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Abstract The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a valuable and easy-to-use method for assessing acute ischemic stroke. It aids in identifying suitable candidates for thrombolytic therapies and evaluating treatment effectiveness. However, ASPECTS evaluation primarily relies on visual observation in current clinical practice, lacking a common standardized space. Additionally, different doctors may have varying clinical experiences, leading to a poor inter-reader agreement and potential errors in the final ASPECTS scoring. To address these issues and fill in the absence of a publicly available ASPECTS atlas, this work constructs age-specific Chinese ASPECTS atlases based on non-contrast computed tomography images of 281 healthy subjects across different age groups. Images of different age groups are warped into respective common averaged spaces, where the average intensity atlases are computed. More importantly, 10 ASPECTS regions can be obtained during this process. We develop an automated ASPECTS region mapping pipeline and collect an independent dataset to validate our atlas. The results prove that the age-specific ASPECTS atlas is of great promise in clinical availability.

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors; however, it has some drawbacks. This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images. Methods: The most suitable process was selected through multiple experiments by comparing multiple methods and features for classification. First, the U-net was applied to segment the image. Next, the nucleus was extracted from the segmented image, and the minimum spanning tree (MST) diagram structure that can capture the topological information was drawn. The third step was to extract the graph-curvature features of the histopathological image according to the MST image. Finally, by inputting the graph-curvature features into the classifier, the recognition results for benign or malignant cancer can be obtained. Results: During the experiment, we used various methods for comparison. In the image segmentation stage, U-net, watershed algorithm, and Otsu threshold segmentation methods were used. We found that the U-net method, combined with multiple indicators, was the most suitable for segmentation of histopathological images. In the feature extraction stage, in addition to extracting graph-edge and graph-curvature features, several basic image features were extracted, including the red, green and blue feature, gray-level co-occurrence matrix feature, histogram of oriented gradient feature, and local binary pattern feature. In the classifier design stage, we experimented with various methods, such as support vector machine (SVM), random forest, artificial neural network, K nearest neighbors, VGG-16, and inception-V3. Through comparison and analysis, it was found that classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into the SVM classifier. Conclusion: This study created a unique feature, the graph-curvature feature, based on the MST to represent and analyze histopathological images. This graph-based feature could be used to identify benign and malignant cells in histopathological images and assist pathologists in diagnosis.

A benchmark histopathological Hematoxylin and Eosin (H&E) image dataset for Cervical Adenocarcinoma in Situ (CAISHI), containing 2240 histopathological images of Cervical Adenocarcinoma in Situ (AIS), is established to fill the current data gap, of which 1010 are images of normal cervical glands and another 1230 are images of cervical AIS. The sampling method is endoscope biopsy. Pathological sections are obtained by H&E staining from Shengjing Hospital, China Medical University. These images have a magnification of 100 and are captured by the Axio Scope. A1 microscope. The size of the image is 3840 × 2160 pixels, and the format is “.png”. The collection of CAISHI is subject to an ethical review by China Medical University with approval number 2022PS841K.These images are analyzed at multiple levels, including classification tasks and image retrieval tasks. A variety of computer vision and machine learning methods are used to evaluate the performance of the data. For classification tasks, a variety of classical machine learning classifiers such as k-means, support vector machines (SVM), and random forests (RF), as well as convolutional neural network classifiers such as Residual Network 50 (ResNet50), Vision Transformer (ViT), Inception version 3 (Inception-V3), and Visual Geometry Group Network 16 (VGG-16), are used. In addition, the Siamese network is used to evaluate few-shot learning tasks. In terms of image retrieval functions, color features, texture features, and deep learning features are extracted, and their performances are tested. CAISHI can help with the early diagnosis and screening of cervical cancer. Researchers can use this dataset to develop new computer-aided diagnostic tools that could improve the accuracy and efficiency of cervical cancer screening and advance the development of automated diagnostic algorithms.

Frailty is a common clinical condition in older adults, which is characterized by decreased steady-state reserve capacity and anti-stress ability. The incidence of frailty is high in China and is associated with many adverse outcomes, bringing heavy burden to the family and society. Therefore, frailty monitoring and early detection will contribute to the implementation of relevant interventions and to delay the onset of frailty or even reverse frailty status and finally to a good maintenance in health condition. Since frailty is the result of multi-organ functional declines, evaluations on biological factors related to body function may provide a new solution for frailty monitoring. In this paper, we summarize the research on frailty-related biomarkers of metabolism, immunity, oxidative stress, inflammation and nutrition for providing a reference to monitoring and early diagnosis of frailty.

Regional wall motion abnormality (RWMA) is a common manifestation of ischemic heart disease detected through echocardiography. Currently, RWMA diagnosis heavily relies on visual assessment by doctors, leading to limitations in experience-based dependence and suboptimal reproducibility among observers. Several RWMA diagnosis models were proposed, while RWMA diagnosis with more refined segments can provide more comprehensive wall motion information to better assist doctors in the diagnosis of ischemic heart disease. In this paper, we proposed the STGA-MS model which consists of three modules, the spatial-temporal grouping attention (STGA) module, the segment feature extraction module, and the multiscale downsampling module, for the diagnosis of RWMA for multiple myocardial segments. The STGA module captures global spatial and temporal information, enhancing the representation of myocardial motion characteristics. The segment feature extraction module focuses on specific segment regions, extracting relevant features. The multiscale downsampling module analyzes myocardial motion deformation across different receptive fields. Experimental results on a 2D transthoracic echocardiography dataset show that the proposed STGA-MS model achieves better performance compared to state-of-the-art models. It holds promise in improving the accuracy and reproducibility of RWMA diagnosis, assisting clinicians in diagnosing ischemic heart disease more reliably.

This eight-week feeding trial was conducted to investigate whether adding yeast cell wall extract mixed with bacteria producing mycotoxin-degrading enzymes (YEMDE) to an aflatoxin B1 (AFB1)-contaminated diet affects the growth performance, liver health, antioxidant status, and intestinal microbiota status of juvenile turbot. A total of 480 fish were divided into four groups (three replicate tanks each group): basal diet (Control); Control diet supplemented with 100 μg/kg AFB1 (AFB1); and AFB1 supplemented with 500 mg/kg or 1000 mg/kg YEMDE (AFY500 and AFY1000, respectively). The results showed that a minor variation in growth performance was observed among all groups (P > 0.05). Compared to the Control diet, the AFB1 diet significantly deceased the activities of triglyceride and catalase in the serum (P

Abstract Background The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes. Results Experimental results show that the proposed method is superior to classical algorithms and advanced methods in feature number and accuracy, and the selected features have more significance. Method This paper proposes a feature selection method based on a graph neural network. The proposed method uses the actual dependencies between features and the Pearson correlation coefficient to construct graph-structured data. The information dissemination and aggregation operations based on graph neural network are applied to fuse node information on graph structured data. The redundant features are clustered by the spectral clustering method. Then, the feature ranking aggregation model using eight feature evaluation methods acts on each clustering sub-cluster for different feature selection. Conclusion The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy, which can potentially select potential biomarkers.

Abstract Precise segmentation of the lung airways is essential for a quantitative assessment of airway diseases. However, because of the complexity of the airway structure and the different thicknesses of the trachea at different positions, it is extremely difficult to segment the fine bronchial structure using chest computed tomography (CT). Traditional lung airway segmentation methods are generally based on the grayscale, geometric shape of the image, or the use of prior knowledge of anatomy. In recent years, deep learning techniques such as fully convolutional neural networks (FCNs) have achieved a great success in the field of image segmentation. Specifically, the symmetric encoder–decoder network represented by U‐Net has achieved high accuracy in many medical image segmentation tasks. In the airway segmentation challenge task of the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020), 9 of the 12 teams participating in the final round used the U‐Net network or one of its other forms, obtaining good results for lung airway segmentation. Methods used to improve the segmentation accuracy include attention mechanisms and multiscale feature information fusion. This article provides a detailed description of the methods used by these 12 teams and analyses their results.

Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.

A novel superhydrophilic and underwater superoleophobic modified PVDF membrane for oil/water separation was fabricated through a modified blending approach. Pluronic F127 and amphiphilic copolymer P (MMA-AA) were directly blended with PVDF as a hydrophilic polymeric additive to prepare membranes via phase inversion induced by immersion precipitation. Then, the as-prepared microfiltration membranes were annealed at 160 °C for a short time and quenched to room temperature. The resultant membranes exhibited contact angles of hexane larger than 150° no matter whether in an acidic or basic environment. For 1, 2-dichloroethane droplets, the membrane surface showed a change from superoleophilic to superoleophobic under water with aqueous solutions with pH values from 2 to 13. This as-prepared membrane has good mechanical strength and can then be applied for oil and water mixture separation.

Background: The association between abnormal invasive fractional flow reserve (FFR) and the fat attenuation index (FAI) of lesion-specific peri-coronary adipose tissue (PCAT) is unclear.Method: Data of patients who underwent coronary computed tomography angiography (CTA) and subsequent invasive coronary angiography (ICA) and FFR measurement within 1 week were retrospectively included. Lesion-specific FAI (FAIlesion), lesion-free FAI (FAInormal), epicardial adipose tissue (EAT) volume and attenuation was collected, along with stenosis severity and plaque characteristics. Lesions with FFR

This study was conducted to comprehensively investigate the beneficial effects of a mannan oligosaccharide product (hereinafter called MOS) on Litopenaeus vannamei and optimum level of MOS. Five isonitrogenous and isolipid diets were formulated by adding 0%, 0.02%, 0.04%, 0.08%, and 0.16% MOS in the basal diet. Each diet was randomly fed to one group with four replicates of shrimp in an 8-week feeding trial. The results showed that dietary MOS improved the growth performance and the ability of digestion of shrimp. Dietary MOS significantly increased the activity of total superoxide dismutase, catalase, and glutathione peroxidase and decreased the content of malondialdehyde in plasma of shrimp. Dietary MOS significantly increased the activity of alkaline phosphatase and lysozyme in plasma and the hemocyte counts. Dietary MOS significantly upregulated the expression of Toll, lysozyme, anti-lipopolysaccharide factor, Crustin, and heat shock protein 70 in the hepatopancreas. And dietary MOS significantly upregulated the expression of intestinal mucin-2, mucin-5B, and mucin-19, while it decreased the expression of intestinal mucin-1 and macrophage migration inhibitory factor. Dietary MOS improved the bacterial diversity; increased the abundance of Lactobacillus, Bifidobacterium, Blautia, and Pseudoalteromonas; and decreased the abundance of Vibrio in the intestine. Shrimp fed MOS diets showed lower mortality after being challenged by Vibrio parahaemolyticus. Notably, this study found a decrease in antibiotic resistance genes and mobile genetic elements after MOS supplementation for the first time. The present results showed that diet with MOS supplementation enhanced the organismal antioxidant capacity and immunity, improved intestinal immunity, optimized intestinal microecology, mitigated the degree of antibiotic resistance, and increased the resistance to V. parahaemolyticus in L. vannamei, especially when supplemented at 0.08% and 0.16%.

Deoxynivalenol (DON) is one of the most common mycotoxins in animal feed worldwide and causes significant threats to the animal health. Increased use of plant ingredients in aquaculture feeds increased the risk of mycotoxin contamination. To evaluate the effects of dietary deoxynivalenol (DON) on growth performance, immune response and intestinal health of turbot and the mitigation efficacy of yeast cell wall extract (YCWE) toward DON, nine isonitrogenous and isolipidic diets were formulated: Diet 1 (control): No DON added; Diets 2–5 or Diets 6–9: 0.5 or 3.0 mg added DON/kg diet + 0%, 0.1%, 0.2%, or 0.4% YCWE, respectively. Results showed that Diet 6 (3 mg/kg DON, 0% YCWE) significantly decreased weight gain, specific growth rate and feed efficiency ratio of fish and reduced immunoglobulin M and complement 4 concentrations in serum. Fish fed Diet 6 presented morphological alterations, lower activity of superoxide dismutase, catalase and total antioxidant capacity but higher malondialdehyde content, lower claudin-4 and occludin expression but higher interleukin-1β expression in intestine. Besides, Diet 6 decreased the abundance of potential helpful bacteria but increased the abundance of potential pathogens in intestine. While, dietary YCWE, especially Diet 8 (3 mg/kg DON, 0.2% YCWE) and 9 (3 mg/kg DON, 0.4% YCWE), markedly improved growth performance and immune response and enhanced the intestinal health of turbot. In conclusion, dietary YCWE could mitigate the toxic effects induced by DON in turbot, and could be used as an effective strategy to control DON contamination in fish feed.

This study aimed to assess dietary organic zinc on zinc homeostasis, antioxidant capacity, immune response, glycolysis and intestinal microbiota in white shrimp (Litopenaeus vannamei Boone, 1931). Six experimental diets were formulated: Control, zinc free; S120, 120 mg·kg−1 zinc from ZnSO4·7H2O added into control diet; O30, O60, O90 and O120, 30, 60, 90 and 120 mg·kg−1 zinc from Zn-proteinate added into control diet, respectively. The results showed that organic zinc significantly promoted zinc content and gene expression of ZnT1, ZIP11 and MT in the hepatopancreas and enhanced antioxidant capacity and immunity (in terms of increased activities of T-SOD, Cu/Zn SOD, PO, LZM, decreased content of MDA, upregulated expressions of GST, G6PDH, ProPO, LZM and Hemo, and increased resistance to Vibrio parahaemolyticus). Organic zinc significantly upregulated GluT1 expression in the intestine, increased glucose content of plasma and GCK, PFK and PDH activities of hepatopancreas, and decreased pyruvate content of hepatopancreas. Organic zinc improved intestinal microbiota communities, increased the abundance of potentially beneficial bacteria and decreased the abundance of potential pathogens. Inorganic zinc (S120) also had positive effects, but organic zinc (as low as O60) could achieve better effects. Overall, organic zinc had a higher bioavailability and was a more beneficial zinc resource than inorganic zinc in shrimp feeds.

At present, with the development of an aging society and an increase in the number of elderly people, in order to ensure the ability and enthusiasm of the elderly to live independently, it is necessary to ensure that they can understand the world in a normal way. More and more elderly people have cognitive impairment, and virtual reality (VR) technology is more effective in cognitive diagnosis and treatment than traditional methods. This review article describes some studies on cognitive diagnosis and training for the elderly, and puts forward some suggestions for current studies, in the hopes that VR technology can be better applied to cognitive diagnosis and training.

This study aimed to investigate the effects of dietary AFB1 on growth performance, health, intestinal microbiota communities and AFB1 tissue residues of turbot and evaluate the mitigation efficacy of yeast cell wall extract, Mycosorb® (YCWE) toward AFB1 contaminated dietary treatments. Nine experimental diets were formulated: Diet 1 (control): AFB1 free; Diets 2–5 or Diets 6–9: 20 μg AFB1/kg diet or 500 μg AFB1/kg diet + 0%, 0.1%, 0.2%, or 0.4% YCWE, respectively). The results showed that Diet 6 significantly decreased the concentrations of TP, GLB, C3, C4, T-CHO, TG but increased the activities of AST, ALT in serum, decreased the expressions of CAT, SOD, GPx, CYP1A but increased the expressions of CYP3A, GST-ζ1, p53 in liver. Diet 6 increased the AFB1 residues in serum and muscle, altered the intestinal microbiota composition, decreased the bacterial community diversity and the abundance of some potential probiotics. However, Diet 8 and Diet 9 restored the immune response, relieved adverse effects in liver, lowered the AFB1 residues in turbot tissues, promoted intestinal microbiota diversity and lowered the abundance of potentially pathogens. In conclusion, YCWE supplementation decreased the health effects of AFB1 on turbot, restoring biomarkers closer to the mycotoxin-free control diet.