A coarse-to-fine cascade deep learning neural network for segmenting cerebral aneurysms in time-of-flight magnetic resonance angiography.

BACKGROUND: Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA) has been the most commonly used method for screening aneurysms due to its noninvasiveness. The methods based on deep learning technologies can assist radiologists in achieving accurate and reliable analysis of the size and shape of aneurysms, which may be helpful in rupture risk prediction models. However, the existing methods did not accomplish accurate segmentation of cerebral aneurysms in 3D TOF-MRA. METHODS: This paper proposed a CCDU-Net for segmenting UCAs of 3D TOF-MRA images. The CCDU-Net was a cascade of a convolutional neural network for coarse segmentation and the proposed DU-Net for fine segmentation. Especially, the dual-channel inputs of DU-Net were composed of the vessel image and its contour image which can augment the vascular morphological information. Furthermore, a newly designed weighted loss function was used in the training process of DU-Net to promote the segmentation performance. RESULTS: A total of 270 patients with UCAs were enrolled in this study. The images were divided into the training (N = 174), validation (N = 43), and testing (N = 53) cohorts. The CCDU-Net achieved a dice similarity coefficient (DSC) of 0.616 ± 0.167, Hausdorff distance (HD) of 5.686 ± 7.020 mm, and volumetric similarity (VS) of 0.752 ± 0.226 in the testing cohort. Compared with the existing best method, the DSC and VS increased by 18% and 5%, respectively, while the HD decreased by one-tenth. CONCLUSIONS: We proposed a CCDU-Net for segmenting UCAs in 3D TOF-MRA, and the obtained results show that the proposed method outperformed other existing methods.

[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

GRIM19 is involved in WT1 expression and epithelial-to-mesenchymal transition in adenomyotic lesions.

The epithelial-to-mesenchymal transition may play a role in adenomyosis. GRIM19 expression is downregulated in adenomyotic lesions, and the effects of this downregulation in adenomyosis remain relatively unclear. In this study, we aimed to explore whether aberrant GRIM19 expression is associated with the epithelial-to-mesenchymal transition in adenomyosis and found that the expression of both GRIM19 and WT1 was low, and epithelial-to-mesenchymal transition, which included significant changes in CDH1, CDH2 and KRT8 expression, occurred in adenomyotic lesions, as confirmed by Western blotting and quantitative real-time PCR. We provided novel insights into WT1 expression in adenomyosis, revealing that WT1 expression was increased in the endometrial glands of adenomyotic lesions by immunohistochemistry. In vitro, knockdown of GRIM19 expression by small interfering RNA (siRNA) promoted the proliferation, migration and invasion of Ishikawa cells, as measured by Cell Counting Kit-8, wound healing assay and Transwell assays. Western blotting and quantitative real-time PCR confirmed that WT1 expression increased and epithelial-to-mesenchymal transition was induced, including the upregulation of CDH2 and downregulation of CDH1 and KRT8after transfecting the GRIM19 siRNA to Ishikawa cells. Furthermore, WT1 expression was upregulated and epithelial-to-mesenchymal transition was observed, including downregulation of CDH1 and KRT8in GRIM19 gene-knockdown mice. Upregulation of Wt1 expression in the endometrial glands of Grim19 knockdown mice was also verified by immunohistochemistry. Taken together, these results reveal that low expression of GRIM19 in adenomyosis may upregulate WT1 expression and induce epithelial-to-mesenchymal transition in the endometria, providing new insights into the pathogenesis of adenomyosis.

Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.

BACKGROUND: Preoperative differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) is important to guide neurosurgical decision-making. PURPOSE: To validate the generalization ability of radiomics models based on multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM. STUDY TYPE: Retrospective. POPULATION: In all, 240 patients with GBM (n = 129) or PCNSL (n = 111). FIELD STRENGTH/SEQUENCE: 3.0T scanners (two vendors). Sequences: fluid-attenuation inversion recovery, diffusion-weighted imaging (DWI), and contrast-enhanced T1 -weighted imaging (CE-T1 WI). Apparent diffusion coefficients (ADCs) were derived from DWI. ASSESSMENT: Cross-vendor and mixed-vendor validation were conducted. In cross-vendor validation, the training set was 149 patients' data from vendor 1, and test set was 91 patients' data from vendor 2. In mixed-vendor validation, a training set was 80% of data from both vendors, and the test set remained at 20% of data. Single and multisequence radiomics models were built. The diagnoses by radiologists with 5, 10, and 20 years' experience were obtained. The integrated models were built combining the diagnoses by the best-performing radiomics model and each radiologist. Model performance was validated in the test set using area under the ROC curve (AUC). Histological results were used as the reference standard. STATISTICAL TESTS: DeLong test: differences between AUCs. U-test: differences of numerical variables. Fisher's exact test: differences of categorical variables. RESULTS: In cross-vendor and mixed-vendor validation, the combination of CE-T1 WI and ADC produced the best-performing radiomics model, with AUC of 0.943 vs. 0.935, P = 0.854. The integrated models had higher AUCs than radiologists, with 5 (0.975 vs. 0.891, P = 0.002 and 0.995 vs. 0.885, P = 0.007), 10 (0.975 vs. 0.913, P = 0.029 and 0.995 vs. 0.900, P = 0.030), and 20 (0.975 vs. 0.945, P = 0.179 and 0.995 vs. 0.923, P = 0.046) years' experiences. DATA CONCLUSION: Radiomics for differentiating PCNSL from GBM was generalizable. The model combining MP-MRI and radiologists' diagnoses had superior performance compared to the radiologists alone. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies. PURPOSE: To investigate the use of a convolutional neural network (CNN) model for differentiation of PCNSL and GBM without tumor delineation. STUDY TYPE: Retrospective. POPULATION: A total of 289 patients with PCNSL (136) or GBM (153) were included, the average age of the cohort was 54 years, and there were 173 men and 116 women. FIELD STRENGTH/SEQUENCE: 3.0 T Axial contrast-enhanced T1 -weighted spin-echo inversion recovery sequence (CE-T1 WI), T2 -weighted fluid-attenuation inversion recovery sequence (FLAIR), and diffusion weighted imaging (DWI, b = 0 second/mm2 , 1000 seconds/mm2 ). ASSESSMENT: A single-parametric CNN model was built using CE-T1 WI, FLAIR, and the apparent diffusion coefficient (ADC) map derived from DWI, respectively. A decision-level fusion based multi-parametric CNN model (DF-CNN) was built by combining the predictions of single-parametric CNN models through logistic regression. An image-level fusion based multi-parametric CNN model (IF-CNN) was built using the integrated multi-parametric MR images. The radiomics models were developed. The diagnoses by three radiologists with 6 years (junior radiologist Y.Y.), 11 years (intermediate-level radiologist Y.T.), and 21 years (senior radiologist Y.L.) of experience were obtained. STATISTICAL ANALYSIS: The 5-fold cross validation was used for model evaluation. The Pearson's chi-squared test was used to compare the accuracies. U-test and Fisher's exact test were used to compare clinical characteristics. RESULTS: The CE-T1 WI, FLAIR, and ADC based single-parametric CNN model had accuracy of 0.884, 0.782, and 0.700, respectively. The DF-CNN model had an accuracy of 0.899 which was higher than the IF-CNN model (0.830, P = 0.021), but had no significant difference in accuracy compared to the radiomics model (0.865, P = 0.255), and the senior radiologist (0.906, P = 0.886). DATA CONCLUSION: A CNN model can differentiate PCNSL from GBM without tumor delineation, and comparable to the radiomics models and radiologists. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Nitrate Reductase-Mediated Nitric Oxide Regulates the Leaf Shape in Arabidopsis by Mediating the Homeostasis of Reactive Oxygen Species.

As a gaseous biological signaling molecule, nitric oxide (NO) regulates many physiological processes in plants. Over the last decades, this low molecular weight compound has been identified as a key signaling molecule to regulate plant stress responses, and also plays an important role in plant development. However, elucidation of the molecular mechanisms for NO in leaf development has so far been limited due to a lack of mutant resources. Here, we employed the NO-deficient mutant nia1nia2 to examine the role of NO in leaf development. We have found that nia1nia2 mutant plants displayed very different leaf phenotypes as compared to wild type Col-0. Further studies have shown that reactive oxygen species (ROS) levels are higher in nia1nia2 mutant plants. Interestingly, ROS-related enzymes ascorbate peroxidase (APX), catalases (CAT), and peroxidases (POD) have shown decreases in their activities. Our transcriptome data have revealed that the ROS synthesis gene RBOHD was enhanced in nia1nia2 mutants and the photosynthesis-related pathway was impaired, which suggests that NO is required for chloroplast development and leaf development. Together, these results imply that NO plays a significant role in plant leaf development by regulating ROS homeostasis.

ARTEMISININ BIOSYNTHESIS PROMOTING KINASE 1 positively regulates artemisinin biosynthesis through phosphorylating AabZIP1.

The plant Artemisia annua produces the anti-malarial compound artemisinin. Although the transcriptional regulation of artemisinin biosynthesis has been extensively studied, its post-translational regulatory mechanisms, especially that of protein phosphorylation, remain unknown. Here, we report that an ABA-responsive kinase (AaAPK1), a member of the SnRK2 family, is involved in regulating artemisinin biosynthesis. The physical interaction of AaAPK1 with AabZIP1 was confirmed by multiple assays, including yeast two-hybrid, bimolecular fluorescence complementation, and pull-down. AaAPK1, mainly expressed in flower buds and leaves, could be induced by ABA, drought, and NaCl treatments. Phos-tag mobility shift assays indicated that AaAPK1 phosphorylated both itself and AabZIP1. As a result, the phosphorylated AaAPK1 significantly enhanced the transactivational activity of AabZIP1 on the artemisinin biosynthesis genes. Substituting the Ser37 with Ala37 of AabZIP1 significantly suppressed its phosphorylation, which inhibited the transactivational activity of AabZIP1. Consistent overexpression of AaAPK1 significantly increased the production of artemisinin, as well as the expression levels of the artemisinin biosynthesis genes. Our study opens a window into the regulatory network underlying artemisinin biosynthesis at the post-translational level. Importantly, and for the first time, we provide evidence for why the kinase gene AaAPK1 is a key candidate for the metabolic engineering of artemisinin biosynthesis.

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules.

Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

Combining SENSE and reduced field-of-view for high-resolution diffusion weighted magnetic resonance imaging.

BACKGROUND: In diffusion-weighted magnetic resonance imaging (DWI) using single-shot echo planar imaging (ss-EPI), both reduced field-of-view (FOV) excitation and sensitivity encoding (SENSE) alone can increase in-plane resolution to some degree. However, when the two techniques are combined to further increase resolution without pronounced geometric distortion, the resulted images are often corrupted by high level of noise and artifact due to the numerical restriction in SENSE. Hence, this study is aimed to provide a reconstruction method to deal with this problem. METHODS: The proposed reconstruction method was developed and implemented to deal with the high level of noise and artifact in the combination of reduced FOV imaging and traditional SENSE, in which all the imaging data were considered jointly by incorporating the motion induced phase variations among excitations. The in vivo human spine diffusion images from ten subjects were acquired at 1.5 T and reconstructed using the proposed method, and compared with SENSE magnitude average results for a range of reduction factors in reduced FOV. These images were evaluated by two radiologists using visual scores (considering distortion, noise and artifact levels) from 1 to 10. RESULTS: The proposed method was able to reconstruct images with greatly reduced noise and artifact compared to SENSE magnitude average. The mean g-factors were maintained close to 1 along with enhanced signal-to-noise ratio efficiency. The image quality scores of the proposed method were significantly higher (P < 0.01) than SENSE magnitude average for all the evaluated reduction factors. CONCLUSION: The proposed method can improve the combination of SENSE and reduced FOV for high-resolution ss-EPI DWI with reduced noise and artifact.

Establishment of lysozyme gene RNA interference systemand its involvement in salinity tolerance in sea cucumber (Apostichopus japonicus).

The lysozyme gene was silenced using RNA interference (RNAi) to analyze the function of lysozyme in sea cucumber under salt stress. The interfering efficiency of four lysozyme RNAi oligos ranged from 0.55 to 0.70. From the four oligos, p-miR-L245 was used for further interfering experiments because it had the best silencing efficiency. Peristomial film injection of p-miR-L245 (10 µg) was used for further interfering experiments. The lowest gene expression, determined by RT-PCR assay, in muscle, coelomic fluid, and parapodium occurred 48 h after p-miR-L245 injection, while that of body wall and tube foot was 96 h and 24 h, respectively. Lysozyme activity in muscle and body wall was significantly lower than the controls. The lowest lysozyme activity in muscle, body wall and parapodium, was found at 48, 72, and 48 h, respectively, which was consistent with the transcription expression of lysozyme. The lowest point of lysozyme activity was at 96 h in coelomic fluid and tube foot, which was laid behind lysozyme expression in transcription level. The expression profile of the lysozyme transcription level and lysozyme activity in the body wall and tube foot increased at 12 h after p-miR-L245 injection before the interference effect appeared. NKA gene expression was expressed at a high level in the positive control (PC) and negative control (NC) groups at 12, 24, and 48 h, while NKA was expressed at low levels in the lysozyme RNAi injection group at 12 and 24 h. The level of NKA gene expression recovered to the level of the PC and NC group at 48, 72, and 96 h after the lysozyme RNAi injection. NKCC1 gene expression was high in the PC and NC groups at 96 h, while the NKCC1 was expressed at a low level 96 h after lysozyme RNAi injection. The results suggest that lysozyme decay involves NKA and NKCC1 gene expression under salinity 18 psµ. The K+ and Cl- concentration after lysozyme RNAi injection was lower than in the PC and NC group.

Effects of Narrative Exposure Therapy for Treating Depressive and Anxious Disorders: A Systematic Review and Meta-Analysis.

OBJECTIVE: Narrative exposure therapy (NET) has been used in various contexts for the treatment of the effects of trauma, with promising results in clinical trials. However, its effects on anxiety and depression are still unclear. The present study is a systematic review and meta-analysis of the effects of NET on depression and anxiety. METHODS: The Embase, Cumulative Index of Nursing and Allied Health Literature, PubMed, Web of Science core collection, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biomedical Database, and Wangfang databases were searched from the earliest records to March 2023. Two researchers independently screened the literature, extracted data, evaluated the risk of bias, and cross-checked the data. Meta-analysis was performed using the program RevMan 5.3. RESULTS: Eleven randomized controlled trials with a total of 754 participants were included in the study. The results showed that NET reduced both the depression (standard mean difference [SMD]=-0.51, 95% confidence interval [CI] -0.73--0.29, p<0.00001) and anxiety (SMD=-0.65, 95% CI -1.13--0.18, p=0.007) scores of the patients. Furthermore, NET was found to alleviate negative emotions associated with guilt (mean difference [MD]=-3.60, 95% CI -5.52--1.68, p=0.0005) and negative change (MD=-5.80, 95% CI -9.76--1.83, p=0.004). CONCLUSION: This analysis showed that NET can alleviate depression and anxiety. It may thus be used in clinical settings to alleviate patients' negative feelings and aid their overall recovery.

R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Contrast-enhanced sonography of juxtapleural pulmonary tuberculoma.

OBJECTIVES: The purpose of this study was to describe the findings of juxtapleural pulmonary tuberculoma on contrast-enhanced sonography and investigate their correlation with histologic findings. METHODS: From April 2008 to April 2012, 21 patients with biopsy or clinically proven juxtapleural pulmonary tuberculomas underwent contrast-enhanced sonography with an intravenous bolus injection of 4.8 mL of a sulfur hexafluoride-filled microbubble contrast agent. Enhancement patterns and functional parameters (time to enhancement, time to peak enhancement, and peak signal intensity) derived from a time-intensity curve were evaluated. Enhancement patterns were correlated with their histologic findings. RESULTS: A rim enhancement pattern was presented in 12 (57.1%), a homogeneous enhancement pattern in 5 (23.8%), and a heterogeneous enhancement pattern in 4 (19.1%) of 21 tuberculomas. A pathologic study confirmed that the nonenhancing center of the rim enhancement pattern corresponded to caseous or liquefied necrosis, and homogeneously enhanced portions corresponded to granulomatous inflammation. The medians (25th-75th interquartile ranges) for the time to enhancement, time to peak enhancement, and peak signal intensity were 14 seconds (9-14 seconds), 22 seconds (21-26 seconds), and 83 dB (55-92 dB), respectively. CONCLUSIONS: Contrast-enhanced sonography of juxtapleural pulmonary tuberculoma is feasible. Juxtapleural pulmonary tuberculomas usually show rim, homogeneous, or heterogeneous enhancement. Enhancement patterns of juxtapleural pulmonary tuberculomas are well correlated with their pathologic features.

Discriminating normal and inflammatory mice models by viscosity changes with a two-photon fluorescent probe.

Studies have found that the intracellular viscosity changes have close relationship with many diseases, therefore design and synthesis of fluorescent probe for testing intracellular viscosity is of great significance to the development of clinical. Herein, we developed a new two-photon near infrared probe (HCT) for viscosity imaging to discriminate normal and inflammatory models. Experimental results displayed that HCT has great sensitivity for the detection of viscosity, and based on the excellent performance of its photostability and lower cytotoxicity, HCT was successfully utilized for single-photon/ two-photon fluorescence imaging of the viscosity in living cells. More importantly, we employ HCT to further showcase in living tissues. Additionally, HCT could be used to discriminate between normal and inflamed mice, heralding its practical application in biomedical aspects.

Activation of Gossypium hirsutum ACS6 Facilitates Fiber Development by Improving Sucrose Metabolism and Transport.

Cotton fiber yield depends on the density of fiber cell initials that form on the ovule epidermis. Fiber initiation is triggered by MYB-MIXTA-like transcription factors (GhMMLs) and requires a sucrose supply. Ethylene or its precursor ACC (1-aminocyclopropane-1-carboxylic acid) is suggested to affect fiber yield. The Gossypium hirsutum (L.) genome contains 35 ACS genes (GhACS) encoding ACC synthases. Here, we explored the role of a GhACS family member in the regulation of fiber initiation. Expression analyses showed that the GhACS6.3 gene pair was specifically expressed in the ovules during fiber initiation (3 days before anthesis to 5 days post anthesis, -3 to 5 DPA), especially at -3 DPA, whereas other GhACS genes were expressed at very low or undetectable levels. The expression profile of GhACS6.3 during fiber initial development was confirmed by qRT-PCR analysis. Transgenic lines overexpressing GhACS6.3 (GhACS6.3-OE) showed increased ACC accumulation in ovules, which promoted the formation of fiber initials and fiber yield components. This was accompanied by increased transcript levels of GhMML3 and increased transcript levels of genes encoding sucrose transporters and sucrose synthase. These findings imply that GhACS6.3 activation is required for fiber initial development. Our results lay the foundation for further research on increasing cotton fiber production.

Implicit Neural Representations With Structured Latent Codes for Human Body Modeling.

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Furthermore, we combine Neural Body with implicit surface models to improve the learned geometry. To evaluate our approach, we perform experiments on both synthetic and real-world data, which show that our approach outperforms prior works by a large margin on novel view synthesis and 3D reconstruction. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

The predictive value of eGFR combined with BNP detection in acute kidney injury after acute myocardial infarction.

Objective: The combined detection of eGFR and BNP may provide some value in predicting the occurrence of AKI after AMI, and the study is designed to propose and validate this hypothesis. Methods: In retrospective research, AMI patients hospitalized at Weifang People's Hospital from January to December 2020 were included. Whether AKI occurred within a week of admission, patients were divided into two groups. Clinical data from two groups of patients were collected, and the Logistic regression model analysed the risk factors for AKI after AMI. The association between eGFR and BNP was analysed using Pearson linear correlation. The predictive value of eGFR and BNP alone and combined detection on AKI after AMI was analysed using the receiver operating characteristic (ROC) curve. Results: Multivariate logistic regression showed that eGFR, BNP, HDLC, UA, and K ions were AKI risk factors (P < 0.05). The eGFR correlates negatively with BNP (R = -0.324, P < 0.05). The area under the curve (AUC) of eGFR and BNP alone and combined prediction for post-AMI AKI were 0.793, 0.826, and 0.831, respectively. Conclusion: The combined detection of eGFR and BNP has a high predictive value for AKI development in AMI patients.

Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance.

PURPOSE: Multimodal registration is a key task in medical image analysis. Due to the large differences of multimodal images in intensity scale and texture pattern, it is a great challenge to design distinctive similarity metrics to guide deep learning-based multimodal image registration. Besides, since the limitation of the small receptive field, existing deep learning-based methods are mainly suitable for small deformation, but helpless for large deformation. To address the above issues, we present an unsupervised multimodal image registration method based on the multiscale integrated spatial-weight module and dual similarity guidance. METHODS: In this method, a U-shape network with our multiscale integrated spatial-weight module is embedded into a multi-resolution image registration architecture to achieve end-to-end large deformation registration, where the spatial-weight module can effectively highlight the regions with large deformation and aggregate discriminative features, and the multi-resolution architecture further helps to solve the optimization problem of the network in a coarse-to-fine pattern. Furthermore, we introduce a special loss function based on dual similarity, which represents both global gray-scale similarity and local feature similarity, to optimize the unsupervised multimodal registration network. RESULTS: We verified the effectiveness of the proposed method on liver CT-MR images. Experimental results indicate that the proposed method achieves the optimal DSC value and TRE value of 92.70 ± 1.75(%) and 6.52 ± 2.94(mm), compared with other state-of-the-art registration algorithms. CONCLUSION: The proposed method can accurately estimate the large deformation field by aggregating multiscale features, and achieve higher registration accuracy and fast registration speed. Comparative experiments also demonstrate the effectiveness and generalization ability of the algorithm.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Distinguishing normal and inflammatory models by viscosity changes with sensitively mitochondrial-trackable fluorescent probe.

Biological microenvironment plays a momentous role in the regulation of various vital activities, and its abnormal changes are often closely related to some diseases. Viscosity, as an indispensable part of microenvironment parameters, has always been one of the research hotspots of investigators. Herein, we constructed a new red-emitting fluorescent probe (HVM) to identify the abnormal situation of mitochondria through viscosity changes in the biological microenvironment. Interestingly, HVM has excellent optical properties such as large stokes shift (160 nm), viscosity sensitivity (195-fold), high photostability, and biochemical properties with low cytotoxicity and excellent biocompatibility. For these reasons, the novel probe could successfully be used to identify the normal and inflammatory models via viscosity changes in biological experiments. Therefore, we provided a convenient synthetic route to obtain viscosity sensor HVM with excellent application properties.