Exploration of glutaredoxin-1 oxidative modification in carbon nanomaterial-induced hepatotoxicity.

Herein, we present toxicological assessments of carbon nanomaterials in HL-7702 cells, and it was found that reactive oxygen species (ROS) levels were elevated. Mass spectrometry results indicated that cysteine sulfhydryl of glutaredoxin-1 (GLRX1) was oxidized to sulfenic acids and sulfonic acids by excessive ROS, which broke the binding of GLRX1 to apoptosis signal-regulating kinase 1, causing the activation of the JNK/p38 signaling pathway and ultimately hepatocyte apoptosis. However, a lower level of ROS upregulated GLRX1 instead of sulfonation modification of its active sites. Highly expressed GLRX1 in turn enabled the removal of intracellular ROS, thereby exerting inconspicuous toxic effects on cells. Taken together, these findings emphasized that CNM-induced hepatotoxicity is attributable to oxidative modifications of GLRX1 arising from redox imbalance.

Incorporating variable RBE in IMPT optimization for ependymoma.

PURPOSE: To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity-modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)-based optimization. METHODS: This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard-of-care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose-averaged LET (LETd ) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LETd , LET-weighted dose, and equivalent uniform dose between the different optimization approaches. RESULTS: We found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans. CONCLUSION: We demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients.

Novel AIE Probe for In Situ Imaging of Protein Sulfonation to Assess Cigarette Smoke-Induced Inflammatory Damage.

Cysteine sulfonic acid, a product of protein oxidative damage, is an important sign by which the body and cells sense oxidative stress. Cigarette smoke (CS) can trigger inflammatory reactions in humans that lead to higher levels of oxidative stress and reactive oxygen species (ROS) in the body. Available evidence indicates a possible relationship between protein oxidative damage and cigarette smoke, which is poorly understood due to the limitations of analytical techniques. Herein, we developed a donor-acceptor structured aggregation-induced emission (AIE) fluorescence probe H-1, which exhibited excellent optical properties for the highly sensitive and specific detection of sulfonic acid biomacromolecules. The probe could be easily synthesized by click chemistry conjugating triazole heterocycles onto a triphenylamine fluorophore, followed by a cationization reaction. Due to low cytotoxity, the probe was successfully applied for in situ imaging of intracellular protein sulfonation, achieving visualization of protein sulfonation in cigarette smoke stimulation-induced inflammatory RAW264.7 cell models. Moreover, an immunofluorescence study of the aorta and lung revealed that significant blue fluorescence signals could be observed only in CS-stimulated vascular. It indicated that CS-stimulated vascular sulfonation injury can be monitored using H-1. This study will provide an efficient method for revealing CS-induced oxidative damage-relevant diseases.

Intensity modulated proton arc therapy via geometry-based energy selection for ependymoma.

PURPOSE: We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. METHODS: Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle such that each target voxel is covered by sufficient scanning spots as specified by the planner, with dose contributions above the specified threshold. Finally, IMPAT plans are generated by robustly optimizing scanning spots of the selected energy layers using a commercial proton treatment planning system (TPS). The IMPAT plan quality was assessed for four ependymoma patients. Reference three-field IMPT plans were created with similar planning objective functions and compared with the IMPAT plans. RESULTS: In all plans, the prescribed dose covered 95% of the clinical target volume (CTV) while maintaining similar maximum doses for the brainstem. While IMPAT and IMPT achieved comparable plan robustness, the IMPAT plans achieved better homogeneity and conformity than the IMPT plans. The IMPAT plans also exhibited higher relative biological effectiveness (RBE) enhancement than did the corresponding reference IMPT plans for the CTV in all four patients and brainstem in three of them. CONCLUSIONS: The proposed method demonstrated potential as an efficient technique for IMPAT planning and may offer a dosimetric benefit for patients with ependymoma or tumors in close proximity to critical organs. IMPAT plans created using this method had elevated RBE enhancement associated with increased linear energy transfer (LET) in both targets and abutting critical organs.

Hepatotoxicity-Related Oxidative Modifications of Thioredoxin 1/Peroxiredoxin 1 Induced by Different Cadmium-Based Quantum Dots.

The hepatotoxicity of cadmium-based quantum dots (Cd-QDs) has become the focus with their extensive applications in biomedicine. Previous reports have demonstrated that high oxidative stress and consequent redox imbalance play critical roles in their toxicity mechanisms. Intracellular antioxidant proteins, such as thioredoxin 1 (Trx1) and peroxiredoxin 1 (Prx1), could regulate redox homeostasis through thiol-disulfide exchange. Herein, we hypothesized that the excessive reactive oxygen species (ROS) induced by Cd-QD exposure affects the functions of Trx1 or Prx1, which further causes abnormal apoptosis of liver cells and hepatotoxicity. Thereby, three types of Cd-QDs, CdS, CdSe, and CdTe QDs, were selected for conducting an intensive study. Under the same conditions, the H2O2 level in the CdTe QD group was much higher than that of CdS or CdSe QDs, and it also corresponded to the higher hepatotoxicity. Mass spectrometry (MS) results show that excessive H2O2 leads to sulfonation modification (-SO3H) at the active sites of Trx1 (Cys32 and Cys35) and Prx1 (Cys52 and Cys173). The irreversible oxidative modifications broke their cross-linking with the apoptosis signal-regulating kinase 1 (ASK1), resulting in the release and activation of ASK1, and activation of the downstream JNK/p38 signaling finally promoted liver cell apoptosis. These results highlight the key effect of the high oxidative stress, which caused irreversible oxidative modifications of Trx1 and Prx1 in the mechanisms involved in Cd-QD-induced hepatotoxicity. This work provides a new perspective on the hepatotoxicity mechanisms of Cd-QDs and helps design safe and reliable Cd-containing nanoplatforms.

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose-volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper-lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re-generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge-based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically-generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.

Proximity-Induced Hybridization Chain Reaction-Based Photoacoustic Imaging System for Amplified Visualization Protein-Specific Glycosylation in Mice.

Glycosylation is a key cellular mechanism that regulates several physiological and pathological functions. Therefore, identification and characterization of specific-protein glycosylation in vivo are highly desirable for studying glycosylation-related pathology and developing personalized theranostic modalities. Herein, we demonstrated a photoacoustic (PA) nanoprobe based on the proximity-induced hybridization chain reaction (HCR) for amplified visual detection of protein-specific glycosylation in vivo. Two kinds of functional DNA probes were designed. A glycan probe (DBCO-GP) was attached to glycans through metabolic oligosaccharide engineering (MOE) and protein probe (PP)-targeted proteins by aptamer recognition. Proximity-induced hybridization of the complementary domain between the two kinds of probes promoted conformational changes in the protein probes and in situ release of the HCR initiator domain. Gold nanoparticles (AuNPs) modified by complementary sequences (Au-H1 and Au-H2) self-assembled into Au aggregates via the HCR, thereby converting DNA signals to photoacoustic signals. Due to the high contrast and deep penetration of photoacoustic imaging, this strategy enabled in situ detection of Mucin 1 (MUC1)-specific glycosylation in mice with breast cancer and successfully monitored its dynamic states during tunicamycin treatment. This imaging technique provides a powerful platform for studying the effects of glycosylation on the protein structure and function, which helps to elucidate its role in disease processes.

Luminescence-Resonance-Energy-Transfer-Based Luminescence Nanoprobe for In Situ Imaging of CD36 Activation and CD36-oxLDL Binding in Atherogenesis.

Macrophage foam cell formation mediated by CD36 receptor dependent internalization of oxidized low-density lipoprotein (oxLDL) is an important hallmark of early atherosclerosis. Activation of CD36 and its binding to oxLDL are the key points in foam cell formation. Herein, we develop a site-specific luminescence resonance energy transfer (LRET) system for the simultaneous imaging of CD36 activity and CD36-oxLDL binding on the cell surface. The system utilizes CD36-antibody-modified, SiO2-coated upconversion luminescent nanoparticles (UCNPs) as an energy donor to target the plasma membranes of macrophages, and DiI-oxLDL (energy acceptor) binds to CD36 and passes through the membrane during macrophage foam cell formation. Upon excitation at 980 nm, the LRET signal can be obtained because of the short distance between DiI-oxLDL and the nanoprobe. Additionally, the very specific fluorescence can be used to visualize distinct features of CD36. The nanoprobe also exhibits high sensitivity, good stability, simplicity, and low cost for the accurate detection and evaluation of macrophage foam cell formation. Moreover, using this novel nanoprobe, we also investigate the mechanism by which reactive oxygen species (ROS) signaling enhances the binding of oxLDL to CD36. ROS, especially O2·-, alter endothelial permeability and facilitate CD36 clustering, ultimately promoting the entry and internalization of oxLDL. Because of these advantages, this nanoprobe may provide a versatile platform for monitoring the progression of atherogenesis and elucidating atherogenesis signaling at the cellular level.

A Redox-Responsive Self-Assembled Nanoprobe for Photoacoustic Inflammation Imaging to Assess Atherosclerotic Plaque Vulnerability.

Inflammation triggered by oxidative stress is the main determinant of atherosclerotic plaque disruption, which is the leading cause of myocardial infarctions and strokes. Hence, noninvasive mapping of alterations in redox status in vivo is highly desirable for accurate assessment of plaque inflammatory activity and vulnerability. Herein, two types of near-infrared fluorescence probes, specific for glutathione (GSH)/hydrogen peroxide (H2O2) redox couple, were used to introduce the self-assembly of bovine serum albumin (BSA), forming a BSA-Cy-Mito nanoprobe for in vivo photoacoustic imaging of redox status. Such BSA-based self-assemblies on one hand processed good biocompatibility and long blood circulation for high EPR effect and plaque accumulation and on the other hand displayed strong GSH- and H2O2-dependent absorbance at 765 and 680 nm, which enabled simultaneous photoacoustic detection of GSH/H2O2 with high specificity and sensitivity. Using BSA-Cy-Mito as an in vivo GSH/H2O2 indicator, accurate detection of the redox-related inflammatory process was realized both in oxidized low-density lipoprotein (ox-LDL)-activated macrophages and high fat diet-fed apolipoprotein E-deficient (ApoE-/-) mice. Systemic administration of BSA-Cy-Mito further enabled differentiation of vulnerable plaques from stable ones based on their different redox states. Therefore, this sensitive redox-responsive PA nanoprobe may be a powerful tool for early identification of rupture-prone plaques and help in implementing successful preventative therapeutic strategies.

CeO2 Nanowire-BODIPY-Adenosine Triphosphate Fluorescent Sensing Platform for Highly Specific and Sensitive Detection of Arsenate.

Effective and sensitive monitoring of arsenate in drinking water is significant for risk management of public health. Here, we demonstrated that a CeO2 nanowire acted as an efficient quencher for small fluorescent molecules with a phosphate group, BODIPY-adenosine triphosphate (BODIPY-ATP) and riboflavin-5'-phosphate (Rf-P), and developed a CeO2 nanowire-BODIPY-ATP platform for highly selective and sensitive detection of arsenate. The response strategy was based on the competitive coordination chemistry of CeO2 nanowire between arsenate and phosphate group of BODIPY-ATP. Arsenate displaced adsorbed BODIPY-ATP to enhance fluorescence, allowing detection of arsenate down to 7.8 nM, which is lower than the WHO-defined limit of 130 nM. An excellent linear range of 20-150 and 150-1000 nM was obtained. Importantly, this system was simple in design and convenient in operation. Also, the platform exhibited excellent selectivity for arsenate without the interference of phosphate ions. Finally, the proposed method had been successfully employed for determination of arsenate in real water samples.

Visualizing miR-155 To Monitor Breast Tumorigenesis and Response to Chemotherapeutic Drugs by a Self-Assembled Photoacoustic Nanoprobe.

MicroRNA-155 (miR-155), which facilitates breast tumor growth and invasion by promoting tumor cell proliferation and inhibiting cell apoptosis, is considered an ideal early diagnostic and prognostic marker. Herein, we developed a self-assembled hybridization chain reaction (HCR)-based photoacoustic (PA) nanoprobe for highly sensitive in situ monitoring of dynamic changes in miR-155 expression during breast tumorigenesis and chemotherapy. The PA nanoprobes (Au-H1/PEG and Au-H2/PEG) were constructed by linking poly(ethylene glycol) (PEG) and two hairpin DNA strands (H1 and H2, respectively) to the surface of gold nanoparticles (AuNPs). In the presence of miR-155, the PA nanoprobes self-assembled into Au aggregates via HCR between H1, H2, and miR-155. The decreased interparticle distance in these aggregates enhanced the surface plasmon resonance (SPR) in the AuNPs. Consequently, the absorption peak of the PA nanoprobes red-shifted, and strong PA signals were generated. This strategy enabled the sensitive and quantitative detection of miR-155 with a low detection limit of 0.25 nM. As a result, PA signals of miR-155 were captured on the second day after tumor inoculation when the solid tumor had not yet formed. Dynamic changes in miR-155 during tumor growth and chemotherapy were also monitored in real time to assess the therapeutic effects via PA imaging. By virtue of these advantages, the PA nanoprobes may provide a powerful platform for in situ detection of miR-155 and thus real-time monitoring of tumorigenesis and drug response in breast cancer.

Visualization and Inhibition of Mitochondria-Nuclear Translocation of Apoptosis Inducing Factor by a Graphene Oxide-DNA Nanosensor.

High concentrations of oxidized low density lipoprotein (oxLDL) induce aberrant apoptosis of vascular smooth muscle cells (VSMCs) in atherosclerotic plaques. This apoptosis cannot be blocked completely by the inhibition of caspase, and it eventually potentiates plaque disruption and risk for cardiovascular disease. Given the important role of apoptosis inducing factor (AIF) in caspase-independent apoptosis, here we develop an AIF-targeting nanosensor by the assembly of graphene oxide (GO) nanosheets and dye-labeled DNA hybrid structures. This nanosensor selectively localizes in the cytosol of VSMCs, where it exhibits a "turn-off" fluorescence signal. Under oxLDL stimuli, the release of AIF from mitochondria into cytosol liberates the DNA hybrid structures from the surface of GO and results in a "turn-on" fluorescence signal. This nanosensor is shown to possess rapid response, high sensitivity, and selectivity for AIF that enables real-time imaging of AIF translocation in VSMCs. Using this novel nanosensor, a better assessment of the apoptotic level of VSMCs and a more accurate evaluation of the extent of atherosclerotic lesions can be obtained. More importantly, the abundant binding between DNA hybrid structures and AIF inhibits the translocation of AIF into the nucleus and subsequent apoptosis in VSMCs. This inhibition may help stabilize plaque and reduce the risk of heart attack and stroke.

Impact of respiratory motion on variable relative biological effectiveness in 4D-dose distributions of proton therapy.

BACKGROUND: Organ motion during radiation therapy with scanned protons leads to deviations between the planned and the delivered physical dose. Using a constant relative biological effectiveness (RBE) of 1.1 linearly maps these deviations into RBE-weighted dose. However, a constant value cannot account for potential nonlinear variations in RBE suggested by variable RBE models. Here, we study the impact of motion on recalculations of RBE-weighted dose distributions using a phenomenological variable RBE model. MATERIAL AND METHODS: 4D-dose calculation including variable RBE was implemented in the open source treatment planning toolkit matRad. Four scenarios were compared for one field and two field proton treatments for a liver cancer patient assuming (α∕ß)x = 2 Gy and (α∕ß)x = 10 Gy: (A) the optimized static dose distribution with constant RBE, (B) a static recalculation with variable RBE, (C) a 4D-dose recalculation with constant RBE and (D) a 4D-dose recalculation with variable RBE. For (B) and (D), the variable RBE was calculated by the model proposed by McNamara. For (C), the physical dose was accumulated with direct dose mapping; for (D), dose-weighted radio-sensitivity parameters of the linear quadratic model were accumulated to model synergistic irradiation effects on RBE. RESULTS: Dose recalculation with variable RBE led to an elevated biological dose at the end of the proton field, while 4D-dose recalculation exhibited random deviations everywhere in the radiation field depending on the interplay of beam delivery and organ motion. For a single beam treatment assuming (α∕ß)x = 2 Gy, D95% was 1.98 Gy (RBE) (A), 2.15 Gy (RBE) (B), 1.81 Gy (RBE) (C) and 1.98 Gy (RBE) (D). The homogeneity index was 1.04 (A), 1.08 (B), 1.23 (C) and 1.25 (D). CONCLUSION: For the studied liver case, intrafractional motion did not reduce the modulation of the RBE-weighted dose postulated by variable RBE models for proton treatments.

Radiobiological issues in proton therapy.

BACKGROUND: The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

Robust 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.

Macro-scale relationship between body mass and timing of bird migration.

Clarifying migration timing and its link with underlying drivers is fundamental to understanding the evolution of bird migration. However, previous studies have focused mainly on environmental drivers such as the latitudes of seasonal distributions and migration distance, while the effect of intrinsic biological traits remains unclear. Here, we compile a global dataset on the annual cycle of migratory birds obtained by tracking 1531 individuals and 177 populations from 186 species, and investigate how body mass, a key intrinsic biological trait, influenced timings of the annual cycle using Bayesian structural equation models. We find that body mass has a strong direct effect on departure date from non-breeding and breeding sites, and indirect effects on arrival date at breeding and non-breeding sites, mainly through its effects on migration distance and a carry-over effect. Our results suggest that environmental factors strongly affect the timing of spring migration, while body mass affects the timing of both spring and autumn migration. Our study provides a new foundation for future research on the causes of species distribution and movement.

Clinical Translation of a Deep Learning Model of Radiation-Induced Lymphopenia for Esophageal Cancer.

Purpose: Radiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk. Materials and Methods: We conducted "virtual clinical trials" utilizing retrospective data for 10 intensity-modulated radiation therapy (IMRT) and 10 passively-scattered proton therapy (PSPT) esophageal cancer patients. For each patient, additional treatment plans of the modality other than the original were created employing standard-of-care (SOC) dose constraints. Predicted values of absolute lymphocyte count (ALC) nadir for all plans were estimated using a previously-developed DL model. The model also yielded the relative magnitudes of contributions of iOARs dosimetric factors to ALC nadir, which were used to compute iOARs dose-volume constraints, which were incorporated into optimization criteria to produce "IMRT-enhanced" and "intensity-modulated proton therapy (IMPT)-enhanced" plans. Results: Model-predicted ALC nadir for the original IMRT (IMRT-SOC) and PSPT plans agreed well with actual values. IMPT-SOC showed greater immune sparing vs IMRT and PSPT. The average mean body doses were 13.10 Gy vs 7.62 Gy for IMRT-SOC vs IMPT-SOC for patients treated with IMRT-SOC; and 8.08 Gy vs 6.68 Gy for PSPT vs IMPT-SOC for patients treated with PSPT. For IMRT patients, the average predicted ALC nadir of IMRT-SOC, IMRT-enhanced, IMPT-SOC, and IMPT-enhanced was 281, 327, 351, and 392 cells/µL, respectively. For PSPT patients, the average predicted ALC nadir of PSPT, IMPT-SOC, and IMPT-enhanced was 258, 316, and 350 cells/µL, respectively. Enhanced plans achieved higher predicted ALC nadir, with an average improvement of 40.8 cells/µL (20.6%). Conclusion: The proposed DL model-guided strategy to incorporate the immune system as iOAR in IMRT and IMPT optimization has the potential for radiation-induced lymphopenia mitigation. A prospective clinical trial is planned.

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers.

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.

Lin28a maintains a subset of adult muscle stem cells in an embryonic-like state.

During homeostasis and after injury, adult muscle stem cells (MuSCs) activate to mediate muscle regeneration. However, much remains unclear regarding the heterogeneous capacity of MuSCs for self-renewal and regeneration. Here, we show that Lin28a is expressed in embryonic limb bud muscle progenitors, and that a rare reserve subset of Lin28a+Pax7- skeletal MuSCs can respond to injury at adult stage by replenishing the Pax7+ MuSC pool to drive muscle regeneration. Compared with adult Pax7+ MuSCs, Lin28a+ MuSCs displayed enhanced myogenic potency in vitro and in vivo upon transplantation. The epigenome of adult Lin28a+ MuSCs showed resemblance to embryonic muscle progenitors. In addition, RNA-sequencing revealed that Lin28a+ MuSCs co-expressed higher levels of certain embryonic limb bud transcription factors, telomerase components and the p53 inhibitor Mdm4, and lower levels of myogenic differentiation markers compared to adult Pax7+ MuSCs, resulting in enhanced self-renewal and stress-response signatures. Functionally, conditional ablation and induction of Lin28a+ MuSCs in adult mice revealed that these cells are necessary and sufficient for efficient muscle regeneration. Together, our findings connect the embryonic factor Lin28a to adult stem cell self-renewal and juvenile regeneration.