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Cotton (Gossypium spp.) is one of the most important fiber crops worldwide. In the last two decades, transgenesis and genome editing play important roles in cotton improvement. However, genotype dependence is one of the key bottlenecks in generating transgenic and gene-edited cotton plants through either particle bombardment or Agrobacterium-mediated transformation. Here, we developed a shoot apical meristem (SAM) cells-mediated transformation system (SAMT) that allowed transformation of recalcitrant cotton genotypes including widely grown Upland cotton (Gossypium hirsutum), Sea island cotton (G. barbadense) and Asiatic cotton (G. arboreum), respectively. Through SAMT, we successfully introduced two foreign genes, GFP and RUBY, into SAM cells of some recalcitrant cotton genotypes, respectively. Within two to three months, transgenic adventitious shoots generated from the axillary meristem zone could be recovered and grown into whole cotton plants. The GFP fluorescent signal and betalain accumulation could be observed in various tissues in GFP- and RUBY- positive plants, as well as in their progenies, indicating that the transgenes were stably integrated into the genome and transmitted to the next generation. Furthermore, by using SAMT, we successfully generated edited cotton plants with inheritable targeted mutagenesis in the GhPGF and GhRCD1 genes through CRISPR/Cas9-mediated genome editing, respectively. Together, the established SAMT transformation system here in this study bypasses the embryogenesis process during tissue culture in a conventional transformation procedure and significantly accelerates generation of transgenic and gene-edited plants for genetic improvement of recalcitrant cotton varieties. This article is protected by copyright. All rights reserved.

To introduce an ultrasound-based scoring system for radiation-induced breast toxicity and test its reliability.Breast ultrasound (BUS) was performed on 32 patients receiving breast radiotherapy (RT) to assess the radiation-induced acute toxicity. For each patient, both the untreated and irradiated breasts were scanned at five locations: 12:00, 3:00, 6:00, 9:00, and tumor bed to evaluate for heterogenous responses to radiation within the entire breast. In total, 314 images were analyzed. Based on ultrasound findings such as skin thickening, dermis boundary irregularity, and subcutaneous edema, a 4-level, Likert-like grading scheme is proposed: none (G0), mild (G1), moderate (G2), and severe (G3) toxicity. Two ultrasound experts graded the severity of breast toxicity independently and reported the inter- and intra-observer reliability of the grading system. Imaging findings were compared with standard clinical toxicity assessments using Common Terminology Criteria for Adverse Events (CTCAE).The inter-observer Pearson correlation coefficient (PCC) was 0.87 (95% CI: 0.83-0.90, P .001). For intra-observer repeatability, the PCC of the repeated scores was 0.83 (95% CI: 0.78-0.87, P .001). Imaging findings were compared with standard clinical toxicity assessments using CTCAE scales. The PCC between BUS scores and CTCAE results was 0.62 (95% CI: 0.35-0.80, P .001). Among all locations, 6:00 and tumor bed showed significantly greater toxicity compared with 12:00 (P = .04).BUS can investigate the cutaneous and subcutaneous tissue changes after RT. This BUS-based grading system can complement subjective clinical assessments of radiation-induced breast toxicity with cutaneous and subcutaneous sonographic information.

Manual contouring is very labor-intensive, time-consuming, and subject to intra- and inter-observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning.This work investigates an efficient deep-learning-based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning.In this work, we propose a novel deep-learning model utilizing U-shaped multi-layer perceptron mixer (MLP-Mixer) and convolutional neural network (CNN) for multi-organ segmentation in abdomen CT images. The proposed model has a similar structure to V-net, while a proposed MLP-Convolutional block replaces each convolutional block. The MLP-Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token-based MLP-Mixer layer for capturing global features with high efficiency, and a token projector for pixel-level detail recovery. We evaluate our proposed network using: (1) an institutional dataset with 60 patient cases and (2) a public dataset (BCTV) with 30 patient cases. The network performance was quantitatively evaluated in three domains: (1) volume similarity between the ground truth contours and the network predictions using the Dice score coefficient (DSC), sensitivity, and precision; (2) surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS); and (3) the computational complexity reported by the number of network parameters, training time, and inference time. The performance of the proposed network is compared with other state-of-the-art networks.In the institutional dataset, the proposed network achieved the following volume similarity measures when averaged over all organs: DSC = 0.912, sensitivity = 0.917, precision = 0.917, average surface similarities were HD = 11.95 mm, MSD = 1.90 mm, RMS = 3.86 mm. The proposed network achieved DSC = 0.786 and HD = 9.04 mm on the public dataset. The network also shows statistically significant improvement, which is evaluated by a two-tailed Wilcoxon Mann-Whitney U test, on right lung (MSD where the maximum p-value is 0.001), spinal cord (sensitivity, precision, HD, RMSD where p-value ranges from 0.001 to 0.039), and stomach (DSC where the maximum p-value is 0.01) over all other competing networks. On the public dataset, the network report statistically significant improvement, which is shown by the Wilcoxon Mann-Whitney test, on pancreas (HD where the maximum p-value is 0.006), left (HD where the maximum p-value is 0.022) and right adrenal glands (DSC where the maximum p-value is 0.026). In both datasets, the proposed method can generate contours in less than 5 s. Overall, the proposed MLP-Vnet demonstrates comparable or better performance than competing methods with much lower memory complexity and higher speed.The proposed MLP-Vnet demonstrates superior segmentation performance, in terms of accuracy and efficiency, relative to state-of-the-art methods. This reliable and efficient method demonstrates potential to streamline clinical workflows in abdominal radiotherapy, which may be especially important for online adaptive treatments.

Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning.In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images.The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis.In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods.The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

A quality assurance (QA) CT scans are usually acquired during cancer radiotherapy to assess for any anatomical changes, which may cause an unacceptable dose deviation and therefore warrant a replan. Accurate and rapid deformable image registration (DIR) is needed to support contour propagation from the planning CT (pCT) to the QA CT to facilitate dose volume histogram (DVH) review. Further, the generated deformation maps are used to track the anatomical variations throughout the treatment course and calculate the corresponding accumulated dose from one or more treatment plans.In this study, we aim to develop a deep learning (DL)-based method for automatic deformable registration to align the pCT and the QA CT. Our proposed method, named dual-feasible framework, was implemented by a mutual network that functions as both a forward module and a backward module. The mutual network was trained to predict two deformation vector fields (DVFs) simultaneously, which were then used to register the pCT and QA CT in both directions. A novel dual feasible loss was proposed to train the mutual network. The dual-feasible framework was able to provide additional DVF regularization during network training, which preserves the topology and reduces folding problems. We conducted experiments on 65 head-and-neck cancer patients (228 CTs in total), each with 1 pCT and 2-6 QA CTs. For evaluations, we calculated the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), target registration error (TRE) between the deformed and target images and the Jacobian determinant of the predicted DVFs.Within the body contour, the mean MAE, PSNR, SSIM, and TRE are 122.7 HU, 21.8 dB, 0.62 and 4.1 mm before registration and are 40.6 HU, 30.8 dB, 0.94, and 2.0 mm after registration using the proposed method. These results demonstrate the feasibility and efficacy of our proposed method for pCT and QA CT DIR.In summary, we proposed a DL-based method for automatic DIR to match the pCT to the QA CT. Such DIR method would not only benefit current workflow of evaluating DVHs on QA CTs but may also facilitate studies of treatment response assessment and radiomics that depend heavily on the accurate localization of tissues across longitudinal images.

Protein neddylation inactivation is a novel topic in cancer research. However, there are few studies on the mechanism of neddylation underlying the development of sheep follicular granulosa cells (GCs). In this study, the development of follicular GCs in sheep was inactivated by MLN4924, a neddylation-specific inhibitor, which significantly attenuated the proliferation and cell index of sheep follicular GCs. Further, the inactivation of neddylation by MLN4924 caused the accumulation of the cullin ring ligase (CRLs) substrates Wee1 and c-Myc, which could upregulate NOXA protein expression. Meanwhile, the B-cell lymphoma/leukemia 2 (BCL2) family members Bcl-2 and MCL-1 were downregulated, subsequently inducing apoptosis in follicular GCs of sheep. Increasing Wee1 levels caused G2/M-phase arrest. The effects of neddylation inactivation on Akt, the JAK2/STAT3 signaling pathway, and Forkhead box class O(FOXO) family members were evaluated. Neddylation inactivation by MLN4924 increased the levels of phospho-Akt, JAK2, phospho-STAT3, and FOXO1 (p 0.05) and decreased the levels of phospho-FOXO3a and STAT3 (p 0.05). In addition, MLN4924 could alter the mitochondrial morphology of GCs, increase cellular glucose utilization and lactate production, increase reactive oxygen species (ROS) generation, and promote sheep follicular GCs glycolysis, thus causing changes in mitochondrial functions. Together, these findings point to an unrecognized role of neddylation in regulating follicular GCs proliferation in sheep.

Several inverse planning algorithms have been developed for Gamma Knife (GK) radiosurgery to determine a large number of plan parameters by solving an optimization problem, which typically consists of multiple objectives. The priorities among these objectives need to be repetitively adjusted to achieve a clinically good plan for each patient. This study aimed to achieve automatic and intelligent priority tuning by developing a deep reinforcement learning (DRL)-based method to model the tuning behaviors of human planners.We built a priority-tuning policy network using deep convolutional neural networks. Its input was a vector composed of multiple plan metrics that were used in our institution for GK plan evaluation. The network can determine which tuning action to take based on the observed quality of the intermediate plan. We trained the network using an end-to-end DRL framework to approximate the optimal action-value function. A scoring function was designed to measure the plan quality to calculate the received reward of a tuning action.Vestibular schwannoma was chosen as the test bed in this study. The number of training, validation and testing cases were 5, 5, and 16, respectively. For these three datasets, the average scores of the initial plans obtained with the same initial priority set were 3.63 ± 1.34, 3.83 ± 0.86 and 4.20 ± 0.78, respectively, while they were improved to 5.28 ± 0.23, 4.97 ± 0.44 and 5.22 ± 0.26 through manual priority tuning by human expert planners. Our network achieved competitive results with 5.42 ± 0.11, 5.10 ± 0. 42, 5.28 ± 0.20, respectively.Our network can generate GK plans of comparable or slightly higher quality than the plans generated by human planners via manual priority tuning for vestibular schwannoma cases. The network can potentially be incorporated into the clinical workflow as planning assistance to improve GK planning efficiency and help to reduce plan quality variation caused by interplanner variability. We also hope that our method can reduce the workload of GK planners and allow them to spend more time on more challenging cases.

Gadolinium-based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network.The proposed workflow consists of two sequential networks: (1) a retina U-Net, which is first trained to derive semantic features from the non-contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U-Net to take the concatenation of the semantic feature maps and non-contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non-contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five-fold cross-validation and 169 patients for hold-out test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis.The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the hold-out test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five-fold cross-validation and hold-out test showed that the calculated metrics can be significantly enhanced (p-values ≤ 0.002) with the tumor contour supervision in network training.The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non-contrast enhanced MR images when the network training included tumor contours. These results suggest that it may be possible to minimize the use of GBCAs in cranial MR imaging studies.

Motivation: Medical image analysis involves tasks to assist physicians in qualitative and quantitative analysis of lesions or anatomical structures, significantly improving the accuracy and reliability of diagnosis and prognosis. Traditionally, these tasks are finished by physicians or medical physicists and lead to two major problems: (i) low efficiency; (ii) biased by personal experience. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are scarce. This review article could serve as the stepping-stone for related research. Significance: From our observation, though reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field find it hard to understand and deploy in clinics. One cause is lacking well-organized review articles targeting readers lacking professional computer science backgrounds. Rather than providing a comprehensive list of all reinforcement learning models in medical image analysis, this paper may help the readers to learn how to formulate and solve their medical image analysis research as reinforcement learning problems. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers are carefully reviewed and categorized according to the type of image analysis task. We first review the basic concepts and popular models of reinforcement learning. Then we explore the applications of reinforcement learning models in landmark detection. Finally, we conclude the article by discussing the reviewed reinforcement learning approaches' limitations and possible improvements., 30 pages, 13 figures

Early T-cell development from CD4

Based on thieno[2,3-b]quinoline-2-carbohydrazide and salicylaldehyde, a novel fluorescent probe (L) was designed and synthesized. L could be used as a multifunctional sensor to sequentially detect In3+ and Fe3+ through fluorescence enhancement and fluorescence quenching in DMF/H2 O buffer solutions. At the same time, L had good anti-interference ability, which could still detect In3+ and Fe3+ well in the presence of other metal ions. For F- , it could be detected by enhancing the fluorescence change caused by the introduction of Al3+ . When other anions were present, the detection of F- would not be interfered. The detection limits of In3+ , Fe3+ and F- were 1.16 × 10-10 M, 2.03 × 10-8 M and 7.98 × 10-9 M, respectively. The complexation model and sensing mechanism between L and In3+ , Fe3+ and F- were confirmed by calculating structural optimization and energy optimization using Gaussian 09 software.

FLASH radiotherapy (RT) is a novel technique in which the ultrahigh dose rate (UHDR) (≥40 Gy/s) is delivered to the entire treatment volume. Recent outcomes of in vivo studies show that the UHDR RT has the potential to spare normal tissue without sacrificing tumor control. There is a growing interest in the application of FLASH RT, and the ultrahigh dose irradiation delivery has been achieved by a few experimental and modified linear accelerators. The underlying mechanism of FLASH effect is yet to be fully understood, but the oxygen depletion in normal tissue providing extra protection during FLASH irradiation is a hypothesis that attracts most attention currently. Monte Carlo simulation is playing an important role in FLASH, enabling the understanding of its dosimetry calculations and hardware design. More advanced Monte Carlo simulation tools are under development to fulfill the challenge of reproducing the radiolysis and radiobiology processes in FLASH irradiation. FLASH RT may become one of standard treatment modalities for tumor treatment in the future. This paper presents the history and status of FLASH RT studies with a focus on FLASH irradiation delivery modalities, underlying mechanism of FLASH effect, in vivo and vitro experiments, and simulation studies. Existing challenges and prospects of this novel technique are discussed in this manuscript.

With the ability to activate certain signaling pathways, chemokines and their receptors may facilitate tumor progression at key steps, including proliferation, immunomodulation, and metastasis. Nevertheless, their prognostic value and regulatory mechanism warrant thorough studies in liver cancer. Here, by screening the expression profiles of all known chemokines in independent liver cancer cohorts, we found that CCL23 was frequently downregulated at mRNA and protein levels in liver cancer. Decreased CCL23 correlated with shortened patient survival, enrichment of signatures related to cancer stem cell property, and metastatic potential. In addition to serving as a tumor suppressor through recruiting CD8+ T cell infiltration in liver cancer, CCL23 could repress cancer cell proliferation, stemness, and mobility. Mechanistically, the expression of CCL23 was transcriptionally regulated by ESR1. On the other hand, CCL23 could suppress the activation of AKT signaling and thus promote the expression of ESR1, forming a feedback loop in liver cancer cells. Collectively, these findings reveal that loss of CCL23 drives liver cancer progression by coordinating immune evasion and metastasis initiation. Targeting the ESR1/CCL23/CCR1/AKT regulatory axis could be an effective therapeutic strategy., Downregulated expression of CCL23 is associated with poor prognosis in liver cancer. Loss of CCL23 drives liver cancer progression by coordinating immune evasion and metastasis initiation. Mechanistic study reveals an ESR1/CCL23/CCR1/AKT feedback loop.

Purpose Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution 3D US images only reliant on the acquired sparsely distributed 2D images. Methods We propose a self-supervised learning framework using cycle consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are respectively trained with paired original US images and two sets of low-resolution US images. The two sets of low-resolution US images are respectively obtained through down sampling the original US images along the two axes. In US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. By learning the mapping from down-sampled in-plane low-resolution images to original high-resolution US images, cycleGAN can generate through-plane high-resolution images from original sparely distributed 2D images. Finally, high-resolution 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models. Results The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90±0.15, the peak signal-to-noise ratio (PSNR) value of 37.88±0.88 dB, and the visual information fidelity (VIF) value of 0.69±0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factor of 5 and 10 in the prostate cases. Conclusions We have proposed and investigated a new deep learning-based algorithm for reconstructing high-resolution 3D US images from sparely acquired 2D images. Significant improvement on through-plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate high-resolution US imaging.

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

A novel pneumonia‐associated respiratory syndrome named coronavirus disease‐2019 (COVID‐19), which was caused by SARS‐CoV‐2，broke out in Wuhan, China, in the end of 2019. Unfortunately, there is no specific antiviral agent or vaccine available to treat SARS‐CoV‐2 infections. The information regarding the immunological characteristics in COVID‐19 patients remains limited. Here, we collected the blood samples from 18 healthy donors (HD) and 38 COVID‐19 patients to analyze changes on γδ T cell population. In comparison with HD, the γδ T cell percentage decreased, while the activation marker CD25 expression increased in response to SARS‐CoV‐2 infection. Interestingly, the CD4 expression was upregulated in γδ T cells reflecting the occurrence of a specific effector cell population, which may serve as a biomarker for the assessment of SARS‐CoV‐2 infection.

Purpose In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone-beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep-learning-based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy. Methods Twenty-three head-and-neck cancer patients were simulated using a Siemens TwinBeam dual-energy CT (DECT) scanner. Mixed-energy scans (equivalent to a 120 kVp single-energy CT scan) were converted to RSP maps for treatment planning. Cone-beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle-GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave-one-out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT-based and CBCT-based RSP maps. The proposed method was compared to a deformable image registration-based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation. Results The CBCT-based RSP generation method was evaluated on 23 head-and-neck cancer patients. From leave-one-out testing, the MAE between CT-based and CBCT-based RSP was 0.06 ± 0.01 and the ME was -0.01 ± 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three-dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field. Conclusions The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCT-guided adaptive treatment planning for IMPT.

PURPOSE: High-dose-rate (HDR) brachytherapy is an established technique to be used as monotherapy option or focal boost in conjunction with external beam radiation therapy (EBRT) for treating prostate cancer. Radiation source path reconstruction is a critical procedure in HDR treatment planning. Manually identifying the source path is labor intensive and timely inefficient. Recent years, magnetic resonance imaging (MRI) becomes a valuable imaging modality for image-guided HDR prostate brachytherapy due to its superb soft-tissue contrast for target delineation and normal tissue contouring. The purpose of this study is to investigate a deep-learning-based method to automatically reconstruct multiple catheters in MRI for prostate cancer HDR brachytherapy treatment planning. METHODS: Attention gated U-Net incorporated with total variation (TV) regularization model was developed for multi-catheter segmentation in MRI. The attention gates were used to improve the accuracy of identifying small catheter points, while TV regularization was adopted to encode the natural spatial continuity of catheters into the model. The model was trained using the binary catheter annotation images offered by experienced physicists as ground truth paired with original MRI images. After the network was trained, MR images of a new prostate cancer patient receiving HDR brachytherapy were fed into the model to predict the locations and shapes of all the catheters. Quantitative assessments of our proposed method were based on catheter shaft and tip errors compared to the ground truth. RESULTS: Our method detected 299 catheters from 20 patients receiving HDR prostate brachytherapy with a catheter tip error of 0.37±1.68 mm and a catheter shaft error of 0.93±0.50 mm. For detection of catheter tips, our method resulted in 87% of the catheter tips within an error of less than ±2.0 mm, and more than 71% of the tips can be localized within an absolute error of no greater than 1.0 mm. For catheter shaft localization, 97% of catheters were detected with an error of less than 2.0 mm, while 63% were within 1.0 mm. CONCLUSIONS: In this study, we proposed a novel multi-catheter detection method to precisely localize the tips and shafts of catheters in 3D MRI images of HDR prostate brachytherapy. It paves the way for elevating the quality and outcome of MRI-guided HDR prostate brachytherapy.

PURPOSE: Current clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy. METHODS: Thirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison. RESULTS: At the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans. CONCLUSIONS: The image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.

With many plan variables to determine, manual forward planning for Gamma Knife (GK) radiosurgery is very challenging. Inverse planning eases GK planning by determining the variables via solving an optimization problem. However, due to the vast search space, most inverse planning algorithms, including the one provided in Leksell GammaPlan (LGP) treatment planning system, have to predetermine the isocenter locations using some geometric methods and then optimize the shot shapes and durations at these preselected isocenters. This sequential planning scheme does not necessarily lead to optimal isocenter locations and hence globally optimal plans. In this study, we proposed a multiresolution-level (MRL) inverse planning approach, attempting to approach this large-scale GK optimization problem via an iterative method.In our MRL approach, several rounds of optimizations were performed with a progressively increased resolution used for isocenter candidates. At each round, an optimization problem was solved to optimize the beam-on time for each collimator and sector at each isocenter candidate. The isocenters that obtained nonzero beam-on times at the previous round and their neighbors on a finer resolution were used as new isocenter candidates for the next round of optimization. After plan optimization, shot sequencing was performed to group the optimized sectors to deliverable composite shots.We have tested our MRL approach on six GK cases previously treated in our institution. For the five cases that have a single target, with similar target coverage obtained, our MRL inverse planning approach achieved better plan quality compared to manual forward planning and LGP inverse planning, with higher selectivity (0.73 ± 0.07 vs 0.72 ± 0.08 and 0.62 ± 0.10), lower gradient index (2.71 ± 0.25 vs 2.78 ± 0.24 and 3.00 ± 0.29), lower brainstem DThis preliminary study has demonstrated the efficacy and feasibility of our MRL inverse planning approach for GK radiosurgery.

Quality assurance computed tomography (QACT) is the current clinical practice in proton therapy to evaluate the needs for replan. QACT could falsely indicate replan because of setup issues that would be solved on the treatment machine. Deforming the treatment planning CT (TPCT) to the pretreatment CBCT may eliminate this issue. We investigated the performance of replan evaluation based on deformed TPCT (TPCTdir) for proton head and neck (HN) therapy.Twenty-eight HN datasets along with pretreatment CBCT and QACT were used to validate the method. The changes in body volume were analyzed between the no-replan and replan groups. The dose on the TPCTdir, the deformed QACT (QACTdir), and the QACT were calculated by applying the clinical plans to these image sets. Dosimetric parameters' changes, including ΔD95, ΔDmean, and ΔD1 for the clinical target volumes (CTVs) were calculated. Receiver operating characteristic curves for replan evaluation based on ΔD95 on QACT and TPCTdir were calculated, using ΔD95 on QACTdir as the reference. A threshold for replan based on ΔD95 on TPCTdir is proposed. The specificities for the proposed method were calculated.The changes in the body contour were 95.8 ± 83.8 cc versus 305.0 ± 235.0 cc (p 0.01) for the no-replan and replan groups, respectively. The ΔD95, ΔDmean, and ΔD1 are all comparable for all the evaluations. The differences between TPCTdir and QACTdir evaluations were 0.30% ± 0.86%, 0.00 ± 0.22 Gy, and -0.17 ± 0.61 Gy for CTV ΔD95, ΔDmean, and ΔD1, respectively. The corresponding differences between the QACT and QACTdir were 0.12% ± 1.1%, 0.02 ± 0.32 Gy, and -0.01 ± 0.71 Gy. CTV ΔD95 2.6% in TPCTdir was chosen as the threshold to trigger QACT/replan. The corresponding specificity was 94% and 98% for the clinical practice and the proposed method, respectively.The replan evaluation based on TPCTdir provides better specificity than that based on the QACT.

Background Agglomeration of myeloid‐derived suppressor cells (MDSCs) in tumors impedes immunotherapeutic effects. Doxorubicin (DOX) is currently the most specific drug used for the selective removal of MDSCs. Here, we study the feasibility and mechanism of eliminating MDSCs by DOX to improve antigen‐specific cytotoxic T lymphocyte (CTL)‐killing neuroblastoma (NB) cells in vitro. Methods CTL and MDSC were prepared; then, CTLs, NB cells, MDSCs, and DOX were mixed and cultivated in different collocation patterns and divided into different groups. The levels of cluster of differentiation 3ζ chain (CD3ζ) and L‐selectin in CTL in different groups were detected. Thereafter, the killing rate of NB cells and secretion of interleukin‐2 and interferon‐γ were measured and compared. Results By real‐time polymerase chain reaction (PCR) and Western blot test respectively, the proliferation and killing effect of CTLs on NB cells were all inhibited by MDSC through downregulating CD3ζ (p = 0.002; p = 0.001) and L‐selectin (p = 0.006; p 0.05). Conclusions Targeted elimination of MDSCs by DOX can improve Ag‐specific CTL killing of NB cells in vitro by upregulating CD3ζ and L‐selectin. This study provides a novel method to enhance the immunotherapeutic effects of NB., High aggregation of myeloid‐derived suppressor cells (MDSCs) in the tumor microenvironment of neuroblastoma results in immunosuppression and affects its therapeutic effectiveness. Targeted elimination of MDSC by doxorubicin can improve antigen‐specific cytotoxic T lymphocyte killing neuroblastoma cells in vitro by upregulating the levels of cluster of differentiation 3ζ chain and L‐selectin. The present study provided a novel method to enhance immunotherapeutic effects for neuroblastoma.

BACKGROUND: We previously reported a prospective study showing axillary lymph node dissection (ALND) is associated with increased breast skin thickening during and 6 weeks post-radiation therapy (RT), and now report ALND’s long-term impact at 1 year. METHODS: Among 66 women who received whole breast RT after lumpectomy, objective ultrasound measurements of epidermal thickness over four quadrants of the treated breast were measured at five time points: before RT, week 6 of RT, and 6 weeks, 6 months, and 1 year post-RT. Skin thickness ratio (STRA) was generated by normalizing for corresponding measurements of the contralateral breast. RESULTS: A total of 2,436 ultrasound images were obtained. Among 63 women with evaluable data at 1 year, mean STRA significantly increased at 6 months (absolute mean increase of 65%, SD 0.054), and remained elevated at 1 year post-RT (absolute mean increase of 44%, SD 0.048). In multivariable analysis, ALND compared to sentinel lymph node biopsy, longer interval between surgery and RT, increased baseline STRA, and Caucasian race predicted for more severe changes in STRA at 1 year compared to baseline (all P < .05). CONCLUSIONS: In the setting of whole breast RT, our findings suggest that ALND has long-term repercussions on breast skin thickening.

Purpose Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbersome and has low reproducibility. Therefore in this paper, by using the convolutional neural networks (CNNs), we develop a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the VW of carotid artery and for automated diagnosis of the carotid atherosclerosis with the black-blood (BB) VW-MRI (i.e., the T1-weighted MRI) in a slice-by-slice manner. Methods The proposed DeepMAD network consists of a segmentation subnetwork and a diagnosis subnetwork for performing the segmentation and diagnosis tasks on the BB-VW-MRI images, where the manual labeled lumen area, the manual labeled outer wall area and the manual labeled lesion Types based on the modified American Heart Association (AHA) criteria are used as the ground-truth. Specifically, a deep U-shape CNN with a weighted fusion layer is designed as the segmentation subnetwork, where the lumen area and the outer wall area can be simultaneously segmented under the supervision of the triple Dice loss to provide the vessel wall map as morphological information. Then, the image stream from the BB-VWMRI image and the morphology stream from the obtained vessel wall map are extracted from two deep CNNs and combined to obtain the diagnosis results of atherosclerosis in the diagnosis subnetwork. In addition, the triple input set is formed by three carotid regions of interest (ROIs) from three consecutive slices of the MRI sequence and input to the DeepMAD network, where the first and last slices used as additional adjacent slices to provide 2.5D spatial information along the carotid artery centerline for the intermediate slice, which is the target slice for segmentation and diagnosis in the study. Results Compared to other existing methods, the DeepMAD network can achieve promising segmentation performances (0.9594 Dice for the lumen and 0.9657 Dice for the outer wall) and better diagnosis Accuracy of the carotid atherosclerosis (0.9503 AUC and 0.8916 Accuracy) in the test dataset (including invisible subjects) from same source as the training dataset. In addition, the trained DeepMAD model can be successfully transferred to another test dataset for segmentation and diagnosis tasks with remarkable performance (0.9475 Dice for the lumen and 0.9542 Dice for the outer wall, 0. 9227 AUC and 0.8679 Accuracy for diagnosis). Conclusions Even without the intervention of reviewers required for previous works, the proposed DeepMAD network automatically segments the lumen and the outer wall together and diagnoses the carotid atherosclerosis with high performances. The DeepMAD network can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate the normal carotid from the atherosclerotic arteries and outlining the vessel wall contours.

PURPOSE: Automated synthetic CT (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle-consistent generative adversarial networks (cycle GAN), a deep-learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. METHODS AND MATERIALS: The cycle GAN-based model was developed to generate sCT images in a patch-based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one-to-one mapping. Dense block-based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference loss and a novel distance loss metric between sCT and original CT. RESULTS: Leave-one-out cross-validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. CONCLUSION: We developed and validated a novel learning-based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high-quality sCT in minutes. The proposed method offers strong potential for supporting near real-time MRI-only treatment planning in the brain and pelvis.

Purpose Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter- and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation. Methods and materials We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing. Results Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 ± 0.03, 3.94 ± 1.55, 0.60 ± 0.23, and 0.90 ± 0.38 mm, respectively. Conclusion We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.

Purpose Accurate and timely organs-at-risk (OARs) segmentation is key to efficient and high-quality radiation therapy planning. The purpose of this work is to develop a deep learning-based method to automatically segment multiple thoracic OARs on chest computed tomography (CT) for radiotherapy treatment planning. Methods We propose an adversarial training strategy to train deep neural networks for the segmentation of multiple organs on thoracic CT images. The proposed design of adversarial networks, called U-Net-generative adversarial network (U-Net-GAN), jointly trains a set of U-Nets as generators and fully convolutional networks (FCNs) as discriminators. Specifically, the generator, composed of U-Net, produces an image segmentation map of multiple organs by an end-to-end mapping learned from CT image to multiorgan-segmented OARs. The discriminator, structured as an FCN, discriminates between the ground truth and segmented OARs produced by the generator. The generator and discriminator compete against each other in an adversarial learning process to produce the optimal segmentation map of multiple organs. Our segmentation results were compared with manually segmented OARs (ground truth) for quantitative evaluations in geometric difference, as well as dosimetric performance by investigating the dose-volume histogram in 20 stereotactic body radiation therapy (SBRT) lung plans. Results This segmentation technique was applied to delineate the left and right lungs, spinal cord, esophagus, and heart using 35 patients' chest CTs. The averaged dice similarity coefficient for the above five OARs are 0.97, 0.97, 0.90, 0.75, and 0.87, respectively. The mean surface distance of the five OARs obtained with proposed method ranges between 0.4 and 1.5 mm on average among all 35 patients. The mean dose differences on the 20 SBRT lung plans are -0.001 to 0.155 Gy for the five OARs. Conclusion We have investigated a novel deep learning-based approach with a GAN strategy to segment multiple OARs in the thorax using chest CT images and demonstrated its feasibility and reliability. This is a potentially valuable method for improving the efficiency of chest radiotherapy treatment planning.

Purpose Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time-consuming and is subject to inter- and intraobserver variation. We developed an automated deep learning-based method to address this technical challenge. Methods We propose a three-dimensional (3D) fully convolutional networks (FCN) with deep supervision and group dilated convolution to segment the prostate on magnetic resonance imaging (MRI). In this method, a deeply supervised mechanism was introduced into a 3D FCN to effectively alleviate the common exploding or vanishing gradients problems in training deep models, which forces the update process of the hidden layer filters to favor highly discriminative features. A group dilated convolution which aggregates multiscale contextual information for dense prediction was proposed to enlarge the effective receptive field of convolutional neural networks, which improve the prediction accuracy of prostate boundary. In addition, we introduced a combined loss function including cosine and cross entropy, which measures similarity and dissimilarity between segmented and manual contours, to further improve the segmentation accuracy. Prostate volumes manually segmented by experienced physicians were used as a gold standard against which our segmentation accuracy was measured. Results The proposed method was evaluated on an internal dataset comprising 40 T2-weighted prostate MR volumes. Our method achieved a Dice similarity coefficient (DSC) of 0.86 ± 0.04, a mean surface distance (MSD) of 1.79 ± 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 ± 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 ± 10.82. A public dataset (PROMISE12) including 50 T2-weighted prostate MR volumes was also employed to evaluate our approach. Our method yielded a DSC of 0.88 ± 0.05, MSD of 1.02 ± 0.35 mm, 95% HD of 9.50 ± 5.11 mm, and aRVD of 8.93 ± 7.56. Conclusion We developed a novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation. The proposed technique could be a useful tool for image-guided interventions in prostate cancer.

Purpose Dual‐energy computed tomography (DECT) using TwinBeam CT (TBCT) is a new option for radiation oncology simulators. TBCT scanning provides virtual monoenergetic images which are attractive in treatment planning since lower energies offer better contrast for soft tissues, and higher energies reduce noise. A protocol is needed to achieve optimal performance of this feature. In this study, we investigated the TBCT scan schema with the head‐and‐neck radiotherapy workflow at our clinic and selected the optimal energy with best contrast‐noise‐ratio (CNR) in organs‐at‐risks (OARs) delineation for head‐and‐neck treatment planning. Methods and materials We synthesized monochromatic images from 40 keV to 190 keV at 5 keV increments from data acquired by TBCT. We collected the Hounsfield unit (HU) numbers of OARs (brainstem, mandible, spinal cord, and parotid glands), the HU numbers of marginal regions outside OARs, and the noise levels for each monochromatic image. We then calculated the CNR for the different OARs at each energy level to generate a serial of spectral curves for each OAR. Based on these spectral curves of CNR, the mono‐energy corresponding to the max CNR was identified for each OAR of each patient. Results Computed tomography scans of ten patients by TBCT were used to test the optimal monoenergetic image for the CNR of OAR. Based on the maximized CNR, the optimal energy values were 78.5 ± 5.3 keV for the brainstem, 78.0 ± 4.2 keV for the mandible, 78.5 ± 5.7 keV for the parotid glands, and 78.5 ± 5.3 keV for the spinal cord. Overall, the optimal energy for the maximum CNR of these OARs in head‐and‐neck cancer patients was 80 keV. Conclusion We have proposed a clinically feasible protocol that selects the optimal energy level of the virtual monoenergetic image in TBCT for OAR delineation based on the CNR in head‐and‐neck OAR. This protocol can be applied in TBCT simulation.

Purpose Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. Materials and methods An auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high-image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images. Results The learning-based CBCT correction algorithm was evaluated using the leave-one-out cross-validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE = 12.81 ± 2.04 and 19.94 ± 5.44 HU, mean PSNR = 40.22 ± 3.70 and 31.31 ± 2.85 dB, mean NCC = 0.98 ± 0.02 and 0.95 ± 0.01, and SNU = 2.07 ± 3.36% and 2.07 ± 3.36% for brain and pelvis data. Conclusion Preliminary results demonstrated that the novel learning-based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT-guided adaptive radiotherapy.

Purpose Denoising has been a challenging research subject in medical imaging, since the suppression of noise conflicts with the preservation of texture and edges. To address this challenge, we develop a content-oriented sparse representation (COSR) method for denoising in computed tomography (CT). Methods An image is segmented into a number of content areas and each of them consists of similar material. Having been ex-painted, each content area is sparsely coded using the dictionary learnt from patches extracted from the corresponding content area. By constraining sparsity, noise is suppressed and the final image is formed by aggregating all denoised content areas. The performance of COSR method is examined with images simulated by computer and generated by multidetector row CT (MDCT), cone beam CT (CBCT), and micro-CT, in which water phantom, anthropomorphic phantom, a human subject, and a small animal are engaged, using the figures of merit, such as standard division (SD), contrast to noise ratio (CNR), and thresholded edge keeping index (EKIth ) and structural similarity index (SSIM). In addition, the optimization of performance by parameter tuning is also investigated. Results Quantitatively gauged by metrics of noise, EKIth and SSIM, the performance evaluation shows that the proposed COSR method is effective in denoising (>50% reduction in noise) while it outperforms the conventional sparse representation method in preservation of texture and edge by ~20% (gauged by SSIM). It has also been shown that the COSR method is tolerable to inaccuracy in content area segmentation and variation in dictionary learning. Moreover, the computational efficiency of COSR can be substantially improved using prelearnt dictionaries. Conclusions The COSR method would find its utility in clinical and preclinical applications, such as low-dose CT, image segmentation, registration, and computer-aided diagnosis. The proposal of COSR denoising is of innovation and significance in the theory and practice of denoising in medical imaging. A demonstration code package is available at https://github.com/xiehq/COSR.

Hydrothermally stable, acid-resistant nickel catalysts are highly desired in hydrogenation reactions, but such a catalyst remains absent owing to the inherent vulnerability of nickel under acidic conditions. An ultra-durable Ni-N-C single-atom catalyst (SAC) has now been developed that possesses a remarkable Ni content (7.5 wt %) required for practical usage. This SAC shows not only high activities for hydrogenation of various unsaturated substrates but also unprecedented durability for the one-pot conversion of cellulose under very harsh conditions (245 °C, 60 bar H2 , presence of tungstic acid in hot water). Using integrated spectroscopy characterization and computational modeling, the active site structure is identified as (Ni-N4)⋅⋅⋅N, where significantly distorted octahedral coordination and pyridinic N constitute a frustrated Lewis pair for the heterolytic dissociation of dihydrogen, and the robust covalent chemical bonding between Ni and N atoms accounts for its ultrastability.

Purpose Preeclampsia (PE) is a severe disease that endangers the safety of mothers and fetuses worldwide. In the absence of specific treatments, more studies on novel predictive and diagnostic biomarkers for PE are required. Experimental design Data-independent acquisition proteomics, with five biological replicates, was used to investigate the protein expression profiles of placental tissues from patients with PE and normal pregnant women. Results In total, 52 differentially expressed proteins were identified, 34 of them were upregulated and 18 downregulated. Bioinformatics analyses revealed that PE was associated with multiple GO terms and KEGG pathways. Arginase-1 (ARG1), ferritin light chain (FTL), and RNA cytidine acetyltransferase (NAT10) were identified as hub proteins, which were further validated in placental tissues and maternal plasma by western blot and ELISA. Conclusions and clinical relevance FTL expression was significantly lower in the placental tissues and early and late pregnancy plasma of patients with PE compared to that in normal pregnant women. This study is the first to propose that FTL may be a potential predictive and diagnostic biomarker for PE; it provides a proteomics insight for understanding the pathological mechanism of this disease. This article is protected by copyright. All rights reserved.