The structures of enzymes reflect two tendencies that appear opposed. On one hand, they fold into compact, stable structures; on the other hand, they bind a ligand and catalyze a reaction. To be ...stable, enzymes fold to maximize favorable interactions, forming a tightly packed hydrophobic core, exposing hydrophilic groups, and optimizing intramolecular hydrogen-bonding. To be functional, enzymes carve out an active site for ligand binding, exposing hydrophobic surface area, clustering like charges, and providing unfulfilled hydrogen bond donors and acceptors. Using AmpC β-lactamase, an enzyme that is well-characterized structurally and mechanistically, the relationship between enzyme stability and function was investigated by substituting key active-site residues and measuring the changes in stability and activity. Substitutions of catalytic residues Ser64, Lys67, Tyr150, Asn152, and Lys315 decrease the activity of the enzyme by 10
3–10
5-fold compared to wild-type. Concomitantly, many of these substitutions increase the stability of the enzyme significantly, by up to 4.7
kcal/mol. To determine the structural origins of stabilization, the crystal structures of four mutant enzymes were determined to between 1.90
Å and 1.50
Å resolution. These structures revealed several mechanisms by which stability was increased, including mimicry of the substrate by the substituted residue (S64D), relief of steric strain (S64G), relief of electrostatic strain (K67Q), and improved polar complementarity (N152H). These results suggest that the preorganization of functionality characteristic of active sites has come at a considerable cost to enzyme stability. In proteins of unknown function, the presence of such destabilized regions may indicate the presence of a binding site.
Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions ...is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.
We evaluated the impact of a virtual radiation oncology clerkship.
We developed a 2-week virtual radiation oncology clerkship that launched on April 27, 2020. Clerkship components included a virtual ...clinic with radiation oncology faculty and residents, didactic lectures, student talks, and supplemental sessions such as tumor boards and chart rounds. Medical students completed pre- and post-clerkship self-assessments. Faculty and resident participants also completed surveys on their experience with virtual lectures and clinics. Pre- and post-clerkship results were compared using a 2-sided paired t test. An analysis of variance model was used to analyze the clerkship components.
Twenty-six medical students, including 4 visiting students, enrolled over 2 clerkship periods (4 weeks). All students completed the pre- and post-clerkship self-assessments and agreed that the clerkship improved their understanding of radiation oncology. Compared with 3 (11.5%) students who agreed that they understood the daily responsibilities of a radiation oncologist before the clerkship, 22 (84.6%) students agreed and 3 (11.5%) strongly agreed that they understood the daily responsibilities of a radiation oncologist after the clerkship (P < .0001). Although 15 students (57.7%) reported an increased interest in radiation oncology because of the clerkship, the mean level of interest in radiation oncology as a career remained the same, with pre- and post-clerkship scores of 3.0 (±0.9) and 3.0 (±1.1) on a 5-point scale, respectively (P = .7). Students found virtual clinic and didactic lectures to be the most valuable components of the clerkship. Most respondents agreed (30.8%) or strongly agreed (65.4%) to recommend the clerkship to their classmates.
Our virtual clerkship was effective in increasing medical student interest in and knowledge about radiation oncology. These data will help optimize a new paradigm of virtual radiation oncology education for medical students during COVID-19 and beyond.
Purpose
To fully automate CT‐based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques.
Methods
We automated three different radiotherapy ...planning techniques for locally advanced cervical cancer: 2D 4‐field‐box (4‐field‐box), 3D conformal radiotherapy (3D‐CRT), and volumetric modulated arc therapy (VMAT). These auto‐planning algorithms were combined with a previously developed auto‐contouring system. To improve the quality of the 4‐field‐box and 3D‐CRT plans, we used an in‐house, field‐in‐field (FIF) automation program. Thirty‐five plans were generated for each technique on CT scans from multiple institutions and evaluated by five experienced radiation oncologists from three different countries. Every plan was reviewed by two of the five radiation oncologists and scored using a 5‐point Likert scale.
Results
Overall, 87%, 99%, and 94% of the automatically generated plans were found to be clinically acceptable without modification for the 4‐field‐box, 3D‐CRT, and VMAT plans, respectively. Some customizations of the FIF configuration were necessary on the basis of radiation oncologist preference. Additionally, in some cases, it was necessary to renormalize the plan after it was generated to satisfy radiation oncologist preference.
Conclusion
Approximately, 90% of the automatically generated plans were clinically acceptable for all three planning techniques. This fully automated planning system has been implemented into the radiation planning assistant for further testing in resource‐constrained radiotherapy departments in low‐ and middle‐income countries.
Purpose
To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with ...implementation of a quality assurance (QA) program specifically designed for the RPA.
Methods
We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four‐field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program.
Results
In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top‐ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician.
Conclusions
Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.
The Impact of Age on Outcome in Early-Stage Breast Cancer Beadle, Beth M., MD, PhD; Woodward, Wendy A., MD, PhD; Buchholz, Thomas A., MD
Seminars in radiation oncology,
2011, 2011-Jan, 2011-01-00, 20110101, Letnik:
21, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Multiple studies have shown that breast-conserving therapy (BCT) and mastectomy have equivalent outcomes for large populations of women with early-stage breast cancer. For individual treatment ...decisions, however, it is important to appreciate the heterogeneity of disease. Recent molecular studies have suggested that “breast cancer” includes biologically distinct classes of disease; although these molecular distinctions are important, other patient-related factors also affect outcome and influence prognosis. One of the most important of these patient factors is the age of the patient at diagnosis. Numerous studies have shown very different breast cancer outcomes based on patient age; younger women typically have more aggressive tumors that are more likely to recur both locoregionally and distantly, and older women more commonly have less aggressive disease. The overall disease-specific outcomes, techniques, and doses for adjuvant radiation therapy and toxicity of treatments should be discussed within the context of age because breast cancer is a very different disease based on this factor. Arguments can be made that more aggressive locoregional therapy is warranted in populations of young women with breast cancer and perhaps less aggressive therapy in the elderly.
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