Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models. Osteosarcoma is one of the most common bone malignancies, which develops from mesenchymal cell line . AlthougThe diversity of osteosarcoma leads to scattered information about it in the medical literature, particularly in imaging results. The images of diverse osteosarcoma generated in the identical environment and process are various as well, and it is hard to tell the difference between healthy tissue and lesion areas manually in some cases ,15. GeneThe mortality of osteosarcoma is very high, but early detection and timely treatment can greatly improve the survival rate . HoweverIn recent years, with the increasing attention to AI, some medical image detection technologies have also been applied to the diagnosis of osteosarcoma ,30. AlthAccording to the above contents, we propose an MRI image segmentation and AI-assisted diagnosis system based on Attention Condenser for osteosarcoma (OMSAS) to assist doctors. This intelligent medical system is designed to assist doctors in identifying MRI images of patients with osteosarcoma and to automatically segment osteosarcoma, thus providing doctors with more powerful and intuitive tips and aids. The analysis results provided by the system can be used as an auxiliary basis for doctors to diagnose patients, which can effectively improve the efficiency and accuracy of diagnosis and reduce the cost of diagnosis and treatment for patients. Firstly, we carry out data augmentation in the dataset and use a variety of methods to expand, standardize and classify the original dataset. Data preprocessing reduces the degree of over-fitting and makes the model more generalized. In terms of model design, we improve AttendSeg , replace(1)In this article, the region in the image can be focused more accurately through the Attention Condenser. The multilayer condenser structure can further locate the boundary of the tumor, and reduce a large number of unnecessary calculations in the later stage, to improve the efficiency and accuracy of training.(2)When using the model to predict, OMSAS uses a compound decision module that turns a single input into multiple copies of the input. Then, the model makes predictions by multiple copies simultaneously, and then combines the multiple outputs to get the final result. Multiple decisions can reduce the wrong prediction caused by some unknown situations and make the output more accurate. Meanwhile, it can improve the stability of the model.(3)More than 4000 images provided by the First People’s Hospital of Huaihua and the Second People’s Hospital of Huaihua were used for testing. The result indicates that our ACRNet in OMSAS is better than other existing segmentation models. The model has high training efficiency and prediction accuracy as well as small resource consumption, which is important in assisting doctors to diagnose osteosarcoma in patients.The specific contributions of the essay are divided into the following points:The paper is arranged as follows: Through investigation and research, we find that many technologies use artificial intelligence knowledge for medical decision-making and image processing. In modern medical systems, more and more artificial intelligence (AI) algorithms are used for image segmentation, health prediction, and other functions. In the diagnosis of osteosarcoma, how to process MRI images and accurately mark the tumor area has become a research hotspot. This section will introduce some mainstream algorithms in related directions.Nasor et al.  proposedArunachalam et al.  used supDionisio f.c.f et al.  took HauIn the field of image segmentation, there are many good models in recent years, which can be divided into threshold-based, cluster-based, edge-based, region-based, etc . U-Net pThrough the above research, we found that with the continuous development of computer technology, the research scope of artificial intelligence technology in the field of paramedicine, especially in the direction of image recognition, is expanding. However, due to the variability of the morphology and structure of osteosarcoma, existing medical image recognition techniques are difficult to achieve the expected results in MRI image segmentation of osteosarcoma. To improve the segmentation accuracy and better adapt to some medical devices with relatively poor performance, we design a new strategy for MRI image segmentation for osteosarcoma based on Attention Condenser. The approach enhances the efficiency and accuracy of osteosarcoma detection by reducing the device requirements via tactics such as data preprocessing, residual network, LayerNorm and attention condenser.Considering that we have submitted two similar papers ,60, the We have conducted a comparative analysis of the innovations in the three papers. First, all three papers address the fact that osteosarcoma is extremely dangerous for human beings and that the diagnosis and treatment of osteosarcoma in developing countries is extremely difficult due to the shortage of medical resources. Moreover, there are few specialized physicians due to the large volume and complexity of patient data. Therefore, we designed an osteosarcoma artificial intelligence method to assist physicians in picture analysis and clinical diagnosis.However, the three studies have different emphases, and there are differences in the problems they each address.For literature , it usesFor literature , this meCompared with the above two papers, our paper uses a more novel MRI image segmentation model for osteosarcoma, ACRNet. The ACRNet model is more novel, and the model training is more efficient and achieves better segmentation results. This is reflected in the following points.In terms of data preprocessing, we use a simpler and more efficient processing method that maintains a good segmentation performance. We binarize and regularize the images to filter out the valid regions in the images and eliminate the effect of different brightness levels between images on the training. Additionally, we enhance the dataset to improve the generalization of the model.In terms of model design, we refactored the main component of AttendSeg, “Attention Condenser”. We use the attention mechanism and combine it with the residual structure to further enhance the attention of the model to the osteosarcoma region, so that the model can shift more attention to refine the details between regions in the osteosarcoma MRI image, and understand the global view of the image, correct the results in the reconstruction, and effectively improve the segmentation effect. In addition, we transformed the original general convolutional layer combination into a reserved block and a shrinkage block, which greatly reduces the number of parameters of the general convolutional operation and allows the model to better extract various features of the image and run more efficiently.In the output part of the model, we use the composite decision to integrate the output of multiple different angles of the same image and unify the results of the same source, which effectively enhances the accuracy and robustness of the output results. We plot the output results as a black-and-white image to assist the doctor’s diagnosis, thus greatly reducing the burden of the doctor’s film reading.In terms of the practicality of the segmentation system, the network has a lightweight structure with only 6.91 M parameters and a SETT value of 174, which makes the model simpler and more efficient in training and more adaptable to low-configuration medical equipment, which helps the implementation of the medical-assisted segmentation system on the ground. As a result, ACRNet ensures high accuracy in segmenting osteosarcoma MRI images while improving efficiency, which will save more human and financial resources for developing countries and improve the efficiency of osteosarcoma diagnosis in hospitals.This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.At this stage, in most developing countries, the distribution of medical resources is very unbalanced, and many advanced medical resources are concentrated in the region where very few people are located. Additionally, generally speaking, a particularly good analytical instrument can greatly reduce the working pressure of doctors. However, the high manufacturing and maintenance costs make it very difficult to promote the medical equipment of image-assisted diagnosis.Considering the osteosarcoma patients living in developing countries, if the early diagnosis is not timely, the lack of technology or the very high cost of later medical treatment will often make them give up their last chance of survival. Therefore, what we need to do is to improve the early diagnosis rate. For hospitals, the prolonged manual diagnosis and the shortage of talents result in the difficulty of diagnosis. It is necessary to introduce an osteosarcoma MRI image segmentation medical system to assist doctor. Moreover, to promote this system more widely, we expect that the built-in process should be simple and efficient enough, the model should have high accuracy, and the requirements for equipment should be as low as possible. Thus, based on these cognitions, this paper proposes a segmentation method for osteosarcoma MRI image based on Attention Condenser (OMSAS), which can accurately depict the tumor area in the image, relieving the burden of reading films for doctors. The overall design of the system is shown in According to Some symbols involved in the chapter are explained, as shown in (1)The osteosarcoma MRI images in the dataset have different brightness and darkness due to different instruments, contrast agent dose, and other external environmental reasons;(2)The amount of data is still insufficient for training a model with high accuracy, and the training is prone to instability.We find that the data in the original dataset is not suitable for model training directly, and there are the following problems:To address the above two problems, we have carried out a series of data preprocessing to optimize the effect of the model by improving the quality of data and eliminating interference factors. The specific operation is shown in First, we select the region of all the osteosarcoma MRI images. After the image is binarized by (1), it will be filled to obtain a picture with only pure black and pure white areas. Then, we select the smallest rectangular area in the picture that can just contain the pure white area, which is the selected effective area. Selecting the effective region can simplify the training and recognition of the model, and make the model pay more attention to the feature extraction of the effective region.In addition, we regularize all the osteosarcoma MRI images to avoid the impact of some excessively bright and dim images on the training. The brightness gap and different density distribution between images make it easy for the model to take these unnecessary factors into account, which not only reduces the speed of model training, but also may affect the effect of the model. Therefore, we use a unified regularization process to transform all MRI images with (2).The regularized image addresses the first problem. At this time, the image has met the minimum standard of training. However, to enhance the robustness of our model and the generalization of it, we also need data augmentation. For data augmentation, we rotate each picture by 90°, 180°, and 270° at first, and then flip each picture horizontally and vertically. After that, the amount of data is 8 times that of the original dataset. We then randomly select some images and add noise to them to reduce the overfitting phenomenon of the model when learning high-frequency features.After data preprocessing, the quality of the MRI images of osteosarcoma in the dataset was improved, and the high-quality dataset can be used as a reference basis for clinical diagnosis, which is beneficial for doctors to make more effective film reading. At the same time, the enhanced dataset can provide a good fit for the model training.3.2. Brief Introduction of Attention.The model of this paper involves the mechanism of attention, so attention will be briefly introduced in this part.M. and the size of M is . Moreover, w means width, h represents its height, and c is its number of channels. Then, the compression process can be expressed as (3).We know that the inspiration of the attention mechanism comes from our physiological perception of the environment. Our visual system will actively select the information we need to pay attention to in the impression and ignore the irrelevant information in the field of vision. Similarly, in the neural network, when we segment an osteosarcoma MRI image or recognize the boundary, we also hope the network to pay more attention to the region we expect, and ignore the irrelevant information. The attention mechanism can be divided into three steps: compression, activation, and attention. For example, we now have an osteosarcoma MRI image input In order to evaluate the importance of each channel, we need to activate the previous operation results as a whole. The weight calculation formula on each channel is shown in (4), where After the activation, we assign In our model design, attention can be understood as the process of (1)The osteosarcoma MRI images will be rotated and flipped to obtain a group of pictures before each prediction of it.(2)The set of pictures from the previous step will be used as input for the residual network with multiple attention condensers, and a set of results will be output.(3)This group of results is inversely changed according to the original change direction to obtain a group of results consistent with the original image direction. This group of results is calculated and processed to obtain the final prediction tumor region.On the osteosarcoma image segmentation model, we designed a residual network based on Attention Condenser (ACRNet), and its overall structure is shown in In recent years, a large number of scholars have emerged to conduct research on attention networks. The attention condenser proposed by Alexander Wong et al.  uses theAttention Condenser consists of four parts: a condensed layer that reduces the dimension of spatial channels, an embedded structure that represents the activation relationship of joint spatial channels, an extended layer that increases the dimension, and an attention mechanism that applies selective attention. In ACRNet, we use two linear structures and the middle embedding layer to perform the above scaling operation. In addition, we introduce a variable called ratio, which can manually adjust the expansion and contraction times of the condenser, to meet the needs in different situations better.The residual structure is used many times in the whole network. The residual unit is realized in the form of layer skip connection. Some layers are regarded as a unit. The initial input of the unit and the final output will be added before activation. The residual structure addresses the degenerate problem of the deep neural network. Under the same number of layers, the training speed of the residual network is faster, which can make the operation efficiency of the model higher.In order to better extract various features of the osteosarcoma MRI image, we use the idea of the block to combine and transform the original general convolution layer into the reservation block and the retracting block. The reservation block includes a 1 × 1 convolution layer, a Layer Normalization, and a ReLU activation function, which only changes the number of channels of input without affecting the size of the image. Adding LayerNorm can normalize the data before entering the activation function to avoid the problem of vanishing gradient in the saturated region of the activation function. Meanwhile, the use of LayerNorm accelerates the convergence speed of the model again.The retracting block consists of a 3 × 3 and a 1 × 1 convolution layer. Its purpose is to simply extract image features. A retracting block instead of single 3 × 3 convolution layer makes a convolution kernel responsible for only one channel. A channel will be convoluted by only one convolution kernel, and the change of channel is handed over to 1×1 convolution layer. This can significantly reduce the parameters number of the ordinary convolution operation.In the output, we use compound decision-making to integrate multiple outputs from different angles of the same image, since that for neural networks, every rotated or flipped image is a new image, and the prediction results are not necessarily the same. Unified processing of homologous output can enhance the accuracy and robustness of results. The specific formula for output calculation is as follows.Medical image segmentation models often encounter the phenomenon of data imbalance. Training unbalanced data is easy to cause the problem of high precision but low recall. Moreover, in the diagnosis of osteosarcoma, missed diagnosis of tumor area is a more intolerable serious error than the wrong diagnosis of the normal area. Therefore, when designing the model, we choose Tversky Loss as the loss function. Tversky loss is a special dice loss, which is a combination of dice loss and Jaccard coefficient. Generally, the weight of dice loss for FP and FN is equal, so the model will not focus on the improvement of recall Sufficiently during training. Therefore, we use Tversky Loss with parameters β as the loss function in model training. The specific definition is as shown in (7).α and β control the penalty strength of the loss function for FP and FN, respectively. The higher the β, the greater the penalty of the loss function on FN, and the easier it is to improve the recall. After testing, we adopt α = 0.25, β = 0.75.By using our model, we can easily perform region detection on osteosarcoma MRI images and the annotation regions are comparable to the “Gold Standard” of doctor annotation. Our model ensures accuracy while greatly reducing the burden on doctors when reading MRI images. For hospitals, the time and money spent on processing related cases are greatly reduced.The data in the article are collected from the First People’s Hospital of Huaihua and the Second People’s Hospital of Huaihua. During the experiment, we collected more than 4000 MRI images and other related index data of 210 patients with different degrees of osteosarcoma. During the experiments, all segmentation models are built on the same dataset, so the performance tests of the models are comparable. To ensure that the sample is closer to the standard of developing countries, we selected data mostly from patients with not very high income in the lower social class. Additionally, the data are mostly from early imaging and a small percentage from mid to late. We selected three different cross-sectional slices to allow the model to better fit multiple input cases and to achieve stable segmentation results. All MRI images are selected and manually labeled by doctors. We divided the dataset into the training set and the test set in the ratio of 7:3. Of the 210 real cases of patients we owned, 147 are put into training set and 63 are in test set. We used a 10-fold cross-validation to ensure the validity of experimental results.The training, test environment, and some important parameters settings during the training of the model in this paper are shown in In the process of training and testing, better, we will use some common comparative index and single epoch training time (SETT) as metrics to evaluate the performance of our ACRNet model and other segmentation models. Moreover, we will also make a comparison of the number of parameters of the different models.We used the confusion matrix to analyze and explain the model. TP indicates that both the predicted results and the actual results are osteosarcoma areas, and TN indicates that both the predicted results and the actual results are normal areas. FP represents the normal area misjudged as the disease area, and FN represents the lesion area misjudged as the normal area. The specific definition and calculation formula of the above indicators are as follows.Definition 1.Accuracy. It represents the proportion of all samples with correct prediction. Its definition is as shown below.Definition 2.Precision. It represents the proportion of all samples judged as tumor areas that are actual tumor areas. The formula for Precision is:Definition 3.Recall. It represents the percentage of all samples judged as normal areas that are really normal areas. Compared with precision, we expect the emergence of high recall in medical experiments, and false negative is more intolerable than false positive. Its definition is as follows.Definition 4.F1-Score. It is an index based on precision and recall, which can represent the robustness of a model. The higher the F1-Score, the greater the robustness. Its definition is as shown below.Definition 5.IOU. It represents the intersection and union ratio between the tumor region in the prediction result and the tumor region in the real result, reflecting the coincidence between the prediction result and the real region. Its definition formula is as follows, where represents the tumor region in the predicted result and  represents the tumor region in the real result.Definition 6.DSC. It describes the similarity between the prediction result and the truth and is an index with a value of 0-1. The closer DSC is to 1, the closer the result is to the real situation. Its definition formula is as follows.Definition 7.SETT. It represents the average time required for an epoch of model training under the same amount of data, and the unit is seconds (s).For the purpose of further evaluating the segmentation effectiveness as well as the structural complexity of our ACRNet, we used U-Net , MSFCN , MSRN 5, FPN 5757 and FCFirstly, we trained the model without preprocessing. The training and test results are shown in We trained the model and finally got a trained ACRNet model. Then, we put the prepared test data into it for testing and test it synchronously with other prepared image segmentation models. The tumor region prediction images are shown in According to To compare each model more accurately and digitally, we counted the segmentation results of each model and calculated and sorted out its indicators. We used several indicators selected and defined in section C to compare our model with other models. To achieve a better prediction effect, we often need to train more epochs to make the model fit the dataset better. We often expect a good model to converge to a higher accuracy as soon as possible, and the higher the final convergence value, the better. Based on this, we tested different models as shown in In addition to comparing accuracy, we also made statistics on recall and F1-Score. Similarly, we took the first 50 epochs for research. During the experiment, we also conducted additional research on the attention part. We visualized the attention mechanism and superimposed the binary mask output by the middle layer with the input picture to obtain a more intuitive information range of attention focus, namely the attention map. The brighter the area, the more focused the attention. We selected 18 maps of different categories for comparison and display, and the results are shown in In the article, the ACRNet was trained and tested by more than 4000 osteosarcoma MRI images. Through comparison and statistical analysis, we find that OMSAS has good prediction results in many indicators such as accuracy and recall. OMSAS not only improves the efficiency and accuracy of the model but also saves the cost with fewer parameters and shorter training time. The experimental results show that the segmentation model ACRNet in OMSAS has a better effect and more obvious advantages in osteosarcoma MRI image segmentation, and is suitable for application in developing countries with unbalanced medical resources distribution and poor medical care.In the future, with further expansion and improvement of the dataset, we can continuously improve the prediction ability of the model, so as to detect the region of more complex osteosarcoma MRI images or even achieve similar good results on MRI of other cancers. At the same time, with the improvement of computing power and the need for later medical treatment, we can expand more modules in the model, such as outputting the size of the tumor area and predicting the future diffusion of tumor area, to provide more professional assistance for medical diagnosis.


Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer's self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians. Osteosarcoma is the most common malignant primary bone tumor, with the highest incidence among adolescents . Among aFor patients with osteosarcoma, if not diagnosed and treated early, there is a tendency to develop extensive metastases in other soft tissues . In addiMost developing countries face a huge dilemma in the diagnosis and treatment of osteosarcoma. Take China as an example. China is a vast country with unbalanced medical development between regions, and the awareness of osteosarcoma is still unfamiliar in primary hospitals and even some provincial and municipal hospitals. And the occurrence, development, transformation, or deterioration of osteosarcoma are a dynamic process of change. With the different age, gender, physique, and even living conditions of patients, the morphology and size of osteosarcoma often show great variability and randomness. This places a high demand on the expertise of physicians. However, the poor and backward regions of China have poor medical equipment and shortage of professional physicians . On the Modern medical imaging technology is developing rapidly, and computer-aided diagnosis (CAD) system is also constantly innovating. Sathiyamoorthi et al.  used adaAs the most popular encoder-decoder network in medical image segmentation , U-Net  segmenteInspired by the self-attention mechanism of transformer , this paIn this paper, we optimize the classification of dataset by mean-teacher model to alleviate the influence of noisy labels on model training; remove irrelevant backgrounds in osteosarcoma MRI images through dataset preprocessing to reduce the burden of input images on segmentation network; and then improve the accuracy and segmentation efficiency of osteosarcoma segmentation.The incorporation of multilevel guided self-aware attention module (MGAM) into the encoder-decoder architecture of U-Net, which combines deep semantic information and spatial information through jump connections, provides the necessary fine-grained and high-resolution features for deconvolution, and facilitates the recovery of spatial information of osteosarcoma. In addition, the application of dense residual learning to the convolution module, combined with multiscale jump connections and aggregation of different semantic amplification sampling functions, facilitates the reduction of interference from osteosarcoma MRI image noise, and better preserves detailed information such as osteosarcoma edge features.Two components, transformer self-attention component (TSAC) and global context aggregation component (GCAC), are designed in UATransNet to enhance the ability to construct remote feature dependencies and capture global contextual information. Among them, TSAC adaptively integrates local features with their global dependencies and GCAC optimizes the feature representation effect of osteosarcoma by aggregating contexts to learned features. Meanwhile, the UATransNet method has few parameters, simple computation, and low hardware equipment requirements, which alleviates the difficulties of backward medical equipment in developing countries and achieves a balance of speed and accuracy in osteosarcoma detection and segmentation, greatly improving the diagnostic efficiency of physicians.Experimental analysis was performed using over 80,000 osteosarcoma MRI images acquired from Xiangya II Hospital of Central South University. Our proposed osteosarcoma image segmentation architecture exhibits better segmentation performance compared with some state-of-the-art baselines. Furthermore, it can enable the diagnostic accuracy of osteosarcoma images to approach the level of expert physicians, providing information support for decision-making in regions and countries lacking high-level physicians.The detailed contributions of this study are as follows:Osteosarcoma is an aggressive malignant bone tumor that occurs mostly in the extremity bones of children and adolescents, with an extremely poor natural prognosis and a tendency to develop blood metastases at an early stage. Imaging is an important tool for the diagnosis and clinical evaluation of osteosarcoma. In recent years, computer-aided decision-making systems have become a popular research direction in the medical field to analyze the health status of patients mainly through healthcare data and diagnostic images.Due to the cellular heterogeneity in the dataset, pathologists are often faced with high complexity processing and disagreement in classifying osteosarcoma tumors. Similarly, segmentation and classification of tissues in tumor images remain extremely challenging due to interclass similarity and intraclass variability, despite H&E staining. Chen and Zhao  proposedVarious osteosarcoma detection methods are used for early detection of osteosarcoma, but evaluation of slides under the microscope to detect the extent of tumor necrosis and tumor outcome remains a major challenge in the medical field. Therefore, Badashah et al.  developeDiffusion-weighted imaging (DWI) can capture cellular changes in tumor tissue without contrast injection in the early stages of patient treatment, facilitating early diagnosis and prognosis of malignant disease . OsteosaIn recent years, automatic segmentation based on deep learning has been widely used. Among them, U-Net is one of the most important semantic segmentation frameworks for convolutional neural networks (CNNs) and is widely used for lesion segmentation and classification in the field of medical image analysis. U-Net  is succeIn order to better extract edge features and texture features, etc. using the shallow edge output module, we extract semantic features using the deep output module, calculate the loss value between the predicted map and the actual tumor image by the side output module, and then backpropagate the loss information. Zhang et al.  proposedBased on the above analysis, in order to better assist clinical diagnosis and treatment, the research on osteosarcoma image preprocessing, image segmentation, and image analysis by model construction has been developed significantly in recent years. However, the MRI images of osteosarcoma are susceptible to noise and have complex edge contours, making the segmentation effect poor. To optimize the segmentation effect of MRI images of osteosarcoma, we combine the depth-separable U-shaped network with transformer and propose a new perspective to improve the performance of semantic segmentation, which achieves more effective feature fusion by multilevel guided attention, i.e., collaborating with transformer's self-attention and global spatial attention, and multiscale exploration of connected contextual semantic information to ensure the semantic embedding consistency.With the development of intelligent medicine, image processing plays an indispensable role in the diagnosis, treatment, and prognosis of diseases. Osteosarcoma MRI images have complex features and contain a lot of redundant data, which makes manual screening and detection by physicians alone time-consuming and laborious. U-Net is widely used for MRI image segmentation of osteosarcoma because of its better segmentation of small targets and scalable structure. However, as the performance requirement of medical image segmentation increases, U-Net shows the limitation of information decline due to the lack of ability to effectively construct remote feature dependencies and capture global contextual information. On the other hand, osteosarcoma MRI images are susceptible to noise and prone to overfitting resulting in loss of edge features. Based on this, we propose a lightweight UATransNet osteosarcoma image segmentation model by combining the encoder-decoder architecture of U-Net with transformer. We successively perform dataset classification optimization and remove irrelevant backgrounds from MRI images by averaging teacher models and normalizing preprocessing. In order to break the limitation of information degradation exhibited by U-Net due to the lack of ability to effectively construct remote feature dependencies and capture global contextual information, we designed two components: transformer self-attentive component (TSAC) and global contextual aggregation component (GCAC). TSAC adaptively integrates local features with their global dependencies to better preserve osteosarcoma edge features and other detailed information, and GCAC optimizes the osteosarcoma feature representation by aggregating context to the learned features.In addition, this paper applies dense residual learning to the convolution module, combined with multiscale jump junction, to improve the feature extraction capability. Using over 80,000 osteosarcoma MRI images acquired at Xiangya II Hospital of Central South University for experimental analysis, our proposed osteosarcoma image segmentation architecture exhibits better segmentation performance compared to some state-of-the-art baselines. In addition, the diagnostic accuracy of osteosarcoma images is close to that of expert physicians, providing intuitive and accurate information support for decision-making in regions and countries that lack high-level physicians.In this section, the proposed UATransNet model is introduced in terms of dataset optimization, normalized preprocessing, multilevel guided attention mechanism, and multiscale jump connection. The overall design of the proposed UATransNet osteosarcoma image segmentation model in this paper is shown in We selected over 80,000 osteosarcoma MRI images as the dataset for our experiments, but among them, some of the images had too small or blurred osteosarcoma regions, which led to significant degradation of the experimental model performance. To improve the accuracy of the test, we combined the mean-teacher network , ResNet To cope with the possible gradient disappearance problem as the number of network layers increases, we use residual connections and three 3 × 3 convolutional blocks to capture the high-dimensional feature information of the image and use group normalization instead of batch normalization in the Res-Block to cope with the possible performance degradation caused by small batches, as shown in D1 and D2. Among them, the D1 dataset contains Label1 and the D2 dataset does not contain the label. The overall design of the mean-teacher semisupervised algorithm is shown in First, we randomly divide the original dataset into two parts, D1 and D2 datasets are input to the student model, and the predicted likelihood of Pstu  de  nt1 and Pstu  de  nt2 is output; the D2 dataset is input to the teacher model, and the predicted likelihood Pteacher2 is output. Second, the loss value V1 is calculated based on the loss function Loss1 and Pstu  de  nt1 and Pteacher1 can calculate the loss value V1, which is calculated as follows [Mean-teacher semisupervised algorithm: first, the  follows :(1)Loss1V2 is calculated based on the loss function Loss2 and Pstu  de  nt2 and Pteacher2. The network parameters of the teacher model of UATransNet  θt′ are obtained by updating the network parameters of the student model  θt by moving average.Similarly, the loss value θt of student model are obtained by updating the parameters through loss gradient descent. Among them, the loss function contains two parts: the first part is the supervised loss function, as shown in equation  scattering relative entropy loss function [The network parameters equation . Loss1 ifunction ; the KL Loss2 is calculated as follows:However, there is a problem of asymmetry in the KL function. In order to make the prediction distribution of the teacher model and the student model consistent, we adopted the Jensen–Shannon (JS) algorithm  to compeFinally, we divided the osteosarcoma MRI image dataset into effective part and difficult part with the proportion of 39.4% and 60.6%, respectively, and input them into the UATransNet network model sequentially to achieve better training effect.To remove irrelevant backgrounds from the osteosarcoma MRI images and reduce the burden of the input images on the network, we segmented the input osteosarcoma MRI images into osteosarcoma-suspected regions and the processing steps are shown in First, a multiscale deep belief network (m-DBN) and Gaussian mixture classifier (GMC)  were useIn the image matrix, the minimum weight of histogram is 5%. The upper bound and the next bound of the adjusted weight are less than 5%. The image is then median filtered to remove noise using a window of a certain size, which is 5% of the maximum value of the length height of the region of interest. However, since the use of median filtering  leads toThe Otsu method  was usedμ is the average intensity of the image pixels in the segmented region, Ix′,y′ is the intensity of the candidate pixel points located at , and θ is the determined threshold. Then, the threshold value obtained in the third step of osteosarcoma size estimation is used as the initial threshold value, and when no remaining pixels satisfy the seed expansion conditions specified by the above two equations, the size of the segmented region obtained in the current evolutionary stage is checked in relation to the predicted osteosarcoma size; if the currently obtained region size is smaller than the estimated value, the threshold value is lowered to continue expanding the region; if the currently obtained region is larger than the estimated value, the segmentation is stopped.Based on a priori knowledge, in osteosarcoma MRI images, the tumor region is generally brighter than the surrounding healthy tissue. Therefore, the brighter multiple pixel points in the center of the region of interest are selected as the initial seed points. The following two principles are followed when selecting the pixel points adjacent to the seed points to be added to the seed points:The threshold is adjusted adaptively according to the size of the osteosarcoma, and the input osteosarcoma MRI image is segmented into the suspected region of the osteosarcoma. The grayscale characteristics of the image can be directly used, the calculation is simple, and the calculation efficiency is high, but for a few grayscale differences that are not obvious, images with a large disparity in the size and proportion of the target and the background, you need to combine manual experience selection to have a better effect. Therefore, this paper mainly focuses on most of the MRI images of osteosarcoma with obvious gray difference.The U-Net network model for segmenting osteosarcoma MRI images is able to fuse deep semantic information (such as the color and shape of the osteosarcoma) with the information contained in high-precision features but is unable to model the contextual relationships of features that are far away, resulting in insufficient accuracy for osteosarcoma segmentation. Like the opposite, the transformer network model employs a self-attentive mechanism, which facilitates the extraction of global information but lacks information at local details. Based on this, we propose a lightweight UATransNet osteosarcoma image segmentation model with a multilevel guided self-aware attention module (MGAM) between the bottom encoder and decoder of the U-shaped architecture, which helps to capture a broader and richer contextual representation and better fuses the features of semantic inconsistency between transformer and U-Net network models. It is beneficial to reduce the interference of osteosarcoma MRI image noise, so that detailed information such as osteosarcoma edge features is better preserved and excellent segmentation results can be obtained. In addition, represented by MGAM, UATransNet has a small number of parameters and simple computation, which reduces the consumption of resources and achieves a balance of accuracy and efficiency of osteosarcoma MRI image segmentation.The core component of the UATransNet proposed in this paper: the multilevel guided self-aware attention module (MGAM) consists of the transformer self-attention component (TSAC) and the global context aggregation component (GCAC).Referring to transformer's multihead self-attention mechanism, UATransNet focuses on semantic information from the global contextual representation subspace and first adds the learned position encoding of the osteosarcoma MRI image to the input of the encoder features and shares it among all attention layers for a given query/key value sequence, enabling the capture of contextual information about absolute and relative positions , which iRc×h×w as input is first reconstructed as a query matrix Q ∈Rc×(h × w) and key matrices K ∈Rc×h×w and V ∈Rc×h×w [Q, K, and V matrices contain the texture features of the osteosarcoma MRI image.Wq, Wk, and Wv are embedding matrices for different linear projections. Then, a scaled dot product operation with softmax normalization between the transposed versions of the query and key matrices is used to generate a matrix of contextual attention maps CAM ∈Rc×c and captures the necessary fine-grained and high-resolution features in the osteosarcoma MRI images. The matrix exhibits similarity to the global elements of the key matrix compared to the given elements from the query matrix. To further implement the aggregation of values weighted by attention weights, we multiplied the contextual attention map CAM by V and recovered the detailed information in the osteosarcoma MRI images. Finally, in the TSAC, we can represent the multiheaded attention as follows:The concrete implementation of the TSAC is shown in  ∈Rc×h×w , and theIt should be noted that equation . FinallyUATransNet encodes broader contextual location information into local features via GCAC and selectively aggregates global context into the learned features. The specific implementation is shown in Featurepc0 ∈ Rc0×h×w and Featurepc1 ∈ Rc1×h×w, where c1=c0/8, respectively, using two convolution operations via the encoder feature Featureenco  de  r. Then, Featurepc0 is reshaped into Upc0 ∈ Rc0×(h × w) and Featurepc1 is reshaped into Vpc1 ∈ Rc1×(h × w) and Wpc1 ∈ Rc1×(h × w). Next, the matrix multiplication operation with softmax normalization is executed on the reshaped V and W to obtain the position attention map L ∈ Rh × w)×(h × w).1 and ∝2 are scale parameters initialized to 0. In this way, we can further optimize the feature representation of osteosarcoma MRI images with semantic consistency to obtain more accurate segmentation of osteosarcoma MRI images and provide intuitive, accurate, and efficient decision information support for physicians.In order to fully utilize the obtained contextual information and spatial relationships, the multilevel guided self-aware attention function module takes a dynamic planning approach to optimally combine the original features and attention feature embeddings of osteosarcoma through attention embedding fusion, which is the important reason why UATransNet can acquire spatial information of osteosarcoma and accurately segment the MRI images of osteosarcoma.υ (·), ⊕, and f (·) are the upsampling, concatenation, and mixed convolution operations, respectively.In this paper, feature maps from all scales are directly connected to form a unified tensor through cascade connection, and then, the features of the target are extracted, as shown in The higher-level network has a larger perceptual field with strong semantic information representation capability; however, this structure results in low resolution of feature and lacks spatial geometric feature details; the lower-level network has a smaller perceptual field with strong geometric detailed information representation capability and weak semantic information representation capability though high resolution. In order to fuse multiscale features to be equally effective in encoding global and local contexts, UATransNet guides the upsampling process in the decoder subnetwork through a new multiscale jump connection scheme, i.e., residual connection and dense connection, and the network structure is shown in As an input module, the model improves the structure of MRI images of osteosarcoma and solves the problem of unbalanced classification of MRI images of osteosarcoma .(12)F=fnTo cope with the medical dilemma of outdated hardware equipment in developing countries, UATransNet employs a dense connection module in the encoder-decoder architecture. Dense concatenation takes the upsampled feature set of the previous encoder block as the input to the current block and the output feature map as the input to all subsequent blocks, as expressed in the upcoming equation. One advantage of dense connectivity is that it has fewer parameters than traditional convolutional networks because it does not need to relearn redundant feature maps, reducing the requirement for hardware equipment and alleviating the difficulties of poor medical equipment in poor areas.UATransNet aggregates multiple decoder features at different semantic scales by bilinear interpolation, which better preserves detailed information such as osteosarcoma edge features. In addition, the dense concatenation mechanism generates the most discriminative feature representation in contrast to using one-time cascade concatenation. In these ways, the UATransNet osteosarcoma image segmentation model can not only mitigate the loss of details due to excessive upsampling but also alleviate the problems of gradient disappearance and overfitting.UATransNet successively performs dataset classification optimization and removes MRI image irrelevant background by averaging the teacher model and normalized preprocessing, which reduces the waste of computational resources, reduces the requirement for hardware devices, and greatly improves the efficiency of osteosarcoma segmentation. Then, UATransNet integrates local features and global dependencies in TSAC and GCAC at the bottom of the encoder-decoder architecture, which effectively reduces the interference of osteosarcoma MRI image noise. In addition, UATransNet applies dense residual learning to the convolution module and combines multiscale jump connections to better retain detailed information such as osteosarcoma edge features, which can provide doctors with fast and accurate decision suggestions, effectively simplify the diagnosis process, save time, and reduce the burden of tedious medical work for doctors.The dataset used in the experiment is over 80,000 osteosarcoma MRI images and related indexes of 286 osteosarcoma patients in Xiangya II Hospital of Central South University in recent years provided by the Ministry of Education Mobile Health Information-China Mobile Joint Laboratory and Xiangya II Hospital of Central South University . In ordeU-Net: the U-structure U-Net combines deep semantic information and spatial information through skip connections of the encoder and decoder, providing necessary high-resolution features for deconvolution, which is beneficial to recover valuable spatial informationThe core of pyramid scene parsing network (PSPNet): PSPNet aggregates contextual information from different regions to obtain global information through the pyramid pooling moduleMSFCN: based on a fully convolutional network, multiscale feature learning is performed with three supervised output layers, and both local and global features are capturedMultiscale residual network (MSRN): convolutional kernels of different sizes are introduced for adaptive detection of image features at different scalesFully convolutional network: FCN classifies images at the pixel level and uses a jump structure to achieve fine segmentationFeature pyramid network (FPN): both high-resolution features in the lower layers and high semantic information features in the higher layers are fused and predicted separately for each fused feature layerIn our experiments, we applied two different approaches to implement UATransNet, UATransNet Residual, and UATransNet Dense osteosarcoma segmentation models based on residual connections and dense connections, respectively. In addition, we also used a wide range of algorithms such as U-Net, PSPNet , MSFCN , MSRN 5, FCN, an1=1, ∝2=0, ∝1=0.75, ∝2=0.25, ∝1=0, ∝2=1, ∝1=0.5, ∝2=0.5, ∝1=0.25,  and ∝2=0.75 by setting different ∝1 and ∝2 values and observe PRE, IOU, DSC, and other changes in the evaluation indicators to find the parameter combination with the best performance of the model.UATransNet was implemented using PyTorch, Cuda 11.3, and all experiments were run on 1 RTX A4000 GPU with 32 G of memory. Before training the UATransNet model, we extended the dataset by scaling up  images, rotating images, and flipping images to enhance the robustness of the model. During training, our proposed UATransNet framework was trained for 100 epochs with an initial learning rate set to 0.001, which changed to 0.0001 when training reached 50 epochs and was dynamically adjusted using CosineAnnealingLR. Through experiments, it can be seen that each epoch in the training process takes about 1–5 minutes. Finally, 100 epochs of verification are performed using the checkpoints that store the trained models, and the changes in the evaluation metrics for each epoch are recorded. The verification time of each epoch is about 6–10 s. In the process of parameter adjustment, we mainly set different parameter combinations such as ∝To measure the similarity between the predicted mask and ground truth, we used accuracy (ACC), precision (PRE), recall (REC), dice similarity coefficient (DSC), intersection over union (IOU), and F1-score (F1) as measures, which were calculated in pixels and used to quantitatively evaluate the performance of the UATransNet model for osteosarcoma image segmentation. The specific definitions of each indicator are as follows:In the UATransNet osteosarcoma image segmentation model, we first perform classification optimization of the dataset, as shown in In order to evaluate the performance of different osteosarcoma segmentation models more clearly and accurately, we performed a quantitative representation of the segmentation effect. We tested by training with 100 epochs, using accuracy (ACC), precision (PRE), recall (REC), dice similarity coefficient (DSC), intersection over union (IOU), and F1-score (F1) as measures for comparative analysis. The comparative results of the evaluation metrics are shown in As shown in However, our proposed UATransNet Residual and UATransNet Dense showed good performance in the osteosarcoma image segmentation task, and both models outperformed other baseline models in accuracy (ACC), precision (PRE), recall (REC), intersection over union (IOU), dice similarity coefficient (DSC), and F1-score(F1). UATransNet Residual and UATransNet Dense not only greatly optimize the segmentation accuracy, but also their FLOPs were only 161.01G and 163.20G, respectively, as shown in As shown in For medical disease detection, if the recall rate is low, a bad situation such as a missed diagnosis can easily occur. Therefore, we pay particular attention to the analysis of the recall rate when we evaluate the performance of the osteosarcoma image segmentation model. In this experiment, we compared the recall rate of the UATransNet model with other baseline models, as shown in In addition, we carefully analyzed how the accuracy of UATransNet and the individual baseline models changed with the progression of 100 epochs, as shown in The F1-score is a statistical measure of the accuracy of a binary classification model. It takes into account both the accuracy and recall of the classification model. As can be seen from Osteosarcoma MRI image segmentation helps physicians in clinical diagnosis by providing precise contours of osteosarcoma and assisting them in the clinical process. We designed a novel network, UATransNet, based on a modified U-Net, by introducing a self-attentive mechanism, incorporating a self-aware attention module, and combining it with a mean-teacher model, which produced good segmentation performance with IOU and DSC of 0.922 ± 0.03 and 0.921 ± 0.04, respectively. To further explore the effectiveness of MRI image segmentation in supporting the neuro-radiosurgery treatment planning stage, Rundo et al.  and MiliThis paper proposes a novel lightweight osteosarcoma image segmentation model, UATransNet, based on over 80,000 osteosarcoma image datasets collected from Xiangya II Hospital of Central South University for extensive experiments. UATransNet significantly improves the accuracy of osteosarcoma segmentation based on the original computational power through dataset classification optimization and preprocessing, and achieves the trade-off between accuracy and speed; utilizes TSAC and GCAC components are used to optimize the feature representation effect of osteosarcoma; applying dense residual learning to the convolution module, combined with multiscale jump junction, better preserves the edge features of osteosarcoma, provides effective decision information support for clinicians, and provides a new option for early diagnosis and treatment of osteosarcoma in developing countries.However, this paper is not clear enough for edge segmentation of osteosarcoma MRI images with little grayscale difference. With the development of medical image segmentation technology, in future research work, we will comprehensively utilize and optimize the edge features and texture features of osteosarcoma MRI images to further optimize the segmentation effect of osteosarcoma MRI images.


Porphyromonas gingivalis , activates the host immune response to release numerous proinflammatory cytokines. Here, we aimed to clarify Leuconostoc mesenterica (L. mesenteroides) LVBH107 probiotic effects based on the inhibition of P.gingivalis activities while also evaluating the effectiveness of an in vitro P.gingivalis lipopolysaccharide-stimulated RAW 264.7 cell-based inflammation mode. L. mesenteroides LVBH107 survived at acid, bile salts, lysozyme, and hydrogen peroxide conditions, auto-aggregated and co-aggregated with P. gingivalis, exhibited strong hydrophobicity and electrostatic action, and strongly adhered to gingival epithelial and HT-29 cells . Moreover, L.mesenteroides LVBH107 exhibited sensitivity to antibiotics erythromycin, doxycycline, minocycline, ampicillin, and others (thus indicating it lacked antibiotic resistance plasmids), effectively inhibited P.gingivalis biofilm formation and inflammation (in vitro inflammation model), reduced the secretion of pro-inflammatory cytokines  and inflammatory mediators (NO and PGE2), and decreased the expression levels of inflammation related genes. Thus, L.mesenterica LVBH107 holds promise as a probiotic that can inhibit P.gingivalis biofilm formation and exert anti-inflammatory activity to maintain oral health.Probiotics, active microorganisms benefiting human health, currently serve as nutritional supplements and clinical treatments. Periodontitis, a chronic infectious oral disease caused by  Probiotics are defined by the FAO/WHO as “live microorganisms which when administered in adequate amounts confer a health benefit on the host” . The effLactobacillus and Lactococcus species as prospective probiotic treatments for oral diseases [Lactobacillus reuteri (L. reuteri), Tweetman et al. [Lactobacillus brevis to patients with periodontal disease led to decreased gingivitis severity and dental calculus deposition [L. reuteri [Periodontitis is a common chronic inflammatory oral disease that is caused by oral pathogenic microbes , which idiseases . In an en et al.  found maposition , while r reuteri . Further reuteri  demonstrPorphyromonas gingivalis  is currently recognized as an important pathogen involved in mature dental plaque formation from biofilm generated through activities of a variety of associated pathogenic or nonpathogenic bacteria that adhere to and copolymerize with P. gingivalis cells. In addition, a variety of virulence factors  that participate in biofilm formation are expressed during interactions of these bacteria with host cells [P. gingivalis by the use of probiotic organisms holds promise as an important approach for preventing and slowing the progression of destructive periodontal disease.st cells , wherebyst cells ,21. TherLeuconostoc mesenteroides (L. mesenteroides), a Gram-positive, spherical or short-chain facultative anaerobic lactobacillus, is widely used for the production of fermented foods and bacteriocins. Several studies have shown that L. mesenteroides treatments can exert antioxidant activities, improve immunity, reduce cholesterol levels, and alleviate hyperlipidemia [L. mesenteroides administration could effectively reduce levels of the inflammatory factor IL-6, prompting researchers to speculate that L. mesenteroides may serve as a safe immunomodulatory treatment [L. mesenteroides LVBH107 were isolated from a traditional fermented food (spicy cabbage) and shown to exert an obvious inhibitory effect on P. gingivalis activities as based on Oxford cup assay results -stimulated inflammation, prompting the current study. Here, potential probiotic properties of L. mesenteroides LVBH107 were investigated in vitro, including resistance, surface characteristics, antibiotic resistance, and anti-P. gingivalis activity, assessed inflammatory responses of L. mesenteroides LVBH107 using a model of inflammation based on P. gingivalis LPS-stimulated RAW 264.7 macrophage. Thereby, our results provide experimental and theoretical basis for further using LVBH107 as an auxiliary drug to inhibit periodontal pathogens and treat periodontitis.ipidemia ,23. For reatment . In our  results . Those rLeuconostoc mesenteroides (subsp. mesenteroides) LVBH107 (CGMCC No. 21362) that obtained from a fermented food product (spicy cabbage) were deposited in the China General Microbiological Culture Collection Center . L. mesenteroides LVBH107 that was inoculated  into de Man, Rogosa, and Sharpe (MRS) broth  was cultured in an anaerobic incubator  at 37 °C for 16 h.L. mesenteroides LVBH107 was collected by centrifugation ; then, the pellet was washed three times with phosphate-buffered saline  to obtain the bacteria-free supernatant, which was filtered through a 0.22 μM filter membrane to remove bacterial cells. In addition, a heat-killed L. mesenteroides LVBH107 preparation was prepared by heating the bacteria at 90 °C for 30 min.After culture, P. gingivalis (ATCC 3327) was obtained from the China General Microbiological Culture Collection Center and was propagated on Columbia blood agar  at 37 °C in an anaerobic incubator  for 48 h. Next, brain heart infusion (BHI) broth (Hopebio Company) was inoculated with a P. gingivalis culture  followed by incubation at 37 °C for 48 h.v/v) fetal bovine serum  and 100 U/mL Penicillin-Streptomycin  at 37 °C . Cells were subcultured once every 2 days by splitting cultures 1:3 in fresh medium after harvesting adherent cells by detaching them from flask surfaces using 0.25% (w/v) trypsin-EDTA solution (Sigma-Aldrich).Human colorectal adenocarcinoma HT-29, human gingival epithelial (HGE), and RAW 264.7 (mouse mononuclear macrophage-derived) cell lines were obtained from Otwo Biotech  and cultured in Dulbecco’s Modified Eagle Medium  containing 10% (L. mesenteroides LVBH107 was inoculated at 3% (v/v) into MRS broth containing 0.3% (w/v) pepsin , bovine bile , lysozyme , and hydrogen peroxide ; then, cultures were incubated at 37 °C for 20 h under aerobic conditions. During the culture period, optical density (OD) values at 600 nm were measured using a UV-VIS spectrophotometer  every 2 h.An overnight culture of L. mesenteroides LVBH107 cells were collected by centrifugation  and resuspended in PBS. The OD600 of the suspension was adjusted to 1.0 using a UV-VIS spectrophotometer; then, the suspension was incubated at 37 °C for 8 h, during which OD600 values were measured at 2 h, 4 h, 6 h, and 8 h [T is the OD600 value at 2 h, 4 h, 6 h, and 8 h, and A0 is the OD600 value at 0 h. After overnight culture,  and 8 h . Auto-agL. mesenteroides LVBH107 and P. gingivalis strains were harvested at 16 h or 48 h by centrifugation , cells of each strain were resuspended in sterile PBS; then, the OD600 values of the two strains were adjusted to 0.50 and 0.60, respectively. Next, equal volumes (2 mL) of suspensions of both strains were shaken , and each suspension was separately incubated without shaking for 8 h at 37 °C. During the 8 h incubation, OD600 readings of each suspension were taken at 2 h, 4 h, 6 h, and 8 h [X and AY are absorbance values of L. mesenteroides LVBH107 and P. gingivalis at 0 h and Amix is the absorbance value of the mixture containing both organisms after incubation for 2 h, 4 h, 6 h, and 8 h.After  and 8 h . Co-aggrL. mesenteroides LVBH107 was determined using the microbial adhesion to hydrocarbons (MATH) method with toluene and xylene serving as hydrophobic organic solvents. Chloroform was selected as Lewis acid and ethyl acetate as Lewis base in order to determine surface charge characteristics of L. mesenteroides LVBH107 [L. mesenteroides LVBH107 preparation was the same method as described above except that the bacterial pellet was resuspended in 0.1 M KNO3 buffer (pH 6.2) and adjusted to an OD600 value of 0.60. Next, 1 mL of organic solvent  to 3 mL of the L. mesenteroides LVBH107 suspension were combined to form a two-phase solution that was preincubated at room temperature for 10 min then vortexed for 2 min followed by incubation at room temperature for 30 min. Thereafter, the OD600 value of the aqueous phase was determined; then, the adherence of L. mesenteroides LVBH107 to hydrocarbons was calculated as follows:0 is the OD600 value before treatment with the organic solvent and A1 is the OD600 value of the aqueous phase after treatment with the organic solvent. The surface hydrophobicity of  LVBH107 . The met5 cells/well); then, the plates were incubated until cells adhered completely to well surfaces and reached 70–80% confluence. Next, medium in the wells was replaced with high glucose DMEM medium (without penicillin-streptomycin); then, the plates were incubated at 37 °C for 24 h. Thereafter, monolayers of cells were washed three times with PBS; then, 500 μL of L. mesenteroides LVBH107 (8 Log CFU/mL) was added per well, and then the plates were incubated for 2 h at 37 °C. Next, non-adherent bacteria were removed from each well then 2 mL of 1% (v/v) Triton X-100  was added to each well to detach adherent cells [L. mesenteroides LVBH107 was calculated as follows:1 is the total count of adhered L. mesenteroides LVBH107, and V0 is the number of total added L. mesenteroides LVBH107.HT-29 and HGE cells were transferred to 12-well tissue culture plate wells  culture was evenly spread onto the surface of each newly prepared MRS agar plate after the agar solidified at room temperature. Next, commercial antibiotic disks were placed onto plate surfaces, and then the plates were incubated at 37 °C for 48 h. After incubation, the diameter of each inhibition zone around each disk was measured and expressed as susceptible (S), intermediate (I), or resistant (R), as specified within the instructions provided by the manufacturer . All analyses were performed in triplicate.The antibiotic susceptibility of P. gingivalis cultures that had been incubated for 48 h at 37 °C were adjusted to 6 Log CFU/mL by dilution in BHI broth. Next, L. mesenteroides LVBH107 culture medium or supernatant and P. gingivalis were added to upper and lower chambers of 24-well transwell plates , respectively, at a ratio of 1:1 (v/v). After the incubation of transwell plates for 48 h at 37 °C, the fluid culture medium within the lower chamber was gently removed and discarded; then, the P. gingivalis biofilm at the bottom of each well was gently washed three times with PBS to remove planktonic P. gingivalis. Thereafter, P. gingivalis biofilm in each lower chamber was fixed in 2.5% (v/v) glutaraldehyde solution at 4 °C overnight and dried at room temperature. Next, 100 μL of 0.1% (w/v) crystal violet was added to each lower transwell chamber; then, staining was allowed to proceed at room temperature for 15 min, after which wells that had received stain were rinsed gently with 75% ethanol to remove unbound crystal violet and dried at room temperature [P. gingivalis biofilm structure was observed under a microscope  and photographed then biofilm absorbance values were measured at 540 nm using a microplate reader. The rate of L. mesenteroides LVBH107 inhibition of P. gingivalis biofilm formation was calculated as follows:540 value of P. gingivalis biofilm in the absence of L. mesenteroides LVBH107 culture medium or supernatant, and AC is the OD540 value of the P. gingivalis biofilm treated with L. mesenteroides LVBH107 culture medium or supernatant.perature . ThereafL. mesenteroides LVBH107 culture medium or supernatant and P. gingivalis were prepared in transwell plates as described above. After incubation of transwell plates for 72 h at 37 °C, coverslips coated with P. gingivalis biofilm were stained for 15-20 min in the dark using a LIVE/DEAD® BacLight™ Bacterial Viability Kit  [P. gingivalis biofilm structure on each coverslip was assessed and recorded using a confocal laser scanning microscope .After sterilized coverslips (10 mm × 10 mm) were placed into lower chambers of transwell plates, co-cultures containing MA, USA) . Next, PL. mesenteroides LVBH107 for RAW 264.7 cells was assessed using the Cell Counting Kit-8 (CCK-8) assay . After RAW 264.7 cells were inoculated into wells of 96-well plates and allowed to adherently grow on well surfaces to 70–80% confluence (1 × 106 cells/well), different concentrations of live or heat-killed L. mesenteroides LVBH107 were added to the wells  followed by co-incubation of plates for 6 h at 37 °C. Thereafter, the culture medium was removed from the wells; then, 300 μL of CCK-8 working reagent was added to each well, followed by the incubation of plates at 37 °C for 4 h. Next, the optical density of each well at 450 nm was measured using a multi-function microplate reader . Cell viability was calculated as follows:450 value of RAW 264.7 cells co-incubated with L. mesenteroides LVBH107, and Ac is the OD450 value of RAW 264.7 cells in the absence of L. mesenteroides LVBH107.The cytotoxicity of P. gingivalis anti-inflammatory activity, RAW 264.7 cells were treated with live or heat-killed L. mesenteroides LVBH107 suspensions for 3 h or 6 h and then 1 μg/mL of P. gingivalis LPS  was added to cells followed by incubation of stimulated cells for 24 h . Next, the production of nitric oxide (NO) was determined in P. gingivalis LPS-stimulated RAW 264.7 cells using assay kits to measure levels of each compound . In addition, the effects of L. mesenteroides LVBH107 on the production of TNF-α, interleukin-6 (IL-6), interleukin-1β (IL-1β), and prostaglandin E2 (PGE2) were assessed in P. gingivalis LPS-induced RAW 264.7 cells using enzyme linked immunosorbent assay (ELISA) kits  [To assess , China) .TNF-α, IL-6, IL-1β, cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS). The 2−ΔΔCt method was used to calculate qRT-PCR results. Melting curve analysis was used to evaluate the specificity of the reaction. β-actin served as the internal control. Primer sequences are listed in RAW 264.7 cells were transferred to centrifuge tubes; then, total RNA was extracted according to instructions provided with the TaKaRa MiniBEST Universal RNA Extraction Kit . Next, the Primescript™ RT reagent Kit (TaKaRa) was used to reverse transcribe approximately 1.0 μg of total RNA into cDNA . The cDNp < 0.05 were considered statistically significant.Data are provided as mean ± standard deviation. All experiments were repeated three times. All statistical analyses were performed using SPSS 22.0  software. Significant differences between groups were assessed using one-way ANOVA and LSD post hoc analysis . Values L. mesenteroides LVBH107 survived and exhibited high growth activity after culture in 0.3% pepsin (pH 5.0) and bovine bile salts (0.3% and 0.5%) for 20 h, while in 0.3% pepsin at pH 2.0 and 3.0, the organisms grew slowly and survived only for as long as 10 h. In addition, L. mesenteroides LVBH107 .In order to function within the oral environment of the human host, probiotic organisms must possess strong tolerance to harsh conditions found there in order to remain viable and carry out activities that benefit human health.  LVBH107 B exhibitL. mesenteroides LVBH107 auto-aggregation (5.80%) was observed  revealed a highly organized and uneven biofilm structure containing a large number of P. gingivalis organisms  and decreased numbers of cells exhibiting green fluorescence (live bacteria), as shown in P. gingivalis biofilm generally stained red, indicating that most cells had died  and a portion exhibiting green fluorescence , as shown in L. mesenteroides LVBH107 could effectively inhibit P. gingivalis biofilm formation, with the L. mesenteroides LVBH107 culture medium inhibitory effect found to be greater than of the bacteria-free culture supernatant.Adhesion and aggregation of L. mesenteroides LVBH107 when the strain was added to RAW 264.7 macrophages in vitro, we evaluated the viability of RAW 264.7 macrophage-like cells when exposed to different L. mesenteroides LVBH107 concentrations. As shown in L. mesenteroides LVBH107 to RAW 264.7 cells were associated with obvious greater RAW 264.7 cell viability (to varying degrees) as compared to the control group without observed cytotoxic effects, with cell viability reaching 103.76–126.45% after 6 h co-culture with L. mesenteroides LVBH107. Importantly, the addition of live L. mesenteroides LVBH107 to RAW 264.7 cells achieved a significantly greater effect than was achieved by the addition of heat-killed bacteria. By contrast, under the same conditions as mentioned above, the addition of 9 Log CFU/mL bacteria to RAW 264.7 cells led to decreases in RAW 264.7 cell viability and proliferation. These results suggest that the cytotoxic effect induced by the addition of 9 Log CFU/mL L. mesenteroides LVBH107 resulted in a partial cell death that ultimately led to reduced viable cell numbers. Therefore, 7 Log CFU/mL and 8 Log CFU/mL concentrations of both live and heat-killed L. mesenteroides LVBH107 were used in subsequent experiments.In order to assess potential cytotoxic effects of P. gingivalis LPS for 24 h clearly stimulated the cells and led to increased levels of proinflammatory cytokines . However, the treatment of RAW 264.7 cells with L. mesenteroides LVBH107 suppressed levels of secreted TNF-α, IL-6, and IL-1β to varying degrees after P. gingivalis LPS stimulated  of L. mesenteroides LVBH107 were significantly reduced after P. gingivalis LPS stimulation, while the exposure of these RAW 264.7 cells to heat-killed L. mesenteroides LVBH107 led to a reduced inhibition of proinflammatory cytokine secretion than was observed after addition of live L. mesenteroides LVBH107 to RAW 264.7 cells.As expected, the exposure of RAW 264.7 cells to imulated A–C, thusP. gingivalis LPS stimulation also significantly increased the secretion levels of inflammatory mediators PGE2 and NO in macrophage RAW264.7  were up-regulated by LPS stimulation, the treatment of RAW 264.7 cells with L. mesenteroides LVBH107 led to relative down-regulated expression of these cytokines  and Lactobacillus fermentans (L. fermentans) could survive under conditions with low pH, bile salts, and lysozyme.As a probiotic that must exert its beneficial activity within the oral cavity, lactobacillus must be able to survive within the harsh oral environment and adhere to oral mucosal cells for a certain period of time. Indeed, our results demonstrated that olerance A in addilysozyme B while ml et al. ,35, indiL. mesenteroides LVBH107 was confirmed here to possess an auto-aggregation ability  [L. mesenteroides LVBH107, through auto-aggregation and co-aggregation activities, may form a barrier that protects the host epithelium by blocking binding of potential pathogens to host cell receptors.Importantly, a previous study demonstrated that lactobacillus strains with strong auto-aggregation ability could colonize mucosal surfaces and consequently carry out their probiotic functions better than strains without this ability . Notably ability C that gral cells ,37. In ttococcus  and resutococcus . In addiG (6.1%) . MeanwhiG (6.1%) , with stG (6.1%) . TherefoL. mesenteroides LVBH107 possessed good biosafety characteristics (lacked antibiotic resistance plasmids), as reflected by its sensitivity to erythromycin, doxycycline, minocycline, ampicillin, chloramphenicol, and cefuroxime. However, here, we must note that tolerance to antibiotics can differ depending on species of lactobacillus under study and even among isolates of a given strain obtained from different sources. For example, Tulumoglu et al. [L. plantarum isolated from traditional cured meat in Tunisia revealed that most of them were resistant to rifampicin [L. plantarum isolates obtained from traditional Marseille dairy products were sensitive to penicillin [Due to the fact that antibiotic genes encoded by plasmids can be transferred among bacterial strains , this acu et al.  reportedfampicin . Moreovenicillin , a resulP. gingivalis, the main pathogenic bacterial species that has been reported to be associated with chronic periodontitis, has been detected at a high rate (60.9%) within subgingival tissues of patients with periodontitis [P. gingivalis and subsequent dental plaque formation are main pathogenic factors associated with periodontitis. Dental plaque, a type of bacterial biofilm, is a soft, nonmineralized bacterially derived material that sticks to teeth and covers tooth surfaces. Importantly, dental plaque provides ideal attachment sites that support the colonization and growth of oral pathogens that play key roles in the pathogenesis of periodontitis. Therefore, inhibiting dental plaque biofilm formation is an important way to prevent periodontitis. In the present study, L. mesenterica LVBH107 significantly inhibited formation of P. gingivalis biofilm . Notably, biofilm damage increased after the addition of cell-free L. mesenterica LVBH107 culture supernatant to P. gingivalis cultures, with some P. gingivalis organisms found to be dead (as detected based on red fluorescence) and some found to be alive (as detected based on green fluorescence). Previously reported observations indicate the protection of microorganisms within biofilm due to the encapsulation of organisms by LPS, peptidoglycan, gingival protease, and flagellin that were produced via plaque biofilm metabolic activities [L. mesenterica LVBH107 may inhibit growth of P. gingivalis and biofilm formation by inhibiting P. gingivalis activities within the biofilm, which ultimately reduces the production of extracellular products that support P. gingivalis survival within the oral microenvironment.Biofilm activities can be observed quickly and intuitively using confocal laser scanning microscopy (CLSM) and fluorescent staining. Two commonly used fluorescent stains include SYTO9, which can enter all bacterial cells to produce green fluorescence, and propidium iodide (PI), which can only enter bacterial cells with damaged cell membranes to produce red fluorescence. Here, CLSM results revealed that tivities . Taken t2), reactive oxygen species, proinflammatory cytokines , and cell chemokines [2, iNOS, and COX-2 is regulated by upstream enzymes, with nitric oxide synthase (iNOS) acting as the rate-limiting enzyme that controls the NO synthesis pathway. In fact, the triggering of iNOS synthesis by inflammatory processes induces high-level expression and the release of large amounts of NO that, in turn, can combine with superoxide anion to form nitroso anions that play crucial roles in killing pathogenic microorganisms and tumors [2) that participate in the inflammatory response [2 dilates blood vessels and lowers blood pressure, leading to up-regulated expression of other pro-inflammatory mediators (including cytokines) that may cause severe cell damage resulting from inflammatory disease processes [2 levels in P. gingivalis LPS-stimulated RAW 264.7 cells that were both decreased after treated with L. mesenterica LVBH107 due to L. mesenterica LVBH107 inhibition of mRNA-level expression of iNOS and COX-2. This result aligned with results of similar studies showing that heat-inactivated Weissella cibaria JW15 and L. brevis K65 bacteria could inhibit production of NO and PGE2 by down-regulating iNOS and COX-2 mRNA-level expression [Macrophages are important immune system cells that play key roles in various inflammatory processes . During emokines  that supemokines . Importad tumors . Meanwhiresponse . PGE2 dirocesses . Here, apression ,59. HoweP. gingivalis LPS activated the macrophage inflammatory response and significantly increased mRNA-level expression of pro-inflammatory cytokines TNF-α, IL-6, and IL-1β. By contrast, treated with L. mesenterica LVBH107 bacteria attenuated the RAW 264.7 cell inflammatory state by inhibiting mRNA-level expression of TNF-α, IL-6, and IL-1β to reduce the production of these pro-inflammatory factors. Therefore, these findings collectively suggest that L. mesenterica LVBH107 may function as an anti-inflammatory probiotic, as consistent with results of numerous other studies demonstrating anti-inflammatory effects of other probiotics, such as Bifidobacterium, Lactobacillus paracei, and Lactobacillus swiss, that inhibited the expression and/or secretion of pro-inflammatory cytokines TNF-α, IL-6, and IL-1β [L. mesenterica LVBH107 produced an extracellular metabolite that exerted an anti-inflammatory effect that inhibited TNF-α, IL-1β, and IL-8 production by specifically targeting an anti-inflammatory molecular pathway. Notably, a similar mechanism was reported previously for L. mesenteroides exopolysaccharides S81 and BioE-LMD18 [Pro-inflammatory cytokines mainly include TNF-α, IL-6, and IL-1β, which are produced by macrophages and endothelial cells. IL-1β and TNF-α promote inflammatory cell aggregation and activation, stimulate the release of inflammatory mediators, stimulate fever production, and exacerbate inflammatory responses ,61. IL-6nd IL-1β ,64,65. WoE-LMD18 ,67, whicL. mesenterica LVBH107, a probiotic isolated from a traditional fermented food (spicy cabbage). This biosafe strain can survive in extreme environments, such as low-pH, bile salts, hydrogen peroxide, and lysozyme; at the same time, it has auto-aggregation, co-aggregation, and adhesion capacity abilities that encourage L. mesenterica LVBH107 adhesion and colonization of the oral environment. In vitro studies have shown that L. mesenterica LVBH107 effectively inhibited P. gingivalis biofilm formation, as well as bacterial activities within established biofilm. In addition, L. mesenterica LVBH107 attenuated the inflammatory response of P. gingivalis LPS-stimulated RAW 264.7 cells and reduced both expression and secretion of inflammatory mediators and pro-inflammatory factors, thus exhibiting anti-inflammatory potential (L. mesenterica LVBH107 could serve as a highly promising probiotic with beneficial antibacterial and immunomodulatory activities, which could be one candidate for doctors in the treatment of periodontal inflammation and protecting teeth far from periodontal pathogen infection.In summary, results of this work demonstrated antibacterial and anti-inflammatory effects of otential . Taken t


COX2 were analyzed by real-time quantitative PCR (RT-qPCR); cell viability was measured by cell counting kit-8 (CCK-8) assay; and apoptosis was detected by flow cytometry. In addition, the release of cytokines was determined by Enzyme-linked immunosorbent assay (ELISA), and the binding sites between miR-367-5p and circ_0000080/COX2 were predicted by bioinformatics analysis and confirmed by dual-luciferase reporter and RNA pull-down assays. circ_0000080 was upregulated in patients with MI and in H9c2 cells treated with H2O2 and hypoxia/reoxygenation (H/R). Silencing circ_0000080 reduced the H/R-mediated apoptosis of cardiomyocytes and secretion of pro-inflammatory cytokines. Moreover, circ_0000080 functioned as an miR-367-5p sponge to regulate the expression of COX2. Downregulated miR-367-5p or overexpressed COX2 degraded cellular functions of cardiomyocytes. circ_0000080 knockdown alleviated myocardial hypoxia injury through the miR-367-5p/COX2 axis.This study aimed to investigate the potential role of circRNA circ_0000080 in myocardial hypoxia injury and the underlying mechanisms. Patients with myocardial hypoxia injury who were admitted to Xi’an No. 1 Hospital, China, were included in this study. The expression levels of circ_0000080, miR-367-5p, and   MyocaCompared to traditional linear RNA, circRNA, with a closed circular structure, is more stable ,7. CircuMicroRNAs are a class of non-coding single-stranded RNA, approximately 21–23 nucleotides long ,14. MiR-COX2 axis on myocardial viability, apoptosis, and inflammation. Overall, this study provides an important theoretical basis for the development of therapeutic targets for treating MI.Here, we aimed to reveal the protective effect of circ_0000080 on oxidative stress injury in myocardial cells induced by hypoxia/reoxygenation. We try to elucidate the effect of circ_0000080/miR-367-5p/A total of 38 healthy heart tissues and 45 heart tissues from patients with MI were obtained. The human heart tissue samples were obtained from patients with their consent and their use for research was approved by the ethical review board of Xi’an No. 1 Hospital (ethics number: 2021JM-587).2.Rat embryonic cardiomyocyte H9c2 was purchased from Shanghai Cell Bank of Chinese Academy of Sciences . The cells were cultured in Dulbecco’s modified Eagle medium (DMEM) in an incubator at 37°C, with saturated humidity and 5% CO2 cells exceeded 90%, the supernatant of the culture medium was replaced by DMEM without fetal bovine serum. Thereafter, H9c2 cells were placed in an incubator (5% CO2 and 95% N2) for 4 h, and then cultured in DMEM containing 10% glycerol (5% CO2 and 95% O2) for 3 h, followed by 24 h of oxygen.When the growth density of H9CCardiomyocytes were seeded in a 6-well plate. According to the operating instructions, transfection was performed when the degree of cell fusion exceeded 60%. Briefly, Lipofectamine 2000 and plasmid were diluted in Opti-MEM, respectively. After 5 min, the diluted Lipofectamine 2000 and plasmid were mixed evenly. The transfection complex was then added to the 6-well plate and replaced with fresh medium after 4–6 h of transfection. The transfection plasmid used in this study was constructed by Tsingke Biotechnology . SiRNA was transfected with Lipofectamine 2000. The sequences of siRNAs were as follows: si-NC: 5’-AGTGGGTCTACGGCGATA-3’, si-circ_0000080 1#: 5’-GTTTGGAGGAACTCAACCCTA-3’, and si-circ_0000080 2#: 5’-TTTGGAGGAACTCAACCCTAT-3’.GAPDH, as a reference, the relative content of the target sample gene was calculated. Commercial TaqMan primers for qPCR were purchased from Life Technologies, USA. The primers were: circ_0000080 F: 5’-GAGGAACACTCCATATAATTGGTGA-3’; R: 5’-TCTGTTTTTCTTTGAAGGGCTACCT-3’, miR-367-5p F: 5’-TGCGGACTGTTGCTAATATG-3’; R: 5’-CCAGTGCAGGGTCCGAGGT-3’, COX2 F: 5’-TTCAAATGAGATTGTGGGAAAAT-3’; R: 5’-AGATCATCTCTGCCTGAGTATCTT-3’.For fluorescent probe PCR, the following components were added to a 20 μL PCR mixture : 10 μL TaqMan Gene Expression PCR Master Mix, 1 μL template DNA, 1 μL 20× Prime Probe mixture, 8 μL DNase-free water, and 9.4 μL DNase-free water. The following cycling conditions were used for PCR: 95°C 10 min; 95°C 15s, 60°C 30s, 68°C 30s, 40 cycles. Using the internal reference gene, After H9c2 cells were transfected as indicated and treated with hypoxia/reoxygenation (H/R), 10 μL CCK-8  was added to each well. After 2 h, the absorbance of each sample was measured at 450 nm according to the kit instruction.Flow cytometry was performed according to a previous study . After HCells were lysed using RIPA buffer. After detecting protein concentration using BCA kit , equal protein was added and run on 10% SDS-PAGE. Then the protein was transferring to PVDF membranes, followed by blocked with 5% skim milk for 1 h. The membranes were incubated with primary antibodies  overnight at 4°C, and incubated with secondary antibody  at room temperature for 1 h. The bands were visualized using ECL plus . The original bands were provided in supplemental material 1.After H9c2 cells were transfected as indicated and treated with H/R, the cell culture supernatant was collected and the concentrations of IL-6, TNF-α, and IL-1β were detected according to the instructions of commercial kits . ELISA was performed as previous described .COX2-wt or pMIR-COX2-mut was inserted by COX2 cDNA or mutant COX2. When the growth density of H9C2 cells exceeded 90%, cells were cultured and co-transfected with pMIR-circ_0000080-wt or pMIR-COX2-wt, pMIR-circ_0000080-mut or pMIR-COX2-mut, miR-367-5p mimics, and miR-NC, respectively. After 48 h, luciferase was detected with the dual-luciferase reporter gene assay system  as instructed.Target genes were predicted by Starbase v3.0. PMIR-circ_0000080-wt was formed by cloning circ_0000080 cDNA into the pMIR-Vector; pMIR-circ_0000080-mut was inserted by the mutant circ_0000080. The cloned sequences were provided in supplemental material 2. Similarly, pMIR-COX2 in cells was determined by an RNA pull-down assay [The interaction between circ_0000080 and miR-367-5p or the interaction between miR-367-5p and wn assay . Biotin SPSS 25.0 statistical software was used for data analysis, and the data are expressed as mean ± SEM. One-way ANOVA was used for comparison among multiple groups, and the LSD test was used for pairwise comparison. P < 0.05 was considered statistically significant.In this study, we sought to explore the expression of circ_0000080 in MI and its role in H/R-treated H9c2 cells. We analyzed the effects of circ_0000080 on cell viability, apoptosis, and inflammation. The goal of this study is to provides an important theoretical basis for the development of therapeutic targets for treating MI.This experiment was conducted to detect the expression level of circ_0000080 between MI patients and healthy individuals (controls) to understand the role of circ_0000080 in the heart. The qRT-PCR results revealed that the expression level of circ_0000080 was significantly elevated in MI patients than in healthy individuals ). The coThe expression level of circ_0000080 was markedly reduced by si-circ_0000080 1# compared to si-circ_0000080 2#, while si-circ_0000080 1# did not affect the linear GAPDH expression level ). Such fInflammation after MI is necessary for heart repair; however, inflammation is also involved in the pathophysiological processes, such as heart remodeling and heart failure after MI . As showBased on increasing evidence, circRNA acts as a miRNA sponge ,21. The A rescue experiment was conducted to explore the relationship between circ_0000080 and miR-367-5p in H/R treated H9c2 cells. Based on qPCR, the miR-367-5p inhibitor significantly reduced the expression level of miR-367-5p, while the miR-367-5p mimic markedly increased the expression level of miR-367-5p ). The CCTo determine the effects of miR-367-5p in H/R-treated H9c2 cells, an experiment was conducted to further assess the effects of miR-367-5p on the secretion of inflammatory factors in H9c2 cells in vitro. Knockdown of circ_0000080 reduced the release of inflammatory cytokines including IL-1β, IL-6, and TNF-α, and the miR-367-5p inhibitor was found to increase the secretion of IL-1β, IL-6, and TNF-α ).FigureCOX2 are shown in COX2-wt. Further, the RNA pull-down assay suggested that biotin-miR-367-5p could notably pull down more circ_0000080 . The mRcontrols ), and thcontrols ). Besidec2 cells ) but wasc2 cells ).FigureCOX2 in H/R-treated H9c2 cells. The transfection efficiency of COX2 is shown in COX2 overexpression (COX2 overexpression promoted apoptosis in H9c2 cells (A rescue experiment was performed to confirm the relationship between miR-367-5p and pression ). Moreovc2 cells ).FigureCOX2 significantly increased the release of inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, as shown in The overexpression of COX2. MiR-367-5p knockdown or COX2 overexpression inhibited the role of circ_0000080 in MI.The cardiovascular system is the engine of life. Severe cardiovascular disease is a potentially fatal disease ,23. AlthApoptosis is a form of programmed cell death; it affects cardiomyocytes during MI . The incWe proceeded to explore the mechanism of circ_0000080 in MI. Most circRNAs play regulatory roles in MI by targeting downstream genes ,31. Liu COX2 knockdown may increase the risk of MI [COX2 in H9c2 cells, and the overexpression of COX2 reversed the effect of miR-367-5p in cells. Therefore, circ_0000080 may weaken the targeted inhibitory effect of miR-367-5p on COX2 by binding to miR-367-5p, thereby inhibiting the progression of MI. The circ_0000080/miR-367-5p/COX2 axis may provide a new therapeutic option for MI.To further study the mechanism of miR-367-5p’s biological function in MI, we investigated the downstream target gene of miR-367-5p. Zhang et al. found that sk of MI . Herein,COX2 axis in MI needs to be confirmed using several clinical studies, which will be the focus and a challenge of future research. Additionally, only the roles of miR-367-5p and COX2 were explored in this study. whether other miRNAs and genes influence circ_0000080-mediated regulation in MI still needs to be elucidated.The main limitation of this study is that the role of the circ_0000080/miR-367-5p/COX2 signal axis was found to promote proliferation and impair the apoptosis of H/R treated cells. This study not only revealed the novel roles of the circ_0000080/miR-367-5p/COX2 axis in MI, but also provided a new target for the management of MI.In summary, the circ_0000080/miR-367-5p/Click here for additional data file.