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.

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.

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.

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.

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.

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. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

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.

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.

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. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT's therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.

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.

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. Western blot, qRT-PCR and immunofluorescence staining were used to evaluate myotube hypertrophy/maturation or protein synthesis. Mass spectrometry (MS)-based thermal proteome profiling-temperature range (TPP-TR) technology was used to identify the protein targets that bind the metabolites. Ribo-MEGA size exclusion chromatography (SEC) analysis was used to verify whether the ribosome proteins bound to calcitriol. RESULTS: We discovered both the inactive cholecalciferol and the bioactive calcitriol are amongst the top hits that boost post-mitotic growth. A large number of ribosomal proteins' melting curves were affected by calcitriol treatment, suggesting that calcitriol binds to the ribosome complex directly. Purified ribosomes directly bound to pure calcitriol. Moreover, we found that calcitriol could increase myosin heavy chain (MHC) protein translation and overall nascent protein synthesis in a cycloheximide-sensitive manner, indicating that calcitriol can directly bind and enhance ribosomal activity to boost muscle growth. CONCLUSION: Through the combined strategy of ML-based phenotypic screening and MS-based omics, we have fortuitously discovered a new class of metabolite small molecules that can directly activate ribosomes to promote post-mitotic 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. The proposed model consisted of four channels, one channel based on long short-term memory (LSTM) network and three channels based on neural networks, to process four categories of features followed by a dense layer to integrate the outputs of four channels and predict the weekly ALC values. Moreover, a discriminative kernel was developed to extract temporal features and assign different weights to each part of the input sequence that enabled the model to focus on the most relevant parts. The proposed model was trained and tested on a dataset of 860 esophageal cancer patients who received concurrent chemoradiotherapy. RESULTS: The performance of the proposed model was evaluated based on several important prediction metrics and compared to other commonly used prediction models. The results showed that the proposed model outperformed off-the-shelf prediction methods with at least a 30% reduction in the mean squared error (MSE) of weekly ALC predictions based on pretreatment data. Moreover, using an extended model based on augmented first-week treatment, data reduced the MSE of predictions by 70% compared to the model based on the pretreatment data. CONCLUSIONS: In conclusion, our model performed well in predicting radiation-induced lymphocyte depletion for RT treatment planning. The ability to predict ALC will enable physicians to evaluate individual RT treatment plans for lymphopenia risk and to identify patients at high risk who would benefit from modified treatment approaches.

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 ß-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. In turn, these muscle-secreted cytokines may exacerbate adipose tissue atrophy, support chronic low-grade inflammation, and establish a vicious cycle of local hyperlipidaemia, insulin resistance, and inflammation that spreads systemically, thus promoting the development of sarcopenic obesity (SO). We call this the metabaging cycle. Patients with SO show an increased risk of systemic insulin resistance, systemic inflammation, associated chronic diseases, and the subsequent progression to full-blown sarcopenia and even cachexia. Meanwhile in many cardiometabolic diseases, the ostensibly protective effect of obesity in extremely elderly subjects, also known as the 'obesity paradox', could possibly be explained by our theory that many elderly subjects with normal body mass index might actually harbour SO to various degrees, before it progresses to full-blown severe sarcopenia. Our review outlines current knowledge concerning the possible chain of causation between sarcopenia and obesity, proposes a solution to the obesity paradox, and the role of fat mass in ageing.

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.

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. Model II assumes that lymphocytes deplete exponentially as a function of total delivered dose. The models can be fitted using the weekly absolute lymphocyte counts measurements collected throughout treatment. We randomly selected 45 esophageal cancer patients treated with IMRT, PSPT, or IMPT at our institution (15 per modality) to demonstrate the fitness of the 2 models. A different group of 10 esophageal cancer patients who had received PSPT were included in this study of in silico simulations of multiple modalities. One IMRT and one IMPT plan were created, using our standards of practice for each modality, as competing plans to the existing PSPT plan for each patient. We fitted the models by PSPT plans used in treatment and predicted absolute lymphocyte counts for IMRT and IMPT plans. RESULTS: Model validation on each modality group of patients showed good agreement between measured and predicted absolute lymphocyte counts nadirs with mean squared errors from 0.003 to 0.023 among the modalities and models. In the simulation study of IMRT and IMPT on the 10 PSPT patients, the average predicted absolute lymphocyte count (ALC) nadirs were 0.27, 0.35, and 0.37 K/µL after IMRT, PSPT, and IMPT treatments using Model I, respectively, and 0.14, 0.22, and 0.33 K/µL using Model II. CONCLUSIONS: Proton plans carried a lower predicted risk of lymphopenia after the treatment course than did photon plans. Moreover, IMPT plans outperformed PSPT in terms of predicted lymphocyte preservation.

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

3D printing is an effective technology for recreating skeletal muscle tissuein vitro. 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. Here, we demonstrated a 3D printing strategy followed by sequential culture processes to engineer skeletal muscle tissue. The effects of confined printing on the skeletal muscle during maturation, which impacted the myotube alignment, myogenic gene expression, and mechanical forces, were observed. Our findings demonstrate the dynamic changes of skeletal muscle tissue duringin vitro3D construction and reveal the role of physical factors in the orientation and maturity of muscle fibres.

Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning.

PURPOSE: Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles. METHODS: A single-institution retrospective data of 746 patients with esophageal cancer treated with photons (n = 500) and protons (n = 246) was reviewed. The primary end point of our study was G4RIL. Clustering techniques were applied to identify patient subpopulations with similar pretreatment clinical and radiation dosimetric characteristics. XGBoost was built on a training set (n = 499) to predict G4RIL risks. Predictive performance was assessed on the remaining n = 247 patients. SHapley Additive exPlanations were used to rank the importance of individual predictors. Counterfactual analyses compared patients' risk profiles assuming that they had switched modalities. RESULTS: Baseline absolute lymphocyte count and volumes of lung and spleen receiving ≥ 15 and ≥ 5 Gy, respectively, were the most important G4RIL risk determinants. The model achieved sensitivitytesting-set 0.798 and specificitytesting-set 0.667 with an area under the receiver operating characteristics curve (AUCtesting-set) of 0.783. The G4RIL risk for an average patient receiving protons increased by 19% had the patient switched to photons. Reductions in G4RIL risk were maximized with proton therapy for patients with older age, lower baseline absolute lymphocyte count, and higher lung and heart dose. CONCLUSION: G4RIL risk varies for individual patients with esophageal cancer and is modulated by radiotherapy dosimetric parameters. The framework for machine learning presented can be applied broadly to study risk determinants of other adverse events, providing the basis for adapting treatment strategies for mitigation.

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.

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.