The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment.

Background: The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. Methods: To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients' clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene's test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. Results: Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86% . Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. Conclusions: Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.

Comparative analysis of the expression patterns of TM9SF family members in mice.

Transmembrane 9 superfamily proteins (TM9SFs) define a highly conserved protein family, each member of which is characterized by a variable extracellular domain and presumably nine transmembrane domains. Although previous studies have delineated the potential cytological roles of TM9SFs like autophagy and secretory pathway, their functions during development are largely unknown. To establish the basis for dissecting the functions of TM9SFs in vivo, we employed the open-source database, structure prediction, immunofluorescence and Western blot to describe the gene and protein expression patterns of TM9SFs in human and mouse. While TM9SFs are ubiquitously and homogeneously expressed in all tissues in human with RNA sequencing and proteomics analysis, we found that all mice Tm9sf proteins are preferentially expressed in lung except Tm9sf1 which is enriched in brain although they all distributed in various tissues we examined. In addition, we further explored their expression patterns in the mice central nervous system (CNS) and its extension tissue retina. Interestingly, we could show that Tm9sf1is developmentally up-regulated in brain. In addition, we also detected all Tm9sf proteins are located in neurons and microglia instead of astrocytes. Importantly, Tm9sf3 is localized in the nuclei which is distinct from the other members that are dominantly targeted to the plasma membrane/cytoplasm as expected. Finally, we also found that Tm9sf family members are broadly expressed in the layers of INL, OPL, and GCL of retina and likely targeted to the plasma membrane of retinal cells. Thus, our data provided a comprehensive overview of TM9SFs expression patterns, illustrating their ubiquitous roles in different organs, implying the possible roles of Tm9sf2/3/4 in lung functions and Tm9sf1 in neurodevelopment, and highlighting a unique cell biological functions of TM9SF3 in neuronal and microglia.

Hierarchical Multiple Precursors Induced Heterogeneous Structures in Super Austenitic Stainless Steels by Cryogenic Rolling and Annealing.

Multiple deformed substructures including dislocation cells, nanotwins (NTs) and martensite were introduced in super austenitic stainless steels (SASSs) by cryogenic rolling (Cryo-R, 77 K/22.1 mJ·m-2). With the reduction increasing, a low stacking fault energy (SFE) and increased flow stress led to the activation of secondary slip and the occurrence of NTs and martensite nano-laths, while only dislocation tangles were observed under a heavy reduction by cold-rolling (Cold-R, 293 K/49.2 mJ·m-2). The multiple precursors not only possess variable deformation stored energy, but also experience competition between recrystallization and reverse transformation during subsequent annealing, thus contributing to the formation of a heterogeneous structure (HS). The HS, which consists of bimodal-grained austenite and retained martensite simultaneously, showed a higher yield strength (~1032 MPa) and a larger tensile elongation (~9.1%) than the annealed coarse-grained Cold-R sample. The superior strength-ductility and strain hardening originate from the synergistic effects of grain refinement, dislocation and hetero-deformation-induced hardening.

Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model.

Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients. This imaging information is of great significance in assisting doctors to determine the patient's treatment plan and prognosis. Currently, great progress has been made in the field of computer-aided diagnosis technology in medicine by using artificial intelligence. However, in related research based on deep learning algorithms, researchers usually only use single-phase data for training, lacking the temporal dimension information of multi-phase image data. This makes it difficult for the model to learn more comprehensive and effective collateral circulation feature representation, thereby limiting its performance. Therefore, combining data for training is expected to improve the accuracy and reliability of collateral circulation evaluation. In this study, we propose an effective hybrid mechanism to assist the feature encoding network in evaluating the degree of collateral circulation in the brain. By using a hybrid attention mechanism, additional guidance and regularization are provided to enhance the collateral circulation feature representation across multiple stages. Time dimension information is added to the input, and multiple feature-level fusion modules are designed in the multi-branch network. The first fusion module in the single-stage feature extraction network completes the fusion of deep and shallow vessel features in the single-branch network, followed by the multi-stage network feature fusion module, which achieves feature fusion for four stages. Tested on a dataset of multi-phase cranial CTA images, the accuracy rate exceeding 90.43%. The experimental results demonstrate that the addition of these modules can fully explore collateral vessel features, improve feature expression capabilities, and optimize the performance of deep learning network model.

Merging Multiphase CTA Images and Training Them Simultaneously with a Deep Learning Algorithm Could Improve the Efficacy of AI Models for Lateral Circulation Assessment in Ischemic Stroke.

We aimed to build a deep learning-based, objective, fast, and accurate collateral circulation assessment model. We included 92 patients who had suffered acute ischemic stroke (AIS) with large vessel occlusion in the anterior circulation in this study, following their admission to our hospital from June 2020 to August 2021. We analyzed their baseline whole-brain four-dimensional computed tomography angiography (4D-CTA)/CT perfusion. The images of the arterial, arteriovenous, venous, and late venous phases were extracted from 4D-CTA according to the perfusion time-density curve. The subtraction images of each phase were created by subtracting the non-contrast CT. Each patient was marked as having good or poor collateral circulation. Based on the ResNet34 classification network, we developed a single-image input and a multi-image input network for binary classification of collateral circulation. The training and test sets included 65 and 27 patients, respectively, and Monte Carlo cross-validation was employed for five iterations. The network performance was evaluated based on its precision, accuracy, recall, F1-score, and AUC. All the five performance indicators of the single-image input model were higher than those of the other model. The single-image input processing network, combining multiphase CTA images, can better classify AIS collateral circulation. This automated collateral assessment tool could help to streamline clinical workflows, and screen patients for reperfusion therapy.

3D visualization model construction based on generative adversarial networks.

The development of computer vision technology is rapid, which supports the automatic quality control of precision components efficiently and reliably. This paper focuses on the application of computer vision technology in manufacturing quality control. A new deep learning algorithm is presented, Multi-angle projective Generative Adversarial Networks (MapGANs), to automatically generate 3D visualization models of products and components. The generated 3D visualization models can intuitively and accurately display the product parameters and indicators. Based on these indicators, our model can accurately determine whether the product meets the standard. The working principle of the MapGANs algorithm is to automatically infer the basic three-dimensional shape distribution through the product's projection module, while using multiple angles and multiple views to improve the fineness and accuracy of the three-dimensional visualization model. The experimental results prove that MapGANs can effectively reconstruct two-dimensional images into three-dimensional visualization models, and meanwhile accurately predict whether the quality of the product meets the standard.

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

BACKGROUND: The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM. METHODS: One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC). RESULTS: Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness. CONCLUSIONS: We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

New trends in the electrochemical detection of endocrine disruptors in complex media.

Endocrine disruptors (EDCs) are substances existing in the environment which affect animal and human endocrine functions and cause diseases. A small quantity of EDCs can have a serious impact on the body. Currently, enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and other traditional methods are used to detect EDCs. Although their sensitivity and reliability are good, these methods are complex, expensive, and not feasible to use in the field. Electrochemical techniques present good potential for the detection of EDCs owing to their low cost, simple, and wearable instrumentation. This paper presents the new trends in this field over the last 3 years. Some simple materials can allow some EDCs to be directly detected. New designs of biosensors, such as aptasensors, allow a femtomolar limit of detection to be reached. Many types of nanomaterial-based sensors were tested; carbonaceous nanomaterials, such as multiwalled carbon nanotubes (MWCNTs) and reduced graphene oxide (rGO), associated or not with other types of nanoparticles were included in numerous designs. Molecularly imprinted polymer (MIP)-based sensors constitute an emerging field. All the presented electrochemical sensors were successfully tested for the detection of EDCs in different types of real samples.

Liquid biopsy of circulating tumor DNA and biosensor applications.

Circulating tumor DNA (ctDNA) as a class of liquid biopsy is a type of gene fragment that contains tumor-specific gene changes in body fluids such as human peripheral blood. More and more evidences show that ctDNA is an excellent tumor biomarker for diagnosis, prognosis, tumor heterogeneity and so on. ctDNA is a tumor code in the blood. Liquid biopsy of ctDNA is firstly summarized. Compared with the traditional detection technologies of ctDNA, the biosensor is an excellent choice for the detection of ctDNA because of its portability, sensitivity, specificity and ease of use. This review mainly evaluates various biosensors applied to the detection of ctDNA. We discuss the most commonly used bioreceptors to specifically identify and bind ctDNA, including complementary DNA (cDNA), peptide nucleic acid (PNA) and anti-5 MethylCytosines, and the biotransducers which convert biological signals to analysable signs. The review also discusses signal amplification strategies in biosensors to detect ctDNA.

Investigation of the Richtmyer-Meshkov instability with double perturbation interface in nonuniform flows.

A nonuniform SF6 gas flow initial condition has been actualized in the context of shock tube experiment for the Richtmyer-Meshkov instability study. Two kinds of amplitude have been used to design the membrane supports which initially materialize the gaseous interface. The visualizations of air/SF6 sinusoidal interfaces and shock wave propagations in the nonuniform field were obtained by Schlieren photography. Experiments are in very good agreement with simulations for the air/SF6 case, but due to the initial nonuniform effects, Sadot model and Zhang-Sohn theory are far beyond the experimental and calculation results.

Experimental and numerical study of shock-accelerated elliptic heavy gas cylinders.

We studied the evolution of elliptic heavy SF6 gas cylinder surrounded by air when accelerated by a planar Mach 1.25 shock. A multiple dynamics imaging technology has been used to obtain one image of the experimental initial conditions and five images of the time evolution of elliptic cylinder. We compared the width and height of the circular and two kinds of elliptic gas cylinders and analyzed the vortex strength of the elliptic ones. Simulations are in very good agreement with the experiments, but due to the different initial gas cylinder shapes, a certain difference of the initial density peak and distribution exists between the circular and elliptic gas cylinders, and the latter initial state is more sensitive and more inenarrable.