Citations

Abstract Expression of Runx2/p57 is a hallmark of the osteoblast‐lineage identity. Although several regulators that control the expression of Runx2/p57 during osteoblast‐lineage commitment have been identified, the epigenetic mechanisms that sustain this expression in differentiated osteoblasts remain to be completely determined. Here, we assess epigenetic mechanisms associated with Runx2/p57 gene transcription in differentiating MC3T3 mouse osteoblasts. Our results show that an enrichment of activating histone marks at the Runx2/p57 P1 promoter is accompanied by the simultaneous interaction of Wdr5 and Utx proteins, both are components of COMPASS complexes. Knockdown of Wdr5 and Utx expression confirms the activating role of both proteins at the Runx2‐P1 promoter. Other chromatin modifiers that were previously described to regulate Runx2/p57 transcription in mesenchymal precursor cells (Ezh2, Prmt5, and Jarid1b proteins) were not found to contribute to Runx2/p57 transcription in full‐committed osteoblasts. We also determined the presence of additional components of COMPASS complexes at the Runx2/p57 promoter, evidencing that the Mll2/COMPASS‐ and Mll3/COMPASS‐like complexes bind to the P1 promoter in osteoblastic cells expressing Runx2/p57 to modulate the H3K4me1 to H3K4me3 transition.

AbstractObjectivesMultisource feedback (MSF) has potential value in learner assessment, but has not been broadly implemented nor studied in emergency medicine (EM). This study aimed to adapt existing MSF instruments for emergency department implementation, measure feasibility, and collect initial validity evidence to support score interpretation for learner assessment.MethodsResidents from eight U.S. EM residency programs completed a self‐assessment and were assessed by eight physicians, eight nonphysician colleagues, and 25 patients using unique instruments. Instruments included a five‐point rating scale to assess interpersonal and communication skills, professionalism, systems‐based practice, practice‐based learning and improvement, and patient care. MSF feasibility was measured by percentage of residents who collected the target number of instruments. To develop internal structure validity evidence, Cronbach's alpha was calculated as a measure of internal consistency.ResultsA total of 125 residents collected a mean of 7.0 physician assessments (n = 752), 6.7 nonphysician assessments (n = 775), and 17.8 patient assessments (n = 2,100) with respective response rates of 67.2, 75.2, and 77.5%. Cronbach's alpha values for physicians, nonphysicians, patients, and self were 0.97, 0.97, 0.96, and 0.96, respectively.ConclusionsThis study demonstrated that MSF implementation is feasible, although challenging. The tool and its scale demonstrated excellent internal consistency. EM educators may find the adaptation process and tools applicable to their learners.

AbstractRobust optimization of intensity‐modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so‐called “worst case dose” and “minmax” robust optimization approaches and conventional planning target volume (PTV)‐based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull‐based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP‐PTV‐based, NLP‐PTV‐based, LP‐worst case dose, NLP‐worst case dose, LP‐minmax, and NLP‐minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP‐based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP‐based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP‐based methods was superior for the skull‐based and head and neck cancer patients. Overall, LP‐based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull‐based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV‐based optimization, and the NLP‐PTV‐based optimization outperformed the LP‐PTV‐based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP‐based methods had notably fewer scanning spots than did those generated using NLP‐based methods.