The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors  recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN  and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing , operating , and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management. The construction industry has been identified as one of the most hazardous industries. And, the nature of construction projects lead to a high incidence of accidents. The interaction between man-machine, man-material, man-environments makes it complex for safety management on construction sites . ManagerIn recent years, with the rapid development of artificial intelligence technology, construction industry practitioners have begun to realize its potential in improving construction safety management, especially in monitoring and managing construction workers’ unsafe behaviors. Many automated technologies have been proposed to monitor the behaviors of construction workers on construction sites to improve the efficiency and accuracy of unsafe behavior management ,10,11,12However, most existing research or products focused only on the workers’ behaviors  recognition in construction sites and very limited studies considered the interaction between man-machine, man-material, or man-environments. For application, those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. For example, suppose throwing a hammer is an unsafe behavior on the construction site, if a worker throws rubbish  using very similar motions, it is very difficult to judge whether the worker’s behavior is safe only based on the motion recognition result. Therefore, identifying unsafe interactions between man-machine/material is necessary and more meaningful, which could provide more direct and valuable information for safety management. To achieve the above goal, it not only needs to recognize the motion and objects, but also needs to detect the interaction. In other words, it needs to make decision rules, which is used to automatically judge whether unsafe interactions between man-machine/material occur.Considering the importance of identifying construction workers’ unsafe interaction between man-machine/material and the limitations of existing research, this study aims to develop a method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (for motion recognition) and YOLO . In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with construction workers. Then, an ST-GCN model was trained to detect and identify construction workers’ behaviors. Lastly, decision rules were made, and the algorithm was developed to detect whether unsafe interactions between man–machine/material exist.For motion recognition, motion capture is the foundation and the popular computer vision-based motion capture technologies are human posture estimation algorithms such as OpenPose  and RGB-As mentioned above, it is more meaningful to detect and identify unsafe interactions between man–machine/material, in which the object  recognition is also necessary. In the aspect of object recognition, a number of methods have been proposed, and detection accuracy has soared since deep learning became popular. There are mainly two types of object detection methods, one is Region Proposal based methods, and the other is the end-to-end method. The most representative of Region Proposal-based methods is the R-CNN series, including R-CNN , Fast R-The above indicates that technologies in motion recognition or object recognition are quite mature and have been widely used in construction workers’ unsafe behaviors management. However, the methods based on motion recognition or object recognition cannot provide enough valuable information for the identification of interaction behaviors. At present, the ways to identify the interaction between man–machine/material in construction sites are mainly integrating computer vision with natural language processing ,39. For This study elaborated the current research on the identification of unsafe behaviors at construction sites from three directions: motion recognition, object recognition, interaction recognition. And, it provided an overview of related research, as shown in Based on the above, most of existing research or products focused only on the workers’ behaviors  recognition or object recognition, very limited research considered the interaction between man–machine/material. Considering the importance of identifying construction workers’ unsafe interaction between man–machine/material and the limitations of existing research, this study contributes a method that combines object recognition with motion recognition, which is very important for interaction identification. Furthermore, decision rules were made, and the algorithm was developed to judge whether the workers’ interaction behaviors are safe or not. The findings of the study could have some practical implications for safety management, especially workers’ behavior monitoring and management.Based on our on-site investigation, the construction workers’ unsafe interaction between man–machine/material falls into two groups: the unsafe physical contact with machine/material (Type I) and no physical contact but unsafe distance to machine/material (Type II). This paper selected six behaviors see , throwinAs mentioned above, motion capture is the foundation of recognition, one of the popular computer vision-based human posture estimation algorithms is OpenPose. We utilized OpenPose for real-time 2D pose estimation from images or videos . This meSpatial Temporal Graph Convolutional Networks (ST-GCN) is the first to apply graph convolution network (GCN) to skeleton-based motion recognition tasks. ST-GCN constructs a skeleton spatial temporal graph of the skeleton keypoints sequence obtained by OpenPose, and a skeleton spatiotemporal graph As shown in In this study, YOLO v5 was adopted for objects detection, which is an advanced object detection algorithm with important improvement in accuracy and speed compared to the previous YOLO versions Input module includes Mosaic data enhancement, image size processing, and adaptive anchor frame calculation. All YOLO algorithms need to transform the size of the input image into a fixed size, and then send it into the detection model for training. The standard size of the designed image in this paper is 640 × 360 × 3.(2)Backbone module is a kind of convolutional neural network, including Focus structure and CSP structure, which aggregates and forms image features with different image granularity. After the input image, the focus slice operation is used to extract the features more fully. At the same time, the CSPNet structure, which can extract rich features.(3)Head module adopts the structure of FPN+PAN. FPN is top-down, and the information is transferred and fused by means of up-sampling to obtain the predicted feature map. PAN uses a bottom-up feature pyramid.Type I behaviors were identified as flows:Step One: objects detection. YOLO v5 model was trained, and then was used to detect all the objects in each frame of the video, the object’s information, including classes of the objects, coordinates (coordinates of the upper left and lower right corners of the bounding box), and confidence level can be obtained. The detected objects include all the machines, tools, materials, safety signs, etc. contained in the image.Step Two: motion capture and recognition. OpenPose was adopted to capture the worker’s motions, and the skeleton time sequence data, including the coordinates of 18 keypoints of the body can be obtained. In addition, when the workers perform Type I behaviors selected in this study, the body part that interacts with objects is the left or right hand. So, the skeleton time sequence data, including the coordinates of four keypoints of each hand will be specially collected. ST-GCN was trained and then used to recognize the workers’ motions, which provides the predicted probability of each motion.Step Three: interaction behaviors identificationFor Type I behaviors, whether the interaction between man-objects occurs can be judged by whether the hand keypoints are within the range formed by the bounding box. If the hand keypoints are within the range, class of objects and confidence level of objects and four hand keypoints will be recorded.This study introduced the consideration of the number of interactions, i.e., how many times the interaction occurs. Because of the complexity of construction workers’ motions and to prevent misidentification caused by miscontact between human and machine/material, we also introduced the consideration of continuity of man-machine/material contact, i.e., the last time (number of frames) of continuous contact. The number of the frame will be recorded, which man–machine/material contact occurs.For Type I motion identification, the discriminant parameter of each video is calculated as follows:For each motion, the motion prediction probability is only multiplied by the corresponding object, e.g., the prediction probability of throwing hammer For Type II behaviors, whether the interaction between man–objects occurs can be judged by relative space position relations between body part and objects. Taking Crossing Railing (CR) and Crossing Obstacle (CO) as examples, this study firstly calculates the line function of the railing/obstacle based on the detection results of YOLO.Secondly, whether the interaction between man-objects occurs can be judged by the change of left/right ankle’s coordinates.The discriminant parameter of each video is calculated as follows:For each motion, the motion prediction probability is only multiplied by the corresponding object, e.g., the prediction probability of crossing rail After behavior identification, its risk should be evaluated according to the safety management and relevant regulations. This study tried to detect and recognize the safety signs in the workplace see , and theAn experiment was designed and conducted to collect a large amount of motion data of simulated construction workers’ behaviors, which was used for training and testing models.Fourteen healthy young males  volunteered to participate in this study. Each participant signed an informed consent form on the experimental protocol.In this study, two cameras were used to collect video data, with a recording resolution of 1920 × 1080 at a frequency of 24 FPS. The two cameras, with 30 degrees downward, were placed on the left and right of the participant see . MoreoveAfter collecting the experimental data, the training and testing of the YOLO and ST-GCN network models were carried out on a laptop computer. The configuration parameters of the software and hardware platform in this study are shown in For YOLO network model training, the dataset was divided in the randomly partitioned dataset into a training set and a validation set in a ratio of 8:2. The The performance of the models was tested using the following methods. For binary classification, For multi-class classification, macro-average was used to evaluate the model. The formulas are shown as follows.For the video shooting, we shot 5040 videos in total, as shown in Input the training set photos into the YOLO neural network for training, and the results are shown in In order to compare the differences in performance between the ST-GCN method alone and the proposed YOLO-ST-GCN method, this paper first used only the ST-GCN method to recognize the above two types of behaviors, and the results were as follows.This study selected the weight model with the best performance on the validation set for Type I behaviors and tested it on the test set. The prediction results were then drawn into a confusion matrix, as shown in The results show the overall identification accuracy of Type I behaviors were 56.70%, and the overall accuracy of Throwing and Operating were 51.79% and 61.61%, respectively. The accuracy of throwing hammer, throwing bottle, turning on switch, and putting bottle were 89.29%, 14.29%, 62.50% and 60.71%, respectively. Especially since, the rate of which the throwing bottle was misidentified as throwing hammer and was 85.71%. The evaluation indicators were also calculated: Similarly, this study selected the weight model with the best performance on the validation set to test the test set, and then draw the prediction results into a confusion matrix, as shown in The results show the overall identification accuracy of Type II behaviors was 58.04%, the accuracy of crossing railing and crossing obstacle was 71.43% and 44.64%, respectively. Especially since, the rate of which for the crossing obstacle was misidentified as crossing railing was 55.36%. The crossing railing was set as positive samples, crossing obstacle was set as negative samples. The evaluation indicators were also calculated: For the Type I behavior, this study set The results show, the overall identification accuracy of Type I behaviors was 92.41%, and the overall accuracy of Throwing and Operating was 85.71% and 99.11%. The accuracy of throwing hammer, throwing bottle, turning on switch, and putting bottle were 85.71%, 85.71%, 98.21%, and 100.00%, respectively. Especially since, the rate of which for the throwing hammer was misidentified as throwing bottle and was 14.29%, and throwing bottle was wrongly identified as throwing hammer and was 12.50%. And, almost all Operating behaviors were identified correctly, with only 1.79% of the turning on switch was misidentified as putting bottle. The crossing railing was set as positive samples, crossing obstacle was set as negative samples. The evaluation indicators were also calculated: For Type II behaviors, this study set The results show the overall identification accuracy of Type II behaviors was 100.00%, and the accuracy of crossing railing and crossing obstacles were both 100.00%. The crossing railing was set as positive sample, crossing obstacle was set as negative sample. The evaluation indicators were also calculated: As mentioned above, the risk of behaviors was evaluated by detecting and recognizing the safety signs in the workplace. The meaning of detected safety signs was used for judging whether the identified behavior is safe or not. If the behavior identified by the YOLO-ST-GCN method is the same as the forbidden behavior corresponding to the safety signs, it would be identified as an unsafe behavior; otherwise, it will be identified as a safe behavior. For example, if the No Throwing safety sign and throwing harmer behavior were detected in the same workplace, the behavior of throwing hammer would be identified as unsafe behavior. In this study, throwing hammer under the safety sign of No Throwing was considered unsafe behavior, while the other behaviors were considered safe behavior. Turning on switch under the safety sign of No Operating was considered as unsafe behavior, while the other behaviors were considered as safe behavior. Crossing railing under the safety sign of No Crossing was considered as unsafe behavior, while the other behaviors were considered as safe. The identification results were drawn into a confusion matrix, as shown in For No Throwing, the overall accuracy of No Throwing was 93.30%, the accuracy of Unsafe Behavior (UB) was 85.71%, and the accuracy of Safe Behavior (SB) is 95.83%. The Unsafe Behavior (UB) was set as positive samples and Safe Behavior (SB) was set as negative samples. The evaluation indicators were calculated: For No Operating, the overall accuracy of No Operating was 99.11%, the accuracy of Unsafe Behavior (UB) was 98.21%, and the accuracy of Safe Behavior (SB) is 99.40%. The Unsafe Behavior (UB) was set as positive samples and Safe Behavior (SB) was set as negative samples. The evaluation indicators were calculated: For No Crossing, the overall accuracy of No Crossing was 100.00%, the accuracy of Unsafe Behavior (UB) was 100.00%, the accuracy of Safe Behavior (SB) was 100.00%. The Unsafe Behavior (UB) was set as positive samples and Safe Behavior (SB) was set as negative samples. The evaluation indicators were calculated: The above results show the overall accuracy was above 90.00%, the accuracy of No Operating was close to 100.00%, and No Crossing can be identified correctly completely. The above indicated that the behaviors risk evaluation by detecting and recognizing the safety signs in workplace was feasible and effective.At present, limited studies investigated the identification of unsafe interaction behaviors on construction sites, most of the research only focused on motion recognition, itself, which might limit its application on real construction site. This study proposed a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man–machine/material, based on ST-GCN and YOLO. Identifying the interaction between man–machine/material and evaluating the risk of behaviors by detecting and recognizing safety signs could improve the practicability of the proposed method, which could provide more direct and valuable information for safety management.In this study, objects  were detected by using YOLO technology, and the performance was very good see . These rThe results of this study show that the performance of motion recognition only based on ST-GCN was poor. The overall identification of Throwing, Operating and Crossing was 51.79%, 61.61% and 58.04% see . The reaConsidering the good performance of ST-GCN in non-similar motions recognition and poor performance in similar motions recognition, this study still chose ST-GCN for motion recognition, it is just that YOLO was added and integrated, which was used for object identification. It could improve the identification accuracy of similar motions in the case when the worker performs similar motions, but the objects that interacted with the worker are different. Since, for application, those interactions are very important for judging whether the workers’ behaviors are safe or not from the standpoint of safety management. The results of this study show that compared with only using ST-GCN, the method based on YOLO-ST-GCN proposed in this paper greatly improved the identification accuracy. The overall accuracy increased from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, for throwing, operating, and crossing behaviors. And, all the interactions between man–objects were well detected and identified. As mentioned above, there is limited research that integrated motion identification with objects recognition to detect interaction behaviors between man–machine/material. Liu et al.  studied To evaluate the effectiveness of other object detection algorithms compared to YOLOv5, we used the latest YOLO-NAS object detection algorithm. The dataset was divided randomly into a training set and a validation set in a ratio of 8:2. The For Type I behaviors, the results show the overall identification accuracy of Type I behaviors was 91.96%, and the overall accuracy of Throwing and Operating were 83.93% and 100.00%. The accuracy of throwing hammer, throwing bottle, turning on switch, and putting bottle were 75.00%, 92.86%, 100.00%, and 100.00%, respectively. The evaluation indicators were also calculated: For Type II behaviors, the results show the overall identification accuracy of Type II behaviors was 100.00%, the accuracy of crossing railing, and crossing obstacle were both 100.00%. The crossing railing was set as positive samples, crossing obstacle was set as negative samples. The evaluation indicators were also calculated: The results show that there is little difference between the accuracy of behavior identification based on YOLOv5 and YOLO-NAS. Although the latest YOLO-NAS offers state-of-the-art target detection with unmatched accuracy and speed performance, outperforming other models of the YOLO family such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8 , the perThis study proposed the YOLO-ST-GCN method for interaction behaviors identification, the foundation was motion and object recognition. This method also has some limitations in the case that a worker performs different tasks with similar motions and interacted with the same objects. This study added one more task, hammering nail see B, which The limitations of the research need to be acknowledged. Firstly, a more completed dataset for training and testing the models is expected, Since, a more completed dataset that covers more work tasks, different scenarios, different angles, and different lighting conditions could improve its application to real construction sites. Secondly, the experimental tasks (i.e., behaviors in This study developed a new method of identifying construction workers’ unsafe interaction behaviors, i.e., unsafe interaction between man–machine/material, based on ST-GCN and YOLO. The research achieved the following findings. Firstly, YOLO, OpenPose, and ST-GCN performed well in object detection, motion capture and motion recognition, respectively. In addition, compared with object recognition, motion recognition is more susceptible to many factors. Therefore, the choice of motion recognition technology is particularly important. Secondly, the experimental tasks (i.e., behaviors in


This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis.A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study . The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models  and three clinical-radiomics models . The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models.P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model  achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models.Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction. Ewing sarcoma (ES) is a highly aggressive bone sarcoma, that occurs in any age with a peak incidence in children and young adults. The most powerful adverse prognostic factor across different treatment strategies in ES is metastases, with the lungs and bone being the most common metastatic locations. At presentation, approximately 20–25% of patients show metastases, usually affecting the lungs (70–80%) and the bone (40–45%) , 2. At pComputed tomography (CT) is widely used for diagnosing pulmonary metastases as a non-invasive and reliable method. However, because micro-metastases are not identified by the current radiological techniques, improving the accuracy in detecting pulmonary metastases at the time of diagnosis is necessary . RadiomiThis study aimed to develop and validate radiomics models based on CT to predict pulmonary metastases in patients with ES, which could be a potential tool for guiding more personalized medicine.This retrospective study was approved by the Ethics Committee of our hospital, and the need for a written informed consent from patients was waived. The inclusion criteria were as follows: (a) histopathologically-confirmed ES by biopsy or tumor resection; (b) with initial CT images, including plain and CT- enhanced (CTE) images of the primary bone lesion, and chest CT at presentation. The exclusion criteria were as follows: (a) poor quality images, which were inadequate for the following analysis; (b) without follow-up of 2 years after diagnosis. The patients were randomly divided into training cohort and validation cohort at a ratio of 8:2, the traAll patients underwent follow-up of more than 2 years after diagnosis, and enhanced chest CT were monitored once every 3 months. The patients who suffered pulmonary metastases of ES within 2 years after diagnosis were defined as the presence (MT) cohort, and those without any suspicious nodules on chest CT images during the 2-year follow-up after diagnosis were defined as the absence (non-MT) cohort. The MT group were those patients with multiple nodules at presentation or nonspecific nodules that increased in number or size, as detected by chest CT during the 2-year follow-up. The potential clinical risk factors that may be associated with pulmonary metastases were obtained from medical records as follows: gender, age, tumor location , lactate dehydrogenase (LDH) level, alkaline phosphatase (ALP) level, major length and therapeutic method.The CT and CTE images were obtained from the picture archiving and communication system (PACS). CT images were acquired using multidetector row CT (MDCT) systems , with the following parameters: 120 kV, 240 to 260 mAs, collimations of 64 × 0.6 mm, and slice thicknesses of 5 mm. CTE acquisition was performed after a 70s delay following intravenous administration, with 1.5 mL/kg iodinated contrast  by using an automatic pump injector  at a rate of 2.5 mL/s through the antecubital vein.The regions of interest (ROIs) were handcrafted along the boundary of each tumor on each slice by using uAI Research Portal  (Shanghahttps://www.python.org), based on algorithms provided in Pyradiomics [Z-score normalization method was implemented in CT images to minimize the intensity discretization before feature extraction. A total of 2600 radiomics features were extracted using Python software (adiomics  (versionadiomics .P＜0.05) were introduced to build the clinical-radiomics model by the stepwise multiple logistic regression. Gaussian process (GP), logistic regression (LR) and partial least squares discrimination analysis (PLS-DA) classifiers with high stability were investigated.After feature selection was conducted, radiomics and clinical-radiomics models were built to predict the pulmonary metastases of ES. The radiomics model incorporated only radiomics features, and then the clinical risk factors  curve. The area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were also reported for the radiomics and clinical-radiomics models. The comparisons between AUCs of the models were conducted by using DeLong test.P < 0.05 considered as statistically significant. Comparisons of patient continuous clinical risk factors were conducted by t-test, and χ2 test or Fisher’s exact test was applied for categorical variables.All statistics were performed using SPSS 22.0 , with Finally, a total of 143 patients with  and without  pulmonary metastases were recruited in this study. The clinical characteristics are shown in Table P＞0.05), except for the therapeutic method (P＜0.05).Among all the cases, 85 lesions were located in pelvic bone, 39 lesions were located in the bones of extremities, and 19 were located in other areas . No significant difference were found in gender, age, tumor location, LDH level, ALP level and major length between the MT and non-MT groups  models, respectively.The ComB model had the highest AUC of 0.829, 0.852 and 0.848 among the models in the validation cohort by using GP, LR and PLS-DA classifiers respectively. The AUC, ACC, sensitivity and specificity of the CT, CTE, and ComB models are presented in Table For GP and LR classifiers, the CTE_clinical model had the highest AUCs of 0.843 and 0.843 in the validation cohort, respectively. Whereas the ComB_clinical model achieved the highest AUC of 0.843 in the validation cohort by using the PLS-DA classifier. Table The calibration curve of the clinical-radiomics models showed good calibration results in the training and validation cohorts Fig. . The DCATo the best of the author’s knowledge, this study was the first to predict the risk of developing pulmonary metastases in patients with ES on the basis of CT radiomics features, in a 2-year follow-up period after diagnosis. The clinical-radiomics models based on combined features and the radiomics models demonstrated similar performance, and the sensitivity and specificity of the clinical-radiomics models and radiomics models were excellent.Among the clinical risk factors, gender, age, tumor location, LDH level, ALP level and the major length of the tumor were confirmed to be not the prognostic factors for the occurrence of pulmonary metastases, without statistical difference between the MT and non-MT groups. By contrast, therapeutic method showed correlation with pulmonary metastases. Some previous studies reported that gender was not a risk factor for pulmonary metastases of ES, larger tumors had a higher chance of pulmonary metastases, and the results of age, and primary location were controversial. Li et al. found that surgery was a protective factor against pulmonary metastases and chemotherapy had no significant difference between MT and non-MT groups –18.In the past decades, radiomics as a promising methodology has been widely used in the diagnosis, differentiation, staging and monitoring of tumors, owing to the progress in extracting vital high throughout analysis features and screening large numbers of features. In prior studies, radiomic analysis has been identified as a powerful method for the predicting pulmonary metastases of soft tissue and bone sarcomas –21.For prediction of pulmonary metastases in ES, radiomics features based on CT were used to develop and validate three different radiomics models, which achieved high performance. Among the three models, the ComB model presented the highest performance in the validation set by using the LR classifier, but there were no statistical difference was observed between the AUCs of the models. Three well-known classifiers were used in this study, including GP, LR and PLS-DA. As the most widely used method in the past studies related to radiomics analysis, LR is a stratification algorithm suitable for analyzing large data sets of features in small samples, and designed to avoid overfitting and predict the class probability of a given categorical dependent variable . GP is aFurthermore, three clinical-radiomics models were established for patients with ES. The CTE_clinical and ComB_clinical models offered preferable prognostic ability  in the prediction of pulmonary metastases, and there were no statistic differences between the AUCs of the clinical-radiomics models. The prediction ability of the three clinical-radiomics models were not markedly enhanced relative to that of the radiomics models, and such results may be partially due to the sample size being relatively small and only one clinical risk factor being combined in the clinical-radiomics models.There are several limitations in the retrospective study. First, due to the low incidence rate of ES, the patient population was relatively small, with all patients coming from one single center, and internal validity was used rather than external validity. Therefore, large-scale studies with external validation are required before widespread implementation of the models in the clinical practice. Second, most cases were diagnosed by surgical specimens, but a small fraction of the pathological diagnosis was obtained by biopsy, which could result in some bias. Third, multiparametric MR images with better soft-tissue resolution should be implemented in future studies to improve the precision and robustness of the models. Forth, manual segmentation is time consuming, and it can’t deliver reproducible results. But manual segmentation was used as a gold standard, and it has been applied in many previous studies and yielded excellent results. Fifth, the relationship between the radiomic analysis of the primary tumor and the possibility to develop pulmonary metastases was unclear, and this need more research in future.In summary, the CT based radiomics model was effective in predicting the pulmonary metastases in patients with ES, and thus could be a potential tool for the accurate risk stratification and precision medicine.


In PwMS, MFIS scores correlated positively with age, total number of relapses, total disease duration, disability status, and Beck Depression Inventory-II score and negatively with cognitive performance. Patients with relapsing-remitting MS had significantly lower fatigue levels than those with secondary progressive MS (p = 0.0010). Fatigue levels were significantly lower among male than female PwMS (p = 0.0120). Other determinant factors of fatigue in our study proved to be the marital and occupational status, as well as the presence of children, but in a linear multivariate regressions analysis with MFIS score as the dependent variable, the fatigue levels were influenced only by sex, occupational status, marital status, children status, and BDI-II test results. Considering the significant impact of fatigue on the quality of life of PwMS, clinicians must diagnose fatigue as early as possible, identify its modifiable determinants, and manage it effectively to increase their quality of life.Fatigue is the most common and disabling symptom in patients with multiple sclerosis (PwMS), representing one of the main determinants of reduced quality of life among PwMS due to its interference with social activities and work capacity. This study aimed to identify the sociodemographic determinants of fatigue in a cohort of 150 PwMS and 100 healthy controls (HCs). Fatigue was assessed using one of the most suitable and appropriate tools for measuring the degree of fatigue: the Modified Fatigue Impact Scale (MFIS). By comparing the median scores for the MFIS, we observed that the PwMS group had significantly higher MFIS scores than the HCs ( Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system (CNS). It is the most common cause of non-traumatic disability in young adults, affecting > 2.5 million worldwide ,2,3.MS is a highly heterogeneous disease, encompassing variations in pathophysiological processes, immunological changes, responses to treatment, and clinical manifestations. Several phenotypes of its clinical evolution have been described. In approximately 85% of cases, MS begins with episodes characterized by new neurological signs and symptoms affecting the CNS or the worsening of pre-existing symptoms. These episodes typically exhibit complete recovery under corticosteroid treatment followed by periods of remission, a condition known as relapsing-remitting MS (RRMS). After approximately 20 years of disease progression, approximately 60–70% of patients with an initial relapsing MS course will convert to secondary progressive MS (SPMS), defined retrospectively by gradual worsening disability with or without acute clinical exacerbations. Fewer patients will undergo a progressive evolution of neurological deficits from MS onset, known as primary-progressive MS (PPMS). PPMS and SPMS are part of the progressive MS phenotypic spectrum ,5.While the severity of MS was initially evaluated solely based on the degree of physical disability , in the last two decades, physicians have begun paying closer attention to “silent” non-motor manifestations of MS, such as fatigue, mood disorders, cognitive disorders, and anxiety ,7.The definition of fatigue, which is a subjective symptom, has been a source of controversy for many years. It has been variously defined as “tiredness”, “lack of energy”, “malaise”, “exhaustion”, and “motor weakness”. The Fatigue Guidelines Development Panel of the Multiple Sclerosis Council for Clinical Practice Guidelines defines fatigue as a “subjective lack of physical and/or mental energy that is perceived by the individual or caregiver to interfere with usual or desired activity”. Recently, some have called for the adoption of a different definition of fatigue put forth by Mills and Young, who defined it as “reversible motor and cognitive impairment, with reduced motivation and desire to rest” ,8,9,10.Fatigue is the most common symptom of patients with MS (PwMS), reported by approximately 75% at some point during the course of the disease. It represents one of the main causes of reduced quality of life among PwMS and is considered by some to be the most disabling symptom, in some cases surpassing motor deficit and pain. It interferes with the social activities of PwMS and is also associated with decreased work capacity, leading to job loss and therefore having a significant socioeconomic impact ,15,16,17Despite recent studies on the topic, the pathophysiology of fatigue in PwMS is not yet fully understood. It is unanimously accepted that immunological factors are involved, but fatigue is also frequently associated with mood and sleep disorders, symptoms frequently reported in PwMS .The main objectives of the study were to identify the sociodemographic determinants of fatigue in a cohort of Caucasian PwMS and also to evaluate the existing relationship between the presence and severity of fatigue and other neuropsychiatric manifestations of the disease . Another objective was to examine how the sociodemographic determinants of fatigue differ between PwMS and healthy controls (HCs).We conducted a cross-sectional study involving 150 consecutive patients diagnosed with RR or SPMS who were under the care of the Regional MS Center in Targu Mures. These patients had been previously diagnosed and were actively receiving disease-modifying therapies as part of the MS treatment protocol. We also included a control group of 100 HCs.Inclusion criteria for the PwMS group were age > 18 years, signing the informed consent form, and an MS diagnosis according to the McDonald 2017 criteria [The inclusion criteria for the HC group were the absence of MS, signed informed consent form, and age > 18 years. The exclusion criteria for the HC group were almost similar to those applied to PwMS: the presence of any condition associated with a physical disability, diagnosis of a psychiatric condition, diagnosis of mental retardation, autoimmune diseases, or cancer.All PwMS and HCs completed a questionnaire with demographic questions , and tobacco and alcohol consumption). For PwMS, questions related to their disease were also included .The degree of disability was evaluated using the Expanded Disability Status Scale (EDSS), a unified scale evaluating the level of neurological impairment in PwMS in eight functional systems .Fatigue was assessed using one of the most suitable and appropriate tools for measuring the degree of fatigue: the Modified Fatigue Impact Scale (MFIS). The MFIS measures fatigue’s impact on normal and habitual physical, cognitive, and psychosocial functioning over the past four weeks. It comprises 21 questions, each answered with a value from 0 to 4. The scores for the physical (PS), cognitive (CS), and psychosocial (PSS) subscales and the total score are obtained by summing these values. The higher the score, the higher the level of fatigue and the greater its impact on the life of PwMS. A total score of ≤38 points is considered no fatigue impact, 39–58 points is considered a low fatigue impact, and ≥59 points is considered a high fatigue impact on the quality of life ,20.The level of depression was determined using the most frequently used depression assessment questionnaire in PwMS: the Beck Depression Inventory-II (BDI-II). The BDI-II comprises 21 questions assessing symptoms and behaviors suggestive of depression, such as feelings of guilt, pessimism, fatigue, and irritability; changes in sleep; and loss of appetite. Therefore, it assesses the somatic, cognitive, affective, and motivational symptoms characteristic of depression. Each question has four answers with scores from 0 to 3, giving a maximum score of 63. These scores can be used to define different degrees of depression, from mild to severe. A score of 11–16 indicates a mild mood disorder, 17–20 indicates borderline depression, 21–30 indicates mild depression, 31–40 indicates severe depression, and >40 indicates extreme depression [Cognitive disorders were assessed using the Symbol Digit Modalities Test (SDMT), one of the most sensitive tests for measuring information processing speed and general cognitive impairment. This test comprises a set of symbols assigned numbers from 1 to 9 and nine rows of symbols. The patient must indicate the number corresponding to each symbol within 90 s. The SDMT’s primary outcome is the total sum of the digits correctly indicated next to each symbol; the higher the score, the better the information processing speed. The oral version of the SDMT was used, and the number of correct answers was recorded .This study was performed according to the principles stated in the Declaration of Helsinki and approved by the local ethics committee.After the PwMS and HCs signed the informed consent form, they completed the questionnaire with demographic questions. For the PwMS, data related to MS were obtained from their anamnesis and medical files. The MFIS, BDI-II, and SDMT were later administered to all study participants. A neurologist then evaluated the PwMS, and their EDSS score was calculated.t-test. For non-parametric data, we used the Mann–Whitney U test and the Kruskal–Wallis test (with Dunn–Bonferroni post hoc correction).Differences between PwMS and HC regarding the outcome variables were described using standard statistics and evaluated for significance according to the empirical distribution toward normality probability. Normality was tested using the Kolmogorov-Smirnov test, quartile plots (Q-Q plots), skewness, and kurtosis. In the case of a normal distribution, we applied parametric tests such as the p-values. The Chi-Square test was used to determine whether there was a statistically significant association between two categorical variables. Linear univariate and multivariate regressions were employed to evaluate associations between fatigue and demographic and disease-related data in PwMS, considering the MFIS score as the dependent variable.Correlations between continuous variables were evaluated by calculating Pearson’s or Spearman’s coefficients, depending on whether the linearity assumption was satisfied. The coefficients were estimated with 95% confidence intervals and The statistical analysis was conducted using SPSS version 26, and GraphPad Prism version 9.3.0 was utilized for figure generation.This study included 150 PwMS and 100 HCs. The demographic and disease-related data of the PwMS and HCs are presented in p = 0.1100, chi-square test). Regarding marital status, the percentage of divorced individuals was higher in the PwMS group (8.7%) than in the HC group (1.0%). The percentage of individuals with children was significantly higher in the PwMS group (69.3%) than in the HC group . However, BMI did not differ significantly between the PwMS and HC groups.PwMS and HCs did not differ significantly in education level . Regarding occupational status, fatigue levels were significantly lower among PwMS who worked full-time or were students than PwMS who worked part-time, were on a disability pension, or were retired (p = 0.0001). However, fatigue levels did not differ significantly by sex or occupational status among HCs.The influence of patient sex on fatigue was statistically examined using the Mann–Whitney test. Fatigue levels were significantly lower among males than females PwMS (p = 0.0070). In contrast, single HCs had the lowest fatigue levels, followed by unmarried HCs living with a partner and married HCs, with widowed or divorced HCs having the highest fatigue levels (p = 0.0040)  .p = 0.0180 and p = 0.0001, respectively). However, we consider it important to mention that PwMS with children were older than those without children (45.66 ± 7.95 vs. 34.7 ± 9.1), respectively, they had higher values on the BDI-II (11.72 ± 10.48 vs. 8.08 ± 7.47). However, fatigue levels were not significantly associated with smoking or alcohol consumption in PwMS or HCs.In both PwMS and HCs, those with children had significantly higher fatigue levels than those without children . While 90% of HCs and 58.7% of PwMS had normal results, 9% of HCs and 22% of PwMS had moderate mood disorders; 1% of HCs and 7.3% of PwMS had clinical depression; 0% of HCs and 6.7% of PwMS had moderate depression; 0% of HCs and 2.7% of PwMS had severe depression; and 0% of HCs and 2% of PwMS had extreme depression. Therefore, 18.7% of PwMS had clinically significant depression .Similarly, based on BDI-II scores, HCs with clinical depression had significantly higher fatigue levels than those with a normal BDI-II score . In PwMS, PS, CS, and PSS scores were influenced by their degree of disability, cognitive performance, and mood disorders. While their age influenced the PS and PPS scores, it did not influence their CS scores .The scores for the three MFIS subscales were also evaluated by clinical MS form. Patients with RRMS had significantly lower PS, CS, and PSS scores than patients with SPMS  and PSS (p = 0.0010) scores than male PwMS.The scores obtained for the three subscales did not differ significantly by sex in HCs. However, female PwMS had significantly higher PS . There was no statistically significant age difference between treatment groups.We compared the MFIS scores among patients treated with interferon β, glatiramer acetate, and natalizumab. Patients treated with natalizumab presented significantly higher fatigue levels compared to the interferon β and glatiramer acetate treated groups  and antispasmodic and anxiolytic therapies .Fatigue is also influenced by disease evolution, and is more severe in periods of MS exacerbation. Physical exertion and heat exposure also play a role, with fatigue worsening during the day, especially in the summer months ,23.Fatigue is a complex symptom comprising three domains . For this reason, self-rated questionnaires were developed to evaluate all three aspects of fatigue in PwMS. One of the most used of these is the MFIS, which comprises 21 questions ,9. The tOur study evaluated modifiable and non-modifiable determinants of fatigue in PwMS and HCs. Age was a non-modifiable determining factor of fatigue in both groups. However, sex influenced fatigue severity only in PwMS, with male PwMS having significantly lower fatigue levels than female PwMS. In HCs, fatigue levels did not differ significantly between males and females. When the sex and age of PwMS were examined as predictors of fatigue in the three MFIS subdomains, they influenced the PS and PSS but not the CS. Studies have reported contradictory findings on the influence of sex on fatigue. Trojan et al. reported no statistically significant difference in fatigue severity between female and male patients .The exact reasons for the gender differences in the experience of fatigue in MS are not entirely understood; there are several factors that may contribute to higher fatigue levels in female MS patients compared to males. These factors include hormonal differences, sociocultural factors, immune system differences, and coexisting conditions. Fluctuations in hormone levels, particularly estrogen, can impact fatigue levels. Women experience hormonal changes throughout their menstrual cycle, during pregnancy, and in menopause, which can influence fatigue. Gender roles and societal expectations may also play a role. Women often take on more responsibilities related to family and caregiving, which can be physically and emotionally taxing, and this can contribute to fatigue. Additionally, women may be more likely to report fatigue and seek medical attention, leading to a perception of higher fatigue levels ,34.Weiland et al.  found thRegarding the disease-related data, as in Weiland et al. , fatigueRegarding modifiable lifestyle factors, our study found alcohol consumption, smoking, and BMI not to be factors determining fatigue in PwMS or HCs, contradicting a previous Australian study . These cIn our study, depression was a significant predictor of fatigue in both PwMS and HCs in univariate analyses and remained a contributor to fatigue in the multivariate regression models. In addition, depression was associated with increased scores for all three MFIS subdomains in PwMS and HCs. Other studies have reported similar results, suggesting that fatigue and depression share common mechanisms, such as psychosocial factors and brain lesions ,40. ConsCognitive impairment was a predictor of PS and CS fatigue in both PwMS and HCs but of PSS fatigue only in PwMS. Previous studies have found a significant relationship between fatigue and cognitive impairment spatially in the memory and attention domains, raising the hypothesis of a bidirectional relationship between the two symptoms: the presence of fatigue implies an increased level of attention and concentration for the performance of cognitive tasks, while attention disorders can interfere with the performance of some activities, resulting in increased fatigue ,43,44,45Considering the increased prevalence of fatigue among PwMS and its significant negative impact on their quality of life, work capacity, and daily activities, identifying and treating fatigue is essential. The multifactorial etiology of fatigue makes its treatment challenging for clinicians. Currently, treatment for fatigue is limited to pharmacological therapy with antidepressants and wake-promoting agents, cognitive behavior therapy, exercise therapy, and cooling therapy ,46,47,48Our study reinforced the view that, besides numerous sociodemographic and non-modifiable disease-related factors, fatigue is also associated with diverse modifiable factors. Regardless of fatigue’s pathophysiological mechanism, identifying these potentially modifiable factors should be valuable for clinicians because their control could be associated with a significant decrease in the fatigue level of PwMS. The severity of fatigue could be significantly affected by the early identification of conversion from RRMS to SPMS and escalation of disease-modifying therapy, identification of depression and institution of appropriate antidepressant treatment, and identification of cognitive disorders and initiation of cognitive behavioral therapy.In relation to differences in fatigue levels depending on the choice of disease-modifying therapy, we observed that patients treated with natalizumab had significantly higher MFIS scores compared to those receiving interferon β and glatiramer acetate. Natalizumab is known for its effectiveness in managing disease activity in PwMS. However, it is important to acknowledge that there are patients who exhibit a suboptimal response to natalizumab and describe a phenomenon known as “wearing off”. A study conducted by Bringeland GH et al. revealed that patients experiencing the “wearing off” phenomenon reported higher levels of fatigue compared to those without this phenomenon. One limitation of our study is that we did not assess the presence of the “wearing off” phenomenon in patients treated with natalizumab. Additionally, another possible explanation for the higher MFIS scores among patients treated with natalizumab could be attributed to the significantly higher degree of disability observed among patients in this treatment group .The main limitations of our study are its relatively small number of participants and the possibility that the data it collected may have been influenced by responder bias, especially the answers related to lifestyle . Another limitation of the study is the fact that patients with primary progressive multiple sclerosis were not included because, at the time of the study in our country, ocrelizumab was not reimbursed for the treatment of primary progressive multiple sclerosis. Nevertheless, our study is strengthened by its evaluation of associations between fatigue and various disease-related factors , mood disorders, and cognitive performance in addition to demographic factors.Our study revealed a strong correlation between the severities of fatigue, overall and for each MFIS subdomain, and depression (cognitive disorders) in PwMS and HCs. The other factors determining fatigue in PwMS were their degree of disability and disease duration. Considering the significant impact of fatigue on the quality of life of PwMS, clinicians must diagnose fatigue in PwMS as early as possible, identify its modifiable determinants, and manage it effectively to increase their quality of life.


Evaluating signs of anxiety related to body appearance is becoming increasingly important in contemporary society and, in this sense, the Social Appearance Anxiety Scale (SAAS) seems an interesting alternative of measurement.To evaluate the psychometric properties of the Portuguese version of the SAAS when applied to Brazilian adults who practice physical exercise and verify the influence of individual characteristics on participants’ social appearance anxiety.This was a cross-sectional study conducted online. The participants completed the SAAS and a demographic questionnaire. The psychometric properties of the SAAS one-factor model were evaluated using confirmatory factor analysis. A structural model was built for men and women to verify the influence of individual characteristics of the participants on social appearance anxiety.1,495 individuals participated in the study . The data obtained with the SAAS presented good indicators of validity and reliability for both genders . For both men and women, greater levels of social appearance anxiety were observed among younger participants, who had a higher body mass index, self-reported an eating disorder, and perceived a change in their body after the onset of the COVID-19 pandemic. For women specifically, higher income and having started physical exercise more recently were associated with greater levels of social appearance anxiety.The findings supported the validity and reliability of the data obtained with the SAAS and revealed that when investigating social appearance anxiety in future research and clinical protocols, specific individual characteristics should be considered. This emotion is understood as a future-oriented, long-acting, and broadly focused response to a threat . When anThe concept of social physique anxiety emerged in the 1980s when The SAAS has 16 items, written as statements, with a 5-point Likert-type response scale . The scaUnderstanding the relationship between social appearance anxiety and individual characteristics is also relevant to develop appropriate scientific and clinical protocols. Relationships have been found between higher scores of social appearance anxiety and younger age, as well as unemployment . CharactGiven the above, this study was conducted with two aims. The first aim was to evaluate the psychometric properties of the Portuguese version of the SAAS when applied to Brazilian adults who practice physical exercise. Given the evidence that the SAAS one-factor model  presents adequate parameters of validity and reliability in different contexts, the hypothesis of the present study was that this model would also present good psychometric indicators in the Brazilian context. The second aim was to verify the influence of participants’ characteristics on social appearance anxiety. For that, a structural model was elaborated, which included the characteristics mentioned in the literature as relevant to social appearance anxiety. This model was tested for men and women separately, as these groups have different experiences in relation to body image, which is already well explained in the literature . For the22.1This was a cross-sectional study with non-probabilistic sampling. The minimum sample size was estimated using the recommendation of 10 participants for each parameter estimated in the factorial model of the SAAS and in the final structural model . A total2.2Brazilian adults who self-reported practicing physical exercise – including those performed at home, in a public environment such as parks or squares, or at gyms – participated in the study. Inclusion criteria were age between 18 and 59 years old and exercising for at least 3 months. Exclusion criteria were pregnant or lactating women. To ensure the eligibility of participants, a screening questionnaire was used before the definitive data collection.Data collection was conducted from May to August of 2021 through a non-probabilistic online survey using Google Forms® . When accessing the survey, participants had to be logged in with an e-mail to limit one response per person. Participants were invited to voluntarily complete the survey through various channels, aiming to obtain a broad and diverse sample. Initially, the invitations were sent from the researchers’ email addresses to the directors and coordinators of several undergraduate and graduate courses at state, federal, and private universities in Brazil. This strategy especially reached the academic community . Additionally, the research was also promoted through WhatsApp groups and Instagram profiles, where the research aims, and participation process were shared. When individuals agreed to participate, they were asked to promote the study to other people through their social networks and individual groups, adopting a snowball sampling strategy.All individuals who agreed to participate first voluntarily accessed the link to the survey and signed the informed consent form (first page). Afterward, the participants completed the items for sample characterization (demographic questionnaire), followed by the SAAS and the SPAS. This study followed the guidelines of the Declaration of Helsinki and was approved by the Research Ethics Committee of the Faculty of Pharmaceutical Sciences of Araraquara (UNESP) (CAAE: 22051619.8.0000.5426).2.32.3.1A demographic questionnaire was developed by the authors to include data on age, gender , region of Brazil residing , marital status , race , level of education , work activity , monthly family income , weight and height, how long have been exercising  and weekly frequency (1 to 7 or more), and if they were a professional athlete . Having already received a medical diagnosis of an eating disorder  and self-perception of changes in body appearance and exercise practice after the onset of the COVID-19 pandemic  were also included.2.3.2This scale was originally proposed in English by 2.3.3This scale was originally proposed in English by 2.3.4The questionnaire was proposed by 2.4Analyzes were performed separately for men and women. First, mean, median, mode, standard deviation, skewness, and kurtosis were calculated for all SAAS items to verify the distribution of participants’ responses and identify the psychometric sensitivity of the data. Absolute values of skewness and kurtosis lower than 3 and 7, respectively, were indicative of a non-severe violation of the normal distribution of the data .λ) of each SAAS item was calculated and those with values greater than 0.50 were kept in the model. To assess convergent validity, that is, the adequacy of the items for the composition of the factor, the average variance extracted (AVE) was estimated from the values of the factor loadings (p < 0.05).The factorial validity of the SAAS one-factor model was estimated using Confirmatory Factor Analysis (CFA). The Weighted Least Squares Mean and Variance Adjusted (WLSMV) estimation method was used. The goodness-of-fit indices used in CFA were the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Standardized Root Mean Square Residual (SRMR) . Values loadings . Values loadings . To asseloadings  and the loadings . In thisα) and omega (ω) coefficients and values ≥0.70 indicated adequacy  was randomly split into two parts and the analysis was performed for men (subsample 1: n = 213 vs. subsample 2: n = 213) and women (subsample 1: n = 529 vs. subsample 2: n = 529). Initially, the configural invariance (baseline model) was guaranteed. Subsequently, the equivalence of factor loadings (metric model), thresholds , and residuals  was evaluated, which were compared two by two through the difference (Δ) in the CFI values. When a reduction in CFI values < −0.01 was observed, the one-factor SAAS model showed strong invariance  were analyzed and compared to the critical ratios of z , monthly income , BMI (continuous), length of exercising , self-reported diagnosis of an eating disorder , and self-report of change in body appearance during the COVID-19 pandemic . The pathways . The mean BMI of the total sample was 24.7 (SD = 4.7) kg/m2 . GSLTPAQ data showed that 87.8% of the participants were physically active , 8.8% were moderately active  and 3.4 insufficiently active . No participant was sedentary, as this was an exclusion criterion.The mean age for the total sample was 29.5 (SD = 8.9) years . The correlation between SAAS and SPAS was 0.11 (p < 0.05) for men and 0.21 (p < 0.05) for women.About SPAS, the one-factor model presented adequate fit to the data of both samples of the present study , direct comparisons were not possible. In the present work, the convergent validity was analyzed from the factorial loadings of the items to investigate the extent of shared variance to assess the construct. This methodology was proposed by  and is rr = 0.59, p = 0.005) between the SAAS and SPAS for the total sample including men and women. This value in our study had a slightly smaller magnitude, and this disparity may be due to cultural issues, indicating that, in the Brazilian context, participants may have perceived the concepts captured by the SAAS and SPAS as different.The study that developed the SAAS  and otheα and ω > 0.86), demonstrating that the SAAS one-factor model was able to produce consistent results to assess the latent concept . These findings corroborate with the original study of the SAAS . 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.GD: Conceptualization, Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft. JC: Conceptualization, Methodology, Visualization, Writing – review & editing. PS: Conceptualization, Investigation, Methodology, Writing – review & editing. JM: Investigation, Methodology, Software, Writing – review & editing. LC: Conceptualization, Funding acquisition, Methodology, Resources, Software, Visualization, Writing – review & editing. WS: Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – original draft.


In terms of brushing, only Biolon samples showed statistically significant increased roughness after brushing with all three types of brushes, in comparison to Erkodur A1, where differences were not statistically significant. Thermocycling increased the mass of all samples, but a statistically significant difference was found only in Biolon (p = 0.0203), while after brushing, decreased mass was found in all specimens, statistically significant only in Essix C+ (CS 1560: p = 0.016). PETG material showed instability when exposed to external influences- thermocycling produced an increase in roughness and mass, and brushing mostly caused an increase in roughness and decrease in mass. Erkodur A1 demonstrated the greatest stability, whereas Biolon demonstrated the lowest.The aim was to assess the impact of thermocycling and brushing on the surface roughness and mass of PETG material—the most commonly used for orthodontic retainers. A total of 96 specimens were exposed to thermocycling and brushing with three different kinds of toothbrushes depending on the number and thickness of the bristles. Surface roughness and mass were evaluated three times: initially, after thermocycling, and after brushing. In all four brands, both thermocycling and brushing increased surface roughness significantly ( Retention and dental arch form maintenance after active orthodontic treatment is a very challenging part of the orthodontic specialty. There are several types of retainers available on the market, and it is up to the orthodontic clinician to decide which one is most suitable for the situation.In 1971, Ponitz  proposedThermoplastic orthodontic retainers are becoming more popular each day. Low hardness, transparency, aging resistance, high elasticity, and resilience are desirable qualities of these orthodontic appliances . There aPETG material, on the other hand, is less common in industry than PET material since it cannot crystallize [The PET-G  is an amorphous copolymer of PET with the additional glycol group ,9. This stallize . Howeverstallize .It is synthesized by the polycondensation of 1,4-cyclohexanedimethanol, ethylene glycol, and terephthalic acid in certain proportions ,9. It haMoreover, manufacturing of thermoplastic retainers is simple and low cost. Even auxiliary personnel with only minimal instruction and experience can fabricate these retainers. No knowledge about acrylic material or technical proficiency in wire bending is necessary .Thermoplastic retainers are easily accepted by patients due to their simplicity of application and esthetic appearance because they are made of versatile transparent polymers ,17,18,19Patients are advised to maintain the hygiene of their retainers. The cleaning of retainers is of great importance in terms of oral and systemic health . Their pThere are no studies addressing the impact of brushing on the mass changes of thermoplastic materials at the moment. It has been proved that thermocycling can also increase the surface roughness of various dental materials ,35. IhssThe null hypothesis of the study is that brushing and thermocycling do not affect the surface roughness and mass of thermoplastic PETG material.For the purpose of this study, ninety-six specimens were prepared from polyethylene terephthalate glycol (PETG), the most commonly used material for orthodontic retainers. PETG material from four different manufacturers was evaluated in this study: Erkodur-A1 , Biolon , Track A  and Essix C+ . The thickness of the material was 1 mm. Models were prepared according to the protocol of the previous study . A totalThe maximum number of thermocycles (50 cycles) that could be acquired from the literature to simulate one day’s wear was used . A commoSimulated brushing was performed on a self-made device modeled after SD Mechatronik Germany. Depending on the number and thickness of bristles, three different kinds of toothbrushes were used to brush the samples . Each type of toothbrush was used for eight samples of each thermoplastic material brand. To mimic 30 days of cumulative brushing, each specimen was brushed for a total of 30 min–15 min on each side (top and bottom) (30 s per day). The cleaning force was set to 2 N, and the cleaning movement was set at 3 cm longitudinally at a rate of 120 strokes per minute [The surface roughness of each specimen was measured with a high-precision profilometer , according to the ISO 4287:1997 standard  at the cSurface roughness and mass were evaluated three times: initially, after thermocycling, and after brushing. After thermocycling, each specimen was dried with a paper towel to ensure accurate mass and roughness measurements.The study was approved by the Ethic committee of the School of Dental Medicine, University of Zagreb .p < 0.05 was considered significant.An analysis of data normality using the Shapiro–Wilk test and asymmetry tests revealed a non-normal distribution of initial surface roughness and mass values. Considering there was no difference in top side initial surface roughness values between brand groups, Wilcoxon matched pairs test was applied for intragroup comparison , while for intergroup comparison Kruskal–Wallis test with post hoc Dunn’s test for multiple p comparison. Bottom side specimens showed statistically different values in initial measurement, so the effect of thermocycling and brushing was presented as a change. One-way ANOVA was utilized for analysis with the post-hoc Tukey HSD test since, according to the Leaven test, the assumption of homogeneity of variances was met. p = 0.01). The highest mass was found in Essix C+, while the lowest was in Erkodur A1 samples.The initial surface roughness values of the top side did not vary significantly between the studied brands. Track A had the lowest values, while Essix C+ had the greatest .. Essix p < 0.01). The effect of thermocycling on surface roughness was highest in Erkodur A1 and least in Biolon .Thermocycling increased the mass of all brands tested, but only Biolon showed a statistically significant rise (p < 0.01)  .p = 0.0006). Within brand group analysis revealed statistically significant differences in surface roughness between ultra-soft (CS 5460) and soft (CS 1560) toothbrushes in Biolon (p = 0.0043) and Essix C+ (p = 0.021) samples. In Erkodur A1 and Track A specimens, differences were insignificant.According to the number and thickness of the filaments, a statistically significant difference was found between ultra-soft (CS 5460) and soft toothbrushes (CS 1560) in all samples , while in Erkodur A1 samples, the difference was not statistically significant.Surface roughness significantly increased in Biolon samples after brushing with all three types of toothbrushes compared to values after thermocycling (CS 5460: Super soft (CS 3960) and soft (CS 1560) toothbrushes significantly increased roughness in Track A and Essix C+ samples .p = 0.0124). Only the CS 1560 toothbrush caused a significant decrease (p = 0.028) in Essix C+ specimens.Brushing caused a decrease in mass in all examined specimens . The effect was highest in Track A and lowest in Erkodur A1   . In all p = 0.028; p = 0.03; In Biolon and Essix C+ samples, surface roughness increased significantly after they were exposed to a soft brush in comparison to an ultra-soft brush . This weight reduction of the specimens after brushing may be the result of the removal of the absorbed water particles adhering to the surface of the sample . AccordiFinal surface roughness values after exposure of PETG material to thermocycling and brushing were all above 0.2 μm, which is clinically relevant in terms of bacterial adhesion ,54,55. FIn the present study, thermocycling and brushing simulated the wear of the material for 30 days. This could be a limitation of this study. It would be more accurate to simulate a period of one or even a few years because removable retainers are usually replaced every two to three years. Future studies are needed to investigate the duration of the thermoplastic retainer and the period when the worn-out retainer needs to be replaced with the new one. Another limitation is that we only observed PETG, and the stability of other orthodontic retainer materials was not evaluated. In future studies, other thermoplastic materials should also be assessed. Moreover, the flat shape of our specimens did not completely reflect the configuration of the real retainer. Furthermore, investigation of other mechanical and stability properties of orthodontic retainer material is recommended. In vivo studies would be more accurate.PETG material showed instability when exposed to external influences- thermocycling produced an increase in roughness and mass, and brushing mostly caused an increase in roughness and decrease in mass. Of all brands, Erkodur A1 demonstrated the greatest stability under external influence, whereas Biolon demonstrated the lowest. Due to the results of this study, it should be proposed to our patients that retainers, when they are not in use, need to be kept on some dry surface/place, away from the water and humid environment. Moreover, hot drinks and food need to be avoided. Removable retainers should be brushed with an ultra-soft brush in order to maintain the surface stability of the material.


Berscheid (1999), taken from Lacunza & Contini (2016), indicated that social relations were the foundation of the human condition. From positive psychology, Park et al. (2013) point out good relationships as a factor that contributes to a good psychological life, since they provide emotional and instrumental support in times of stress and challenge, indicating, in turn, normal evolutionary development and the avoidance of psychopathological problems.To establish the relationship between positive relationships and the presence of stressors in adolescents.A cross-sectional, descriptive-correlational study was carried out in 109 (N= 109) adolescents. The SISCO Inventory was used to study academic stress as well as the Ryff Psychological Well-Being Scale.A negative magnitude correlation was found between positive relationships and stressors. ( Table 1).As a secondary result, 60.6% of the evaluated adolescents presented life purpos as the factor with the highest score in the psychological well-being variable. This points to authors such as Erikson , who define adolescence as a space characterized by feelings of creativity, productivity, new ideas, and a period of cognitive and social maturation, which leads to a definitive commitment to life itself. (Graph 1).Image:Image 2:Positive relationships in adolescents decrease the presence of stimuli considered stressors.None Declared


This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD).The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30–F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model.β = −0.02, SE = 0.02), social dysfunction , time interval between first onset and first dose , polarity at onset , and times of hospitalization . We assessed the ability of the nomogram model to recognize suicidality, with good results . Indicating that the nomogram had a good consistency . The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks.LASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts. According to the World Health Organization (WHO) statistics, approximately 70.3 million people commit suicide each year, equating to one suicide every 40 s . SuicideIn recent years, numerous studies have investigated the mechanisms underlying suicide to identify accurate risk factors. Previous research has suggested that Hypothalamic–pituitary–adrenal (HPA) axis activity is implicated in suicide risk, regardless of whether psychiatric conditions are present or not. Additionally, abnormalities in the HPA axis, particularly hyperactivity, may significantly influence suicide risk, while impaired stress response mechanisms also contribute to this risk . MoreoveConversely, many researchers propose that approximately 90% of suicide attempts are accompanied by a mental disorder, with emotional disorders being one of the primary contributing factors . The sitThis study aims to identify effective predictors for suicide risk in female patients with MD who have attempted suicide by analyzing their sociodemographic and clinical characteristics. The goal is to develop a predictive model that establishes the association between suicide attempts and diagnosed affective disorders in females. To achieve this, the study will utilize the LASSO Logistic Regression model and nomogram model to systematically analyze risk factors related to suicide attempts in MD patients and establish a reliable predictive model.2.2.1.The present study was conducted at Beijing Anding Hospital from 2019 to 2021 and designed as a cross-sectional retrospective analysis. Two attending psychiatrists independently enrolled patients based on the diagnostic criteria for mood disorders in the International Statistical Classification of Diseases and Related Health Problems 10th edition (ICD-10) . All par2.2.The complete electronic medical records of female patients who met the admission criteria were transferred and reviewed by specialized psychiatrists. Personal information was removed to ensure anonymity. Patients with other psychiatric disorders such as schizophrenia, schizoaffective disorder, personality disorder, and intellectual disability were excluded from the study. Patients with a history of psychiatric illness comorbid with alcohol or drug abuse were also excluded. The use of atypical antipsychotics or antidepressant medications did not influence the data collection of enrolled patients.2.3.Detailed sociodemographic profiles and clinical characteristics were collected, including age, education, employment status, body mass index (BMI), marital status, income, residential address, age of first onset, duration of first onset, medication time (since first dose), psychotic symptoms at the first episode, polarity at onset, duration of overall illness, times of hospitalizations, social dysfunction, family history of mood disorders, and somatic diseases.Social function refers to a person’s ability to manage everyday activities, engage in leisure and occupational activities . The PerFollowing Silverman et al. , suicida2.4.p-value of less than 5% was considered statistically significant for comparisons between MD-S and MD-N. Multiple imputations were performed using the R MI package with five replications and a chained equations approach to handle missing data  and the R programming environment (version 4.0.2). Categorical data were presented as counts (percentages), while continuous data were presented as medians (interquartile ranges). Two-group comparisons were conducted using the Wilcoxon rank-sum test or χ2 test, as appropriate. A two-sided ing data .The LASSO regression model  was used3.3.1.A total of 396 hospitalized female patients  were included in the present study, and 48% of the patients had suicidal attempt. The baseline characteristics of all the participants are shown in 3.2.β = −0.02, SE = 0.02), social dysfunction , time interval between first onset and first dose , polarity at onset , and times of hospitalization .Totally 18 variables were included in the LASSO regression. 3.3.Since the relative contribution of each individual factor to the development of SA in MD may be small, it is important to consider the combined contribution of selected significant factors. We constructed a risk prediction nomogram model for SA in MD patients with the combination of the 5 risk factors mentioned above . To bettThe ROC analysis assessed the ability of the nomogram model to recognize suicidality, with good results  . The pre3.4.The constructed nomogram model was internally validated by Bootstrap resampling with 100 replicates. For MD with SA, the C-statistic in the derived cohort was 0.756 with good internal validation (bootstrapped C-statistic of 0.740) and excellent calibration of predicted and observed risks.4.In this study, we found five factors including BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization were significantly associated with the development of suicidality in MD patients. Specifically, we found that individuals who experienced depression at onset (as opposed to manic episodes) and those with social dysfunction were more likely to attempt suicide. On the other hand, higher BMI, timely hospitalization, and shorter intervals between first onset and medication initiation were associated with a lower risk of suicidal attempts. Importantly, the inclusion of these factors in the nomogram model can strongly predict the risk of suicide in MD patients with considerable accuracy.In detail, we concluded that MD patients with a first depressive episode was associated with a higher risk of SA. A prospective study by Daban et al.  involvinGenerally, suicide risk varied according to illness characteristics and stage of illness, with suicidal behavior mainly associated with depression and mixed phase illness. Major depressive episodes were associated with the highest risk of suicide, followed by the mixed phase and finally the manic phase, which was associated with the lowest risk of suicide . SA wereOur LASSO Logistic Regression analysis suggests that individuals with mood disorders (MD) and increased BMI are less likely to exhibit suicidal tendencies. These findings may be attributed to the treatment of MD, as it is well-recognized that regular use of drugs for psychiatric disorders can lead to adverse effects such as weight gain. For instance, antipsychotics (AP) used in the treatment of schizophrenia are known to cause weight gain in a significant percentage of patients, ranging from 15 to 72% . SimilarRegarding social dysfunction, our findings highlight that it is a risk factor that exacerbates suicidal behavior in individuals with MD. Appropriate social functioning forms the basis for the formation and development of satisfying and lasting relationships, which are essential for physical and psychological well-being throughout the lifespan . HoweverThe results of our analysis showed that two risk factors, the number of hospitalizations in MD patients and the interval between the first onset of first dose, had negative and positive feedback, respectively, with suicidal tendency. On the one hand, the former results of our analysis mean that patients who actively receive inpatient treatment with cooperative doctors have relatively stable disease control. On the other hand, if the first depressive or manic episode requires timely medication, the delay in the condition causes a poor outcome. However, study demonstrated that delays in help-seeking were common in MD patients even in offspring of patients with depressive and/or anxiety disorders . This inThis study has several limitations. First, the nature of this study was cross-sectional and could not confirm the causal relationship between risk factors and outcomes, because the time dimensions of risk factors and SA do not correspond (with some “risk factors” occurring after SA onset). Although an advanced analytic technique is employed, the data are cross-sectional, risk factors studied in this manuscript are well-established that make interpretation of results attenuated. So future cohort studies with higher levels of evidence are needed for further determination. Secondly, the lack of external validation prevents the nomogram model from being better replicated in other centers. However, the results of our single-center study may provide a reference for suicide prevention for female with MD nationwide. Thirdly, further sample size expansion is needed. An insufficient sample size might have influenced the validity of the nomogram model.5.In summary, several factors influence the development of suicidality in individuals with mood disorders, including polarity of onset, social dysfunction, BMI, number of hospitalizations, and interval between first onset and medication. The nomogram model developed in this study holds potential in facilitating early identification and implementing prevention strategies, thereby improving the prognosis of suicidal attempts in this population.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving humans were approved by Beijing Anding Hospital, Capital Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin because the present study was designed as a cross-sectional retrospective analysis. All participants were informed about the research at the time of admission and provided consent for the anonymous sharing of their medical records.SS had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. JZ and SL: formal analysis, methodology, writing – original draft, and writing – review and editing. XL, DL, FZ, and LX: data curation and conceptualization. JL: critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.This study was supported by High-level Public Health Technology Talent Development Program, Project Training Plan Number: Discipline Backbone-02-37.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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.