Abstract
Experimental and epidemiological studies suggest that maternal nutritional status during early pregnancy, including the period around the time of conception, may induce long-lasting ...epigenetic changes in the offspring. However, this remains largely unexplored in livestock. Therefore, the objective of this study was to evaluate if modification of the maternal diet of sheep (CTR: control; UND: 50% undernutrition) during the periconceptional period (42 d in total: −14/+28 from mating), would impact CpG methylation in muscle tissue (Longissimus dorsi) of adult offspring (11.5 mo old). Reduced representation bisulfite sequencing identified 262 (Edge-R, FDR < 0.05) and 686 (logistic regression, FDR < 0.001) differentially methylated regions (DMRs) between the UND and CTR groups. Gene ontology analysis identified genes related to development, functions of the muscular system, and steroid hormone receptor activity within the DMRs. The data reported here show that nutritional stress during early pregnancy leads to epigenetic modifications in the muscle of the resulting offspring, with possible implications for cardiac dysfunction, muscle physiology, and meat production.
Lay Summary
The formation of the epigenetic pattern of an organism is highly sensitive to environmental factors, especially during early mammalian development, when epigenetic reprogramming of the whole genome takes place. In utero adverse conditions experienced during early pregnancy, such as maternal undernutrition, may induce long-lasting epigenetic changes in the resulting offspring. This study investigated the CpG methylation variations in muscle tissue of adult offspring induced by differences in the diet of their mothers during pregnancy. Our data show that undernutrition during pregnancy leads to epigenetic alterations in the muscle of the offspring, with a potential impact on animal health and productivity.
Alteration of the maternal diet of sheep impacts the CpG methylation of genes related to muscular tissue development in muscle tissue of adult offspring.
Cardiac toxicity is a well-recognized risk after radiation therapy (RT) in patients with non-small cell lung cancer (NSCLC). However, the extent to which treatment planning optimization can reduce ...mean heart dose (MHD) without untoward increases in lung dose is unknown.
Retrospective analysis of RT plans from 353 consecutive patients with locally advanced NSCLC treated with intensity modulated RT (IMRT) or 3-dimensional conformal RT. Commercially available machine learning-guided clinical decision support software was used to match RT plans. A leave-one-out predictive model was used to examine lung dosimetric tradeoffs necessary to achieve a MHD reduction.
Of all 232 patients, 91 patients (39%) had RT plan matches showing potential MHD reductions of >4 to 8 Gy without violating the upper limit of lung dose constraints (lung volume V receiving 20 Gy (V20 Gy) <37%, V5 Gy <70%, and mean lung dose MLD <20 Gy). When switching to IMRT, 75 of 103 patients (72.8%) had plan matches demonstrating improved MHD (average 2.0 Gy reduction, P < .0001) without violating lung constraints. Examining specific lung dose tradeoffs, a mean ≥3.7 Gy MHD reduction was achieved with corresponding absolute increases in lung V20 Gy, V5 Gy, and MLD of 3.3%, 5.0%, and 1.0 Gy, respectively.
Nearly 40% of RT plans overall, and 73% when switched to IMRT, were predicted to have reductions in MHD >4 Gy with potentially clinically acceptable tradeoffs in lung dose. These observations demonstrate that decision support software for optimizing heart-lung dosimetric tradeoffs is feasible and may identify patients who might benefit most from more advanced RT technologies.
Normal physiology relies on the organization of transmembrane proteins by molecular scaffolds, such as tetraspanins. Oncogenesis frequently involves changes in their organization or expression. The ...tetraspanin CD151 is thought to contribute to cancer progression through direct interaction with the laminin-binding integrins α3β1 and α6β1. However, this interaction cannot explain the ability of CD151 to control migration in the absence of these integrins or on non-laminin substrates. We demonstrate that CD151 can regulate tumor cell migration without direct integrin binding and that integrin-free CD151 (CD151(free)) correlates clinically with tumor progression and metastasis. Clustering CD151(free) through its integrin-binding domain promotes accumulation in areas of cell-cell contact, leading to enhanced adhesion and inhibition of tumor cell motility in vitro and in vivo. CD151(free) clustering is a strong regulator of motility even in the absence of α3 expression but requires PKCα, suggesting that CD151 can control migration independent of its integrin associations. The histologic detection of CD151(free) in prostate cancer correlates with poor patient outcome. When CD151(free) is present, patients are more likely to recur after radical prostatectomy and progression to metastatic disease is accelerated. Multivariable analysis identifies CD151(free) as an independent predictor of survival. Moreover, the detection of CD151(free) can stratify survival among patients with elevated prostate-specific antigen levels. Cumulatively, these studies demonstrate that a subpopulation of CD151 exists on the surface of tumor cells that can regulate migration independent of its integrin partner. The clinical correlation of CD151(free) with prostate cancer progression suggests that it may contribute to the disease and predict cancer progression.
A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images ...acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.
The purpose of this research is to develop a 4DCBCT-based dose assessment method for calculating actual delivered dose for patients with significant respiratory motion or anatomical changes during ...the course of SBRT. To address the limitation of 4DCT-based dose assessment, we propose to calculate the delivered dose using time-varying ('fluoroscopic') 3D patient images generated from a 4DCBCT-based motion model. The method includes four steps: (1) before each treatment, 4DCBCT data is acquired with the patient in treatment position, based on which a patient-specific motion model is created using a principal components analysis algorithm. (2) During treatment, 2D time-varying kV projection images are continuously acquired, from which time-varying 'fluoroscopic' 3D images of the patient are reconstructed using the motion model. (3) Lateral truncation artifacts are corrected using planning 4DCT images. (4) The 3D dose distribution is computed for each timepoint in the set of 3D fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach is validated using six modified XCAT phantoms with lung tumors and different respiratory motions derived from patient data. The estimated doses are compared to that calculated using ground-truth XCAT phantoms. For each XCAT phantom, the calculated delivered tumor dose values generally follow the same trend as that of the ground truth and at most timepoints the difference is less than 5%. For the overall delivered dose, the normalized error of calculated 3D dose distribution is generally less than 3% and the tumor D95 error is less than 1.5%. XCAT phantom studies indicate the potential of the proposed method to accurately estimate 3D tumor dose distributions for SBRT lung treatment based on 4DCBCT imaging and motion modeling. Further research is necessary to investigate its performance for clinical patient data.
Purpose:
The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course ...of stereotactic body radiation therapy (SBRT).
Methods:
A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient‐specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basis DVFs. (2) During the treatment procedure, 2D time‐varying projection images (either kV or MV projections) are acquired, from which time‐varying “fluoroscopic” 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time‐varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio‐torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT‐based planning and to the actual delivered dose that was calculated using “ground truth” XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario.
Results:
For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction‐based dose estimates.
Conclusions:
With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern.
Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to ...deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.
Purpose:
In this work the authors develop and investigate the feasibility of a method to estimate time‐varying volumetric images from individual MV cine electronic portal image device (EPID) images.
...Methods:
The authors adopt a two‐step approach to time‐varying volumetric image estimation from a single cine EPID image. In the first step, a patient‐specific motion model is constructed from 4DCT. In the second step, parameters in the motion model are tuned according to the information in the EPID image. The patient‐specific motion model is based on a compact representation of lung motion represented in displacement vector fields (DVFs). DVFs are calculated through deformable image registration (DIR) of a reference 4DCT phase image (typically peak‐exhale) to a set of 4DCT images corresponding to different phases of a breathing cycle. The salient characteristics in the DVFs are captured in a compact representation through principal component analysis (PCA). PCA decouples the spatial and temporal components of the DVFs. Spatial information is represented in eigenvectors and the temporal information is represented by eigen‐coefficients. To generate a new volumetric image, the eigen‐coefficients are updated via cost function optimization based on digitally reconstructed radiographs and projection images. The updated eigen‐coefficients are then multiplied with the eigenvectors to obtain updated DVFs that, in turn, give the volumetric image corresponding to the cine EPID image.
Results:
The algorithm was tested on (1) Eight digital eXtended CArdiac‐Torso phantom datasets based on different irregular patient breathing patterns and (2) patient cine EPID images acquired during SBRT treatments. The root‐mean‐squared tumor localization error is (0.73 ± 0.63 mm) for the XCAT data and (0.90 ± 0.65 mm) for the patient data.
Conclusions:
The authors introduced a novel method of estimating volumetric time‐varying images from single cine EPID images and a PCA‐based lung motion model. This is the first method to estimate volumetric time‐varying images from single MV cine EPID images, and has the potential to provide volumetric information with no additional imaging dose to the patient.
Objective
This study aims to explore young people's perspectives of emerging technologies and health systems research in an adolescent health community of practice.
Methods
The context of this ...integrated knowledge translation study is the Wellbeing Health & Youth Centre of Research Excellence in Adolescent Health. A theory-building, non-systematic review was conducted to examine the concepts and interrelationships of emerging technologies associated with digital innovation and health systems. This typology informed the design of an online workshop with young people to explore their views, concerns, and ideas about health systems research.
Results
A digital innovation typology was identified to differentiate and explain emerging technology concepts and interrelationships that can be applied to the health systems context. Aligned with this typology, youth perspectives about digital health challenges and opportunities were identified to support future research, policy, and practice.
Conclusion
The integrated findings from this study can assist the navigation of complex emerging technologies, and the negotiation of equitable health systems research, between youth and adult stakeholders. Further, with these typology-related resources, mutual learning and the public involvement of young people in health systems research and priority setting agendas can be supported.
Accurate understanding and modeling of respiration-induced uncertainties is essential in image-guided radiotherapy. Explicit modeling of the overall lung motion and interaction among different organs ...promises to be a useful approach. Recently, preliminary studies on 3D fluoroscopic treatment imaging and tumor localization based on principal component analysis motion models and cost function optimization have shown encouraging results. However, the performance of this technique for varying breathing parameters and under realistic conditions remains unclear and thus warrants further investigation. In this work, we present a systematic evaluation of a 3D fluoroscopic image generation algorithm via two different approaches. In the first approach, the model's accuracy is tested for changing parameters for sinusoidal breathing. These parameters include changing respiratory motion amplitude, period and baseline shift. The effects of setup error, imaging noise and different tumor sizes are also examined. In the second approach, we test the model for anthropomorphic images obtained from a modified XCAT phantom. This set of experiments is important as all the underlying breathing parameters are simultaneously tested, as in realistic clinical conditions. Based on our simulation results for more than 250 s of breathing data for eight different lung patients, the overall tumor localization accuracies of the model in left-right, anterior-posterior and superior-inferior directions are 0.1 ± 0.1, 0.5 ± 0.5 and 0.8 ± 0.8 mm, respectively. 3D tumor centroid localization accuracy is 1.0 ± 0.9 mm.