ABSTRACT
Amid the COVID-19 pandemic, cancer continues to be the most devastating disease worldwide. Liquid biopsy of circulating tumor cells (CTCs) has recently become a painless and noninvasive tool for obtaining carcinoma cell samples for molecular profiling. Here, we report efficient detection and collection of cancer cells in blood samples by combining stem cell antigen (CD44)-specific immunosilica particles and immunofluorescent quantum dots with spectrally and temporally resolved single-photon counting. We accurately detect 1-10 cells among 100 cancer cells of the breast, lungs, or cervix in 1 mL blood samples. In addition, the bright and narrowband emission of CdSe/ZnS quantum dots enables temporally and spectrally resolved photon counting for multiplexed cancer cell detection. The cancer cell-specific and large immunosilica particles helped us collect the specific cells. We validate the detection efficiency and multimodality of this strategy by time-stamped and energy-dispersed single-photon counting of orange- and red-emitting quantum dots and green-fluorescing nuclei stained with Syto-13/25 dye. Thus, the present work highlights the prospects of multimodal CTC detection for noninvasive cancer screening and postsurgical or therapeutic follow-up.
ABSTRACT
Purpose or Objective The COVID-19 pandemic forced radiation oncology departments to alter clinical workflows to reduce exposure risks in the clinic. Performing patient-specific quality assurance (PSQA) is one of the most resource intensive and time-consuming tasks. With technological advancements in radiotherapy treatment planning and quality assurance, research towards measurement-free PSQA has become a focus within the field. Most of these techniques involve modeling the relationship between treatment plan complexity and corresponding PSQA outcomes. However, to our knowledge, none of these efforts have been assessed and prospectively validated for clinical use. We implemented and a machine learning-based virtual VMAT QA (VQA) workflow to assess the safety and workload reduction of measurement-free patient-specific QA at a multi-site institution in light of COVID-19. Materials and Methods An XGBoost machine learning model was trained and tuned to predict QA outcomes of VMAT plans, represented as percent differences between the planned dose and measured ion chamber point dose in a phantom. The model was developed using a dataset of 579 previous clinical VMAT plans and associated QA measurements from our institution. 30 classes of complexity features were extracted from each VMAT plan and used as input for the model, which was tuned using a grid search over learning rate and tree depth hyperparameters and evaluated with 10-fold cross-validation. The final model was implemented within a webbased VQA application to predict QA outcomes of clinical plans within our existing clinical workflow. The application also displays relevant plan-specific feature importance and nearest neighbor analyses relative to database plans for physicist evaluation and documentation (Figure 1). VQA predictions were prospectively validated over one month of measurements at our clinic to assess the safety and efficiency gains of clinical implementation. $Φg Results 147 VMAT plans were measured at our institution over the course of one month, taking an average of approximately 20 minutes per plan for QA. VQA predictions for these plans had a mean absolute error of 0.97 +/- 0.69%, with a maximum absolute error of 2.75% (Figure 2). Employing a prediction decision threshold of 1% - meaning plans with absolute predictions of less than 1% would not need measurements - would flag all plans that may have ion chamber disagreements greater than 4%. This translates to a 73% reduction in QA workload in terms of time. A more conservative implementation of this workflow, where all SBRT plans will continue to be measured, would still result in a 46% reduction in QA workload. $Φg Conclusion To our knowledge, this is the first prospective clinical implementation and validation of VQA, which we observed to be safe and efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for patient-specific QA, leading to a more effective allocation of clinical resources.
ABSTRACT
Purpose: This research aims to uncover coronavirus disease 2019’s (COVID-19's) impact on shipping and logistics using Internet articles as the source. Design/methodology/approach: This research applies web mining to collect information on COVID-19's impact on shipping and logistics from Internet articles. The information extracted is then analyzed through machine learning algorithms for useful insights. Findings: The research results indicate that the recovery of the global supply chain in China could potentially drive the global supply chain to return to normalcy. In addition, researchers and policymakers should prioritize two aspects: (1) Ease of cross-border trade and logistics. Digitization of the supply chain and applying breakthrough technologies like blockchain and IoT are needed more than ever before. (2) Supply chain resilience. The high dependency of the global supply chain on China sounds like an alarm of supply chain resilience. It calls for a framework to increase global supply chain resilience that enables quick recovery from disruptions in the long term. Originality/value: Differing from other studies taking the natural language processing (NLP) approach, this research uses Internet articles as the data source. The findings reveal significant components of COVID-19's impact on shipping and logistics, highlighting crucial agendas for scholars to research. © 2021, Pacific Star Group Education Foundation.
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Large-scale health emergencies like COVID-19 oftentimes result in widespread humanitarian impacts. Due to their long-standing relationships and involvement within local communities, along with extensive networks and support from faith-affiliated institutions, faith-based NGOs carry a unique advantage in reaching the most vulnerable during such crises. The Adventist Development & Relief Agency's (ADRA) experience during its global COVID-19 response showcases how keeping a local presence in-country and fostering partnerships with affiliated faith institutions and constituents can result in a wide reach of programming. By providing dedicated personnel and small seed-funding, developing a flexible global strategy involving strong business continuity plans and emphasis on its faith base, and supporting the sharing of information and lessons learned among local offices, faith-based NGOs are capable of quickly delivering life-saving interventions to vulnerable communities. ADRA and the affiliated Seventh-day Adventist Church have proved during the first year of COVID-19 that they are stronger together, highlighting the importance of utilizing a faith base when implementing humanitarian interventions. © 2021 Center for Health in Mission. All rights reserved.
ABSTRACT
PURPOSE: Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019. METHODS AND MATERIALS: In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements. RESULTS: In the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement. CONCLUSIONS: It is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.