Personal protective equipment (PPE) is crucially important to the safety of both patients and medical personnel, particularly in the event of an infectious pandemic. As the incidence of Coronavirus Disease 2019 (COVID-19) increases exponentially in the United States and many parts of the world, healthcare provider demand for these necessities is currently outpacing supply. In the midst of the current pandemic, there has been a concerted effort to identify viable ways to conserve PPE, including decontamination after use. In this study, we outline a procedure by which PPE may be decontaminated using ultraviolet (UV) radiation in biosafety cabinets (BSCs), a common element of many academic, public health, and hospital laboratories. According to the literature, effective decontamination of N95 respirator masks or surgical masks requires UV-C doses of greater than 1 Jcm-2, which was achieved after 4.3 hours per side when placing the N95 at the bottom of the BSCs tested in this study. We then demonstrated complete inactivation of the human coronavirus NL63 on N95 mask material after 15 minutes of UV-C exposure at 61 cm (232 μWcm-2). Our results provide support to healthcare organizations looking for methods to extend their reserves of PPE.
ABSTRACT DISCLAIMER This article does not represent the official recommendation of the Cleveland Clinic or Case Western Reserve University School of Medicine, nor has it yet been peer reviewed. We are releasing it early, pre-peer review, to allow for quick dissemination/vetting by the scientific/clinical community given the necessity for rapid conservation of personal protective equipment (PPE) during this dire global situation. We welcome feedback from the community. Personal protective equipment (PPE), including face shields, surgical masks, and N95 respirators, is crucially important to the safety of both patients and medical personnel, particularly in the event of an infectious pandemic. As the incidence of Coronavirus Disease (COVID-19) increases exponentially in the United States and worldwide, healthcare provider demand for these necessities is currently outpacing supply. As such, strategies to extend the lifespan of the supply of medical equipment as safely as possible are critically important. In the midst of the current pandemic, there has been a concerted effort to identify viable ways to conserve PPE, including decontamination after use. Some hospitals have already begun using UV-C light to decontaminate N95 respirators and other PPE, but many lack the space or equipment to implement existing protocols. In this study, we outline a procedure by which PPE may be decontaminated using ultraviolet (UV) radiation in biosafety cabinets (BSCs), a common element of many academic, public health, and hospital laboratories, and discuss the dose ranges needed for effective decontamination of critical PPE. We further discuss obstacles to this approach including the possibility that the UV radiation levels vary within BSCs. Effective decontamination of N95 respirator masks or surgical masks requires UV-C doses of greater than 1 Jcm −2 , which would take a minimum of 4.3 hours per side when placing the N95 at the bottom of the BSCs tested in this study. Elevating the N95 mask by 48 cm (so that it lies 19 cm from the top of the BSC) would enable the delivery of germicidal doses of UV-C in 62 minutes per side. Effective decontamination of face shields likely requires a much lower UV-C dose, and may be achieved by placing the face shields at the bottom of the BSC for 20 minutes per side. Our results are intended to provide support to healthcare organizations looking for alternative methods to extend their reserves of PPE. We recognize that institutions will require robust quality control processes to guarantee the efficacy of any implemented decontamination protocol. We also recognize that in certain situations such institutional resources may not be available; while we subscribe to the general principle that some degree of decontamination is preferable to re-use without decontamination, we would strongly advise that in such cases at least some degree of on-site verification of UV dose delivery be performed.
Abstract Hypoxia and the development of spatially heterogeneous hypoxic and/or necrotic regions in non-small cell lung cancer (NSCLC) and other solid tumors is associated with chemotherapy and radiation resistance. One mechanism by which this could occur is through modification of the cell-cell interactions (or games) between the resistant and naïve cells within the tumor. Cell-cell interactions can modify the fitness of cells and thereby change the evolutionary dynamics of a tumor. Hypoxia may, in this way, change the game dynamics between cells and directly support maintenance of a population of resistant cells. To understand the mechanism by which hypoxic heterogeneity modulates evolutionary dynamics and therapy response, we combine computational modeling and in vitro experiments under a spatial hypoxia gradient. We utilize in-house game assay protocols to probe the evolutionary games between cell types. The experimental system is adapted from Carmona-Fontaine's MEMIC plate. This system allows us to create stable oxygen and nutrient gradients for 12 evolutionary replicates of NSCLC PC-9 cells. Fluorescence labeled gefitinib-resistant PC-9 cells and naïve parental PC-9 cells are co-cultured in a closed chamber with limited availability to oxygen and nutrient supply at one end. A green hypoxia probe is used to verify the establishment of a hypoxia gradient and a red Annexin V probe monitors apoptosis. Once microenvironmental gradients are established due to cellular consumption of oxygen and nutrients, images of cells are taken every 1.5 hours for 5 days. We carry out multiple experiments in which we vary the initial proportions of parental and resistant cells. Computationally, green and red fluorescence from the images captured by the microscopy system is quantified by an in-house image processing pipeline. We apply a random forest machine learning classifier, illumination correction, and object identification to each image to gather fluorescence counts for each well. These counts are used to compute the background rate of cell death and absolute vs. relative oxygen levels. The subsequent cell counts over time were used to calculate the growth rate (fitness) of the cell types in different environments and therefore the strength of environmental and frequency dependent interactions between the cell types. This framework allows for tightly coupled computational and in vitro experiments aimed at understanding and predicting the clinical implications of hypoxia resistance across a range of tumor types and microenvironments. We have measured the effect of hypoxia upon game interactions between gefinib-resistant and susceptible NSCLC cells. The evolutionary game changes with changing levels of hypoxia–and for the first time we are able to write down a continuous equation representing the game as a function of oxygen. Citation Format: Mina Dinh, Bezhou Feng, Jeff Maltas, Emily Dolson, Masahiro Hitomi, Steph Owen, Jacob Scott. Exploring the effect of hypoxia and spatial interactions on the dynamics between gefitinib resistant and naïve NSCLC cell lines [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A027.
ABSTRACT Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies work to delay this inevitability by promoting the maintenance of tumoral heterogeneity. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. Reproducibility of the success seen with this treatment paradigm is contingent on formal elucidation of underlying subclonal interactions. One such consequence of these interactions, “competitive release”, is an evolutionary phenomenon that describes the unopposed proliferation of resistant populations following maximally tolerated systemic therapies. While often assumed in evolutionary models of cancer, here we show the first empiric evidence of “competitive release” occurring in an in vitro tumor environment. We found that this phenomenon is modulated by both drug dose and initial population composition. As such, we observed that monotypic fitness differentials were insufficient to accurately predict the outcomes of this phenomenon. Instead, derivation of underlying frequency dependent evolutionary game dynamics is essential to understand resulting sub-population shifts through time. To evaluate the impact of these non-autonomous growth behaviors over longer time series, we used a range of commonly employed growth models, some of which are the foundation of ongoing clinical trials. While useful for identifying persistent qualitative features, we observed significant fragility and model specific behaviors that limited the ability of these models to make consistent quantitative predictions, even when the parameters were empirically derived.
Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies.
ABSTRACT Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. In this study we experimentally measure the frequency-dependent interactions between a gefitinib resistant non-small cell lung cancer (NSCLC) population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. In addition, we show that frequency-dependent growth rate changes may ultimately result in a safe harbor for resistant populations to safely accumulate, even those with significant cost of resistance. Using frequency-dependent growth rate data we then show that gefitinib treatment results in competitive exclusion of the ancestor, while absence of treatment results in a likely, but not guaranteed exclusion of the resistant strain. Finally, using our empirically derived growth rates to constrain simulations, we demonstrate that incorporating ecological growth effects can dramatically change the predicted time to sensitive strain extinction. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Taken together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and the clinic.
Abstract OBJECTIVES Medulloblastoma is the most common malignant pediatric brain neoplasm, but its pattern of care among adult patients has not been standardized. Disparity in socioeconomic factors influence treatment decision and prognosis in many cancers. This study aims to analyze the impact of insurance and socioeconomic status on the clinical outcome of adult patients with medulloblastoma in the United States. METHODS Adult patients (age 18 or older) with medulloblastoma in the brain diagnosed from 2004 to 2016 were identified from the National Cancer Database (NCDB). OS was evaluated with the Kaplan-Meier and Cox proportional hazards methods. Logistic regression was performed to assess the effect on dichotomized dependent variable. RESULTS A total of 1,282 adult patients with medulloblastoma were identified. The mean age was 29 years (range 18-85). 30.9% of patients were uninsured or had Medicaid, 83.2% were white, 81.9% received radiation therapy, and 59.9% received chemotherapy. Using univariate analysis, insurance status, radiation, and chemotherapy were statistically associated with OS. Uninsured/Medicaid patients were associated with poorer OS (HR 1.36, 95% CI 1.08-1.71, p < 0.01) compared to those with private insurance. Treatment with radiation therapy (HR 0.43, 95% CI 0.34-0.55, p < 0.01) or chemotherapy (HR 0.51, 95% CI 0.41-0.62, p < 0.01) portended improved OS. After adjusting for age, sex, race, and Charlson-Deyo comorbidity score, statistically significant differences in OS were seen for patients with no insurance/Medicaid. Using multivariate logistic regression, patients with either no insurance or Medicaid were less likely to receive radiation therapy (OR 0.69, 95% CI 0.51-0.95, p = 0.02) compared to patients with private insurance. CONCLUSION Using a large national cohort of adult medulloblastoma patients, this study demonstrated the statistically significant disparity of patient outcomes with insurance status. An effort to address healthcare disparity and increase access to cancer care may improve the clinical outcome for adult patients with medulloblastoma.
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