A decrease in integrin α5β1/FAK is associated with increased apoptosis of aortic smooth muscle cells in acute type a aortic dissection

BACKGROUND

Acute type A aortic dissection (AAAD) is a devastating disease. Human aortic smooth muscle cells (HASMCs) exhibit decreased proliferation and increased apoptosis, and integrin α5β1 and FAK are important proangiogenic factors involved in regulating angiogenesis. The aim of this study was to investigate the role of integrin α5β1 and FAK in patients with AAAD and the potential underlying mechanisms.

METHODS

Aortic tissue samples were obtained from 8 patients with AAAD and 4 organ donors at Zhongshan Hospital of Fudan University. The level of apoptosis in the aortic tissues was assessed by immunohistochemical (IHC) staining and terminal-deoxynucleotidyl transferase-mediated nick end labeling (TUNEL) assays. The expression of integrin α5β1 and FAK was determined. Integrin α5β1 was found to be significantly expressed in HASMCs, and its interaction with FAK was assessed via coimmunoprecipitation (Co-IP) analysis. Proliferation and apoptosis were assessed by Cell Counting Kit-8 (CCK-8) assays and flow cytometry after integrin α5β1 deficiency.

RESULTS

The levels of integrin α5β1 and FAK were both significantly decreased in patients with AAAD. Downregulating the expression of integrin α5β1-FAK strongly increased apoptosis and decreased proliferation in HASMCs, indicating that integrin α5β1-FAK might play an important role in the development of AAAD.

CONCLUSIONS

Downregulation of integrin α5β1-FAK is associated with increased apoptosis and decreased proliferation in aortic smooth muscle cells and may be a potential therapeutic strategy for AAAD.

MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis

AIM

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases.

MATERIALS AND METHODS

117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models.

RESULTS

Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI).

CONCLUSION

The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

Gold(I) ion and the phosphine ligand are necessary for the anti-Toxoplasma gondii activity of auranofin

Auranofin, an FDA-approved drug for rheumatoid arthritis, has emerged as a promising antiparasitic medication in recent years. The gold(I) ion in auranofin is postulated to be responsible for its antiparasitic activity. Notably, aurothiomalate and aurothioglucose also contain gold(I), and, like auranofin, they were previously used to treat rheumatoid arthritis. Whether they have antiparasitic activity remains to be elucidated. Herein, we demonstrated that auranofin and similar derivatives, but not aurothiomalate and aurothioglucose, inhibited the growth of Toxoplasma gondii in vitro. We found that auranofin affected the T. gondii biological cycle (lytic cycle) by inhibiting T. gondii's invasion and triggering its egress from the host cell. However, auranofin could not prevent parasite replication once T. gondii resided within the host. Auranofin treatment induced apoptosis in T. gondii parasites, as demonstrated by its reduced size and elevated phosphatidylserine externalization (PS). Notably, the gold from auranofin enters the cytoplasm of T. gondii, as demonstrated by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDS) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).IMPORTANCEToxoplasmosis, caused by Toxoplasma gondii, is a devastating disease affecting the brain and the eyes, frequently affecting immunocompromised individuals. Approximately 60 million people in the United States are already infected with T. gondii, representing a population at-risk of developing toxoplasmosis. Recent advances in treating cancer, autoimmune diseases, and organ transplants have contributed to this at-risk population's exponential growth. Paradoxically, treatments for toxoplasmosis have remained the same for more than 60 years, relying on medications well-known for their bone marrow toxicity and allergic reactions. Discovering new therapies is a priority, and repurposing FDA-approved drugs is an alternative approach to speed up drug discovery. Herein, we report the effect of auranofin, an FDA-approved drug, on the biological cycle of T. gondii and how both the phosphine ligand and the gold molecule determine the anti-parasitic activity of auranofin and other gold compounds. Our studies would contribute to the pipeline of candidate anti-T. gondii agents.

Remnant cholesterol is associated with hip BMD and low bone mass in young and middle-aged men: a cross-sectional study

PURPOSE

Remnant cholesterol (RC) is a contributor to cardiovascular diseases, obesity, diabetes, and metabolic syndrome. However, the specific relationship between RC and bone metabolism remains unexplored. Therefore, we aimed to investigate the relationships of RC with hip bone mineral density (BMD) and the risk of low bone mass.

METHODS

Physical examination data was collected from men aged < 60 years as part of the Kailuan Study between 2014 and 2018. The characteristics of the participants were compared between RC quartile groups. A generalized linear regression model was used to evaluate the relationship between RC and hip BMD and a logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for low bone mass. Additional analyses were performed after stratification by body mass index (BMI) (≥ or < 24 kg/m2). Sensitivity analyses were performed by excluding individuals who were taking lipid-lowering therapy or had cancer, cardiovascular diseases, or diabetes.

RESULTS

Data from a total of 7,053 participants were included in the analysis. After adjustment for confounding factors, RC negatively correlated with hip BMD (β =  - 0.0079, 95% CI: - 0.0133, - 0.0025). The risk of low bone mass increased from the lowest to the highest RC quartile, with ORs of 1 (reference), 1.09 (95% CI: (0.82, 1.44), 1.35 (95%CI: 1.02, 1.77), and 1.43 (95% CI: 1.09, 1.89) for Q1, Q2, Q3, and Q4, respectively (P for trend = 0.004) in the fully adjusted model. Compared to RC < 0.80 mmol/l group, the risk of low bone mass increased 39% in RC ≥ 0.80 mmol/l group (P < 0.001). The correlation between RC and hip BMD was stronger in participants with BMI ≥ 24 kg/m2 group (β =  - 0.0159, 95% CI: - 0.0289, - 0.0029). The results of sensitivity analyses were consistent with the main results.

CONCLUSION

We have identified a negative correlation between serum RC and hip BMD, and a higher RC concentration was found to be associated with a greater risk of low bone mass in young and middle-aged men.

Comparison of different dosages of propofol combined with its equivalent alfentanil in outpatient abortion: a prospective, double-blinded, randomized trial

OBJECTIVE

This study aimed at determining the optimal dose combination of alfentanil and propofol for outpatient abortion anesthesia.

PATIENTS AND METHODS

The study was separated into two parts. In the first part, patients were to determine the median effective dose (ED50) and the 95% effective dose (ED95) of alfentanil in combination with 2.5 mg·kg-1 propofol to inhibit body movements during the abortion using the Dixon up-and-down sequential allocation method. In the second part, 170 patients were randomly divided into group C (2.0 mg·kg-1 propofol with alfentanil 12.16 μg·kg-1) and group E (2.5 mg·kg-1 propofol with its ED95) to compare the anesthetic effect. The primary outcome was the sedation level during general anesthesia. The secondary outcomes were circulation, respiratory complications, and postoperative recovery quality.

RESULTS

The ED50 and the ED95 values of alfentanil were 3.37 μg·kg-1 (95% CI: 2.58-3.97 μg·kg-1) and 4.68 μg·kg-1 (95% CI: 4.04-9.32 μg·kg-1). The frequency of deep sedation in group E was significantly higher than in group C (76.5% vs. 60%). Patients in group C showed more wakefulness even during the surgery (14.3% vs. 4.4%). The results of our exploratory analyses did not reveal differences in respiratory depression, circulatory depression, postoperative side effects, or recovery outcomes.

CONCLUSIONS

The combination of 2.5 mg·kg-1 propofol and 4.68 μg·kg-1 alfentanil produces a better sedative effect than the combination of 2.0 mg·kg-1 propofol and 12.16 μg·kg-1 alfentanil without increasing additional risks associated with anesthesia.

A follow-up study on the recovery and reinfection of Omicron COVID-19 patients in Shanghai, China

Limited follow-up data is available on the recovery of Omicron COVID-19 patients after acute illness. It is also critical to understand persistence of neutralizing antibody (NAb) and of T-cell mediated immunity and the role of hybrid immunity in preventing SARS-CoV-2 reinfection. This prospective cohort study included Omicron COVID-19 individuals from April to June 2022 in Shanghai, China, during a large epidemic caused by the Omicron BA.2 variant. A total of 8945 patients from three medical centres were included in the follow up programme from November 2022 to February 2023. Of 6412 individuals enrolled for the long COVID analysis, 605 (9.4%) individuals experienced at least one sequelae, mainly had fatigue and mental symptoms specific to Omicron BA.2 infection compared with other common respiratory tract infections. During the second-visit, 548 (12.1%) cases of Omicron reinfection were identified. Hybrid immunity with full and booster vaccination had reduced risk of SARS-CoV-2 reinfection by 0.29-fold (95% CI: 0.63-0.81) and 0.23-fold (95% CI: 0.68-0.87), respectively. For 469 participants willing to the hospital during the first visit, those who received full (72 [IQR, 36-156]) or booster (64 [IQR, 28-132]) vaccination had significantly higher neutralizing antibody titers than those with incomplete vaccination (36 [IQR, 16-79]). Moreover, non-reinfection cases had higher neutralizing antibody titers (64 [IQR, 28-152]) compared to reinfection cases (32 [IQR, 20-69]).

MGA-seq: robust identification of extrachromosomal DNA and genetic variants using multiple genetic abnormality sequencing

Genomic abnormalities are strongly associated with cancer and infertility. In this study, we develop a simple and efficient method - multiple genetic abnormality sequencing (MGA-Seq) - to simultaneously detect structural variation, copy number variation, single-nucleotide polymorphism, homogeneously staining regions, and extrachromosomal DNA (ecDNA) from a single tube. MGA-Seq directly sequences proximity-ligated genomic fragments, yielding a dataset with concurrent genome three-dimensional and whole-genome sequencing information, enabling approximate localization of genomic structural variations and facilitating breakpoint identification. Additionally, by utilizing MGA-Seq, we map focal amplification and oncogene coamplification, thus facilitating the exploration of ecDNA's transcriptional regulatory function.

A Self-Attention-Based Neural Network for Predicting Immune Checkpoint Inhibitors Response

PURPOSE/OBJECTIVE(S)

Cancer cells evade immune system by negatively regulating T cells via immune checkpoints (e.g., PD-1). By blocking these checkpoints, the ability of immune system to recognize and kill cancer cells restores. Individual response rate of checkpoint blockade varies among patients, with 50%-80% in specific types of cancer such as melanoma, while only 15%-30% in most other tumors. Yet it is still an open question what is the set of biomarkers that are crucial to the response to immune checkpoint inhibitors (ICI). The overall goal of this study is to develop and validate a biologically-aware interpretable deep learning model to identify the biomarkers that can predict the survival outcome to ICI treatment.

MATERIALS/METHODS

The self-attention mechanism could yield interpretable results where important biomarkers may have more "attention". However, in classical self-attention mechanism, the prior biological knowledge of protein interactions (PPI) and gene pathways are not incorporated. In this study, we propose a weighted biologically-aware attention score, where it is weighted against the gene centrality and pathway length. The genes that are closely connected to mutated genes receive 'high attention', while the genes that are far away from mutated genes along the pathway receive "lower attention". We then train, validate and test our model using 1,660 patients of nine types of cancer. To validate the prediction, 1. We evaluate the accuracy via concordance index. 2. We identified the genes that receive high attention and verify their functions in existed literature. 3. We perform sanity check by removing these genes from the data, re-training and predicting again, and comparing the prediction accuracy.

RESULTS

Our framework has achieved an average accuracy (measured via c-index) of 0.60 ± 0.06 for NSCLC and 0.58 ± 0.07 for melanoma, which is superior to both the gold standard COX-PH model (0.57 ± 0.06 for NSCLC and 0.53 ± 0.03 for melanoma) and DeepSurv (0.54 ± 0.05 for NSCLC and 0.51 ± 0.10 for melanoma). Genes that receive high attention have been validated by supporting literature, which provides an additional means of verifying the prediction in comparison to "black box" deep learning models, where there is no way to comprehend the reason behind predictions. Removing the top 8% high-attention genes (∼25 genes) from the data while using the remaining 92% for making predictions resulted in a drop in accuracy to 0.55 ± 0.073 for NSCLC and 0.56 ± 0.03 for melanoma, underscoring the significance of these genes. Patient stratification is also performed by dividing patients into responders and non-responders based on prediction score.

CONCLUSION

In this study, we propose and validate a biologically-aware self-attention based deep learning model which outperforms commonly-used survival models. Additionally, this tool has the potential to identify key biomarkers while assist in clinical decision-making, which demonstrates a promising step for immunotherapy response prediction.

Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

PURPOSE/OBJECTIVE(S)

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way.

MATERIALS/METHODS

We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM.

RESULTS

Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method.

CONCLUSION

The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.

Reinforcement Learning Powered Station Parameter Optimized Radiation Therapy (SPORT): A Novel Treatment Planning and Beam Delivery Technique

PURPOSE/OBJECTIVE(S)

Conventional intensity modulated radiation therapy (IMRT) with a typical 5-20 fixed beams often does not provide sufficient angular sampling required for conformal dose shaping, whereas current volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points without considering the differential need for intensity modulation of different angles, leading to undersampling at some angles while oversampling at some other angles. Our goal is to develop a node or station parameter optimized radiation therapy (SPORT) strategy with simultaneously optimized angular sampling and beam modulation by leveraging state-of-the-art reinforcement learning and the unique capability of modern digital LINACs in dose delivery through programmable nodal points.

MATERIALS/METHODS

We developed a SPORT optimization framework, in which, the process of programming control points (or station parameters) was formulated as a stochastic dynamic programming problem, which was solved by a reinforcement learning-based algorithm. On-policy reinforcement learning method, namely, state-action-reward-state-action (SARSA) was integrated with deep convolutional neural network to predict station parameters by utilizing the patient's anatomical structures meanwhile considering the delivery capability of a typical digital LINAC machine. Here, the deep convolutional neural network estimated the state-action value by using the quality of the plan with current station parameters when a next potential station parameter was selected. The state-action value was then updated by SARSA learning. The quality of the plan was quantified by dosimetry constraints. The model was assessed by a retrospective study on a cohort of patients underwent head-and-neck radiation therapy. Dosimetric analysis and delivery efficiency comparisons were used to evaluate the performance of the proposed framework.

RESULTS

Our model was used to generate 16 plans unseen in the original training set. All the plans predicted by our model achieved better dose distributions without violating clinical planning constraints. Moreover, instead of using 4 full standard arcs in the original clinically used plans obtained via manual optimization, the predicted plans only used one full standard arc (about 178 control points) plus boost from a few sub-arcs (less than 30 degrees of gantry angles), which significantly improved the efficiency of the beam delivery. We are in the process of integrating the sub-arcs into the full arc by considering the programmable capability of modern LINACs.

CONCLUSION

We demonstrated that a machine learning-based SPORT framework capable of optimizing the spatial sampling and beam modulation simultaneously for modern radiation therapy. The framework not only significantly improves the quality and efficiency of beam delivery, but also has the potential to be incorporated into current clinical workflow to improve the efficiency of dose planning and delivery.

Automated Small Tumor Segmentation by a Template-Based Global Hierarchical Attention Method

PURPOSE/OBJECTIVE(S)

Accurate segmentation of tumors is significant for radiation therapy treatment planning and clinical decision-making. While deep convolutional neural network-based methods have found valuable applications in automatic medical segmentation, tumor segmentation, especially small tumor segmentation, remains challenging due to deficiencies of current deep learning in convolutional and pooling operations, which often results in the loss of small object information. This research proposes a global hierarchical attention-based method for accurate and automated segmentation of small tumors by exploiting the associations between small tumors and the feature maps of large tumors.

MATERIALS/METHODS

This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1-2.0, 2.1-5.0, 5.1-10.0, and 10.1-20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a tumor template-based hierarchical attention method to quantify the relation between small and large tumors by computing their feature maps. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation.

RESULTS

Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively.

CONCLUSION

We found that the small-large tumors relation can significantly improve small tumor segmentation, which is valuable for treatment planning, and clinical decision-making. Our experimental results show that our method can significantly improve the accuracy of segmenting small liver tumors compared to existing deep-learning-based models. The method is quite general and can be extended to other types of tumor detection and segmentation. We discovered that the relationship between small and large tumors can significantly enhance the segmentation of small tumors, which has significant value for treatment planning and clinical decision-making. Our experiments demonstrate that our approach significantly improves the accuracy of small liver tumor segmentation compared to existing deep learning-based models. Our method is quite versatile and can be extended to other types of tumor detection and segmentation.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

PURPOSE/OBJECTIVE(S)

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden.

MATERIALS/METHODS

The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2.

RESULTS

The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation.

CONCLUSION

The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

The Role of CT-Based Radiomics Nomogram in Differential Diagnosis of Immune Checkpoint Inhibitor-Related Pneumonitis from Radiation Pneumonitis for Patients with ESCC

PURPOSE/OBJECTIVE(S)

The combination of immunotherapy and chemoradiotherapy has widely used for patients with esophageal squamous cell carcinoma (ESCC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by the CT radiomics and clinical or radiological parameters.

MATERIALS/METHODS

A total of 76 ESCC patients with pneumonitis were enrolled in this retrospective study and divided into training dataset (n = 53) and validation dataset (n = 23). A total of 837 radiomics features were extracted from regions of interest (ROIs) based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator (LASSO). A logistic regression was applied to develop radiomics nomogram. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.

RESULTS

No significant difference was detected between training dataset and validation dataset. The radiomics signature which was made up of four radiomics features shown a favorable performance on differentiating between CIP and RP with the α-binormal-based and empirical AUC = 0.831 and 0.843. Patients with RP had a close relationship with location (p = 0.003) and shape of lesions (p = 0.002). The nomogram that combined with radiomics signature and clinical factors improved the classifying performance on discrimination in the training dataset (AUCαbin = 0.963 and AUCemp = 0.964). The results were verified in the validation dataset with AUC = 0.967 and 0.964.

CONCLUSION

CT-based radiomics features have potential values for differentiating between patients with CIP and RP. Addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

PURPOSE/OBJECTIVE(S)

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises.

MATERIALS/METHODS

A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths.

RESULTS

The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively.

CONCLUSION

Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.

Clinical Implementation of an Automated IMRT/VMAT Treatment Planning Tool

PURPOSE/OBJECTIVE(S)

To create an in-house automated treatment planning tool for IMRT/VMAT treatments and evaluate the dosimetric plan quality against manually generated plans.

MATERIALS/METHODS

A scripting application programming interface is employed to interact with a commercial treatment planning system (TPS) to implement automatic plan evaluation and update optimization parameters by mimicking the human planning process. The automated planning performs in an iterative fashion until reaching an acceptable tradeoff among target coverage/dose homogeneity and sparing of critical organs at risk. In each iteration, the dose constraints, priorities, and optimization structures for are automatically updated based on the results of the current iteration. Twenty previously treated plans (10 prostate and 10 head and neck), were preliminarily used to evaluate the performance of the automated planning tool. The differences in target and organ-at-risk metrics from the manually generated clinical plans were analyzed using paired t-test to evaluate clinical acceptability of tour automated planning tool. The current in-house-developed automated planning solution is able to create plans for different disease sites, including head & neck, prostate, pelvis, and lung. So far, the VMAT plans for more than 150 different cases have been generated with the tool. The results for these were also evaluated.

RESULTS

Compared to the manually generated clinical head and neck plans, all auto plans achieved PTV D95% coverage and critical organs at risk sparing without statistically significant change in average global Dmax (107.4% for manual vs 107.3% for automated plans). The auto-planning solution provided reduced maximum doses to brainstem and spinal cord (average reductions with standard deviations of 5.1 ± 2.6 Gy and 2.9 ± 1.4 Gy, respectively, all p <0.03), reduced average mean doses to contralateral parotid, ipsilateral parotid, contralateral submandibular gland, pharynx, esophagus, cochleae (reductions of 2.2 ± 2.9 Gy, 4.8 ± 4.7 Gy, 3.6 ± 5.2 Gy, 2.0 ± 7.1 Gy, 3.9 ± 2.6 Gy, 3.8 ± 5.0 Gy, respectively, all p < 0.045). Similar results were observed for the prostate plans. With the same PTV coverage and without statistically significant change in average global Dmax (106.5% for manual vs 106.8% for automated plans), the automated solution provided superior sparing for both bladder and rectum. Bladder V75, V70, V65 were reduced by 0.6% ± 2.1%, 0.8% ± 2.5%, and 0.9% ± 2.9% (all p <0.04), respectively. Rectum V75, V70, V65, V60 were reduced by 1.0% ± 2.3%, 1.2% ± 2.8%, 1.3% ± 3.2%, 1.6% ± 3.6% (all p < 0.01), respectively.

CONCLUSION

Our automated treatment planning solution is capable of efficiently generating VMAT plans for different disease sites with superior dosimetric indices compared to manually generated plans. Our tool is integrated within a commercial TPS platform, so it has the advantage of seamless adoption into the standard workflow to improve plan quality and treatment planning efficiency in our clinic.

Patient-Adaptive Automated Segmentation in Daily kVCT Images for Radiotherapy of Head and Neck and Prostate Cancer

PURPOSE/OBJECTIVE(S)

The purpose of this study was to examine the use of transfer learning in deep learning-based auto-segmentation of daily kilovoltage computed tomography (kVCT) images for patient-specific adaptive radiotherapy. Using data from the first cohort of patients treated with the innovative BgRT system, the objective of this study was to evaluate the potential benefits of this approach in facilitating efficient and effective adaptive radiotherapy.

MATERIALS/METHODS

For the head and neck (HaN) site and pelvic site, we first trained a deep convolutional segmentation network using a population dataset, consisting of 67 and 56 patient cases, respectively. This population network was then fine-tuned for a specific patient using a transfer learning approach, adapting the network weights. The auto-segmentation network utilized in this study was a 23-layer U-Net with batch normalization, a dropout rate of 0.5, and four skip connections between the encoder and decoder at different levels. We used initial planning CT and 5-26 sets of daily kVCT scans with a total of 8,039 images for patient-specific learning in the 6 HaN cases and 4 pelvic cases, particularly analyzing the relationship between the number of sequential patient-specific training data and the performance of the auto-segmentation. We compared the performance of the patient-specific network with the population network and the clinical rigid registration method, using the Dice similarity coefficient (DSC) as the evaluation metric. Additionally, we investigated the corresponding dosimetric impacts of the different auto-segmentation and registration methods.

RESULTS

The patient-specific network showed improved mean DSC scores of 0.88 and 0.90 for three HaN organs at risk (OARs) and eight pelvic targets and OARs, respectively, compared to the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network steadily improved as the number of longitudinal training cases increased, reaching near saturation after 6 training cases. The use of the patient-specific auto-segmentation resulted in a reduction of the mean discrepancy in target and OAR doses between delivery and planning from 5.5% with the clinical rigid registration to 1.1%.

CONCLUSION

The use of patient-specific transfer learning in auto-segmenting kVCT images showed higher accuracy compared to a conventional population network and clinical registration-based method. This approach holds promise for enhancing dose evaluation accuracy in adaptive radiotherapy.

Real Time Volumetric MRI for MR-Guided 3D Motion Tracking via Sparse Prior-Augmented Neural Representation Learning

PURPOSE/OBJECTIVE(S)

To reconstruct volumetric MRI from orthogonal cine acquisition aided by sparse priors of 2 static 3D MRI through implicit neural representation (NeRP) learning, with the goal of eliminating large-scale training datasets for data-driven sparse MRI reconstruction and supporting clinical workflow of real time 3D motion tracking during MR-guided radiotherapy.

MATERIALS/METHODS

A multi-layer perceptron network was trained to learn the NeRP of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first learning the NeRP of 2 static 3D MRI with different breathing motion states, prior knowledge of patient breathing motion was embedded into network weights through optimization. The prior knowledge was then augmented from 2 to 31 motion states by querying the optimized network at interpolated/extrapolated motion state coordinates. Starting from the prior-augmented network as an initialization point, the network was further trained using sparse samples of 2 orthogonal cine slices. The final volumetric reconstruction was obtained by querying the trained network at desired 3D spatial locations. We evaluated the proposed method using 5-minute volumetric MRI time series with 340 ms temporal resolution collected from 7 liver carcinoma patients. The time series was acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two MRI with inhale and exhale states respectively were selected from the first 30 sec of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to 2 orthogonal slices and compared network-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.

RESULTS

Across the 7 patients evaluated, the peak signal to noise ratio between model reconstruction and ground truth was 54.66 ± 6.16 dB and the structural similarity index measure was 0.99 ± 0.01. Gross tumor volume (GTV) contours estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 1.89 ± 1.13 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all 3 orthogonal directions.

CONCLUSION

Volumetric MRI from orthogonal cine acquisition with sparse priors is feasible by modeling prior knowledge through implicit neural representation learning. The model-reconstructed images showed sufficient accuracy in supporting 3D motion tracking of abdominal targets. By eliminating the need for large scale training datasets, the method promises to enable clinical implementation of 3D motion tracking for precision radiation therapy.

Analyzing the Predictive Effects of PD-L1 Expression, Early Changes of bTMB and Circulated CD8+T Cells during Treatment for Responses of RT Combined with ICI in NSCLC

PURPOSE/OBJECTIVE(S)

The beneficial role of immunotherapy and the clinical relevance of current biomarkers remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. A rational combination of biomarkers is needed. Here, we estimated potential predictive factors for responses of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) in a phase II trial to determine the efficacy and safety of combination of moderate hypofractionated RT with ICI in patients with oligometastatic NSCLC (NCT03557411).

MATERIALS/METHODS

Pretreatment tumor tissue samples and longitudinal blood were collected for immune and tumor biomarker analysis. We examined pre-treatment (pre-ICI) PD-L1 expression in tumor cells. Circulating tumor cell (CTC), PD-L1+CTC, blood tumor mutation burden (bTMB), CD8+T cells, CD4+T cells, NK cells, B cells in circulation were acquired pre-ICI and 1 month after ICI starting (1-mth). In addition, early changes of CTC (CTC), PD-L1+CTC (PD-L1+CTC), bTMB (bTMB), CD8+T cells (CD8+T cells), CD4+T cells (CD4+T cells), NK cells (NK cells), B cells (B cells) were also analyzed to estimate the predictive effects for treatment.

RESULTS

High pre-ICI bTMB and increased CD8+T cells at 1 month was associated with better PFS (p = 0.016; p = 0.006). Interaction analyses revealed that each combination of two markers in the 5 markers including PD-L1, pre-ICI bTMB, 1-mth bTMB, 1-mth CD8+T cells and CD8+T cells was significantly associated with PFS, except for CTC, PD-L1+CTC, CD4+T cells, NK cells and B cells in circulation due to low power. Unsupervised cluster analysis based on these markers revealed three sub-cohorts. Cohort-1 was overrepresented by patients with progressive disease (81%) of whom were negative for 3-4 of the 5 biomarkers. Cohort-3 was overrepresented by patients with partial response (70%) of whom were positive for 3-4 of the 5 biomarkers. Survival analyses of the 3 cohorts indicated a significant association with PFS (p = 0.017).

CONCLUSION

This study suggests that a combination of PD-L1 expression, early changes of bTMB and circulated CD8+T cells as a better predictive biomarker for response to RT combined with ICI. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Does Epigallocatechin Gallate as a Radiation Protective Agent Reduce the Anti-Tumor Effect of Radiotherapy in Postoperative Breast Cancer Radiotherapy?

PURPOSE/OBJECTIVE(S)

Based on the previous encouraging results, we further explored whether EGCG would have a protective effect on potential tumor lesions, that is, reduce the efficacy of radiotherapy. We selected patients with stage III breast cancer with or without EGCG. The local control rate, distant metastasis rate, DFS and OS were compared between the two groups.

MATERIALS/METHODS

Patients with stage III breast cancer who were treated with EGCG and radiotherapy was selected from a phase II clinical study (ClinicalTrials.gov, No. NCT02580279). Each patient was matched with one control patient without EGCG From the medical database of our hospital matching for age and stage. The control group of stage-and age-matched patients was selected at random from the medical database of our hospital RESULTS: We identified 43 EGCG patients and 43 matched controls. The median age was 45 years (range: 26-67). Between the two groups, there were no obvious differences in the baseline demographic or clinical features. When compared to the placebo group, the mean radiation-induced dermatitis index (RIDI) in the EGCG group was substantially lower (2.56±1.14 vs 3.36±1.16 T = -3.232, P = 0.002). Repeated measures ANOVA indicated the significant differences in the RTOG score during the course of radiotherapy between the two groups (F = 9.611 P = 0.003). The patients mostly experienced RID two or three weeks after starting radiotherapy, although in the EGCG group, it appeared later (3.19±0.91 weeks) than it did in the placebo group (2.67±0.84 weeks), P = 0.008. The median follow-up for patients in the EGCG group at the time of data collection was 50.6 months with 95% confidence interval (CI) from 43.9 to 57.3. While it was 48.6 months (95% CI: 43.4-53.9) for patients in the control group. There was no significant difference in overall survival (OS), disease free-survival (DFS) and freedom from locoregional (LRF) and distant failure (DMF) (P > 0.05). At the data cut-off (December 2021), the 4-year DFS with EGCG was 71.4% compared to 65.4% with conventional therapy, and the 4-year OS was 77.0% compared to 80.3%.

CONCLUSION

The prophylactic use of EGCG solution reduced the RID score of stage III breast cancer patients without negatively impacting the therapeutic effect of radiotherapy on the tumor. EGCG is safe and feasible choice for RID for breast cancer during radiotherapy.

Prospective Clinical Adoption of Artificial Intelligence for Organ Contouring in Head and Neck Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

Patients that undergo head and neck (H&N) radiation therapy (RT) require laborious delineation of organs-at-risk (OARs) on computed tomography (CT) scans in a treatment planning system (TPS) to minimize radiation to normal tissue. This task can be completed rapidly and accurately with recently developed artificial intelligence-based semantic segmentation models. The current study aims to deploy and evaluate a strategy for improving clinical practice with this technology.

MATERIALS/METHODS

Deep learning models were trained and tested with CT scans and OAR contours from previous H&N RT cases at our clinic. Two medical physicists vetted the models and selected a 2.5D U-Net for further implementation. The model was embedded in a dedicated server at the hospital, programmed to read H&N CT scans staged for import into the TPS, generate auto-contours, and write them into a TPS-compatible format made available alongside the scan. In the pilot implementation, the auto-contouring service was utilized for more than 60 cases, prospectively. The auto-contours were quantitatively evaluated against the treatment-approved contours to determine how much modification was performed by the clinical team.

RESULTS

The 2.5D U-Net selected for clinical integration segments 21 OARs in less than 3 minutes per scan. Across all the prospective cases, the mean Dice score and mean 95th percentile Hausdorff distance (mm) between the auto-contour and treatment-approved contour for each of the 21 OARs were as follows, respectively: brainstem (0.93, 1.94), optic chiasm (0.70, 2.96), left cochlea (0.69, 2.37), right cochlea (0.68, 2.44), esophagus (0.88, 2.46), left globe (0.93, 1.50), right globe (0.93, 1.63), glottis (0.91, 2.13), larynx (0.93, 2.76), mandible (0.90, 4.86), left optic nerve (0.78, 1.64), right optic nerve (0.82, 1.65), oral cavity (0.86, 8.46), left parotid gland (0.91, 2.78), right parotid gland (0.91, 2.39), pharynx (0.85, 2.39), spinal cord (0.87, 2.27), left submandibular gland (0.85, 3.46), right submandibular gland (0.83, 3.69), left temporal lobe (0.94, 2.20), and right temporal lobe (0.95, 2.09). The auto-contours for the optic chiasm, optic nerves, cochleas, and submandibular glands differed substantially from the final contours, a finding corroborated by the clinical team; the rest were clinically acceptable with minor or no edits necessary.

CONCLUSION

The proposed strategy provides a sophisticated starting point for treatment planning that has garnered overall favorable feedback from the participating radiation oncologists and dosimetrists. Consequently, the technique is being extended to other treatment sites.

Particulate suspension coating of capillary tubes

The displacement of a suspension of particles by an immiscible fluid in a capillary tube or in porous media is a canonical configuration that finds application in a large number of natural and industrial applications, including water purification, dispersion of colloids and microplastics, coating and functionalization of tubings. The influence of particles dispersed in the fluid on the interfacial dynamics and on the properties of the liquid film left behind remain poorly understood. Here, we study the deposition of a coating film on the walls of a capillary tube induced by the translation of a suspension plug pushed by air. We identify the different deposition regimes as a function of the translation speed of the plug, the particle size, and the volume fraction of the suspension. The thickness of the coating film is characterized, and we show that similarly to dip coating, three coating regimes are observed, liquid only, heterogeneous, and thick films. We also show that, at first order, the thickness of films thicker than the particle diameter can be predicted using the effective viscosity of the suspension. Nevertheless, we also report that for large particles and concentrated suspensions, a shear-induced migration mechanism leads to local variations in volume fraction and modifies the deposited film thickness and composition.

Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation

Immunocytes dynamically reprogram their gene expression profiles during differentiation and immunoresponse. However, the underlying mechanism remains elusive. Here, we develop a single-cell Hi-C method and systematically delineate the 3D genome and dynamic epigenetic atlas of macrophages during these processes. We propose "degree of disorder" to measure genome organizational patterns inside topologically-associated domains, which is correlated with the chromatin epigenetic states, gene expression, and chromatin structure variability in individual cells. Furthermore, we identify that NF-κB initiates systematic chromatin conformation reorganization upon Mycobacterium tuberculosis infection. The integrated Hi-C, eQTL, and GWAS analysis depicts the atlas of the long-range target genes of mycobacterial disease susceptible loci. Among these, the SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 3D genome structure of immunocytes and sheds insights into the order of genome organization and the coordinated gene transcription during immunoresponse.

Extraction optimization, physicochemical property, antioxidant activity, and α-glucosidase inhibitory effect of polysaccharides from lotus seedpods

BACKGROUND

Lotus seedpods are an agricultural by-product of lotus (Nelumbo nucifera Gaertn.), which is widely cultivated in Southeast Asia and Australia. Most lotus seedpods are considered waste and are abandoned or incinerated, resulting in significant waste of resources and heavy environmental pollution. For recycling lotus seedpods, the extraction optimization, physicochemical properties, antioxidant activity, and α-glucosidase inhibitory effect of the polysaccharides contained therein were investigated in this study.

RESULTS

Hot water extraction of lotus seedpod polysaccharides was optimized by using a response surface methodology combined with a Box-Behnken design, with the optimum conditions being as follows: a liquid/solid ratio of 25.0 mL g^-1 , an extraction temperature of 98.0 °C, and an extraction time of 138.0 min. Under these conditions, an experimental yield of 5.88 ± 0.06% was obtained. Physicochemical analyses suggested that lotus seedpod polysaccharides belong to acidic heteropolysaccharides and are principally composed of rhamnose, arabinose, galactose, glucose, mannose, and galacturonic acid. The polysaccharides content has a broad molecular weight distribution (2.15 × 10⁵ to 1.77 × 10⁷  Da), an α-configuration, and mainly possesses smooth and sheet-like structures. Biological evaluations showed that the polysaccharides possessed good scavenging activity on 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt, 1,1-diphenyl-2-picryl-hydrozyl, and hydroxyl radicals, and exerted an obvious inhibitory effect on α-glucosidase activity. Moreover, the polysaccharides content was determined to be a mixed-type noncompetitive inhibitor of α-glucosidase.

CONCLUSION

The results indicate that lotus seedpod polysaccharides have potential as natural antioxidants and hypoglycaemic substitutes. This study provides the theoretical bases for the exploitation and application of polysaccharides from lotus seedpod by-product resources. © 2022 Society of Chemical Industry.

Aged Callus Skeletal Stem/Progenitor Cells Contain an Inflammatory Osteogenic Population With Increased IRF and NF-κB Pathways and Reduced Osteogenic Potential

Skeletal stem/progenitor cells (SSPCs) are critical for fracture repair by providing osteo-chondro precursors in the callus, which is impaired in aging. However, the molecular signatures of callus SSPCs during aging are not known. Herein, we performed single-cell RNA sequencing on 11,957 CD45-CD31-Ter119- SSPCs isolated from young and aged mouse calluses. Combining unsupervised clustering, putative makers, and DEGs/pathway analyses, major SSPC clusters were annotated as osteogenic, proliferating, and adipogenic populations. The proliferating cluster had a differentiating potential into osteogenic and adipogenic lineages by trajectory analysis. The osteoblastic/adipogenic/proliferating potential of individual clusters was further evidenced by elevated expression of genes related to osteoblasts, adipocytes, or proliferation. The osteogenic cluster was sub-clustered into house-keeping and inflammatory osteogenic populations that were decreased and increased in aged callus, respectively. The majority of master regulators for the inflammatory osteogenic population belong to IRF and NF-κB families, which was confirmed by immunostaining, RT-qPCR, and Western blot analysis. Furthermore, cells in the inflammatory osteogenic sub-cluster had reduced osteoblast differentiation capacity. In conclusion, we identified 3 major clusters in callus SSPCs, confirming their heterogeneity and, importantly, increased IRF/NF-κB-mediated inflammatory osteogenic population with decreased osteogenic potential in aged cells.

Simple Death Risk Models to Predict In-hospital Outcomes in Acute Aortic Dissection in Emergency Department

Objective

We sought to find a bedside prognosis prediction model based on clinical and image parameters to determine the in-hospital outcomes of acute aortic dissection (AAD) in the emergency department.

Methods

Patients who presented with AAD from January 2010 to December 2019 were retrospectively recruited in our derivation cohort. Then we prospectively collected patients with AAD from January 2020 to December 2021 as the validation cohort. We collected the demographics, medical history, treatment options, and in-hospital outcomes. All enrolled patients underwent computed tomography angiography. The image data were systematically reviewed for anatomic criteria in a retrospective fashion by three professional radiologists. A series of radiological parameters, including the extent of dissection, the site of the intimal tear, entry tear diameter, aortic diameter at each level, maximum false lumen diameter, and presence of pericardial effusion were collected.

Results

Of the 449 patients in the derivation cohort, 345 (76.8%) were male, the mean age was 61 years, and 298 (66.4%) had a history of hypertension. Surgical repair was performed in 327 (72.8%) cases in the derivation cohort, and the overall crude in-hospital mortality of AAD was 10.9%. Multivariate logistic regression analysis showed that predictors of in-hospital mortality in AAD included age, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter. A final prognostic model incorporating these five predictors showed good calibration and discrimination in the derivation and validation cohorts. As for type A aortic dissection, 3-level type A aortic dissection clinical prognosis score (3ADPS) including 5 clinical and image variables scored from −2 to 5 was established: (1) moderate risk of death if 3ADPS is <0; (2) high risk of death if 3ADPS is 1–2; (3) very high risk of death if 3ADPS is more than 3. The area under the receiver operator characteristic curves in the validation cohorts was 0.833 (95% CI, 0.700–0.967).

Conclusion

Age, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter can significantly affect the in-hospital outcomes of AAD. And 3ADPS contributes to the prediction of in-hospital prognosis of type A aortic dissection rapidly and effectively. As multivariable risk prediction tools, the risk models were readily available for emergency doctors to predict in-hospital mortality of patients with AAD in extreme clinical risk.

Physicochemical property and antioxidant activity of polysaccharide from the seed cakes of Camellia oleifera Abel

Seed cake refers to the food by‐product of Camellia oleifera Abel, and its insufficient utilization can cause serious environment pollution and resource waste. This study aimed to investigate antioxidant activities of the polysaccharide from the seed cakes of Camellia oleifera Abel (COCP) in vitro and in vivo. The physicochemical property of COCP was also determined. COCP was characterized to be an acidic glycoprotein and mainly consisted of rhamnose (Rha), arabinose (Ara), galactose (Gal), glucose (Glc), xylose (Xyl), mannose (Man), and galacturonic acid (Gal‐UA). COCP exhibited the polysaccharide's characteristic absorption in the Fourier transform infrared (FT‐IR) spectroscopy and showed as sheet‐like structures with a smooth surface under the scanning electron microscope (SEM). COCP exerted good scavenging activities on ABTS, DPPH, and OH radicals, with IC₅₀ values of 2.94, 2.24, and 5.09 mg/ml, respectively. COCP treatment improved learning and memory abilities of D‐galactose‐induced aging mice. Significant decreases were found in the levels of alanine transaminase (ALT), aspartate aminotransferase (AST), creatinine (CRE), blood urea nitrogen (BUN), creatine kinase (CK), and lactate dehydrogenase (LDH) in serum, as aging mice were supplemented with COCP. Aging mice showed obviously higher malondialdehyde (MDA) contents and lower superoxide dismutase (SOD) and glutathione peroxidase (GSH‐Px) activities in serum, brain, liver, kidney, and heart. The phenomena were noticeably reversed when mice were treated with COCP. Results indicated that COCP exerted excellent antioxidant activities in vitro and in vivo, which support its potential application as a natural antioxidant in food and medicine industries.

Expression pattern of CRYAB and CTGF genes in two pig breeds at different altitudes

ABSTRACT Tibetan pigs are characterized by significant phenotypic differences relative to lowland pigs. Our previous study demonstrated that the genes CRYAB and CTGF were differentially expressed in heart tissues between Tibetan (highland breed) and Yorkshire (lowland breed) pigs, indicating that they might participate in hypoxia adaptation. CRYAB (ɑB-crystallin) and CTGF (connective tissue growth factor) have also been reported to be associated with lung development. However, the expression patterns of CRYAB and CTGF in lung tissues at different altitudes and their genetic characterization are not well understood. In this study, qRT-PCR and western blot of lung tissue revealed higher CRYAB expression levels in highland and middle-highland Tibetan and Yorkshire pigs than in their lowland counterparts. With an increase in altitude, the expression level of CTGF increased in Tibetan pigs, whereas it decreased in Yorkshire pigs. Furthermore, two novel single-nucleotide polymorphism were identified in the 5′ flanking region of CRYAB (g.39644482C>T and g.39644132T>C) and CTGF (g.31671748A>G and g.31671773T>G). The polymorphism may partially contribute to the differences in expression levels between groups at the same altitude. These findings provide novel insights into the high-altitude hypoxia adaptations of Tibetan pigs.

GPR174 mRNA Acts as a Novel Prognostic Biomarker for Patients With Sepsis via Regulating the Inflammatory Response

Previous studies indicated that G-protein coupled receptor 174 (GPR174) is involved in the dysregulated immune response of sepsis, however, the clinical value and effects of GPR174 in septic patients are still unknown. This study is aimed to evaluate the potential value of GPR174 as a prognostic biomarker for sepsis and explore the pathological function of GPR174 in cecal ligation and puncture (CLP)-induced septic mice. In this prospective longitudinal study, the expressions of peripheral GPR174 mRNA were measured in 101 septic patients, 104 non-septic ICU controls, and 46 healthy volunteers at Day 1, 7 after ICU (Intensive Care Unit) admission, respectively. Then, the clinical values of GPR174 for the diagnosis, severity assessment, and prognosis of sepsis were analyzed. Moreover, the expressions of GPR174 mRNA in CLP-induced septic mice were detected, and Gpr174-knockout (KO) mice were used to explore its effects on inflammation. The results showed that the levels of GPR174 mRNA were significantly decreased in septic patients compared with non-septic ICU and healthy controls. In addition, the expressions of GPR174 mRNA were correlated with the lymphocyte (Lym) counts, C-reactive protein (CRP), and APACHE II and SOFA scores. The levels of GPR174 mRNA at Day 7 had a high AUC in predicting the death of sepsis (0.83). Further, we divided the septic patients into the higher and lower GPR174 mRNA expression groups by the ROC cut-off point, and the lower group was significantly associated with poor survival rate (P = 0.00139). Similarly, the expressions of peripheral Gpr174 mRNA in CLP-induced septic mice were also significantly decreased, and recovered after 72 h. Intriguingly, Gpr174-deficient could successfully improve the outcome with less multi-organ damage, which was mainly due to an increased level of IL-10, and decreased levels of IL-1β and TNF-α. Further, RNA-seq showed that Gpr174 deficiency significantly induced a phenotypic shift toward multiple immune response pathways in septic mice. In summary, our results indicated that the expressions of GPR174 mRNA were associated with the severity of sepsis, suggesting that GPR174 could be a potential prognosis biomarker for sepsis. In addition, GPR174 plays an important role in the development of sepsis by regulating the inflammatory response.

[Clinicopathological characteristics of SMARCB1(INI1)-deficient sinonasal carcinoma]

Objective: To investigate the clinicopathological characteristics, diagnosis, differential diagnosis and prognostic factors of SMARCB1 (INI1)-deficient sinonasal carcinoma (SDSC). Methods: Sixteen cases of SDSC diagnosed in the Department of Pathology, Beijing Tongren Hospital from January 2016 to September 2020 were enrolled. Ninety-nine cases of small round cell malignant tumors of the head and neck were selected as the control, including poorly-differentiated squamous cell carcinoma (n=10), poorly-differentiated adenocarcinoma (n=5), undifferentiated carcinoma (SNUC, n=4), NUT carcinoma (n=5), neuroendocrine carcinoma (n=10), and other non-epithelial tumors [olfactory neuroblastoma (n=10), rhabdomyosarcoma (n=10), NK/T-cell lymphoma (n=10), malignant melanoma (n=10), Ewing's sarcoma/primitive neuroectodermal tumor (EWS/PNET, n=5)] and non-keratinizing undifferentiated nasopharyngeal carcinoma (n=20). The clinical and pathologic characteristics of SDSC, and immunohistochemical (IHC) expression of broad-spectrum CKpan, CK7, CK8/18, CK5/6, p63, p40, p16, INI1, NUT and neuroendocrine markers (Syn, CgA, CD56) were evaluated. In situ hybridization (ISH) was used to detect EBER and fluorescence in situ hybridization (FISH) to detect INI1 gene deletion. Results: The 16 cases of SDSC accounted for 1.3% (16/1 218) of all malignant sinonasal tumors in the author's unit during this time period, and 2.4% (16/657) of all malignant epithelial tumors. Microscopically, there was no clear squamous and adenomatous differentiation, but "rhabdoid-like" cells, are often seen. All SDSC cases were positive for CKpan and CK8/18, negative for INI1; Epstein-Barr virus was not detected by ISH; and INI1 gene deletion was observed in all 11 SDSC patients with FISH. Twelve cases were followed up for 3-47 months. One died of tumor-related diseases half a year after diagnosis, and the remaining patients were alive with tumor, the longest survival time was 47 months. Conclusion: SDSC should be differentiated from a variety of poorly-differentiated tumors in the sinonasal area. Histologically, SDSC has no clear differentiation, but the tumor cells are characteristically basal-like or rhabdoid-like, with non-specific vacuoles, translucent or vacuolar nuclei, prominent nucleoli and necrotic foci. They are negative for INI1 IHC staining, and FISH demonstrates INI1 gene deletion. The clinical prognosis is still unclear, further studies on its biologic behavior and treatment methods are warranted.