CD3 high and FoxP3 - tumor-infiltrating lymphocytes in the invasive margin as a favorable prognostic marker in patients with invasive urothelial carcinoma of the bladder.

Tumor-infiltrating lymphocytes (TILs) have been extensively explored as prognostic biomarkers and cellular immunotherapy methods in cancer patients. However, the prognostic significance of TILs in bladder cancer remains unresolved. We evaluated the prognostic effect of TILs in bladder cancer patients. Sixty-four bladder cancer patients who underwent surgical resection between 2018 and 2020 in Zhejiang Provincial People's Hospital were analyzed in this study. Immunohistochemistry was used to evaluate CD3, CD4, CD8, and FoxP3 expression on TILs in the invasive margin of tumor tissue, and the presence of TIL subsets was correlated with the disease-free survival (DFS) of bladder cancer patients. The relationship between clinical-pathological features and DFS were analyzed. A high level of CD3 + TILs (CD3 high TILs) ( P = 0.027) or negative expression of FoxP3 TILs (FoxP3 - TILs) ( P = 0.016) was significantly related to better DFS in bladder cancer patients. Those with CD3 high FoxP3 - TILs had the best prognosis compared to those with CD3 high FoxP3 + TILs or CD3 low FoxP3 - TILs ( P = 0.0035). Advanced age [HR 4.57, (1.86-11.25); P = 0.001], CD3 low TILs [HR 0.21, (0.06-0.71); P = 0.012], CD8 low TILs [HR 0.34, (0.12-0.94); P = 0.039], and FoxP3 + TILs [HR 10.11 (1.96-52.27); P = 0.006] in the invasive margin were associated with a worse prognosis (DFS) by multivariate analysis. In conclusion, we demonstrated that CD3 high , FoxP3 - , and CD3 high FoxP3 - TILs in the invasive margin were significantly associated with better DFS. CD8 high and CD4 high TILs in the invasive margin tended to predict better DFS in bladder cancer. Patients with CD4 high CD8 high TILs in the invasive margin were likely to have a better prognosis.

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.

OBJECTIVES: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. METHODS: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). RESULTS: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. CONCLUSIONS: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

Low-rank fusion convolutional neural network for prediction of remission after stereotactic radiosurgery in patients with acromegaly: a proof-of-concept study.

Artificial intelligence approaches to analyze pathological images (pathomic) for outcome prediction have not been sufficiently considered in the field of pituitary research. A total of 5,504 hematoxylin & eosin-stained pathology image tiles from 58 acromegalic patients with a good or poor outcome were integrated with other clinical and genetic information to train a low-rank fusion convolutional neural network (LFCNN). The model was externally validated in 1,536 patches from an external cohort. The primary outcome was the time to the first endocrine remission after stereotactic radiosurgery (SRS). The median time of initial endocrine remission was 43 months (interquartile range [IQR]: 13-60 months) after SRS, and the 24-month initial cumulative remission rate was 57.9% (IQR: 46.4-72.3%). The patient-wise accuracy of the LFCNN model in predicting the primary outcome was 92.9% in the internal test dataset, and the sensitivity and specificity were 87.5 and 100.0%, respectively. The LFCNN model was a strong predictor of initial cumulative remission in the training cohort (hazard ratio [HR] 9.58, 95% confidence interval [CI] 3.89-23.59; p < 0.001) and was higher than that of established prognostic markers. The predictive value of the LFCNN model was further validated in an external cohort (HR 9.06, 95% CI 1.14-72.25; p = 0.012). In this proof-of-concept study, clinically and genetically useful prognostic markers were integrated with digital images to predict endocrine outcomes after SRS in patients with active acromegaly. The model considerably outperformed established prognostic markers and can potentially be used by clinicians to improve decision-making regarding adjuvant treatment choices. © 2022 The Pathological Society of Great Britain and Ireland.

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis.

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to identify good surgical candidates. This study aims to establish a radiomics system for the automatic diagnosis of hippocampal sclerosis with the help of machine learning. A total of 240 cases were analysed to develop a diagnostic model. First, an automatic hippocampal segmentation process was established that exploits a priori knowledge of the relatively fixed location of the hippocampus in brain partitions, as well as a deep-learning segmentation network based on an Attention U-net. Then, we extracted 527 radiomics features from each side of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on the left or right side. When the automatic diagnosis model identified HS and NC, the sensitivity and specificity reached 0.941 and 0.917 in the 10-fold cross-validation set and 0.920 and 0.909 in the independent testing set. When the diagnostic model detected HS lateralization, the sensitivity and specificity reached 0.923 and 0.920 in cross-validation and 0.909 and 0.929 in independent testing. Our results show that the developed radiomics model can help detect TLE patients with hippocampal sclerosis and has the potential to simplify preoperative evaluations and select surgical candidates.

Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints.

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model's performance, outperforming other unsupervised methods.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer.

BACKGROUND: Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa). METHODS: We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m6A methylation genes. RESULTS: Kaplan-Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma - associated herpesvirus infection, IL - 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K - Akt signaling pathway and HIF - 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m6A methylation-related genes were significantly differentially expressed between the two risk groups. CONCLUSION: A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Diversity synthesis of indole derivatives via catalyst control cyclization reaction of 2-indolylmethanols and azonaphthalene.

A series of indole-fused scaffolds and derivatives was synthesized via the cyclization reaction of 2-indolylmethanols with azonaphthalene. These reactions were realized under mild reaction conditions through catalyst control, providing structurally diverse indole derivatives with moderate to excellent yields. This protocol also shows good substrate adaptability, especially in six-membered ring products.

TM4SF1 promotes glioma malignancy through multiple mechanisms.

Transmembrane-4 L Six Family member 1 (TM4SF1) belongs to a family of integral membrane proteins implicated in cell growth and tumor progression. Glioma is the most common and aggressive malignant brain tumor in adults. In this study, we showed that TM4SF1 was highly expressed in glioma tumor tissues and cell lines. The expression levels of TM4SF1 were negatively correlated with patients' survival rates. Silencing TM4SF1 by RNA interference inhibited the proliferation, migration, and invasion of glioma cells. Moreover, TM4SF1 silencing induced glioma cell cycle arrest and early apoptosis. In contrast, overexpression of TM4SF1 in glioma cells exhibited the opposite effects. Mechanistically, we found that loss of TM4SF1 reduced phospho-ATK, Cyclin D1, Bcl-2, and MMP-9 levels in glioma cells. Taken together, these findings provide novel insights into glioma pathogenesis and suggest that TM4SF1 may represent a novel target for glioma intervention.

Combined Radiomics Model for Prediction of Hematoma Progression and Clinical Outcome of Cerebral Contusions in Traumatic Brain Injury.

BACKGROUND: Traumatic brain injury is a common and devastating injury that is the leading cause of neurological disability and death worldwide. Patients with cerebral lobe contusion received conservative treatment because of their mild manifestations, but delayed intracranial hematoma may increase and even become life-threatening. We explored the noninvasive method to predict the prognosis of progression and Glasgow Outcome Scale (GOS) by using a quantitative radiomics approach and statistical analysis. METHODS: Eighty-eight patients who were pathologically diagnosed were retrospectively studied. The radiomics method developed in this work included image segmentation, feature extraction, and feature selection. The nomograms were established based on statistical analysis and a radiomics method. We conducted a comparative study of hematoma progression and GOS between the clinical factor alone and fusion radiomics features. RESULTS: Nineteen clinical factors, 513 radiomics features, and 116 locational features were considered. Among clinical factors, international normalized ratio, prothrombin time, and fibrinogen were enrolled for hematoma progression. As for GOS, treatment strategy, age, Glasgow Coma Scale score, and blood platelet were associated factors. Eight features for GOS and five features for hematoma progression were filtered by using sparse representation and locality preserving projection-combined method. Four nomograms were constructed. After fusion radiomics features, area under the curve of hematoma progression prediction increased from 0.832 to 0.899, whereas GOS prediction went from 0.794 to 0.844. CONCLUSIONS: A radiomic-based model that merges radiomics and clinical features is a noninvasive approach to predict hematoma progression and clinical outcomes of cerebral contusions in traumatic brain injury.

MRI-based brain tumor segmentation using FPGA-accelerated neural network.

BACKGROUND: Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. RESULTS: Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. CONCLUSION: We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N = 188), cross-validation (N = 411), and independent testing (N = 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P = 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P = 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.

PURPOSE: To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis. METHODS: We continuously enrolled magnetic resonance diffusion weighted image (MR-DWI) confirmed first-episode ischemic stroke patients (onset time: less than 9 h) as well as some normal individuals in this study. They all underwent CT plain scan and MR-DWI scan with same scanning range, layer thickness (4 mm) and interlayer spacing (4 mm) (The time interval between two examinations: less than 4 h). Setting MR-DWI as gold standard of infarct core and using deep learning ResNet combined with a maximum a posteriori probability (MAP) model and a post-processing method to detect the infarct core on non-contrast CT images. After that, we use decision curve analysis (DCA) establishing models to analyze the value of this new method in clinical practice. RESULTS: 116 ischemic stroke patients and 26 normal people were enrolled. 58 patients were allocated into training dataset and 58 were divided into testing dataset along with 26 normal samples. The identification accuracy of our ResNet based approach in detecting the infarct core on non-contrast CT is 75.9%. The DCA shows that this deep learning method is capable of improving the net benefit of ischemic stroke patients. CONCLUSIONS: Our deep learning residual network assisted with optimization methods is able to detect early infarct core on non-contrast CT images and has the potential to help physicians improve diagnostic accuracy in acute ischemic stroke patients.

White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction.

BACKGROUND: We developed an image patch classification-based method to detect early ischemic stroke. The accuracy of this method was >75%. We aimed to analyze patients' image data to identify interference factors that would affect its accuracy. METHODS: We conducted a retrospective analysis of 162 patients who were hospitalized with acute ischemic stroke. Factors related to the noncontrast computed tomography (ncCT) determination results were analyzed according to the patient's sex, age, clinical symptoms, cerebral infarction volume, cerebral infarction location, and whether or not the white matter high (WMH) signal was combined. RESULTS: The volume of cerebral infarction was positively correlated with the predicted results. The correct percentages of patients with volumes >1 and <1 mL were 59.18 and 83.19%, respectively, and the difference was statistically significant (p = 0.001). The correct percentage of the internal capsule region (47.1%) was significantly lower than that of the other groups (p = 0.011). The correct percentage of lateral ventricular paraventricular infarction was significantly lower than that of non-lateral ventricle patients (70.8 vs. 85.7%). In patients with lateral ventricular paraventricular infarction, if the WMH was combined, the correct percentage will decreased further as the Fazekas level increased. The correct percentage of lateral ventricle infarction combined with Fazekas 3 was 40.0%, which was statistically significant compared with the patient having Fazekas 0 with lateral ventricular infarction (p = 0.01). CONCLUSIONS: WMH had a similar computed tomography appearance to cerebral infarction and could interfere with the prediction of the cerebral infarction region by ncCT. This result provides a reference for clinicians to choose imaging methods for identifying acute cerebral infarction areas.

Foliar application of selenium and zinc to alleviate wheat (Triticum aestivum L.) cadmium toxicity and uptake from cadmium-contaminated soil.

Due to the large area of agricultural soils contaminated by Cd worldwide, cost-effective and practical method for safety food production are necessary. The roles of micronutrient on reducing Cd accumulation in crops are recently introduced. In the current study, a pot-culture experiment in the greenhouse was conducted to study the foliar spraying of Se (Na2SeO4) and Zn (ZnSO4) on physiological and growth parameters, as well as Cd concentrations in wheat plants grown in Cd-contaminated soil. The foliar was sprayed with four concentration of Se and Zn (0, 10, 20, and 40 mg L-1) at different growth stage (tillering, elongating and heading) and whole wheat plants were collected after maturity. Both foliar spraying with Se and Zn significantly enhanced the photosynthesis, tissue biomass and antioxidant enzyme activity. Additionally, Se and Zn application can also increase Se and Zn concentrations in different plant tissues. Selenium and Zn decreased malondialdehyde (MDA) and Cd concentrations in wheat grains, hulks, leaves, stalks and root in a dose-additive manner. Overall, Se and Zn both efficiently enhanced the wheat growth and Se and Zn concentrations, and simultaneously decreased the Cd concentration in wheat plant. Compared with Zn, Se more efficiently improved wheat growth and reduced Cd concentration in the wheat in a Cd-contaminated soil. Present results suggest that use of foliar spraying, especially Se, could be a cost-effective strategy and could be recommended for remediation of light-or moderate-polluted soils contaminated by Cd.

The CO2 Absorption in Flue Gas Using Mixed Ionic Liquids.

Because of the appealing properties, ionic liquids (ILs) are believed to be promising alternatives for the CO2 absorption in the flue gas. Several ILs, such as [NH2emim][BF4], [C4mim][OAc], and [NH2emim[OAc], have been used to capture CO2 of the simulated flue gas in this work. The structural changes of the ILs before and after absorption were also investigated by quantum chemical methods, FTIR, and NMR technologies. However, the experimental results and theoretical calculation showed that the flue gas component SO2 would significantly weaken the CO2 absorption performance of the ILs. SO2 was more likely to react with the active sites of the ILs than CO2. To improve the absorption capacity, the ionic liquid (IL) mixture [C4mim][OAc]/ [NH2emim][BF4] were employed for the CO2 absorption of the flue gas. It is found that the CO2 absorption capacity would be increased by about 25%, even in the presence of SO2. The calculation results suggested that CO2 could not compete with SO2 for reacting with the IL during the absorption process. Nevertheless, SO2 might be first captured by the [NH2emim][BF4] of the IL mixture, and then the [C4mim][OAc] ionic liquid could absorb more CO2 without the interference of SO2.

[Determination of ten kinds of α-hydroxy acids in cosmetics with quantitative analysis of multi-components by single marker].

OBJECTIVE: To establish a method for determination of ten kinds of α-hydroxy acids in cosmetics with quantitative analysis of multi-components by single marker(QAMS). METHODS: The analytes were separated by high performance liquid chromatography on a Venusil XBP C_8 column(4. 6 mm×250 mm, 5 µm), with the mobile phases of ammonium dihydrogen phosphate buffer-methonal under a gradient elution. The components were detected at the wavelengths of 214 nm using a diode array detector. Citric acid was used as the internal standard to determine the relative correction factors(RCFs) of the nine other α-hydroxy acids, in order to calculate their contents in samples by their RCFs. RESULTS: Good linearity with correlation coefficients greater than 0. 9994 was obtained for all the analytes. Stabilities within 24 h and precision of ten α-hydroxy acids were all good. Recoveries of the method were from 89. 3% to 105. 0% at three concentration levels, with the relative standard deviation(RSD) from 1. 0% to 2. 9%. Nine batches of samples were determined by QAMS, as well as the standard curve method(SCM). The relative average deviations(RAD) were below 3. 2% between the result of the two method, which showed good feasibility and accuracy of QAMS. CONCLUSION: The method is simple, accurate and beneficial to the saving of reference substances, which is suitable for the determination of ten kinds of α-hydroxy acids in cosmetics.

Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

PURPOSE: To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients. METHOD: A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis. RESULT: For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001). CONCLUSION: Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy. KEY POINTS: • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis.

Preparation of Y2O3 Coated CaO Ceramic Cores with Anti-Hydration Performance and High-Interface Stability Against Interface Reaction of Ti-6Al-4V Alloys.

In this study, we describe a novel method for preparing Y2O3@CaO ceramic cores with anti-hydration performance and high-interface stability against interface reaction of Ti-6Al-4V alloys. The effect of Y2O3 coating on microstructure, mechanical, anti-hydration properties of ceramic cores and interface reaction with Ti-6Al-4V alloys was studied. The results show that the surface charge of Y2O3 and CaO are opposite at the pH value of 13, which might result in an electrostatic force and become the main driving force of Y2O3 particles absorb on the surface of CaO particles. The Y2O3 coating improved the anti-hydration properties of the CaO-based ceramic cores after sintering at 1450 °C. Meanwhile, the flexural strength improved from 11.2 to 18.8 MPa. At last, the interaction between the ceramic cores and Ti-6Al-4V metal were studied by centrifugal investment casting. Y2O3 coating can effectively reduce the interface reaction and the thickness of the interaction layer in the casting was less than 10 µm. The results suggest that the Y2O3@CaO ceramic with anti-hydration performance provide excellent mechanical and high-interface stability against interface reaction of Ti-6Al-4V alloys.

Effect of shoe wearing time and midsole hardness on ground reaction forces, ankle stability and perceived comfort in basketball landing.

This study examined the effect of wearing time on comfort perception and landing biomechanics of basketball shoes with different midsole hardness. Fifteen basketball players performed drop landing and layup first step while wearing shoes of different wearing time (new, 2-, 4-, 6- and 8-week) and hardness (soft, medium and hard). Two-way ANOVA with repeated measures was performed on GRF, ankle kinematic and comfort perception variables. Increased wearing time was associated with poorer force attenuation and comfort perception during landing activities (p < 0.05). The new shoes had significantly smaller forefoot (2- and 4-week) and rearfoot peak GRF impacts (all time conditions) in drop landing and smaller rearfoot peak GRF impact (6- and 8-week) in layup; shoes with 4-week of wearing time had significantly better perceptions of forefoot cushioning, forefoot stability, rearfoot cushioning, rearfoot stability and overall comfort than the new shoes (p < 0.05). Compared with hard shoes, the soft shoes had better rearfoot cushioning but poorer forefoot cushioning (p < 0.05). Shoe hardness and wearing time would play an influential role in GRF and comfort perception, but not in ankle kinematics. Although shoe cushioning performance would decrease even after a short wearing period, the best comfort perception was found at 4-week wearing time.

Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images.

BACKGROUND: This study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging. METHODS: A total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models. RESULTS: A total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included. CONCLUSIONS: Radiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.