional MRI that was obtained during treatment the changes of the dose distribution on this specific high dose region. Results: The median time between the planning MRI and additional MRI was 26 days (range 16–37 days). The median time between the planning MRI and start of radiotherapy was relatively short (7 days, range 3–11 days). In 3 patients (12.5%) changes were observed which resulted in a substantial deterioration of both the photon and proton treatment plans. All these patients underwent a subtotal resection, and a decrease in dose coverage of more than 5% and 10% was observed for the photon- and proton-based treatment plans, respectively. Conclusion: Our study showed that only for a limited number of patients anatomical changes during photon or proton based radiotherapy resulted in a potentially clinically relevant underdosage in the subvolume. Therefore, volume changes during treatment are unlikely to be responsible for the negative outcome of dose-escalation studies.
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Despite the diplomatic activities and efforts between China and the EU, the news coverage generally paints a murky picture of the China-EU bilateral relations.
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Background and purpose: Automatic approaches are widely implemented to automate dose optimization in radiotherapy treatment planning. This study systematically investigates how to configure automatic planning in order to create the best possible plans. Materials and methods: Automatic plans were generated using protocol based automatic iterative optimization. Starting from a simple automation protocol which consisted of the constraints for targets and organs at risk (OAR), the performance of the automatic approach was evaluated in terms of target coverage, OAR sparing, conformity, beam complexity, and plan quality. More complex protocols were systematically explored to improve the quality of the automatic plans. The protocols could be improved by adding a dose goal on the outer 2 mm of the PTV, by setting goals on strategically chosen subparts of OARs, by adding goals for conformity, and by limiting the leaf motion. For prostate plans, development of an automated post-optimization procedure was required to achieve precise control over the dose distribution. Automatic and manually optimized plans were compared for 20 head and neck (H&N), 20 prostate, and 20 rectum cancer patients. Results: Based on simple automation protocols, the automatic optimizer was not always able to generate adequate treatment plans. For the improved final configurations for the three sites, the dose was lower in automatic plans compared to the manual plans in 12 out of 13 considered OARs. In blind tests, the automatic plans were preferred in 80% of cases. Conclusions: With adequate, advanced, protocols the automatic planning approach is able to create high-quality treatment plans.
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Het doel van het onderzoek is om te bepalen welke voordelen de fusie van PET-CT en MRI-CT hebben in het voorbereidingstraject van de behandeling van de gynaecologische patiënt met radiotherapie ten opzichte van CT alleen. Hierbij is gekeken naar voordelen met betrekking tot intekenen van doelvolumina en risico organen, effecten op intekenvariaties en ook de effecten op het bestralingsplan. Vooral MRI blijkt nuttig te zijn voor de intekening van lymfeklieren, het gebruik van PET in combinatie met CT laat een afname van het doelvolume zien van de primaire tumor. Bij het maken van het bestralingsplan wordt het gebruik van één van beide modaliteiten daarom aanbevolen.
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Background: Research into termination of long-term psychosocial treatment of mental disorders is scarce. Yearly 25% of people in Dutch mental health services receive long-term treatment. They account for many people, contacts, and costs. Although relevant in different health care systems, (dis)continuation is particularly problematic under universal health care coverage when secondary services lack a fixed (financially determined) endpoint. Substantial, unaccounted, differences in treatment duration exist between services. Understanding of underlying decisional processes may result in improved decision making, efficient allocation of scarce resources, and more personalized treatment.
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The broad field of environmental ethics, animal welfare, animal liberation, and animal rights literature indicate that all encounters between humans and animals are ethically charged. In this article, I shall examine how environmental ethics or animal welfare/rights/liberation literature translate into public media. The case study will delve into the representation of animals in the Dutch newspapers, using content analysis to provide an empirical basis for monitoring public opinion. Assuming that attitudes to animals are influenced by media coverage, the results of this case study will be brought to bear upon the discussion of the representation of animals beyond a specific national context. LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Standard SARS-CoV-2 testing protocols using nasopharyngeal/throat (NP/T) swabs are invasive and require trained medical staff for reliable sampling. In addition, it has been shown that PCR is more sensitive as compared to antigen-based tests. Here we describe the analytical and clinical evaluation of our in-house RNA extraction-free saliva-based molecular assay for the detection of SARS-CoV-2. Analytical sensitivity of the test was equal to the sensitivity obtained in other Dutch diagnostic laboratories that process NP/T swabs. In this study, 955 individuals participated and provided NP/T swabs for routine molecular analysis (with RNA extraction) and saliva for comparison. Our RT-qPCR resulted in a sensitivity of 82,86% and a specificity of 98,94% compared to the gold standard. A false-negative ratio of 1,9% was found. The SARS-CoV-2 detection workflow described here enables easy, economical, and reliable saliva processing, useful for repeated testing of individuals.
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