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Circular RNA (circRNA) is a novel type of RNA that plays an important role in the occurrence and development of many malignant tumors. However, the potential regulatory role and molecular mechanisms of circRNAs in cervical cancer (CC) are still not clear. Here, we explored circRNAs associated with CC from the Gene Expression Omnibus (GEO) datasets GSE113696 and GSE102686. We initially identified circ_0039787, which is derived from exons 2 to 3 of the C16orf70 gene. We observed that circ_0039787 is mainly located in the cytoplasm and is more stable than its linear counterpart, C16orf70. circ_0039787 is significantly upregulated in CC tissues and cells. In addition, functional gain and loss experiments demonstrated that circ_0039787 promotes the proliferation, migration, and invasion of CC cells in vitro and the growth of CC tumors in vivo. Mechanistically, circ_0039787 promotes CC tumor progression by competitively absorbing miR-877-5p to alleviate the inhibitory effect of miR-877-5p on Kirsten Rat Sarcoma viral oncogene homolog (KRAS) expression. Overall, our results suggest that circ_0039787 could serve as a promising diagnostic biomarker and potential therapeutic target for CC patients.

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Scoliosis diagnosis and assessment rely upon Cobb angle estimation from X-ray images of the spine. Recently, automated scoliosis assessment has been greatly improved using deep learning methods. However, in such methods, the Cobb angle is usually predicted based on regression models that don't account for information of the spine structure. Alternatively, the Cobb angle can be estimated indirectly through landmark-detection and vertebra-segmentation, but this approach is still highly sensitive to small detection and segmentation errors. This paper proposes a novel deep-learning architecture, called the vertebra localization and tilt estimation network (VLTENet). This network boosts the Cobb angle estimation accuracy through employing vertebra localization and tilt estimation as network prediction goals. In particular, the VLTENet model innovatively combines a deep high-resolution network (HRNet) and a fully-convolutional U-Net architecture for capturing long-range contextual information, the overall structure, and local details in spinal X-ray images. A feature fusion channel attention (FFCA) module is also proposed to selectively emphasize more informative features and suppress less informative ones. In addition, a joint spine loss function (JS-Loss) is designed to account for the spine shape and other spatial constraints, so that the network focuses more on spine-related regions and ignore irrelevant background regions. Finally, we propose a new Cobb angle estimation method conforms with the clinical Cobb angle calculation guidelines, and produces accurate estimates for different types of scoliosis. Extensive experiments on the publically-available AASCE challenge dataset and on an in-house dataset demonstrated the superiority of our method for the task of automatic assessment of scoliosis.

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Grasping good understanding of the weight-bearing spatial structure of the spine of a human subject in a standing position is critical for the treatment of spinal disorders. Such disorders are commonly diagnosed via 2D X-ray imaging of the human subject in a standing position. However, 3D reconstruction techniques based on bi-planar X-ray imaging can enable better exploration and analysis of the spinal structure. In particular, compared to earlier deformable modeling approaches, the recently-developed deep-learning-based 3D reconstruction methods exhibit higher efficiency and generalizability. But these methods usually employ simple architectures with 2D encoders and 3D decoders. Thus, these methods have several drawbacks, namely, the existence of a semantic gap between dimensionally-inconsistent feature maps, the difficulty of jointly handling multi-view inputs, and the information source limitations for the decoding process. In order to better assist clinicians and tackle these problems, we propose a novel convolutional neural network framework, which we call BX2S-Net, to effectively achieve 3D spine reconstruction based on bi-planar X-ray images. In particular, a dimensionally-consistent encoder-decoder architecture is designed in conjunction with a dimensionality enhancement method in order to reduce the semantic gap between feature maps and achieve information fusion for multi-view inputs. A feature-guided progressive decoding process is developed on the decoder side, where a full-scale feature attention guidance (FFAG) module is introduced to efficiently aggregate image features and guide the decoding process at each level. In addition, a class augmentation method and a spatially-weighted cross-entropy loss function are used for network training with improved reconstruction quality for the vertebral edge region. The experimental results demonstrate the effectiveness of our model in reconstructing high-quality 3D spinal structures from bi-planar X-ray images. The code is available at https://github.com/NBU-CVMI/bx2s-net.

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BACKGROUND: Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred. PURPOSE: To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae. MATERIAL AND METHODS: A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance. RESULTS: Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R2, RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland-Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = -0.01 ± 0.1412 mg/cm2). CONCLUSION: Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.

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INTRODUCTION: Virtual reality (VR) is already being used for cognitive or emotional rehabilitation. However, its role in postoperative cognitive dysfunction (POCD) has not been fully recognised. Due to the lack of an effective postoperative follow-up system, the incidence of POCD in China is not clear, and although many drugs have been proposed to improve POCD in the animal study, their clinical applications are limited, while VR provides an innovative method to provide non-pharmacological management. METHODS AND DESIGN: This is a single-centre, randomised, double-blind, sham-controlled clinical trial. In this study, 600 patients over 55 years old undergoing laparoscopic surgery will be recruited. Participants will be randomly assigned to receive biophilic VR or sham VR (1:1 ratio), all patients have 20 min of exposure per day during the hospital stay. The primary outcome is the impact of VR on the incidence of POCD. Secondary outcomes include perioperative anxiety and instrumental activities of daily living. Changes in the performance of the neurocognitive batteries are measured by a local resident doctor. Serum samples will be collected on the day before surgery and 7 days after surgery. ETHICS AND DISSEMINATION: This trial has ethical approval from the Medical Ethics Committee of the Affiliated Hospital of Medical School of Ningbo University (KY20210302). The study is sponsored by Ningbo University and Ningbo Science and Technology Bureau. CONTACT: Dr. Mao Haijiao, Chair of the hospital medical Ethics committee (ndfylunli@126.com). Trial results will be submitted for publication in peer-reviewed journals, patient recruitment began in April 2021. Written informed consent is obtained for all participants. All information acquired will be disseminated via national or international conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR2000040919.

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INTRODUCTION: Primary hepatic extraskeletal osteosarcoma (ESOS) is a rare tumor with no specific clinical manifestations, and little is known about it. Here, we describe an elderly patient with primary hepatic osteosarcoma confirmed by pathology results to raise awareness. CASE REPORT: We report an unusual case of a 62-year-old man who presented with right upper quadrant pain. The inflammatory indicators were elevated, and alkaline phosphatase (AKP), carbohydrate antigen (CA-199 and CA-125) were slightly increased. Computed tomography images and magnetic resonance images discovered a 7.8 × 7.4 × 6.6 cm mass with irregular radiated and cotton-like tumor bone between the liver and right kidney space. Pathology revealed the mass to be primary exophytic ESOS of the liver. The patient underwent a surgical operation and standard chemotherapy and is still alive with no recurrence and metastasis to date. CONCLUSION: Owing to the rarity of the tumor and the lack of clinical characteristics and specific laboratory indexes, it is difficult to make a correct diagnosis. Medical imaging features mainly behave soft tissue entity with tumor bone composition. Surgical resection combined with adjuvant chemotherapy is the main treatment for ESOS.

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Injectable dynamic hydrogels play a key role in cell transplantation to protect the cells from shear stress during injection. However, it still remains challenging to design dynamic hydrogels with fast gelation and high stability for protecting cells under flow due to the slow formation and exchange of most dynamic bonds. Here, a novel dual-crosslinked hydrogel system with fast dynamic crosslinks is developed by using methacrylate chitosan (CHMA) and aldehyde functionalized hyaluronate (oxidized HA, OHA). Based on the cooperation of electrostatic interaction between cationic amino of chitosan and anionic carboxyl of HA and Schiff-based crosslinking through amino and aldehyde groups, the dynamic CHMA-OHA hydrogel shows rapid gelation and high injectability. Further, the CHMA-OHA hydrogel is photopolymerized for achieving a high modulus and stability. Importantly, such hydrogels loaded with stem cells remains a cell viability (~ 92%) after extrusion. These results indicate that the CHMA-OHA hydrogel is a promising tissue engineering biomaterial for therapeutic cell delivery and 3D printing of encapsulated cell scaffolds.

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The success of glioma chemotherapy is hampered by low intratumoral drug concentration and severe toxicity in normal organs. Glioma diagnosis and total tumor resection depend on enhanced magnetic resonance imaging (MRI) results which provide the best solution for recognizing tumor mass anatomical details with high spatial resolution. Zeolite imidazole frameworks (ZIFs) have pore channel tunability, large specific surface area and porosity, and have broad application prospects in adsorption, catalysis and drug loading. However, there are few reports on post-synthesis ZIF-8 based multifunctional nanocomposites as a theranostic agent for in vivo diagnostic and therapeutic applications simultaneously. In this study, we synthesized a low toxicity bimetallic zeolitic imidazolate framework (Mn-ZIF-8) with good dispersibility and high specific surface area, which could be used for potential high drug loading. Meanwhile, we used Mn-ZIF-8 for the first time for in vivo MRI. T1-weighted MR signals at tumor sites continuously increased over time after injecting Mn-ZIF-8 intravenously. Moreover, 12 hours after injecting Mn-ZIF-8 into a nude mouse bearing U87-MG tumor, a relatively high accumulation of Mn2+ in tumors was observed, probably due to the EPR effect of cancerous tumors. Targeted delivery significantly improves the therapeutic efficacy of Mn-ZIF-8/5-Fu in U87-MG tumor-bearing mice, resulting in 80% survival rate over 40 days of treatment. Mn-ZIF-8/5-Fu has excellent in vivo biocompatibility at a given dose, which induces minimal side effects on the functions of important organs. Therefore, efficient 5-Fu loaded Mn-ZIF-8 with favorable in vivo biocompatibility, pH responsiveness and T1-weighted contrast MRI of tumors can be used as a promising framework for diagnostic and therapeutic applications in the case of glioma simultaneously.

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