Search

PURPOSE/OBJECTIVES: Dosimetric and technical challenges often limit radiotherapy (RT) target coverage for breast cancer patients who require bilateral breast/chest wall and regional nodal irradiation (RNI). We evaluated the feasibility of using volumetric modulated arc therapy (VMAT) to administer bilateral comprehensive RNI including the internal mammary nodes. MATERIALS/METHODS: We analyzed all patients treated at our institution with bilateral RNI using VMAT from 2017–2020. Medical records were reviewed to ascertain clinicopathologic features, radiotherapeutic parameters and treatment-related adverse events. RESULTS: The cohort comprised 12 patients who underwent VMAT for bilateral RNI, with a median follow-up of 14.5 months. Median V5Gy for bilateral lungs was 96.1% (range: 84.5%−99.8%). Median V20Gy for each lung was 27.5% (range: 14.9–38.1%). Cardiac mean dose was a median of 699 cGy (range: 527–1117 cGy). Five patients (41%) developed grade 1 cough/dyspnea, with one patient developing grade 3 dyspnea. Of note, three of these (60%) were current or former smokers. No patient received glucocorticoid therapy or required respiratory intervention and none developed longer term pulmonary complaints. Decline in ejection fraction occurred in one patient with a pre-existing cardiac condition who also received anthracycline-based chemotherapy and trastuzumab. Only one patient experienced locoregional recurrence with synchronous distant progression and subsequently succumbed to her disease. No secondary cancers have been noted to date. CONCLUSION: VMAT appears to be a feasible and tolerable RT modality for breast cancer patients who require bilateral comprehensive adjuvant RT with RNI obtaining excellent target coverage. No patients required medical intervention for pulmonary complaints despite a median bilateral V5 approaching 100%, providing further evidence that V5 is not predictive for complications.

Microbial communities participate in many essential processes from biogeochemical cycles to the maintenance of human health. The structure and functions of these communities are dynamic properties that depend on poorly understood interactions among different species.

TheE. coligenome-scale metabolic model (GEM) is a gold standard for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint model uncertainty and ensure continued development of accurate models. Here we assessed the accuracy of theE. coliGEM using published mutant fitness data for the growth of gene knockout mutants across thousands of genes and 25 different carbon sources. We explored the progress of theE. coliGEM versions over time and further investigated errors in the latest version of the model (iML1515). We observed that model size is increasing while prediction accuracy is decreasing. We identified several adjustments that improve model accuracy – the addition of vitamins/cofactors and re-assignment of reaction reversibility and isoenzyme gene to reaction mapping. Furthermore, we applied a machine learning approach which identified hydrogen ion exchange and central metabolism branch points as important determinants of model accuracy. Continued integration of experimental data to validate GEMs will improve predictive modeling of the mapping from genotype to metabolic phenotype inE. coliand beyond.SynopsisE. coligenome-scale metabolic model flux balance analysis (FBA) prediction accuracy was quantified with published experimental data assaying gene knockout mutant growth across different carbon sources. Insights into model development trends and sources of inaccuracy were revealed.Model representational power (size) has been increasing over time, while accuracy has been decreasing.Adding vitamins/cofactors to the model environment and re-assigning reaction reversibility and isoenzyme gene-to-reaction mapping improves correspondence between model predictions and experimental data.Machine learning reveals hydrogen ion exchange and central metabolism branch points as important features in the determination of model accuracy.

We aimed to develop and study the implementation of a remote system for toxicity assessment and management of acute side effects of breast radiation using electronic patient-reported outcomes (ePROs).A response-adapted Patient-Reported Outcomes Common Terminology Criteria for Adverse Events-based assessment for breast radiation toxicity was administered weekly during and for 8 weeks after radiation from June 2019 to July 2020. The care team received alerts when "severe" symptoms were reported by patients, who were then contacted. Treatment, clinic, and sociodemographic characteristics were abstracted from patient records. A subsample of patients and care team members was qualitatively interviewed at follow-up.Overall, 5787 assessments were sent to 678 patients, of whom 489 (72%) completed 2607 assessments (45%). Moderate or greater toxicity was reported by 419 responders (86%; 95% CI, 82%-89%). Clinician alerts for severe toxicity were generated for 264 assessments among 139 unique patients, of which 83% occurred posttreatment. The proportion of surveys that prompted an alert was significantly higher after treatment (219 [13%]) than during treatment (45 [5%]) (P.001). Survey completion rates in the posttreatment period were higher among patients undergoing partial breast irradiation than postmastectomy radiation (incidence rate ratio, 0.70; 95% CI, 0.60-0.81) (P.001) despite these patients experiencing less severe toxicity. Interviews (15) found that patients had a positive experience with ePROs, although many thought the primary purpose was for research rather than symptom management.With the majority of toxicity occurring after breast radiation has ended, remote symptom monitoring with ePROs appears to fill a gap in clinical practice, particularly for patients undergoing shorter courses of radiation. It is important to properly onboard patients and explain that the purpose of ePROs is to aid clinical care. Further research is needed to determine whether the costs associated with ePROs can be offset by reducing routine clinic visits and whether this approach is acceptable and appropriate.

Background Cutaneous leishmaniasis (CL) is a threat to U.S. Military personnel as they deploy to endemic areas. As treatment may require evacuation, CL undermines operations. Elucidating the epidemiology of CL in this population is key for prevention. Methods We retrospectively reviewed data from a CL sodium stibogluconate treatment trial at Walter Reed Army Medical Center, Washington DC. 412 military members with parasitologically confirmed CL and deployment to Southwest Asia from May 2002 - August 2004 enrolled. Subjects’ CL lesions were counted and measured. 334 subjects completed a risk survey. Given no control group, we used number of CL lesions (NL), total lesion area (TLA), and lesion location as outcomes to assess CL risks. Non-parametric tests and logistic regression were used as appropriate. Results Permethrin treated bed net use was associated with lower NL (p = 0.000), TLA (p = 0.024), and odds of head/face lesion (OR 0.12, p = 0.047). Sleeping in a combat uniform was associated with lower TLA (p = 0.000) and odds of leg/foot lesion (OR 0.39, p = 0.023). Use of a permethrin treated uniform (p = 0.002) and N,N-diethyl-meta-toluamide (DEET) insect repellent at night (p = 0.046) were associated with lower NL. Noting unit members with similar lesions was associated with higher NL (p = 0.007) and odds of head/face lesion (OR 12.1, p = 0.019). National Guard status followed by Active Duty was associated with higher NL (p = 0.002) and TLA (p = 0.000) than Reserve. Sleeping in a building was associated with higher TLA (p = 0.008) and odds of arm lesion (OR 2.28, p = 0.016). Sleeping near animal burrows was associated with higher NL (p = 0.031). Sleeping on a cot (p = 0.006), certain ethnicities (p = 0.020) and certain military occupational specialties (p =0.038) were associated with higher TLA. Increasing age (p = 0.001) and years of service (p = 0.000) were positively correlated with TLA. Conclusion This is the largest group of U.S. Military members with CL reported and provides insight into risks for CL which will guide preventive efforts to reduce the burden of illness in this population. The opinions and assertions expressed herein are those of the author(s) and do not reflect the official policy or position of the Uniformed Services University of the Health Sciences, the Department of Defense, or the U.S. Government. Disclosures Naomi E. Aronson, M.D., Elsevier: Royalties as text editor, honoraria for chapter writing|UpToDate: royalties for writing chapters.

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.