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.

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.

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.

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).

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 (LET ) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.

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 Lin28aPax7 skeletal MuSCs can respond to injury at adult stage by replenishing the Pax7 MuSC pool to drive muscle regeneration.

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.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources.

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.

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.

Reflections on beam configuration optimization for intensity-modulated proton therapy.

Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e.

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.

Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists.

Objectives: To restore tissue growth without increasing the risk for cancer during aging, there is a need to identify small molecule drugs that can increase cell growth without increasing cell proliferation. While there have been numerous high-throughput drug screens for cell proliferation, there have been few screens for post-mitotic anabolic growth.

Materials And Methods: A machine learning (ML)-based phenotypic screening strategy was used to discover metabolites that boost muscle growth.

A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy.

Purpose: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated with poor overall survival of patients with many solid tumors. In this paper, we aimed to predict radiation-induced lymphocyte depletion in esophageal cancer patients during the course of RT based on patient characteristics and dosimetric features.

Methods: We proposed a hybrid deep learning model in a stacked structure to predict a trend toward ALC depletion based on the clinical information before or at the early stages of RT treatment.

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.

Pathogenesis of sarcopenia and the relationship with fat mass: descriptive review.

Age-associated obesity and muscle atrophy (sarcopenia) are intimately connected and are reciprocally regulated by adipose tissue and skeletal muscle dysfunction. During ageing, adipose inflammation leads to the redistribution of fat to the intra-abdominal area (visceral fat) and fatty infiltrations in skeletal muscles, resulting in decreased overall strength and functionality. Lipids and their derivatives accumulate both within and between muscle cells, inducing mitochondrial dysfunction, disturbing β-oxidation of fatty acids, and enhancing reactive oxygen species (ROS) production, leading to lipotoxicity and insulin resistance, as well as enhanced secretion of some pro-inflammatory cytokines.

Controllable assembly of skeletal muscle-like bundles through 3D bioprinting.

3D printing is an effective technology for recreating skeletal muscle tissue. To achieve clinical skeletal muscle injury repair, relatively large volumes of highly aligned skeletal muscle cells are required; obtaining these is still a challenge. It is currently unclear how individual skeletal muscle cells and their neighbouring components co-ordinate to establish anisotropic architectures in highly homogeneous orientations.

Radiation-Induced Lymphopenia Risks of Photon Versus Proton Therapy for Esophageal Cancer Patients.

Purpose: To assess possible differences in radiation-induced lymphocyte depletion for esophageal cancer patients being treated with the following 3 treatment modalities: intensity-modulated radiation therapy (IMRT), passive scattering proton therapy (PSPT), and intensity-modulated proton therapy (IMPT).

Methods And Materials: We used 2 prediction models to estimate lymphocyte depletion based on dose distributions. Model I used a piecewise linear relationship between lymphocyte survival and voxel-by-voxel dose.

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.

Author Correction: Exploring the advantages of intensity-modulated proton therapy: experimental validation of biological effects using two different beam intensity-modulation patterns.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Chromatin-modifying drugs and metabolites in cell fate control.

For multicellular organisms, it is essential to produce a variety of specialized cells to perform a dazzling panoply of functions. Chromatin plays a vital role in determining cellular identities, and it dynamically regulates gene expression in response to changing nutrient metabolism and environmental conditions. Intermediates produced by cellular metabolic pathways are used as cofactors or substrates for chromatin modification.

A biological effect-guided optimization approach using beam distal-edge avoidance for intensity-modulated proton therapy.

Purpose: Linear energy transfer (LET)-guided methods have been applied to intensity-modulated proton therapy (IMPT) to improve its biological effect. However, using LET as a surrogate for biological effect ignores the topological relationship of the scanning spot to different structures of interest. In this study, we developed an optimization method that takes advantage of the continuing increase in LET beyond the physical dose Bragg peak.

Exploring the advantages of intensity-modulated proton therapy: experimental validation of biological effects using two different beam intensity-modulation patterns.

In current treatment plans of intensity-modulated proton therapy, high-energy beams are usually assigned larger weights than low-energy beams. Using this form of beam delivery strategy cannot effectively use the biological advantages of low-energy and high-linear energy transfer (LET) protons present within the Bragg peak. However, the planning optimizer can be adjusted to alter the intensity of each beamlet, thus maintaining an identical target dose while increasing the weights of low-energy beams to elevate the LET therein.

Systematic microdosimetric data for protons of therapeutic energies calculated with Geant4-DNA.

The purpose of this study was to generate physical data needed for microdosimetry-based models of proton RBE. Our focus was on the frequency and dose average lineal energies, y  and y  . We report data for proton energies from 0.