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Tripp Leavitt, Michael S Hu, Clement D Marshall, Leandra A Barnes, Michael T Longaker, H Peter Lorenz Hagey Laboratory for Pediatric Regenerative Medicine, Division of Plastic Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA Abstract: Currently available treatments for chronic wounds are inadequate. A clearly effective therapy does not exist, and treatment is often supportive. This is largely because the cellular and molecular processes underlying failure of wound repair are still poorly understood. With an increase in comorbidities, such as diabetes and vascular disease, as well as an aging population, the incidence of these intractable wounds is expected to rise. As such, chronic wounds, which are already costly, are rapidly growing as a tremendous burden to the health-care system. Stem cells have garnered much interest as a therapy for chronic wounds due to their inherent ability to differentiate into multiple lineages and promote regeneration. Herein, we discuss the types of stem cells used for chronic wound therapy, as well as the proposed means by which they do so. In particular, we highlight mesenchymal stem cells (including adipose-derived stem cells), endothelial progenitor cells, and induced pluripotent stem cells. We include the results of recent in vitro and in vivo studies in both animal models and human clinical trials. Finally, we discuss the current studies to improve stem cell therapies and the limitations of stem cell-based therapeutics. Stem cells promise improved therapies for healing chronic wounds, but further studies that are well-designed with standardized protocols are necessary for fruition. Keywords: stem cells, chronic wounds, cell therapy, wound healing

Many K-12 students are given homework; this can also apply in physical education (PE) classes. Homework in PE can be specific to what the student is doing in their PE class, or students might be given the option of doing an activity of their choice. Research has investigated parents and K-12 students' opinions and perceptions of homework. This study sought to better understand Physical Education Teacher Education (PETE) majors' perceptions and opinions of homework in PE. It was generally found from qualitative survey questions that PETE majors felt that homework in PE can be beneficial, but that it should be fun with no busy work. Results from this study suggest that PETE majors' are not particularly sure if they will implement homework in their PE classes upon graduation. [ABSTRACT FROM AUTHOR]

Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet causal inferences from randomized experiments are especially credible because they depend on a known assignment process, not a probability model of potential outcomes. In this article, I derive a randomization-based procedure for Bayesian inference of causal effects in a finite population setting. I formally show that this procedure satisfies Bayesian analogues of unbiasedness and consistency under weak conditions on a prior distribution. Unlike existing model-based methods of Bayesian causal inference, my procedure supposes neither probability models that generate potential outcomes nor independent and identically distributed random sampling. Unlike existing randomization-based methods of Bayesian causal inference, my procedure does not suppose that potential outcomes are discrete and bounded. Consequently, researchers can reap the benefits of Bayesian inference without sacrificing the properties that make inferences from randomized experiments especially credible in the first place.

We seek to investigate the impact of the COVID19 pandemic on the radiation oncology care path timeline. We hypothesized that the COVID19 pandemic would result in increased time to complete the care path from simulation to start of radiation. Care path data of patients treated at a single institution were included for 3 epochs: Pre COVID (July 2019 – March 2020), C1 [PreV] (April 2020 – Dec 2020), and C2 [PostV] (Jan 2021 – September 2021). The following tasks in the care path were evaluated: simulation start time, contours completed, physician review, plan finalization, IMRT/IMPT QA, and radiation treatment start. Time stamps of completion of tasks in the radiation treatment planning care pathway were extrapolated and used to create 9-time intervals. Demographic, tumor, and treatment characteristics were gathered to stratify patients. Patients with care path times greater than 25 days were excluded from the analysis. Numerical variables are summarized using mean and standard deviation while categorical data is summarized using counts and percentages. The ANOVA test is used to compare means in RT planning times between time epochs. A total of 3557 patients were included in the study analysis: 1095 PC, 1105 C1, and 1357 C2. There were improvements in multiple car path intervals following the start of the pandemic. Across epochs, the mean time from simulation to RT Start was 10.5 days (10.9 PC, 10.3C1, and 10.3 C2) (p =0.008). The mean time from simulation to plan finalization was 5.5 days (5.6 PC, 5.7 C1, 5.2 C2, p =0.008). The mean time for plan review to start time was 5.5 days (5.7 PC, 5.2 C1, and 5.5 C2) (p=0.010). Contour completion to radiation start time was significantly shorter after the pandemic (8.8 PC vs. 8.4 C1 vs. 8.4 C2, p =0.046). The mean time from physician review to IMRT/IMPT QA was 1.7 days (1.8 PC, 1.7 C1, 1.5 C2, p = 0.007). Physician review to RT start took a mean time of 5.5 days (5.7 PC, 5.2 C1, 5.5 C2, p = 0.010). Plan finalization to IMRT/IMPT QA took a mean time of 1.1 days (1.2 PC, 1.1 C1, 1.0 C2, p = 0.01). IMRT/QA to RT start took a mean time of 4.4 days (4.6 PC, 4.0 C1, and 4.5 C2, p =0.002). The mean time from plan finalization to RT start was 5.0 days (5.2 PC, 4.6 C1, 5.0 C2, p = 0.004). There were no significant differences in simulation to contour time (p =0.181) and physician review completion to plan finalization (p =0.223) The COVID19 pandemic has been a disruptive force in the management of patients in Radiation Oncology. However, we demonstrate improved efficiency in care path completion as we entered into the pandemic. Further investigation into care path efficiency is important for provider and patient satisfaction. [ABSTRACT FROM AUTHOR]

Accurate organ-at-risk (OAR) contouring is essential to improve plan quality. Head and neck (H&N) cancer has many OARs and contouring requires substantial time and effort. We developed a deep learning model (DLM) for autosegmentation of H&N OARs and performed a prospective study to assess time savings using DLM to generate contours for review compared to those created by medical dosimetrist's assistants (MDAs) with experience in H&N OAR contouring as part of routine practice. A prospective study was run to assess if the use of the DLM would reduce contouring time by more than 30%. Using a custom 3D U-net architecture operating on 32 × 512 × 512-voxel subvolumes, the DLM was trained to segment 42 OARs for H&N cancer using retrospective planning CTs with OAR contours generated by H&N expert radiation oncologists (ROs) using sufficient protected time to represent the institutional "gold standard" (312 training, 51 model tuning, 82 testing), with 20 held out for prospective validation through an IRB-approved study with H&N specialized MDAs and ROs as consenting participants. The prospective study involved two arms for each hold out CT: the standard arm involved MDAs creating initial contours followed by revision by an RO (Manual arm) and initial contours for the same CT created by the DLM and revised by a different H&N RO (AI arm), the ROs were blinded to the origin of the initial contours and were randomly assigned an equal number of cases from each arm and asked to revise the contours to meet clinical standards for planning. The study was powered to detect greater than 30% reduction in contouring time (α = 0.025). Final RO-edited contours from each arm were compared using Dice Similarity Coefficient (DSC) to the gold standard (GS). Participant surveys were obtained after each session and the completion of the study. The mean total time for the manual arm was 3.4 hours (95% CI, 2.9-3.9 hours), while for the AI arm it was 0.7 hours (95% CI, 0.6-1.6 hours), a reduction of 76% (95% CI, 65%-88%) which was significantly greater than 30% (p<0.0001). 36 OARs (86%) had mean DSC greater than 0.8 on the AI arm compared to 20 (45%) for the Manual arm. The mean DSC for select OARs are given in table below. Survey of ROs showed satisfaction with DLM contours and interest in using for future cases. The DLM demonstrated a significant reduction in the time needed for contouring the OARs for H&N cancer while improving agreement with the institutional gold standard. Integration of the DLM into the clinic for prospective evaluation is underway. [ABSTRACT FROM AUTHOR]

Purpose/objective(s): The COVID-19 pandemic has direct and indirect impact on patients with cancer. Low- and middle-income regions, especially sub-Saharan Africa, are especially vulnerable to a negative impact on cancer resources and outcomes. We report the initial indirect impact of COVID-19 on cancer care in the sub-Saharan Africa region approximately 14 months into the pandemic.Materials/methods: At the start of the pandemic, we created a consortium of African and North American cancer centers and NGOs for the distribution of factual and timely information and data on COVID-19 and cancer care. A survey was distributed to consortium members and other colleagues from the sub-Saharan Africa region to understand the impact of COVID-19 in cancer care resources. Survey respondents represent cancer experts from 8 centers in Ghana, Nigeria, Kenya, Ethiopia, South Africa, Rwanda, and Zimbabwe.Results: All sites report SARS-COv-2 transmission amongst cancer patients and staff. A total of 48 staff developed COVID-19 infection with one site reporting a single death. Additionally, 62.5% of sites report loss of oncology physician or nursing staff due to redeployment for COVID-19 care resulting in minimal (20%), moderate (60%), or other (20%) impact on cancer care. All 8 sites report a government mandated lockdown with a median duration of 2.3 months (IQR .9-4.2 months). Impact of the lockdown on cancer care was reported as none (12.5%), minimal (12.5%), moderate (50%) and severe (25%). Additionally, we surveyed the impact of COVID-19 on resources in radiation, medical and surgical oncology services. A total of 25% of responders reported decreases in radiation resources while 37.5% reported changes in medical and surgical oncology resources. For radiation oncology, the most common impact was access to CT imaging for 3D-conformal planning (25%), access to brachytherapy (12.5%), and medical physics support (12.5%). For medical oncology, the most frequent impact was access to chemotherapy (37.5%) and blood products (12.5%), and loss of oncology ward space (12.5%). The most frequent impact for surgical oncology was access to operating rooms (37.5%), ventilators (12.5%), anesthesia (25%), blood products (25%), and other supply chain issues (25%). Of centers who reported impact on cancer care, severity of impact was none (50%) and moderate (50%) for radiation oncology; mild (25%) and moderate (75%) for medical oncology; and moderate (75%) and severe (25%) for surgical oncology.Conclusion: Our survey identified diffuse impact of COVID-19 on all facets of cancer care across sub-Saharan Africa. Based on physician assessment of impact, the discipline of surgical oncology may be impacted the greatest. Additional studies measuring the impact of COVID-19 on cancer outcomes are ongoing. [ABSTRACT FROM AUTHOR]