Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder

IntroductionAutism Spectrum Disorder (ASD) has a significant impact on the health of patients, and early diagnosis and treatment are essential to improve their quality of life. Machine learning methods, including multi-classifier fusion, have been widely used for disease diagnosis and prediction with remarkable results. However, current multi-classifier fusion methods lack the ability to measure the belief level of different samples and effectively fuse them jointly.MethodsTo address these issues, a multi-classifier fusion classification framework based on belief-value for ASD diagnosis is proposed in this paper. The belief-value measures the belief level of different samples based on distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples), which is more representative than using a single type of information. Then, the complementary relationships between belief-values are captured via a multilayer perceptron (MLP) network for effective fusion of belief-values.ResultsThe experimental results demonstrate that the proposed classification framework achieves better performance than a single classifier and confirm that the fusion method used can effectively fuse complementary relationships to achieve accurate diagnosis.DiscussionFurthermore, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to the diagnosis of other neuropsychiatric disorders

Antibacterial Activity and Biocompatibility of Ag-Montmorillonite/Chitosan Colloidal Dressing in a Skin Infection Rat Model: An In Vitro and In Vivo Study

(1) Background: Traditional dressings can only superficially cover the wound, they have widespread issues with inadequate bacterial isolation and liquid absorption, and it is simple to inflict secondary wound injury when changing dressings. Therefore, it is crucial for wound healing to develop a new kind of antimicrobial colloidal dressing with good antibacterial, hygroscopic, and biocompatible qualities. (2) Methods: Ag-montmorillonite/chitosan (Ag-MMT/CS) colloid, a new type of antibacterial material, was prepared from two eco-friendly materials—namely, montmorillonite and chitosan—as auxiliary materials, wherein these materials were mixed with the natural metal Ag, which is an antibacterial agent. The optimum preparation technology was explored, and Ag-MMT/CS was characterized. Next, Staphylococcus aureus, which is a common skin infection bacterium, was considered as the experimental strain, and the in vitro antibacterial activity and cytocompatibility of the Ag-MMT/CS colloid were investigated through various experiments. Subsequently, a rat skin infection model was established to explore the in vivo antibacterial effect. (3) Results: In vitro studies revealed that the Ag-MMT/CS colloid had a good antibacterial effect on S. aureus, with an inhibition zone diameter of 18 mm and an antibacterial rate of 99.18%. After co-culture with cells for 24 h and 72 h, the cell survival rates were 88% and 94%, respectively. The cells showed normal growth and proliferation, and no evident dead cells were observed under the laser confocal microscope. After applying the colloid to the rat skin infection model, the Ag-MMT/CS treatment group exhibited faster wound healing and better local exudation and absorption in the wound than the control group, suggesting that the Ag-MMT/CS colloid exhibited a better antibacterial effect on the S. aureus. (4) Conclusions: Ag+, chitosan, and MMT present in the Ag-MMT/CS colloid dressing exert synergistic effects, and it has good antibacterial effects, cytocompatibility, and hygroscopicity, indicating that this colloid has the potential to become a next-generation clinical antibacterial dressing

Automatic body region localization in 3D-CT images based on the improved YOLO model

Automatic body region localization in medical three-dimensional (3D)-CT images is a critical step of computerized body-wide Automatic Anatomy Recognition (AAR) system, which can be applied for radiotherapy planning and interest slices retrieving. Currently, the complex internal structure of human body and time consuming computation are the main challenges for the localization. Therefore, this paper introduces and improves the YOLO-v3 model into the body region localization for these problems. First, seven categories of body regions in a CT volume image I are defined based on the modification version of our previous work. Second, an improved YOLO-v3 model is trained to classify each axial slice into one of the seven categories. Then, the effectiveness of the proposed method is evaluated on 3D-CT images that collected from 220 subjects. The experimental results demonstrate that the slice localizing error is less than 3 NoS (Number of slices), which is competitive to the state-of-the-art methods. Beyond this, our method is simple and computationally efficient owing to its less training time, and the average computational time for localizing a volume CT images is about 3 second, which shows potential for a further application