Background: Invasive breast carcinoma is among the most common female cancers worldwide, causing high morbidity and mortality. Considerable disagreement in the interpretation of diagnostically challenging breast lesions based on histology alone has been documented. One of the essential histopathological findings that help distinguish benign from malignant lesions is the presence of the myoepithelial cell layer. Myoepithelial markers such as tumor protein 63 (p63) help distinguish invasive carcinoma from benign proliferations. p63 antibody is superior to other myoepithelial markers as it selectively stains the nuclei and is negative in stromal cells.Objective: To study the expression of p63 in various histological subtypes and grades of breast carcinomas.Methods: After routine hematoxylin and eosin stain, 65 cases of breast lesions were subjected to immunohistochemistry for p63 antigen using Novacastra ready-to-use monoclonal antibody p6. All cases were analyzed for p63 expression, and its staining arrangement was interpreted.Results: In all benign lesions, immunoreactivity was noted in the myoepithelial cells, forming a continuous layer surrounding the luminal epithelial cells. The benign papillary lesions showed p63 staining in the fibrovascular core of the papillary fronds and at the periphery. A few single myoepithelial cells stained by p63 were also seen scattered discontinuously in ductal carcinoma in situ (DCIS). All invasive carcinomas and encapsulated papillary carcinomas were completely devoid of peripheral p63 staining of myoepithelial cells.Conclusion: p63 is a specific nuclear marker of myoepithelial cells in the breast and can, therefore, aid in distinguishing invasive ductal carcinoma from DCIS or rare questionable hyperplastic lesions. They also play a significant role in distinguishing various papillary lesions of the breast and, hence, can be incorporated into routine reporting for definitive diagnosis and accurate treatment. Protein 63 (p63), a member of the p53 family, is found on chromosome 3q27 . The varThis prospective study was conducted in the Department of Pathology at a tertiary care hospital in Southern India over a period of three years, from October 2013 to June 2015, after ethical clearance from the Yenepoya Medical College Institutional Ethical Committee (YUEC 113/15/11/2013). Clinical details were procured from the patients' histopathology requisition forms and the laboratory software. Biopsies, excision, tru-cut needle biopsy, and mastectomy specimens of both benign and malignant breast lesions were included in the study. Congenital breast diseases, inflammatory breast lesions, soft tissue lesions, metastatic deposits to the breast, and cases with prior treatment were excluded from the study. The specimens were fixed in 10% formalin, embedded in paraffin blocks, sectioned at 3-5μ, and stained with hematoxylin and eosin. They were studied under a light microscope and classified into benign lesions and non-invasive and invasive carcinomas. All cases were subjected to immunohistochemistry for p63 antigen using Novacastra ready-to-use monoclonal antibody, and the slides were studied for the presence of myoepithelial cells. Based on the pattern of staining and intensity of p63, the cases were graded as continuously positive with strong intensity, discontinuously positive with weak intensity, and negative. The sample size was calculated to be 65 using nMaster 2.0 software . The chi-square test was used for quantitative analysis of data. Mean±SD and percentage were used to determine data for statistical analysis by using Statistical Product and Service Solutions  , and the p-value was calculated. The p-value <0.05 was considered statistically significant.A total of 65 (N) specimens from breast lesions were evaluated in the study, of which 26 (40%) were needle biopsies, 15 (23.07%) were lumpectomy specimens, and 24 (36.92%) were mastectomy specimens , and 41 were malignant (63%). Among 24 benign lesions, 12 (50%) were fibroadenoma, five (20.83%) were intraductal papilloma, and seven (29.16%) were phyllodes tumor. Among 41 malignant cases, six (14.63%) were non-invasive carcinomas, and 35 (85.36%) were invasive carcinomas. Out of six non-invasive carcinomas, five (83.3%) were ductal carcinomas in situ, and one (16.6%) was encapsulated papillary carcinoma. The most common histological type of invasive carcinoma was invasive ductal carcinoma of no special type accounting for 72.22%. Other histological types were invasive ductal carcinoma with medullary features, invasive ductal carcinoma with metaplastic carcinoma, invasive lobular carcinoma, mixed invasive lobular and ductal carcinoma, invasive papillary carcinoma, and invasive papillary carcinoma with features of lobular carcinoma  showed Scarf-Bloom-Richardson grade I, 19 46.34%) showed grade II, and 15 (36.58%) showed grade III (Table 4% showedIn all benign lesions, p63 immunoreactivity was seen in the myoepithelial cell layer surrounding the epithelial structures Figure .In most cases, the p63 expression was nuclear, and staining was in a continuous manner Figure .p63 staining was also noted in a peripheral rim of myoepithelial cells in the in situ carcinomas. However, the staining intensity was less continuous than benign lesions (Figure The benign papillary lesions showed p63 staining in the fibrovascular core of the papillary fronds and at the periphery with mild attenuation at the dilated part Figure .Encapsulated papillary carcinoma showed a lack of p63 staining both in the fibrovascular core of the papillary fronds and in the periphery Figure .All invasive breast carcinomas, irrespective of their histological type and grade, were devoid of p63 staining in the present study (Figure The difference in the staining pattern of p63 Table  in benigBreast carcinomas are treated based on clinical, radiological, and pathological findings. The presence or absence of invasion is a crucial histopathological feature that helps in the treatment aspect and prognosis. In rare cases, invasive foci may not be detected despite extensive microscopic examination and multiple histological sections of the entire breast tissue with routine staining. Moreover, metastases can develop without an invasion visible under routine microscopic examination. Various morphological criteria, including the presence of tubules, cytoplasmic vacuoles, dissociated tumor cells, the proliferation of fibroblasts, stromal infiltration by neoplastic cells, and the presence of myoepithelial cells around epithelial cell clusters, have been described -13. MyoeIn the present study, all benign lesions showed immunohistochemical expression of p63 in the myoepithelial cell layer surrounding the epithelial cells. In all cases, p63 expression was nuclear, and staining was in a continuous manner. This was similar to studies by Ribeiro-Silva et al., where normal breast tissue and fibroadenomas showed p63 nuclear staining of a single continuous layer of cells surrounding the ductal and alveolar epithelium . The benIn our study, p63 also stained a rim of peripheral myoepithelial cells in DCIS, although the staining intensity was less continuous than benign lesions. No significant difference in staining was noted between high-grade and low-grade DCIS. This was similar to studies done by Khazai et al.  and AbdaAll invasive breast carcinomas, irrespective of their histological type and grade, were devoid of p63 staining in the present study, similar to other studies . The assFew studies showed that metaplastic carcinomas and rarely invasive ductal carcinomas stain positively for p63, but staining tends to be patchy and of less intensity than adjacent normal myoepithelial cells -3. A stuOf 41 malignant cases, two were papillary invasive carcinoma, which showed no p63 staining in the present study. However, studies by Stefanou et al. showed that p63 positivity could be seen in neoplastic cells of 33.3% of invasive papillary carcinomas, probably due to the exhibition of myoepithelial differentiation in these cells . A studyThe findings of this study have to be seen in light of some limitations. The small sample size of the study may not be representative of the entire population, and the evaluation of p63 expression was not done on all histological subtypes of breast carcinomas.From this study, we can conclude that p63 is a useful marker for distinguishing benign epithelial lesions from carcinoma in situ on one end and invasive carcinoma on the other end. It can be judiciously used, in addition to hematoxylin and eosin-stained tissues, in identifying the myoepithelial cell layer in problematic cases and in papillary lesions of the breast. Its variable expression, intensity, and staining pattern seen in various studies suggest that it may have more significance at a molecular level in tumor genesis, in addition to staining the myoepithelial cells, and hence further studies are recommended.


Little is known about the stress levels and associated factors of public health and preventive medicine students in the post-pandemic period. This study aims to investigate the stress levels of these students in the post-COVID-19 era and to determine the association of personal background, employment attitude, and psychological state with stress.t-tests and ANOVA tests were used to compare the differences in PSS scores among variables, and multiple hierarchical regression analysis was used to evaluate the associated factors with stress.A cross-sectional survey was conducted in March 2023 among 620 public health and preventive medicine students from two universities in Changsha, China. The survey included demographic characteristics, employment attitudes, perceived stress scale 10, general anxiety disorder 7, the University of California at Los Angeles loneliness scale 20, and the PTSD checklist-civilian version. Two-sided B = 0.42, p = 0.001), higher grade , having internship experiences , having anti-epidemic experiences , believing that COVID-19 has a negative impact on employment , and having higher GAD scores  and UCLA scores  were significantly associated with high-stress levels. Conversely, being female  and believing that the pandemic had a positive impact on employment  were associated with low-stress levels.The survey was completed by 504 students . 24.8% of the students were screened for a high level of stress. 69.0% thought the epidemic positively impacted employment while 18.5% believed it had a negative impact. The results of regression analysis showed that older age (Public health and preventive medicine students in Changsha, China, experienced a high-stress level in the post-pandemic period, which was influenced by age, gender, grade, employment attitude, internship experience, anxiety, and loneliness. As one of the main guardians of the epidemic, these students should be given more attention and psychological interventions in the future. Since the pandemic has changed the original curriculum and increased concerns about future employment, medical students, as the main body of future clinical works, have been paid wide attention , 8. EvenIn fact, public health and preventive medicine students have been hit hard by the pandemic, but they did not seem to receive much attention. Studies have confirmed that during the severe phase of the epidemic, COVID-19 elicits a strong psychological response among public health workers . In Chin2.2.1.The study was conducted through a cross-sectional online survey in March 2023 in Changsha, Hunan Province, China, which had seen multiple COVID-19 outbreaks in the past 3 years, involving every administrative district. We identified 2 universities, Central South University and Hunan Normal University, which were the only universities in Changsha to offer public health and preventive medicine majors. Both of which have independent schools or departments of public health, and both confer bachelor’s and master’s degrees. There were 620 students in the 2 universities and all students were invited to participate in the survey.This study was approved by the Ethics Committee of Hunan Normal University School of Medicine , and an informed consent was attached to the questionnaire to ensure that each student participated voluntarily. According to the lists provided by the 2 universities, we sent electronic questionnaires to each student by email and issued regular reminders. Students who did not reply more than three times were regarded as declined. All surveys were conducted anonymously, and no personal information would be seen in the background except IP address, and no personal information would be divulged. All items in the questionnaire were required. If any items were omitted, the questionnaire cannot be submitted. If a student was consistent in the choice of some variables , or had obvious logical errors, the questionnaire was considered invalid. The data in this study were all self-reported, which may introduce some bias and limit the ability to draw causal conclusions, but the setting of the census was still expected to identify some of the problems with these students.2.2.2.2.1.A self-made questionnaire was developed to collect participants’ demographic characteristics and employment attitudes. The former includes age, gender, grade, major, family residence, household income, intern experience, and COVID-19 vaccination. The latter includes whether they study their major voluntarily, their preferred employment intention, views on the impact of COVID-19 on employment, whether they want to change careers and the reasons for it, and whether they or others around them have been diagnosed or participated in the fight against COVID-19.2.2.2.R = 0.95). In order to identify students’ stress levels after the pandemic, we highlighted in the questionnaire instructions: In the past month, how often have you experienced the following due to COVID-19?The perceived stress scale 10 (PSS-10) was used to assess students’ perceived stress in the last month. The scale consists of 10 questions and is scored on a five-point scale from 0 (never) to 4 (often) . The hig2.2.3.R with the Beck anxiety inventory (BAI) and the symptom check list-90 (SCL-90) were 0.72 and 0.74, respectively  was used to assess students’ anxiety symptoms after COVID-19. This is a validated screening tool for major anxiety disorders such as generalized anxiety disorder or panic disorder based on the seven-item symptoms . Studentectively , 19.2.2.4.We used the University of California at Los Angeles loneliness scale 20 (UCLA-20) to assess students’ loneliness after the COVID-19 pandemic. The scale consisted of 20 items, and students were asked to choose 1 from 4 answers . Nine items were scored in reverse order, and higher scores indicate higher loneliness. The scale divides loneliness into 5 levels: high loneliness (≥45), moderately high loneliness (39–44), moderate loneliness (34–38), moderately low loneliness (28–33), and low loneliness (0–27). The scale with an internal consistency of 0.89–0.94 and a test-retest reliability of 0.73 (2.2.5.The PTSD checklist-civilian version (PCL-C) was used to assess for post-traumatic stress disorder (PTSD) after the COVID-19 pandemic . Student2.3.t-tests or ANOVA tests were used to examine differences in PSS scores among multiple variables. Pearson correlations were calculated to examine associations between scores on psychological scales. A hierarchical multiple regression with 4 consecutive steps was used to detect factors associated with stress. Step 1 includes demographic variables. In step 2, we examined the association of employment attitudes with stress after controlling for demographic variables. In steps 3 and 4, we tested the association of anxiety and loneliness symptoms with stress after controlling for demographic variables and employment attitudes, respectively, PTSD was not included in the regression because it was homogeneous with stress. At each step, a backward procedure was used to remove relevant factors that did not significantly increase the prediction of stress. Before conducting the regression analysis, collinearity tests and Durbin–Watson (DW) tests were incorporated. All analyses were performed using SPSS 22.0 , and a p-value <0.05 was considered statistically significant.Descriptive statistics  were calculated to describe students’ demographics, employment attitudes, and scores on psychometric scales. Two-sided 3.3.1.A total of 620 questionnaires were distributed and 539 were returned, with a recovery rate of 86.9%. Out of the returned questionnaires, 504 were valid, leading to an effective recovery rate of 93.5%. As shown in 3.2.A total of 84.7% of the students indicated that they voluntarily enrolled in this major. Among the employment intentions, most students chose to go to the hospital (37.3%), followed by CDC (23.0%), and only 0.8% were willing to go to primary health institutions. 18.5% hold that the epidemic had a negative impact on employment, and 12.5% had the idea of changing careers, mainly because they were worried about the increase in workload caused by the epidemic (8.1%). 96.6% of the students said that they or those around them had been diagnosed with COVID-19, and 28.8% participated in epidemic prevention and control .3.3.p < 0.05). In contrast, there were no statistically significant differences in family residence, only child, and diagnosis of COVID-19 (p > 0.05)  . Based o > 0.05) .3.4.p < 0.001, p < 0.001), and internship experience  were significantly associated with higher stress levels while being female , majoring in preventive medicine , and higher monthly household income  were negatively related to stress levels. In step 2, after controlling for demographic variables, those who believed that COVID-19 had a negative impact on employment  and participated in epidemic prevention and control  were associated with higher levels of stress. And those who hold that COVID-19 had a positive impact on employment  showed lower stress level associations. In step 3, after controlling for demographic variables and employment attitudes, higher GAD scores  showed a significant correlation with higher stress levels. In step 4, after controlling for all the above variables, the UCLA score detected a significant association with stress .The results of the collinearity test and DW test showed that the tolerance of all variables was greater than 0.1, and the variance inflation factor (VIF) was less than 10, indicating that there was no multicollinearity between variables. The value of DW was 1.6, meaning that the residuals were independent. As shown in 4.This study focused on a detailed investigation of stress levels and related factors among students majoring in public health and preventive medicine in the post-pandemic period. We found that quite a few students experienced various degrees of psychological problems. 20.4% of the students reported anxiety symptoms, higher than the anxiety level of Chinese medical students (regardless of major) . StudentDifferent from other medical majors, students majoring in public health and preventive medicine have more opportunities to directly face the impact of the epidemic in theoretical learning and practice, including the accurate understanding of the harm of the novel coronavirus, the uncertainty of the safety of nucleic acid testing, and the high workload of epidemiological investigation. Some students were even directly involved in local epidemic prevention and control before graduation, thus showing more psychological problems and higher-stress levels. We found that anxiety, loneliness, and PTSD were positively associated with students’ stress levels, which is similar to the findings of Ye et al. . Stress In the employment intentions of the students, 37.3% expressed their willingness to work in a hospital, which was in line with the overall positive attitude of all medical students towards the medical profession , 30. HowThis study found that older age, higher grades, and male students were significantly associated with higher stress levels. Age and grade increased synchronously. The closer to graduation, the more pressure on students would naturally increase. On the one hand, they have to complete the graduation examination from school, and on the other hand, they have to face the pressures of employment and career selection, both of which would be affected by the epidemic. Higher stress levels in male students might be attributable to social expectations that they take on more responsibilities and experience more economic stress , or boysAccording to our findings, students with internship and pandemic prevention experiences were associated with higher levels of stress. Previous studies also agreed that medical interns were more stressed than non-interns . CombineThere were 18.5% of the students believed the epidemic had a negative impact on employment and was significantly associated with higher stress levels. The COVID-19 epidemic undoubtedly provided more employment opportunities for these students, because it was an inevitable trend that countries needed to develop their public health systems in the post-pandemic era. However, in the situation of high morbidity and certain mortality, making everyone feel comfortable working in the industry is a challenge. A qualitative study revealed that overly positive attitudes towards the healthcare industry could be challenged by demanding and potentially risky working conditions close to reality . AlthougThere were three shortcomings in this study. First, the design of the cross-sectional study made it impossible to establish a causal relationship. Second, sampling and response biases may persist because of low responses due to Internet connectivity and limitations in self-reported data. Third, the data were obtained at only 2 universities in one province, which has experienced several severe outbreaks. However, the severity of outbreaks varies from region to region, and the results reported by subjects from different regions might be biased, so conclusions beyond the scope of the investigation should be drawn with caution. Expanding the study area, increasing the sample size, and maximizing random sampling are the directions for improving these issues and our future research efforts.5.In conclusion, students majoring in public health and preventive medicine in Changsha, China experienced higher stress levels in the post-pandemic period, which was influenced by age, grade, gender, internship experience, anti-epidemic experience, employment attitude, anxiety symptoms, and loneliness symptoms. Conducting fundamental courses on COVID-19, one-on-one psychological counseling, and inviting successful public health professionals to share their experiences were expected to alleviate the stress. Although the COVID-19 pandemic seemed to be coming to an end, the pandemic has been a wake-up call. Strengthening the attention and intervention of potential public health personnel, that is, students of public health and preventive medicine, is an important prerequisite to defend against any future major public health events.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Biomedical Research Ethics Committee, Hunan Normal University, 2022(332). The patients/participants provided their written informed consent to participate in this study.XW: designed the study and wrote the initial draft. JZ and NY: analyzed the data and interpreted results. PH: contributed content to subsequent drafts. XW, JZ, NY, MZ, and PH critically reviewed and approved the final draft. All authors contributed to the article and approved the submitted version.This study was supported by the Scientific Research Fund of Hunan Provincial Education Department (no. 21B0079), Changsha Natural Science Foundation Project (no. kq2202253), the Key Project of Hunan Provincial Evaluation Committee of Social Science Achievement (no. XSP20ZDI013), Open Project of Hunan Normal University Medical College (KF2022008), and Rehabilitation Project of Hunan Disabled Persons’ Federation (2023XK0216).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. First, the leaf object detection algorithm-based YOLOv8 was introduced to reduce the interference of backgrounds on the second stage leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. Densely connected atrous spatial pyramid pooling (DenseASPP) was used to replace the ASPP module, and the strip pooling (SP) strategy was simultaneously inserted, which enabled the backbone network to effectively capture long distance dependencies. The experimental results show that our proposed method, which combines YOLOv8 and the improved DeepLabv3+, achieves a 90.8% mean intersection over the union (mIoU) value for leaf segmentation on our public leaf dataset. When compared with the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPnet), U-Net, DeepLabv3, and DeepLabv3+, the proposed method improves the mIoU of leaves by 8.2, 8.4, 3.7, 4.6, 4.4, and 2.5 percentage points, respectively. Experimental results show that the performance of our method is significantly improved compared with the classical segmentation methods. The proposed method can thus effectively support the development of smart agroforestry. Image segmentation is a key aspect of image analysis and understanding in the field of computer vision. Plant leaf image segmentation is important for obtaining biomass characteristics . The segTraditional plant leaf segmentation methods mainly include the following: edge detection technique, threshold technique, region-based methods, clustering methods, and watershed methods. The edge detection techniques include the Sobel, Canny, Laplacian, and Fuzzy logic techniques . These mThe high precision and migration ability of deep learning techniques, such as FCN , U-Net , PSPNet https://ytt917251944.github.io/dataset_jekyll (accessed on 24 November 2022).Plant leaf object detection and image segmentation datasets were constructed. The original images from a public leaf segmentation dataset  were annTo reduce the interference of the background in leaf segmentation tasks, an object detection algorithm based on YOLOv8 was proposed. The experimental results show that leaf object detection results significantly impact the second stage leaf segmentation task.An improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. In this paper, DenseASPP was used to replace the ASPP module, and the SP strategy was simultaneously introduced, enabling the backbone network to effectively capture long-distance dependencies.This new model, combined with the proposed YOLOv8 and improved DeepLabv3+, was designed to accurately segment individual leaves in various states, such as regular, curled or withered, and dorsal leaves, making the model more feasible for leaf characteristics collected in natural environments.Aiming to address existing problems, we chose to use a previously published public dataset that includes 9763 images of 39 plant species, including indoor and outdoor scenes. Our data amounts and number of species far exceed those of the previous related studies. The YOLOv8 model was proposed for extracting regions of interest and reducing the impact of the background on target segmentation. Then, an improved DeepLabv3+  model waThe experimental setup was as follows: Intel Core i7-12700KF CPU, 64 GB RAM, Nvidia GeForce RTX3090Ti GPU, 10 GB of video memory, CUDA Toolkit 9.0, CUDNN V11.7., Python 3.7.12, and the Ubuntu 22.04 operating system.N is the number of plant leaf species. iP (ir) is the precision at recall ir.To quantitatively evaluate the performance of the proposed method and the compared methods, three metrics were used as the object detection evaluation criteria: precision, recall, and mean average precision (mAP). The metrics were calculated as follows (Equation (1)):Two metrics were used as the segmentation evaluation criteria: mean intersection over union (mIoU) and mean pixel accuracy (mPA). The metrics were calculated as follows (Equation (2)):The YOLOv8 series consists of five models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. For leaf image object detection, the hyperparameters were set as epoch 150, batch size 32, initial learning rate 0.01, momentum 0.937, and weight attenuation coefficient 0.0005. Then, the trained model was evaluated by the validation and test sets. val50 and mAPval50–95 values of YOLOv8m are much higher than those of YOLOv8n and YOLOv8s, and the index values of YOLOv8m are comparable to those of YOLOv8l and YOLOv8x; In this paper, an approach for plant leaf image segmentation based on YOLOv8 and improved DeepLabv3+ was proposed. YOLOv8 was first used to carry out object detection on our leaf dataset, and the detection results were then input into the improved DeepLabv3+ model for segmentation. To better verify the segmentation performance of our proposed DeepLabv3+ (DenseASPP + SP) method, we conducted DeepLabv3+ (ASPP), DeepLabv3+ (DenseASPP), and DeepLabv3+ (SP) experiments. For the segmentation task, the hyperparameters were set as epoch 200, batch size 8, initial learning rate 0.01, momentum 0.9, and weight attenuation coefficient 0.0001. Then, the trained model was evaluated on the validation and test datasets. To further validate the segmentation performance of the proposed method on our leaf dataset, the proposed method was compared with typical models, such as FCN, LR-ASPP, U-Net, and DeepLabv3. The specific comparison results are shown in The results of different segmentation models are shown in Furthermore, introducing DenseASPP or SP networks cannot improve the segmentation performance of DeepLabv3+, but combining DenseASPP and SP networks can do so. Our DeepLabv3+ (DenseASPP + SP) model based on YOLOv8 is 1.8 percentage points higher than the mIoU of the model not based on YOLOv8. The mIoU and mPA values of our model based on YOLOv8 are 2.5 and 1.4 percentage points higher than those of the original DeepLabv3+ (ASPP) model. To some extent, the overall indicator values cannot directly reflect the segmentation results of each species. Therefore, the segmentation results of the proposed method on the validation and test sets are analyzed from the perspective of each species to evaluate the performance of the model.In this subsection, these classical segmentation networks, such as FCN, U-Net, PSPNet, LR-ASPP, and DeepLabv3, are discussed. Then, the network structures of DeepLabv3+ (ASPP), DeepLabv3+ (DenseASPP), DeepLabv3+ (SP), and DeepLabv3+ (DenseASPP + SP) are discussed. Therefore, the effectiveness of our DeepLabv3+ (DenseASPP + SP) is demonstrated by experiment results. The results of the different classical segmentation network structures on the leaf validation set are displayed in Then, the images and leaf labels in the test set were used to calculate the evaluation indicators of various network structures. The results of the different modified structures on the leaf test set are displayed in The example images depicted in Many of the most advanced models require a vast amount of labeled data to obtain strong results. Therefore, collecting sufficient training images is essential. In our previous research, we published an open-source dataset of plant leaf images with indoor and outdoor backgrounds . These pOur dataset was collected in natural environments. We, respectively, captured images of the same plant species on different city streets and under different lighting intensities, object scales, growth stages, and shooting perspectives to increase the diversity of our dataset.We adopted various data augmentation techniques, such as cropping, mirroring, and rotating, to increase the magnitude of our dataset; we then performed image compression processing. A total of 9763 plant leaf images were acquired. The number of different plant species acquired is shown in The leaf images were annotated by a team of 10 professional image annotators. Seven of the annotators performed initial quality assurance, and three verified the annotations. During the object detection task, a rectangular box was used to label the leaf images. For the instance of segmentation task, fine annotations were provided at the pixel level, as shown in The leaf dataset was then divided into training, validation, and test sets at a ratio of 8:1:1. To reduce the impact of image background on leaf segmentation, the YOLOv8 network was proposed for obtaining the regions of interest in the leaf images. On this basis, the improved DeepLabv3+ model was used for leaf image segmentation. The specific leaf object detection and segmentation network is depicted in YOLO is an object detection system that makes predictions based on global image information. Since Joseph Redmon, Ali Farhadi, and others  proposed(1)A new state-of-the-art model is provided. Similar to YOLOv5, different size models of the n/s/m/l/x scale are also provided based on the scaling coefficient to meet the requirements of different scenarios.(2)The backbone network and neck part may refer to the design concept of YOLOv7 ELAN, replacing the C3 structure of YOLOv5 with the C2f structure, which exhibits more abundant gradient flow and adjusts different channel numbers for different scale models, which significantly improves model performance, as shown in (3)Compared with YOLOv5, the head part is changed to the current mainstream decoupling head structure, separating the classification and detection heads and changing the model from anchor-based to anchor-free. The anchor-free model abandons or bypasses the anchor concept, uses a streamlined method to determine positive and negative samples, simultaneously reaches or even exceeds the accuracy of the anchor-based model, and has a faster speed than the anchor-based model.(4)The TaskAlignedAssignor positive sample allocation strategy is adopted for loss function calculation. The classification loss is the VariFocal Loss (VFL), and the regression loss is the Complete-IoU (CIoU) Loss + Distribution Focal Loss (DFL).YOLOv8 builds on previous YOLO releases, introducing new features and improvements to further enhance performance and flexibility. Its core features and modifications can be summarized as follows:s is the prediction score corresponding to the annotated category. u is the IoU of the prediction box and the ground-truth box. μ and β are the weight hyperparameters. When μ and β are multiplied together, the degree of alignment can be measured.The TaskAlignedAssignor uses the matching strategy of selecting positive samples based on weighted classification and regression scores. This is calculated as follows (Equation (3)):VFL , and q is the label. When the sample is positive, q is the IoU between the predicted and ground-truth values. When the sample is negative, q is equal to 0.VFL  primarilCIoU, which is based on IoU, considers the constraints of the central point distance and aspect ratio. CIoU is calculated as follows (Equation (5)):b and gtb represent the center points of the prediction box and ground-truth box, respectively. c represents the diagonal distance of the smallest closed area that can contain the prediction box and the ground-truth box simultaneously. α and v comprise the impact factor. gtw and gth are the width and height of the ground-truth frame, and pw and ph are the width and height of the prediction frame, respectively.DFL  represents the cascading concatenation operation.  represents the feature generated after concatenating the output of all layers before the current layer. ly is the feature map received by the current layer.Each atrous layer in DenseASPP can be represented as follows (Equation (8)):Thus, DenseASPP needs to connect only four convolution layers with different dilation rates ,6,12,18,d are [ASPPR is (Equation (9)):Assuming that the dilation rates d are ,12,18,24DenseASPPR is (Equation (10)):The maximum receptive field Additionally, to more effectively capture bar leaves and slender petioles in the leaf dataset, the SP  strategyx, the size is H × W, the pooling window is  or  and the pooling space range is h × w. Unlike 2D average pooling, SP averages all feature values in a row or column. The horizontal h and vertical v pooling expressions are (Equation (11)):The following is assumed: The 2D tensor is C × H × W, and C is the number of input channels. The tensor x is first input into two parallel paths, each of which contains a horizontal  or vertical  strip pooling layer. The 1D convolution layer of K = 3 is then used to expand the two output feature maps, ensuring that the dimensions of the feature maps of the two branches after expansion are consistent. Next, the corresponding positions of the two feature maps are summed, as shown in Suppose the input size is Scale  represents the bitwise multiplication of elements, σ represents the sigmoid function, and f represents the 1 × 1 convolution.The final result is obtained by point multiplication of the sigmoid function and the original feature map. This is calculated as follows (Equation (13)):In this work, the leaf object detection algorithm-based YOLOv8m was used to reduce the interference of background in the leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. DenseASPP was used to replace the ASPP module, and the SP strategy was simultaneously introduced, enabling the backbone network to effectively capture long-distance dependencies. Combined with the proposed YOLOv8 and improved DeepLabv3+, the new model was designed to effectively reduce the influence of complex backgrounds and significantly improve the accuracy of the segmentation results. The experimental results show that the proposed method achieves 90.8% mIoU on the validation set and 88.1% mIoU on the test set. Our approach exhibits significantly higher accuracy than other models. Therefore, the proposed method achieves the accurate segmentation of single plant leaves in different states, providing support for calculating leaf area, identifying plant species, and obtaining information about plant diseases and insect pests. In subsequent studies, the model will be optimized for spotted leaf segmentation to make it more suitable for practical applications.


Objective: The objective of this study was to explore the level of knowledge and practice of insulin therapy among patients with type 2 diabetes in Saudi Arabia.Materials and methods: In this cross-sectional study, 400 pretested structured questionnaires were administered through an interview with patients in a primary health care center. Responses from 324 participants (81% response rate) were analyzed. The questionnaire comprised three main sections: sociodemographic data, a knowledge assessment, and a practice assessment. The total knowledge score was calculated out of 10: 7-10 was excellent, 5.5-6.9 was satisfactory, and less than 5.5 was poor.Result: Approximately 57% of the participants were ≤ 59 years old, and 56.3% were females. The mean knowledge score was 6.5 (+/-1.6). Participants showed an overall good practice, with 92.5 rotating the site of injection, 83.3% sterilizing the site, and 95.7% taking insulin regularly. The knowledge level was influenced effectively by gender, marital status, educational level, job, frequency of follow-up, having visited a diabetic educator, duration of insulin therapy, and experiencing a hypoglycemic event . Knowledge was revealed to significantly influence self-insulin administration, meal-skipping after taking insulin, use of home glucose monitoring, keeping snacks nearby, and taking insulin in relation to meals . In some of the practice parameters, patients with high knowledge scores had better practice.Conclusion: Knowledge of patients with type 2 diabetes mellitus was satisfactory, with significant differences in knowledge according to gender, marital status, educational level, occupation, duration of diabetes, frequency of follow-up, visiting a diabetic educator, and having an experience of the hypoglycemic episode. Participants showed overall good practice, with better practice being associated with a higher knowledge score. Type 2 diabetes mellitus (DM) results in the human body's inefficient use of insulin. Out of more than 300 million individuals with DM worldwide, more than 90% have type 2 DM ,2. In thInsulin is one of the cornerstones of DM treatment. It is vital for patients with type 1 DM and is sometimes required in patients with type 2 DM ,8. InsufFew studies conducted in Saudi Arabia have investigated prevalence, complications, and patients with DM's adherence to their medication regimens -19. A suFrom 2020 to 2040, a two-fold increase in type 2 DM is expected due to obesity, life expectancy, and the ethnic populations most at risk . An incrThis cross-sectional study was conducted in three major primary healthcare centers at the National Guard Health Affairs (NGHA). This complex includes the Health Care Specialty Center, King Abdul Aziz City Housing, and National Guard Comprehensive Specialized Clinics. The final sample comprised males and females recruited from the aforementioned locations; they consented to participate, and adult patients with type 2 DM. The further inclusion criteria were on insulin therapy, with or without other hypoglycemic agents, and regularly scheduled follow-ups and regular treatment. The exclusion criteria were individuals who refused to fill in the questionnaire or consent or those unable to present at the health centers for regular follow-ups.A previously used questionnaire was translated into the Arabic language by two native speakers in English and Arabic . The surThe sample size ,24 calcuKnowledge was scored based on the appropriate response provided by patients with DM to insulin therapy, while practice demonstrated the knowledge of the study population through action. Data were entered by using IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp. Descriptive statistics were performed in frequencies and percentages for categorical variables, and mean, and standard deviation (SD) were used for continuous variables. For analytical statistics, the chi-square test (χ2) was used to assess the differences between categorical variables, while one-way ANOVA and t-tests were performed to test differences in continuous variables.This study's ethical clearance was obtained from the institutional board review (IRB) at King Abdalla International Medical Research Center on April 23, 2017, study number RC17/065/R. Participants were informed of the objectives of the study, and their acceptance was recorded.This study was conducted in primary health settings, where the patients with type 2 DM received their routine care according to the health system and care for chronic disease guidelines in Saudi Arabia. The essential findings are regarding the knowledge and practice of the use of insulin. The analyzed sample size was 324 (81% response rate) participants of the 400 participants in the total sample. All the participants were patients with type-2 DM who were using insulin. Their knowledge and practice of insulin use were assessed. The results showed that the mean of the total knowledge score was 6.5 (+/-1.6). The mean knowledge score was higher in male than in female participants and for participants who had the following characteristics: married, educational level, visited a diabetic educator, a long DM duration (>10 years), regular follow-ups of their DM with their primary physician, duration of insulin therapy, and experienced hypoglycemia. Notably, these factors affected the level of knowledge significantly  among the studied population. By contrast, the knowledge score did not differ significantly concerning the age of the participants, being on insulin only or insulin and other hypoglycemic agents, and insulin treatment duration . The knowledge significantly influenced the practice properly in regards to self-insulin administration, meal-skipping after taking insulin, use of home glucose monitoring, keeping snacks nearby, and the frequency of taking insulin in relation to meals , stored insulin in a refrigerator , sterilized the site of injection , kept snacks in case of need and monitored glucose level regularly , and regularly checked blood glucose level at home . These measures were mostly practiced by the participants who had higher knowledge scores, but some participants practiced despite their lower knowledge scores.The study population assessed their knowledge related to the use of insulin therapy for type 2 DM. Most participants responded to the question on the multiple types of insulin  correctly, and 137 (42.3%) knew multiple types of insulin delivery devices. Approximately, 233 (71.9%) knew that insulin should not be stopped once the blood sugar level normalizes and that once the insulin started, diet and exercise never became less important, which was correctly answered by 256/324 (79%). Duration of low blood glucose reaction in rapid-acting insulin was known by 152 (46.9%), realizing the right action if just before lunch, and forgot to take insulin at breakfast by 76 (23.5). The other notable responses were as follows: if you take your morning insulin but skip breakfast, your blood glucose level status in the case of taking morning insulin but skipping breakfast , signs of hypoglycemia , causes of low blood glucose , and what to do in case of low blood glucose ; (Table In this study, we explored the knowledge and practice of insulin therapy usage among patients with type 2 DM who attended primary health care centers at NGHA, Riyadh, Saudi Arabia. The study revealed that almost half of the study group either often or sometimes skipped a meal after insulin intake. The importance of this finding is that insulin injection synchronizes with mealtime should be highlighted. More patients in our study were skipping insulin doses or meals after taking insulin than those in studies outside Saudi Arabia ,13. The More than one-fifth of our study subjects were not self-injecting their insulin, which is markedly increased compared to what is reported in a large-scale Indian study . The abdThe overall knowledge of this study’s participants was average, and very few participants scored above the average score. Gender, educational level, occupation, marital status, DM duration, frequency of follow-up, and visiting a diabetic educator were significantly associated with knowledge of insulin use, as lower mean knowledge scores were noted among females, uneducated subjects, homemakers, those with DM for 10 years or less, with insulin therapy for five years or less, having followed up monthly, and not seeing diabetic educator. The same findings were reported by a study conducted in Nepal that assessed DM knowledge in general . SimilarAlthough the sample size was adequate for the evaluation, this study's participants were recruited from primary care centers only. Therefore, the findings may not be generalized to the population with DM in the region. Additionally, cross-sectional studies provide a limited amount of strong evidence because the results are based on self-reporting.Knowledge of patients with type 2 diabetes mellitus was satisfactory, with significant differences in knowledge according to gender, marital status, educational level, occupation, duration of diabetes, frequency of follow-up, visiting a diabetic educator, and having an experience of the hypoglycemic episode. Participants showed overall good practice, with better practice being associated with a higher knowledge score. The study revealed that in some practice parameters, patients with high knowledge scores had better practices, which indicates the need for educational programs to improve the awareness of the practicing knowledge of insulin therapy. There remains a gap in the knowledge and practice that should be filled with the appropriate counseling and additional patient-centered education. Comprehensive insulin usage educational programs that focus on empowering insulin use among patients with DM and provide DM education and related information as essential elements of DM management programs should be widely available.


Background and Objectives: Supraglottic airway devices (SADs) are known to be useful in eliminating the drawbacks of laryngoscopy and tracheal intubation, especially ocular pressure and stress responses. The ultrasonographic measurement of optic nerve sheath diameter (ONSD) reflects increases in intracranial pressure (ICP). In our study, we aimed to compare the effects of SADs on hemodynamic response and ONSD. Materials and Methods: Our prospective study included 90 ASA I–II patients over the age of 18 who did not have a history of difficult intubation or ophthalmic pathology. The patients were randomly divided into three groups based on the laryngeal mask airway (LMA) devices used: ProSeal LMA , LMA Supreme , and I-gel (n = 30). The bilateral ONSD measurements and hemodynamic data of the patients who underwent standard anesthesia induction and monitoring were recorded before induction (T0) and 1 min (T1), 5 min (T5), and 10 min (T10) after SAD placement. Results: At all measurement times, the hemodynamic responses and ONSD values of the groups were similar. In all three groups, intergroup hemodynamic changes at T0 and T1 were similar and higher than those at other times of measurement (p < 0.001). The ONSD values of all groups increased at T1, and they tended to return to baseline values afterward (p < 0.001). Conclusions: We concluded that all three SADs could be used safely because they preserved both hemodynamic stability and ONSD changes in their placement processes, and they did not cause elevations in ONSD to an extent that would lead to increased ICP. The stimulation of the supraglottic region during laryngoscopy and intubation is known to cause a short-lasting (approximately 5 min) elevation in heart rate (HR), blood pressure, intraocular pressure (IOP), and intracranial pressure (ICP) by leading to an increase in plasma catecholamine concentrations through the activation of the sympathoadrenal system ,2. Supra® AuraGainTM-Ambu, Ballerup, Denmark), which was developed in 2007, is a modified single-use form of the pLMA. Its preformed bent shaft has a double lumen, including a central lumen for access to the digestive tract sheathed by a flat, oval-shaped airway lumen for reaching the respiratory tract [The ProSeal laryngeal mask airway (pLMA) , which was developed by modifying the classic LMA (cLMA) in the 2000s, is a reusable SAD. It includes a hard bite block on the occlusal level and a pilot entry to allow placement using a finger or an “introducer”, which is a metal rod that provides ease of placement. Its placement may require a bit more experience than the cLMA does . The Supry tract . The I-gry tract ,4,5,6,7.The optic nerve is part of the central nervous system and is surrounded by cerebrospinal fluid (CSF). With increased ICP, the subarachnoid space, and especially the retrobulbar segment, is affected by the same pressure, which leads to an increase in sheath diameter. This diameter change allows for the indirect measurement of ICP on the optic nerve sheath by transocular ultrasonography (USG) ,9. In a Increases in ICP occur due to various causes in anesthesia and surgical procedures ,19,20,21After receiving permission from Zonguldak Bülent Ecevit University Clinical Research Ethics Committee , this prospective randomized study was conducted in Zonguldak Bülent Ecevit University Hospital, Turkey, between July 2019 and 2020. Informed written consent was obtained from all patients. The flow diagram according to CONSORT guidelines is provided as Ninety patients (aged 18–65 years) in the American Society of Anesthesiologists (ASA) class I–II who were scheduled for elective non-ophthalmic procedures that would last 1–2 h under general anesthesia in the supine position were included in this study. The sample of the study excluded patients with Mallampati and those in ASA class ≥III, and those with a history or suspicion of a difficult airway, more than three SAD placements, past intracranial/ocular surgery, diabetic neuropathy, cerebral edema or elevated ICP, glaucoma, potentially full stomachs, uncontrolled hypertension, obstetric conditions, and a lack of agreement to participate in the study. The demographic data, Mallampati scores, and ASA classes of all patients were recorded.2) measurement, and bispectral index  measurement devices. After the patients were preoxygenated with 100% O2 for 3 min, 1.5 mcg/kg fentanyl citrate , 1.5 mg/kg lidocaine , and 2–2.5 mg/kg propofol  were intravenously (iv) administered. No neuromuscular agent was administered. Until the appropriate depth of anesthesia  for device insertion was achieved, an additional propofol bolus (0.5 mg/kg) was planned.All patients were instructed to fast for at least 8 h before surgery. No premedication was given. When the patient was in the anesthesia room, their head was placed on a soft, 7 cm high pillow before the induction of anesthesia, with their neck flexed and head extended. The patients were connected to electrocardiography, noninvasive blood pressure measurement, peripheral pulse oximeter, end-tidal carbon dioxide (EtCOn = 30), Group S , or Group I , using the sealed envelope technique. The size of the SAD was selected based on the patient’s weight, as recommended by the manufacturer. For the sLMA and pLMA groups, the patients weighing 30–50 kg, 50–70 kg, and >70 kg underwent their procedures with sizes 3, 4, and 5, respectively. For the I-gel group, the recommendations included the use of sizes 3, 4, and 5 for weights of 30–60 kg, 50–90 kg, and >90 kg, respectively. When the BIS values of the patients who were being ventilated through a facemask reached the range of 40–60, the suitable SAD was placed by the same anesthesiologist doctor. The pLMA and sLMA were completely deflated before insertion. The patient’s head was placed in a sniffing position. After the occurrence of post-induction apnea, the chosen SADs were lubricated and inserted based on the instructions of their manufacturers. The sLMA and the I-gel were inserted using the rotational technique, whereas the pLMA was inserted with the index finger. The cuffs of the pLMA and sLMA were inflated to the recommended volumes, and checks were undertaken for the presence of an air leak sound coming from inside the mouth. After SAD insertion, the observation of fluctuations in capnography, bilateral chest movements, and stridor absence were determined as criteria for successful insertion. In the case of insufficient ventilation after two attempts, insertion was accepted as unsuccessful, the practitioner was free to decide on the use of an alternative airway device, and these patients were excluded from the study. The duration of SAD insertion (the time between the moment of picking the device to be placed and the moment of observing EtCO2 waves) was recorded. For the maintenance of anesthesia, the respiratory volume was adjusted to 6–8 mL/kg, the respiratory rate was set at 12/min, and controlled mechanical ventilation was provided with a 50/50% O2/Air mixture, as well as 1–2% sevoflurane  and 0.05–0.2 mcg/kg/min remifentanil hydrochloride  infusion.Each patient was randomly assigned to one of three groups, Group P , HR, and ONSD values of the patients were recorded before induction (T0), 1 min after SAD placement (T1), 5 min after SAD placement (T5), and 10 min after SAD placement (T10). The same observer, who did not have any information about the study, recorded all the parameters that were examined in the study.p < 0.05 was taken as the level of statistical significance.The planned sample size required to detect 95.9% test power (1 − β), 95% confidence (1 − α) and effect size d = 1.16 was 10 people per group. We included 30 patients in each group to compensate for patient dropouts [Clinical and Demographic Characteristicsp > 0.05)  .p > 0.05), in the intragroup comparisons, all groups showed significant differences in their HR values measured at different times (p < 0.001)  .p > 0.05), in the intragroup comparisons, all groups showed significant differences in their MAP value measurements (p < 0.001)  .p > 0.05)  . The chaIn our study, when they were inserted into adult patients with normal airways, the effects of the pLMA, sLMA, and I-gel on hemodynamic responses and ONSD values were similar. In all three groups, the HR and MAP values measured 1 min after SAD insertion were similar to each other, and in the intragroup comparisons, these values were higher than those measured at the other measurement times. In the intragroup comparisons, it was also observed that ONSD values had a tendency to increase 1 min after SAD insertion in all three groups and return to baseline values afterward.In recent years, noninvasive ICP monitoring methods have become more popular. This is because they have fewer complications compared with invasive methods, are cost-effective, and reduce the need for certain factors, such as expertise in neurosurgery, that require some invasive measurement methods. Ideally, a noninvasive ICP assessment method must be accurate, reliable, pathology-independent, and capable of working in a heterogeneous patient population; use instantly available equipment; and be robust in relation to methodical variations, such as the experience of the operator .The ONSD measurement is subject to error and variation because it is observer dependent. Nevertheless, as the measurement methodology is well-standardized, and the results are repeatable, the variability in ONSD measurement can be minimized ,27. AlthIn recent years, ONSD measurements have also been included in anesthesiology practices ,19,20,21Supraglottic airway devices are easy to use and atraumatic, and they create minimal somatic and autonomic responses. HR and MAP values increase during insertion and removal, but these differences are lower compared with those in tracheal intubation and extubation ,31. In aFor the immobilization of patients, their hemodynamic stability, and the early diagnosis and treatment of neurological deficits in endovascular treatments (EVT), practitioners have started to use SADs in anesthesia management. It has been stated that these devices, which are used in EVT to prevent hemodynamic instabilities associated with intubation and extubation, can be a safe alternative in anesthesia management, and there is no complication associated with their usage ,35,36. AThe literature on SADs is vast, and varying results have been reported on their insertion success rates and times. The main factors that could have affected these results in the methodologies of previous studies have been reported as the experience levels of practitioners and differences in the use of muscle relaxants ,33,37,38There were some limitations to this study. One of the most important was that it was not technically possible for the person who recorded the data to be unaware of the airway device being used. Furthermore, as patients with normal airways were included in this study, the results may not be applicable to patients with difficult airways or extended LMA insertion durations. Third, this study was conducted with patients who had low ASA scores, and studies involving those with underlying cardiovascular diseases may provide different results. Another limitation is that we did not measure the ONSD optic nerve sheath diameter just before and after SAD insertion. Finally, the effects of SADs on ICP among patients with intracranial pathologies were not examined. Different results in terms of ONSD may be observed in patients who have a history of elevated ICP or cerebral ischemia.We concluded that all three SADs could be used safely because they preserved both hemodynamic stability and ONSD changes in their placement processes, and they did not cause elevations in ONSD to an extent that would lead to increased ICP.


PIM1 gene, which encodes a protooncogene that has been garnering interest as a potential therapeutic target for PAH, that were associated with PAH with genome wide significance, one (rs192449585) of which lies in the promoter region of the gene. We also identified 15 linked variants in the LINC01491 gene. These results provide genetic evidence supporting the role of PIM1 inhibitors as a potential therapeutic option for PAH.Pulmonary arterial hypertension (PAH) is characterized by remodeling and narrowing of the pulmonary vasculature which results in elevations of pulmonary arterial pressures. Here, we conducted a genome-wide association study (GWAS) using the UK Biobank, analyzing the genomes of 493 individuals diagnosed with primary pulmonary hypertension, based on ICD-10 coding, compared to 24,650 age, sex, and ancestry-matched controls in a 1:50 case-control design. Genetic variants were analyzed by Plink’s firth logistic regression and assessed for association with primary pulmonary hypertension. We identified three linked variants in the  BMPR2, a gene encoding bone morphogenetic protein receptor type 2, account for ~80% of patients with hereditary PAH and ~20% of sporadic cases  describes a heterogeneous group of systemic, heart, and lung disorders characterized by elevations in pulmonary arterial pressures. Pulmonary arterial hypertension (PAH) represents a subgroup of PH, characterized by remodeling and narrowing of the pulmonary vasculature. Idiopathic pulmonary arterial hypertension (IPAH), which was previously coined primary pulmonary hypertension (PPH), describes PAH when no alternative etiology can be identified. Much of our understanding of the pathogenesis of PAH is shaped by the seminal human genetic studies which revealed that mutations in ic cases . Subsequ pathway  as well y -based GWAS for a variety of cardiovascular diseases . Here, uThe present study, which involved deidentified data obtained from the UK Biobank Resource under Application Number 49852, received the proper ethical oversight, including the determination by the University of Maryland, Baltimore Institutional Review Board that the study is not human research (IRB #: HF-00088022).This study utilized data from the UK Biobank, a large ongoing prospective cohort study involving 506,682 participants between 2006 and 2010. Participants were aged 40–69 at the time of recruitment. Our study population was identified using the ICD-10 diagnostic code for primary pulmonary hypertension (I27.0) as either the participant’s main diagnosis  or secondary diagnosis code. This ICD-10 diagnostic code encompasses (1) IPAH, (2) hereditary PAH, (3) primary PAH, and (4) primary group 1 PH and excludes PAH due to secondary causes. This I27.0 code is most similar, but not identical, to Group 1 pulmonary arterial hypertension (PAH) based on the newer World Health Organization (WHO) grouping . Given thttps://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=22018; 1532 subjects), sex chromosome aneuploidy , mismatch between self-reported sex and genetically determined sex , recommended genomic analysis exclusions , and outliers for heterozygosity or missing rate .The UK Biobank contains genetic data with over 820,000 genotyped SNPs and up to 90 million imputed variants available. Using data from the UK Biobank Resource on 487,310 subjects with imputed genotypes, we performed quality control by removing those with genetic relatedness exclusions .Additionally, we chose to limit subject selection to the UK Biobank’s genetic ethnic grouping described as “White British” with very similar genetic ancestry based on principal components analysis and made available as part of the UK Biobank Resource  to a max of 7 years. For ancestry, principal components (PCs) supplied by the UK Biobank were used. We assessed the similarity in ancestry by calculating the mathematical distance in a graph plotting the PC1 × PC2. The tolerance for ancestry ranged from a calculated distance of 2 PC units to a maximum of 80 PC units, with PC1 ranging from 0 to +400 and PC2 ranging from −300 to +100.To perform this GWAS, Plink’s firth logistic regression was used, adjusting for age, sex, and 5 PCs using data supplied by the UK Biobank Resource. Firth regression was used for analysis as it has been shown to yield the best combination of control for type I error and power for detecting low-frequency variants . The PAHA minor allele frequency (MAF) of at least 0.5% and a significance value of < 5E-08 was used to identify variants of interest.t-test.The UK Biobank also contains participant baseline clinical information, including comorbidities, height, weight, body mass index, and basic laboratory values. The ICD-10 code diagnoses, ABO blood type, 5 biometric markers, and 61 biomarkers in the identified PAH cases and the matched controls were examined. The significance of these conditions and variables with PAH patients in comparison to age, sex, and ancestry-matched controls were determined by two-sample A total of 493  individuals were identified to have a diagnosis of PAH from a total of 472,124 participants, representing a prevalence of 1 in 1,000. Among these individuals, the mean (SD) age of diagnosis was 63.4 (8.21) years. Of the 490 cases with available BMI data, 355 (72%) had a BMI of 25 and above.PIM1 gene, which encodes the Pim-1serine/threonine-protein kinase, which is involved in cytokine signaling, cell cycle progression and oncogenesis (p = 4.78E-09), located in the first amino acid of the exonic region of the PIM1 isoform 1 gene in a region of high density Dnase I hypersensitivity signals and predicted transcription start site for several tissues , located in the intergenic region −6,549 bases from the PIM1 gene. The third variant was rs187386578 , located in the intergenic region 56,310 bases from the PIM1 gene. The three variants were found to be linked in our linkage disequilibrium analysis. A locus zoom plot of rs183926708, the top variant, can be found in Our GWAS of variants with MAF of at least 0.5% identified a total of 24 variants in 7 distinct loci . We idenogenesis . One var tissues . Further and TBP , and altnd SIN3A . Additiond SIN3A . An eigend SIN3A . InteresLINC01491. All of the identified variants lie in the intergenic region of LINC01491. The top variant within this locus was rs145733648 . The prevalence of PAH in heterozygotes with the rs145733648 variant was 295 per 100,000, compared to 98 per 100,000 in the wild types  are provided in In addition, 6 other variants at 5 other loci with MAF over 0.5% were found to be associated with PAH. Information regarding these can be found in p < 0.0001), chest pain , syncope and collapse , all of which are common symptoms of PAH. PAH cases were also more likely to be diagnosed with congestive heart failure , chronic kidney disease , and atrial fibrillation/flutter . Additionally, PAH cases were more likely to report a family history of heart disease  than controls.The cases of PAH were more likely to be diagnosed with dyspnea , chronic obstructive pulmonary disease , and pulmonary embolism . Additionally, PAH cases were also more likely to have a co-diagnosis of hypothyroidism , iron deficiency anemia , sleep apnea , non-insulin-dependent diabetes mellitus , pure hypercholesterolemia , and essential hypertension . A full list of co-diagnoses can be found in Interestingly, individuals with a diagnosis of PAH were also more likely to be diagnosed with left ventricular failure  than controls. Individuals with PAH had a higher mean serum urea , creatinine , uric acid  and cystatin C  levels, consistent with the higher rate of diagnosis of chronic kidney disease in that population when compared to controls. While individuals with PAH were more likely to be diagnosed with hypercholesterolemia, they had a lower mean cholesterol  compared to the control population. Consistent with findings regarding diabetes mellitus type 2, the PAH cohort had a higher HbA1c  and fasting glucose level  when compared to the control cohort. These results are shown in On average, PAH cases had higher BMIs  is particularly noteworthy because, while it does not alter the resulting amino acid sequence, it lies in the promoter region of the gene and overlaps with the binding sites of transcription factors such as RNA Polymerase as well as regulatory motifs such as TATA, suggesting it may impact PIM1 expression. This is supported by the variant’s high eigenPC score of 5.046, an aggregate predictive score for functional importance, which helps to prioritize likely causal variants in a given genomic region  protein family, to activate transcription factor nuclear factor of activated T cells (NFAT), which in turn promotes the pro-proliferative and anti-apoptotic phenotype of PAH  recruitment to damaged DNA sites, effectively inhibiting DNA non-homologous end-joining, which allows for increased apoptosis in PAH pulmonary artery smooth muscle cells. In the same study, they found PIM1 inhibition resulted in improved vascular remodeling and pulmonary hemodynamics in mice models  1491. While LINC01491 has not been well characterized in the literature, other lncRNAs have been found to play a role in the pathogenesis of PAH (LINC01491 may be warranted to confirm its role in PAH pathogenesis and explore its utility as a biomarker or therapeutic target.In addition to our findings regarding s of PAH , with lns of PAH . StudiesOur study also provides information corroborating our current understanding of PAH and associated comorbidities. For example, recent studies have found that iron deficiency is correlated with PAH disease severity, with a prevalence ranging from 30% to 65% in patients with IPAH . We founThere are several limitations in this study. First, the identification of genetic variants in the GWAS does not prove functional causality, and further studies should be done to confirm these findings. Additionally, these findings should be validated in a patient population with a more diverse genetic origin. The use of ICD-10 codes to select cases is also a limitation as many diseases may be misdiagnosed in the real world. In particular, PAH is often misdiagnosed, which is one reason referral to specialty centers is recommended. ICD-10 based studies offer an opportunity to study a wide array of diseases and medical conditions, especially rare ones like PAH, in a large, “real world” setting. The prevalence of PAH in the UK Biobank is much higher than the previously reported prevalence of 10–15 cases per million people , reflectPIM1 inhibitors like TP-3654, which was originally developed for advanced solid tumors (PIM1 detection in pulmonary vasculature or elevated plasma PIM1 levels.Our study also found markedly higher rates of chronic and acute renal failure, atherosclerotic heart disease, essential hypertension, and peripheral vascular disease as well as higher mean CRP levels in PAH patients. Given that these co-morbid conditions can be considered vascular diseases, one wonders whether PAH involves a more general vascular pathology. Regardless of these limitations and confounding factors, our findings identify novel genetic variants associated with PAH and provide further support for the notion of repositioning d tumors , for thePIM1 gene in our GWAS of the UK Biobank, further suggesting the role of PIM1 in the pathogenesis of PAH in humans and providing key human genetic evidence in support of the potential of PIM1 inhibitors as a therapeutic option for PAH. We also identified 15 linked variants in the LINC01491 gene, contributing to the growing literature of lncRNAs contributing to the pathogenesis of PAH.Using ICD-10 diagnostic codes for PAH, we identified 3 linked variants in the Data Sheet 1Data Sheet 3Data Sheet 2


Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models  because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI). Colorectal cancer (CRC) is a type of cancer that affects the large intestine, commonly called the colon. It begins when cells in the colon have mutations in their DNA, causing them to grow and divide uncontrollably. If left untreated, these abnormal cells can become tumors that invade neighboring tissues or spread to other body parts . ColorecIn 2019, 142,462 instances of colon and rectal cancer were reported, with 75,581 males and 66,881 females affected in the United States [Deep learning has made essential contributions to the healthcare field by allowing for the development of powerful algorithms capable of analyzing medical data, making predictions, and assisting in various medical tasks such as medical imaging analysis and illness detection ,8,9,10. A convolutional neural network (CNN) is a form of deep learning (DL) algorithm frequently utilized for text mining , as wellEnsemble learning is a powerful machine learning (ML)  techniquA stacking model is developed based on integration of the output of pretrained base models  with a meta-learning (SVM) model to enhance performance;Stacking-SVM models are compared with VGG16, InceptionV3, DenseNet121, and ResNet50 using various evaluation methods and two image databases;Stacking-SVM achieves the best results compared to other models;Black-box deep learning models are represented using explainable AI (XAI).The main contributions of this paper are summarized as follows:The rest of this paper is organized as follows. In previous studies, a wide range of ML approaches have been proposed for the analysis of CRC. Most works have used k-means, KNN, and SVM ,31,32.CNN has been used successfully to classify colon cancer in recent years. CNNs can extract relevant features from medical imaging data and apply them to the classification task, allowing for a more accurate disease diagnosis. By utilizing CNNs, it is possible to create a DL model that can accurately distinguish between benign and malignant tumors with high accuracy rates. Furthermore, by leveraging transfer learning techniques, such as by fine tuning pretrained models on large datasets of labeled images, researchers have achieved even higher performance levels when classifying colon cancer using CNNs . For exaThe authors of  used theIn , the autThis section presents the proposed strategy for detecting colon cancer using histopathology images, as shown in We used two colon image datasets for our experiments.We used a dataset known as LC25000, which contains histopathological images of colon cancer . There aThe WCE colon image dataset collected from Bernal from the Universitat Autonoma de Barcelona  includesData augmentation techniques transform an image by mapping the points in the image in a different location or manipulating its intensity levels. As a result of this operation, the existing dataset is modified and subsequently added back into the data pool, increasing the dataset’s size. These techniques improve the trained model’s performance ,48. We aWe added three layers before the output layer in each pretrained CNN, a flattening layer, and two fully connected layers. The flattening layer converts the output of the convolution layer into a 1D layer that is used as input to the fully connected layers. The final layer is the output layer, which uses a sigmoid activation function for binary classification and a softmax activation function for multiclassification. Detailed descriptions of the pretrained CNN models are provided for each classifier.VGG16 is one of the first CNN models to achieve high accuracy on the ImageNet dataset, which contains over one million images divided into 1000 categories. VGG16 is made up of 16 layers . Convolutional layers are organized into blocks, each with a predetermined number of layers  .InceptionV3 is an image categorization architecture based on CNN. InceptionV3 is made up of several convolutional layers, pooling layers, and fully connected layers. InceptionV3 includes a stack of convolutional layers, a global average pooling layer, numerous fully connected layers, and a Softmax output layer .Resnet50 comprises 50 convolutional layers and includes residual connections with shortcuts that help the model better manage the vanishing gradient problem and effectively train deeper architectures. The architecture is divided into stages, each containing a sequence of convolutional blocks and identity blocks. Each convolutional block contains three convolution layers, whereas each identity block only has one. The ResNet50 architecture’s last layer is a fully connected layer that performs classification .DenseNet121 is a CNN architecture consisting of four layers: the input layer, transition layer, dense block, and output layer. The input layer receives an image or data as input. The transition layers consist of multiple convolutional operations, which reduce the size of feature maps before entering densely connected blocks. Each dense block comprises several sets of batch normalization followed by Relu activation and then a series of 3×3 Conv2d with the same padding to preserve spatial resolution between two consecutive stages in the network, which helps to achieve faster convergence when training models on large datasets .The pretrained models  are trained and saved, then loaded, and all model layers are frozen without the output layers.Training stacking combines the output predictions of the training set for each pretrained model. A metalearner  is trained and optimized using stacking. A grid search is used to optimize SVMs as metalearners.Testing stacking combines the output predictions of each pretrained model. The metalearner (SVM) is then evaluated using accuracy, precision, recall, F1 score, and ROC analysis.The stacking ensemble method is a powerful AI model that combines multiple models to produce better predictive results than any single model. It works by training each base model on the same dataset, then combining their predictions via a metamodel that is used to generate more accurate results than traditional methods. It also allows for greater interpretability of the overall result and provides an avenue for further exploration of potential improvements in performance through hyperparameter optimization techniques . There aAccuracy (ACC), precision (PRE), recall (REC), and F1 score (F1) are the most often-used metrics for classification performance. Equations (1) and (2) illustrate these measures (4).True negative (TN) indicates that an individual is healthy and the test is negative, in contrast to true positive (TP), which indicates that the person is ill and the test is positive. When a test shows positive although the subject is healthy, this is known as a false positive (FP). A false negative (FN) occurs when a person is sick but the test is negativeA confusion matrix (CM) is used to evaluate the performance of models, comprising a table that summarizes an algorithm’s correct and incorrect predictions, with each row representing the actual class and each column representing the anticipated class .Receiver operating characteristic (ROC) and area under the curve (AUC) are performance metrics for classification problems. ROC represents a probability curve, whereas AUC represents the degree of separability. By indicating the degree of separation between classes, the model is able to perform well. Models with higher AUCs predict better .We used different methods to evaluated models:This section describes the experimental setup, as well as the results of CNN models and Stacking-SVM with fixed LR and dynamic LR using two colon image databases.The experiments in this study were implemented using the TensorFlow  library,All CNN models and Stacking-SVM were applied to the LC25000 dataset for binary classification, in which we distinguished as benign and adenocarcinomas. The LC25000 was split into 70% training set and 30% testing set. The PRE, REC, and F1 for each class were registered; CM and ROC curves are displayed.For the benign class, Stacking-SVM recorded the greatest ACC, PRE, REC, and F1 score (100). DenseNet121 recorded the second-highest results. ResNet50 recorded the lowest performance in REC, REC, and F1 score . For the adenocarcinomas class, Stacking-SVM registered the highest PRE, REC, and F1 score (100). DenseNet121 had the second-highest results. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score .In For the Benign class, Stacking-SVM recorded the greatest F1, at 98, and DenseNet121 recorded the highest PRE, at 100, with a REC value of 93. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score .For the adenocarcinoma class, Stacking-SVM registered the highest F1 score, at 98, and VGG16 had the highest PRE, at 100, with a REC value of 87. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score .In All CNN models and Stacking-SVM were applied to the WCE database for multiclassification: normal (N), ulcerative colitis (UC), polyps (P), and esophagitis (E). The dataset was split into 75% training (4500 images) and 25% (1200 images). The PRE, REC, and F1 score for each class were registered; CM and ROC curves are also displayed.For the N class, Stacking-SVM recorded the highest PRE and F1 score . VGG16, InceptionV3, and DenseNet121 recorded the highest REC scores, at 100. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score .For the UC class, Stacking-SVM recorded the highest REC and F1 score . DenseNet121 recorded the highest PRE, at 99. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score.For the P class, Stacking-SVM recorded the highest REC and F1 score . DenseNet121 recorded the highest PRE, at 100. ResNet50 recorded the lowest performance in terms of PRE, REC, and F1 score .For the E class, Stacking-SVM recorded the highest REC and F1 score . DenseNet121 recorded the highest PRE, at 100. ResNet50 recorded the lowest performance in terms of REC and F1 score .A heat map of a post hoc explainable model was generated to better understand the behavior of each model. Grad-CAM explainable models were used to extract relevant rich features from the images and generate the heat map for each colon cancer class in the dataset . Grad-CAA comparison of the proposed model with other models is shown in Worldwide, colon cancer ranks third in terms of prevalence; there were almost two million cases diagnosed in 2020. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. The main goal of our work was to propose Stacking-SVM based on pretrained CNN models  and a metalearner (SVM) to generalize and classify colon cancer using binary classes and multiclasses. The main steps of the proposed framework are data collection, data augmentation, data splitting, model pretraining, and model proposal. In level 1, the output of multiple base models  is combined in stacking (training stacking and testing stacking). In level 2, training stacking is used to train a metalearner (SVM). Testing stacking is used to evaluate the metalearner (SVM) and predict the final result. We conducted experiments using two public image databases (LC25000 and WCE) with both fixed and dynamic learning rates. Stacking-SVM models were compared with different pretrained CNN models using other evaluation metrics: ACC, PRE, REC, F1, ROC, AUC, and CMs. The results show that Stacking-SVM with a fixed learning rate achieved the highest average performance for the two databases. The ACC, PRE, REC, and F1 score of the Stacking-SVM model were 100, 100, 100, and 100, respectively, for the LC25000 database. The ACC, PRE, REC, and F1 score of the Stacking-SVM model were 98, 98, 98, and 98, respectively, for the the WCE database. Stacking-SVM recorded the highest performance compared to existing models  because it takes the predictions made by multiple single models as inputs, learns to combine them in a way that produces a final prediction, and evaluates the metalearner using the output to produce better predictive results than any single model. In our future work, we plan to aggregate more data to ensure the model’s generalizability. Furthermore, we plan to deploy the developed model in a real clinical system to evaluate its performance on a practical dataset.


Four circRNAs showed significant differences in expression, which were validated using qRT-PCR. These results suggested that three circRNAs were consistent with high-throughput sequencing results. Bioinformatics analysis demonstrated that these differentially expressed circRNAs were involved in protein metabolism, regulation, synapses, and other pathophysiological processes during CeAD-induced stroke. Additionally, various pathways related to inflammation were closely associated with circRNAs. Based on our results, we suggest that the aberrant expression of circRNAs in CeAD may serve as a biomarker for its diagnosis and as a potential therapeutic target.Cervical artery dissection (CeAD), a special cerebrovascular disease and the main cause of stroke in young people, can present with ischemic stroke, headache, subarachnoid hemorrhage, and other symptoms, increasing the possibility of misdiagnosis. As a special class of non-coding RNAs, circRNAs are commonly found in organisms and can play regulatory roles in transcription and post-transcription processes, affecting gene expression.CircRNAs have reported to be associated with neurological diseases; however, their role in CeAD has not been discerned. In this study, we aimed to elucidate the pathophysiological changes in patients with CeAD and identify biomarkers. Peripheral blood mononuclear cells from patients with CeAD and healthy controls were sequenced using high-throughput sequencing. We detected 460 differently expressed circRNAs in patients with CeAD ( ArteriaWith increasing RNA research, it has been found that non-coding RNAs are widely involved in various pathophysiological processes, such as epigenetic, transcriptional, and post-transcriptional regulation, and are closely related to many diseases . CompareThe diagnosis of CeAD depends mainly on imaging and clinical manifestations. Cerebrovascular angiography is the gold standard for the diagnosis of CeAD. However, it is a complex examination method, which makes rapid diagnosis difficult. Based on the existing research results on circRNAs in the nervous system, this study used high-throughput sequencing to conduct a prospective controlled study on patients with CeAD and healthy subjects, to clarify the pathophysiological changes in patients with CeAD from a new perspective, and to predict diagnostic markers and future therapeutic targets.2.2.1.We included 10 CeAD patients and 10 normal controls in this study. All CeAD patients included in this study were patients at the Department of Neurology, ShenZhen Hospital of Southern Medical University, from 2020 to 2022, while the healthy controls were volunteers from the Health Management Center at Shenzhen Hospital of Southern Medical University. The study was approved by the Ethics Committee of the Shenzhen Hospital of Southern Medical University (NYSZYYEC20200016), and all samples for the trial were obtained from the patients and controls upon the receipt of signed informed consent forms.2.2.2.2.1.All patients were diagnosed using a Computed Tomography (CT) or Magnetic Resonance Imaging(MRI) scan, according to the American Heart Association/American Stroke Association diagnostic criteria for cerebral infarction .2.2.2.1.Clinical manifestations included stroke event, neurological deficits, headache and neck pain, and Horner’s syndrome.2.Imaging: Ultrasound of the neck indicates double-lumen structure in the blood vessel; CT angiography or magnetic resonance angiography indicates stenosis, occlusion, pseudoaneurysm, intimal flap, and the double-lumen; digital subtraction angiography shows stenosis, occlusion, or the “flame sign,” “rat tail sign,” and “thread-like sign.” For digital subtraction angiography, stenosis, occlusion, “flame sign,” “rat tail sign,” “thread-like sign,” atherosclerosis, and embolism were excluded in all patients.3.CeAD with rheumatic diseases and connective tissue diseases should also be excluded.2.2.3.Venous blood samples (10 mL) were collected from patients with CeAD, and PBMCs were extracted as previously described .2.3.Total RNA was isolated from PBMCs using the Magzol Reagent , as per manufacturer’s protocol. The quantity and integrity of the RNA was assessed using the K5500 micro-spectrophotometer  and the Agilent 2,200 TapeStation , respectively. Briefly, rRNAs were removed from the total RNA using the QIAseq FastSelect-rRNA HMR Kit . The RNA was then treated with RNase R  and fragmented to approximately 200 bp. Subsequently, the purified RNA fragments were subjected to first strand and second strand cDNA synthesis, followed by adaptor ligation and enrichment with a low cycle according to manufacturer’s instructions for the NEBNext® Ultra™ RNA Library Prep Kit for Illumina . The library products were evaluated using the Agilent 2,200 TapeStation and Qubit , and further sequenced using an Illumina , through the paired-end 150 bp approach, at Ribobio Co. Ltd. .2.4.2 (fold change) | > 1 and p ≤ 0.05.The circRNAs were detected using the CIRI2 and CIRCexplorer2 algorithms. Reads were mapped to the human reference genome grch37/hg19 using the BWA-MEM or TopHat tools, respectively.2.5.To ensure the validity of the high-throughput sequencing results, the experiments were validated using qRT-PCR with β-actin as an internal control, and the samples were prepared in triplicates. Total RNA was reverse-transcribed to complementary DNA using a reverse transcription kit (Takara Bio). We performed qRT-PCR to detect circRNA expression levels using the SYBR Green qPCR SuperMix and the 7,500 Sequence Detection System (Applied Biosystems). The primers used are listed in . Ploidy 2.6.2.6.1.p < 0.05 was used as the threshold to determine significant enrichment of the gene sets.GO functional annotations and KEGG pathway enrichment analyses were performed using the clusterProfiler package in the Bioconductor/KOBAS 3.0. For KEGG enrichment analysis, a 2.6.2.Acting as an miRNA sponge, circRNA inhibits the negative regulation of its target mRNA by competitively binding to miRNA with mRNA; therefore, the prediction of miRNA that can be bound by circRNA can effectively predict the potential function of circRNA. Further functional annotation of the three circRNAs identified by qRT-PCR was performed. We used the miRanda, RNAhybrid, and TargetScan tools to predict circRNA and its miRNA binding site and obtained the prediction results from the intersection of each tool.2.7.p < 0.05 identified as a statistically significant difference.In this study, all data are presented as mean ± standard deviation, and the difference between groups was determined using the t-test, with 3.3.1.We included 10 patients diagnosed with CeAD and 10 healthy controls in this trial; The clinical symptoms of CeAD patients include stroke events, neurological deficits, head and neck pain, etc. The average age of CeAD patients was 40.67 ± 7.97 years (age range: 32–55), and a mean NIHSS score of 2.3 (ranging from 1 to 12 points). Among CeAD patients, 5 were caused by minor trauma, including 4 patients who developed clinical symptoms after head and neck massage, and another patient who injured the neck while playing basketball. Clinical characteristics of CeAD patients are detailed in 3.2.By analyzing the PBMCs from the patients and controls using high-throughput sequencing, 30,990 circRNAs were obtained. Using the DESeq2 package, 460 differentially expressed circRNAs, including 240 upregulated circRNAs and 220 downregulated circRNAs, were detected. Hierarchical clustering of the aberrantly expressed circRNAs was performed. Volcano plots were used to visualize circRNAs that differed between the CeAD and control group , and sca3.3.p < 0.05) of the 460 differentially expressed circRNAs in this study, four circRNAs with the greatest difference between the upregulated and downregulated circRNAs  on CeAD, we used the miRanda and TargetScan tools to predict miRNA binding sites on circRNAs. We predicted five miRNA binding sites with the highest match values for the three differentially expressed circRNAs. Subsequently, the miRanda and TargetScan tools were used for the target gene prediction of the 15 miRNAs, and a total of 208 target genes were obtained. After the KEGG pathway enrichment analysis, the top 10 pathways with the most significant enrichment results were screened for 26 target genes, and “total score” > 500 was set as the threshold for miRanda, and six target genes were predicted. Finally, we constructed a network diagram of 3 circRNAs, 15 miRNAs, and 6 target genes .4.This study is the first to analyze patients with CeAD at the circRNA level. By comparing the difference in circRNAs expression in patients with CeAD and healthy controls through high-throughput sequencing, we detected 460 circRNAs that were differentially expressed between the CeAD and control group, including 240 upregulated circRNAs and 220 downregulated circRNAs.Studies on circRNAs have confirmed that they play an important role in ischemic stroke , 19. In To further predict the function of circRNAs in CeAD in this study, KEGG, and GO analyses were performed on differentially expressed circRNAs. GO analysis revealed that the amyloid precursor protein metabolic process, hippocampal mossy fiber to CA3 synapse, deacetylase activity, HSP protein, and other processes are related to protein metabolism, synapse, and regulation. Recent studies on HSP proteins have shown that both HSP27 and HSPB1 are highly expressed in patients with thoracic aortic coarctation and that their levels are closely related to CeAD prognosis .The pathThe KEGG analysis of pathways related to the pathogenesis of CeAD mainly highlighted the phospholipase D signaling, ErbB signaling, relaxin signaling, and estrogen signaling pathways, apart from other pathways. The relaxin signaling pathway has been shown to inhibit inflammatory responses through multiple signaling pathways, which are associated with inflammation and apoptosis . A studyCervical artery dissection is a common cause of stroke in young adults. This study find the changes in circRNAs in patients with CeAD. Based on the characteristics of circRNAs, we hope to make a breakthrough in the diagnosis and treatment of CeAD. Studies demonstrated that the regulation of circRNA through overexpression, gain or loss of function can affect diseases. The overexpression of specific circRNAs can be achieved using adenoviral or lentiviral vectors carrying circRNA sequences . ZolgensThis study has certain limitations. First, the sample size is insufficient, and the number of patients studied needs to be increased to further verify the results of this study. Second, as an exploratory study, another limitation of this study is the lack of correction for multiple comparisons. Third limitation is that we cannot determine with certainty whether ischemic stroke or dissection will lead to changes in circulating RNA, as a non-dissecting stroke could also be a cause. We will establish a group of stroke caused by non-dissection causes in future experiments.In summary, our study demonstrate differentially expressed CircRNAs in PBMCs of patients with CeAD for the first time. In view of the research achievements of CircRNAs in neurological diseases, we found that CircRNAs are involved in the pathophysiological process of CeAD pathogenesis through bioinformatics analysis of CircRNAs, which proposed a new direction for the diagnosis and treatment of CeAD.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.The studies involving humans were approved by Ethics Committee of the Shenzhen Hospital of Southern Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YLiu and LL designed the experiments. YW and ZD performed the experiments and collected the data. JL, YLi, JM, WT, and SY analyzed the data. YW wrote the manuscript. All authors contributed to the article and approved the submitted version.