The aim of the current study is to investigate the relationship between prevolitional processes and video game playing. Models of attitude, the model of goal-directed behavior (MGB) and the extended model of goal-directed behavior (EMGB) are tested with structural equation models to analyze the process that leads to video game playing. More specifically, the roles of affective, motivational, habitual processes in video game playing and the goal underlying video game playing are examined. The participants were 210 video game players who completed measures of Attitude, Subjective Norms, Perceived Behavioral Control, Behavioral Desire, Anticipated Emotions, Intention to Play, intensity of actual and past video game playing (Playing Behavior and Past Playing Behavior) and Goal Desire. The results showed that the initial MGB did not achieve a satisfactory fit and thus, a revised model with more acceptable fit was proposed. It was found that anticipated emotions and attitude are significant predictors of desire to play; desire to play, perceived behavioral control, subjective norms and attitude are significant predictors of intention to play and intention is a significant predictor of playing behavior . Moreover, past playing is a stronger significant predictor of behavior itself than of prevolitional processes in video game playing. Goal desire within the EMGB is a significant predictor of desire to play and the relationship of goal to playing behavior is indirect. Nevertheless, goal desire has an important role in the prevolitional processes of video game playing. In the discussion, potential explanations are further explored. Video games have become increasingly popular over the last years and for many  people they are part of everyday life. The internet and technological innovations have contributed to the rapid development of video games. Video games have become more complex and sophisticated since their first introduction with more realistic graphics and storylines. Broadband internet has enabled gamers to play and interact online with each other. Due to the constant increase in video game playing and its popularity e. g. , it is iAccording to the TPB, the proximal cause of behavior is the intention to perform that given behavior. Besides intention, there are three fundamental constructs: (1) the attitude towards the behavior that corresponds to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question; (2) subjective norms (SN) which involve the perceived social pressure to perform or to not perform the behavior; and (3) perceived behavioral control (PBC), defined as the perceived ease or difficulty of performing the behavior. PBC is also posited as having a direct influence on behavior to the extent that it corresponds to actual behavioral control . AccordiHowever, little research has been devoted to the application of this model in the attitude towards video game playing. The fact that one desires to perform a certain action does not necessarily imply that he/she will intend to do it. People often have desires, fleeting or otherwise, that they never intend to act upon. Intention and desire are distinct and The original conception of the TPB mostly focused on the evaluative aspect without explicitly dealing with the specific contribution of the affective and cognitive components. Among the determinants of desire, the MGB also introduced the concept of anticipated emotions as predictors of intentions to act . AnticipThe authors also distinguish between attitude and anticipated emotions . An attiThe empirical evidence supports the importance of constructs such as intention and volition in predicting human behavior. On the other hand, there is evidence that automatic processes play an important role in human cognition and that they can direct behavior .The last improvement of the TPB concerns goals. Goals play a central role in the explanation of many behaviors because these behaviors are chosen as a means of goal achievement . In attiBuilding on the MGB, According to In the current study both these models  are tested. On the other hand, playing behavior can be better understood in light of the interplay between the goal and behavioral levels. The MGB can be enriched by focusing on the more distal determinants of an action such as the desire to achieve a given goal by means of some specific behavior and increase its predictive power. The EMGB incorporates the mechanisms and the processes through which the goal and the desire to achieve this goal influence behavior. In order to predict and understand playing behavior, this study is based on the (extended) model of goal – directed behavior that results from the well-known and widely adopted model of attitudes; the theory of planned behavior. In the MGB and the EMGB independent variables have been added as parallel predictors of behavioral intentions and behaviors  and to which the TPB has not paid sufficient attention.In testing this extended theoretical model  in the context of gaming behavior, the factors which contribute to video game playing can be identified. A detailed analysis of this behavior can be made in the meaning of how strong various determinants are in the prediction of the prevolitional processes that lead to playing as well as in the prediction of playing behavior itself. In other words, what is the role of the established predictors of the TPB in players’ attitude to play, perceived social pressure to play or perceived behavioral control over playing. Moreover, do affective processes play a role and if so, whether a player´s positive or negative (anticipated) emotions play a more important role and whether playing habits or player´s goals underlying the behavior are significant determinants. Knowledge about the key factors that contribute to video game playing can help us understand what is behind this specific behavior and why playing video games is such a popular activity. Exploring the processes that develop and maintain playing behavior can also help to prevent the negative phenomenon related to increasingly intensive playing.The present study has three main aims. The first aim is to test the MGB in video game playing. The predictive power of the MGB has been demonstrated in different behaviors from various domains e. g. . HoweverThe second aim is to investigate the role of past behavior in the MGB. Past behavior was introduced as a proxy for habit and automatic processes in the MGB . Yet, deConsidering that goals may be an important part of playing behavior, the third aim was to investigate their influence by applying the EMGB  in orderAlthough the study is interested in applying the MGB and the EMGB to video games playing, it is not being suggested that the MGB or the EMGB provide a general model or theory of playing behavior. The MGB and the EMGB are general models that focus on decision making processes and can be applied to a variety of behaviors. The MGB and the EMGB provide much more detail about cognitive, emotional or habitual processes in the specific action.The following hypotheses were proposed:Hypothesis 1: Behavioral desire (BD) will be related to attitude, SN, PBC and PAE and NAE.Hypothesis 2: Intention and PBC will be significantly related to playing behavior.Hypothesis 3: Past playing behavior will be significantly related to BD, intention and playing behavior.Hypothesis 4: In the EMGB, goal desire (GD) will not be a direct predictor of playing behavior.Hypothesis 5: In the EMGB, goal desire (GD) will be a significant predictor of behavioral desire (BD).M = 20.1; SD = 5.7) completed measures of Goal Desire, Attitude, Subjective Norms, Perceived Behavioral Control, Behavioral Desire, Anticipated Emotions (PAE and NAE), Intention to Play and actual intensity of video games playing (Playing Behavior) and intensity of video games playing 6 months ago (Past Playing Behavior).210 participants who were Slovak video game players  from 14 to 35 years old . In order to measure GD, participants were first asked, “What do you think would be the most likely reason why you would play video games?” After the participants had indicated their own reasons, they then responded to the following items by putting his/her own reason instead of letter Y. The desire towards his/her goal was measured with three items (“How strongly would you characterize your desire to reason Y?” “How likely is your desire to reason Y?” and “The intensity of your desire to reason Y can be described as?”). The reliability of this measure was very good .Attitude. Participants were presented with the stem “I think that for me video games playing is?” followed by nine bipolar scales . This achieved very good reliability .Subjective Norms (SN). SN were assessed by three items  .Perceived Behavioral Control (PBC). PBC was assessed with five items  and “If I wanted to, it would be easy for me to play video games”). The reliability of this measure was not satisfactory . The last item in the above list was not related to the others and was eliminated . The reliability of the adjusted measure was at the edge of acceptability and therefore was not excluded from the measures tested in the models.Anticipated Emotions. Positive Anticipated Emotions (PAE) were measured with five items. Participants indicated how delighted, proud, happy, pleased and satisfied they would feel if they succeeded in playing video games. Negative Anticipated Emotions (NAE) were also measured with five items. Participants indicated how disappointed, agitated, guilty, regretful and frustrated they would feel if they failed in playing video games. The reliabilities were high for both Negative (Cronbach’s Alpha was .933) and Positive Anticipated Emotions .Behavioral Desire (BD). BD was measured by three items . Very good reliability was obtained .Intention. Intention was assessed by three items . The reliability of this measure was satisfactory .Playing Behavior and Past Playing Behavior. These were measured by the questions “Approximately how many hours over the week do you play video games these days?” (Playing Behavior) and “Approximately how many hours over the week did you play video games 6 months ago?” (Past Playing Behavior).Participants also completed questions about the kind of video game that they played most frequently, their gender, age as well as choosing whether they were secondary school students, university students or employed.r = -.146, p = .035) and past playing behavior . It means the older video games players are, the fewer the hours they played video games in the past and the fewer the positive anticipated emotions they experience when they successfully play video games.Age did not correlate with attitude, goal desire, subjective norms, negative anticipated emotions, behavior desire, intention to play, perceived behavioral control or playing behavior. Age significantly correlated only with positive anticipated emotions . It means that the male players  reported a higher intensity of playing in the past than the female players . The descriptive characteristics measuring the variables are listed in Female and male players did not differ in attitude, goal desire, subjective norms, negative anticipated emotions, positive anticipated emotions, behavioral desire, intention to play, perceived behavioral control or playing behavior. Female and male players only differed in past playing behavior  participants listed for video games playing. The goals reported by the participants were: to have fun (31.4%), to avoid being bored (18.1%), to be with friends (15.2%), relax (10%), to escape from worry (10%), to win over other players (7.1%) and to advance in the game (6.2%).The models (the MGB and the EMGB further) were formally tested with structural equation models using AMOS 20. At first a full structural equation model was used in order to investigate the goodness of fit for the MGB. The predictive power of the model was tested by examining the amount of variance explained (R2) in the criteria .2 = 33.045, p < .001, χ2/df = 3.672, CFI = .953, TLI = .852, NFI = .938, RMSEA = .113, PCLOSE = .006). Post hoc modification indices suggested an improved fit by direct effect from attitude and subjective norms to intention. The respecified model generated an adequate fit  = 14.292, p = .046, χ2/df = 2.042, CFI = .986, TLI = .942, NFI = .973, RMSEA = .071, PCLOSE = .221). The standardized parameter estimates for the MGB are reported in Goodness of fit. Structural equation modeling was used to test the MGB. Attitude, subjective norms (SN), positive and negative anticipated emotions (PAE and NAE) and perceived behavioral control (PBC) were included as predictors of behavioral desire (BD). BD and PBC were included as predictors of intention. Intention and PBC were included as predictors of behavior . The initial model showed a poor fit  = 16.018, p = .025, χ2/df = 2.288, CFI = .986, TLI = .927, NFI = .976, RMSEA = .079, PCLOSE = .152). After entering past behavior, the beta weights form intention to behavior changed from .34 to .22. The statistical significance for any variables did not change. Hypothesis 3 was partially supported.In the second step, past playing behavior as a predictor of behavioral desire, intention to play and playing behavior were added in order to examine the additional influence of this construct. 2 = 17.552, p = .041, χ2/df = 1.95, CFI = .988, TLI = .940, NFI = .977, RMSEA = .067, PCLOSE = .236). After entering GD, the beta weights for attitude, PAE and NAE changed a little and became lower. However, the statistical significance for none of the variables changed. Goal desire related to playing behavior indirectly through behavioral desire. The standardized parameter estimates for the EMGB are reported in The addition of goal desire (GD) as a predictor of behavioral desire (BD) slightly but significantly increased the amount of variance explained in BD (from 57% to 58%)  and to investigate the goals underlying the behavior as well as the role of those goals in the determination of the desire to play (BD) and playing behavior . With structural equation modeling, the goodness of fit of the MGB and the EMGB model were tested.2 = 33.045, p < .001, χ2/df = 3.672, CFI = .953, TLI = .852, NFI = .938, RMSEA = .113, PCLOSE = .006). Adding direct paths from attitude and SN to intention improved the fit of the model  = 14.292, p = .046, χ2/df = 2.042, CFI = .986, TLI = .942, NFI = .973, RMSEA = .071, PCLOSE = .221).The first aim of this study was to investigate the motivational and affective processes within the MGB that lead to video game playing. It was found that anticipated emotions  and attitude were significant predictors of behavioral desire (BD); BD and perceived behavioral control (PBC) were significant predictors of intention and intention was a significant predictor of behavior. The desire to play (BD) did not fully mediate the direct effect of subjective norms (SN), PBC and attitude on the intention to play. However, the fit of the initial model was not satisfactory . Under these circumstances, self-efficacy beliefs can play a major role in influencing intention. As This study also sheds light on the role of PBC. PBC was significantly related to intentions although PBC has no significant connection to BD or to behavior. The participants were players and obviously considered video games playing as a rather easy behavior  = 16.018, p = .025, χ2/df = 2.288, CFI = .986, TLI = .927, NFI = .976, RMSEA = .079, PCLOSE = .152). Past playing has a much stronger effect on playing behavior itself than on prevolitional processes that lead to playing behavior and increased the amount of variance explained especially in behavior. It seems meaningful because automatic processes that are behind  past playing can directly influence actual video games playing without being mediated by the desire to play (BD) and being mediated by intention only marginally. Habit strength and its direct effect on media use is also emphasized in another model; the model of media attendance  = 17.552, p = .041, χ2/df = 1.95, CFI = .988, TLI = .940, NFI = .977, RMSEA = .067, PCLOSE = .236). The current results confirmed that video game playing can be motivated by many different goals. The contribution of GD on BD was significant and the desire towards the goal one wants to achieve by video games playing (GD) did not relate directly to the emergence of the behavior. However, it had an indirect association through the desire towards video game playing (BD). In other words, wanting to have fun or win over other players will not directly increase the likelihood of actual video game playing, but rather relates to the desire to play video games (BD). The results also reveal that the desire to have fun or win over other players (GD) not only predicts the desire to play video games (BD), but has a more important role than more intuitively compelling and well-established constructs. Indeed, SN and PBC did not play a critical role, whereas PAE, NAE and attitude were significant predictors of one’s desire to play video games (BD). In fact, the emotional and motivational processes that were taken into account by including anticipated emotions and GD, respectively, together with attitude, were three of the main significant predictors of BD. One should also note that there is a consistency between the goals expressed by participants in this study and the significant contribution especially of PAE in determining the desire to play video games.Considering that goals may be an important part of video game playing, it was proposed that their influence should be investigated more thoroughly by applying the EMGB proposed by The limitations of this study need to be acknowledged. This study is correlational and therefore may shed light only indirectly on the causal mechanisms underlying decision-making processes. Nevertheless, the results were consistent with the hypothesized theoretical framework. Future experimental studies could manipulate the key variables explicitly. It also needs to be acknowledged that procedures used to measure some constructs may be improved. Past behavior was introduced as a proxy for habit and automatic processes in the MGB , 2004a aDue to the increase in gaming behavior, it is important to research the prevolitional processes that lead to playing games. The study tested two theoretical models: the model of goal–directed behavior (MGB) and the extended model of goal-directed behavior (EMGB) , 2004a iThe comparison between the predictive power of the tested models for  desire to play, intention to play and playing behavior have shown that the extended models (the MGB with past playing behavior and the EMGB) accounted for more variance than the basic MGB. When applied to video game playing, the basic MGB without past playing behavior (the model of attitude that considers behavior as planned and largely determined by the intention to perform it) explained 57% of the variance for the desire to play, 54% of the variance for intention to play, but only 11% of the variance for playing behavior. The MGB with past playing behavior accounted for more explained variance especially in playing behavior and the EMGB accounted for slightly more variance in desire to play.Practitioners may benefit from knowing the processes that lead to playing games. The determinants of the MGB or the EMGB have a different role in this process. The results have suggested that anticipated emotions and attitude seem to be connected with more distal determinants of playing behavior; the desire to play. This construct does not necessarily imply that the player will intend to play. However, the desire to play was the strongest predictor of a more proximal determinant of playing behavior and together with perceived control/perception of control over playing (PBC), perceived social pressure to play (SN) and attitude, had a direct effect on the intention to play. Only two determinants had a direct effect on actual playing behavior; the intention to play and past playing behavior. Therefore, it is necessary to know a player´s playing habits when analyzing the process of developing and maintaining playing behavior. Moreover, past playing was a stronger predictor of playing behavior itself than of prevolitional processes that lead to playing games. The results have also suggested that the player´s goals played an important role in the prevolitional processes of video game playing. While their relationship to playing behavior was indirect, goal desire had an important role in the desire to play.Based on the findings, there is an assumption that habitual or automatic processes in the prevolitional processes that lead to playing should not be omitted because they can play a role in playing behavior. The current results also support the view that the interplay between goal and behavioral levels of analysis represents an important element in understanding the decision making process that leads to video game playing.


We read with interest the validation study of the commercially available CheckMe blood pressure (BP) monitor by Schoot et al . We agreOne of the reported strengths of the study is that it met European Society of Hypertension International Protocol (ESH-IP) validation guidelines . HoweverFirst, presenting correlation coefficients and scatterplots can help readers assess the relationship between measurements from the devices. Next, the authors present a relative difference in mean systolic BPs, which is near zero. Presenting mean absolute difference between devices would be more informative to the accuracy of the device . A modifWe recognize that it is not possible to adhere to the ESH-IP with novel cuffless BP-measuring devices, since the ESH-IP assumes that the tested monitor does not require user-specific calibration. In response to the ongoing interest in cuffless BP measurement the Institute of Electrical and Electronics Engineers (IEEE) released a guideline specifically for the validation of these devices . BecauseIn conclusion, we encourage the authors to present additional analyses and results to improve the understanding of the CheckMe’s performance, and to follow up with a formal IEEE-protocol validation study. In the present study, the device is reported to be a Conformité Européenne-approved category IIa category medical device compliant with directive 93/42/European Economic Community, but it did not meet that directive’s accuracy requirements EN 1060-3). The CheckMe has United States Food & Drug Administration approval for measurements of heart rate, oxygen saturation, temperature, and activity, but not for measurement of systolic blood pressure [0-3. The


Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum.In order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest.A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer.Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy. A polyp is an abnormal tissue growth and is commonly found in the intestine . Since aCecal intubation time (CIT) provides various data, which may be important indicators . In addiInformation about the time-location of the cecum can also be helpful when checking colonoscopy videos. Because analyzing a video requires much time and concentration, it places a great burden on the physician, especially when the doctor re-watches the video or shares the video due to change of doctor or hospital . KnowingMoreover, the movement data of the colonoscope can be utilized for proficiency assessment . Since iRecently, with advances in computer technology and equipment, gastroenterologists do not need to record these data anymore . In variHorn–Schunk algorithms are the most popular differential algorithms that have been used for many applications and have been referenced for many performance evaluation models . The HorTherefore, in this study, we aimed to provide more useful information to physicians by recording the direction of the scope’s movement and time-location of the cecum using Horn–Schunk algorithm by applying convolutional neural network (CNN).This study was approved by the institutional review board of the Seoul National University Hospital (IRB No. 1509-062-703), and it was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants before any study-related procedures were performed. This prospective, single-center trial enrolled patients aged 19 to 75 years who underwent colonoscopy for screening, surveillance, or therapy such as polypectomy at Seoul National University Hospital, a tertiary referral center in Korea, from August 2016 to December 2016.In this study, colonoscopy was performed using a high-resolution colonoscopy device . Colonoscopy videos were acquired using a video capture card , after signal branching from the CV260SL.The video was converted to an MP4 format to avoid alteration of the resolution, and the resolution was 1920*1080, 30fps. Videos were acquired from 112 patients, and the play time was about 20-40 min. The colonoscopy video was decomposed into frames. A frame was extracted as a PNG file per 0.5 s using Virtualdub software. All frames were cropped to 850*750 pixels to extract only the colonoscopy area, excluding patient information and the settings.As the play time of the colonoscopy videos was long and had much noise, it was inefficient to use the whole video . Thus, fAs the Horn–Schunk algorithm can only estimate small motions and compares the current frame with the previous frame to calculate the motion vector, the time interval between the two frames has a significant effect on the outcome . If the Because the image inside the large intestine had varied shapes and colors depending on the conditions of position, health status, and patient’s condition, it is advantageous to remove other information from the frame image for training, leaving only information about the position using Horn-Schunck algorithm. After Horn-Schunck algorithm application, we acquired the image shown in In this paper, Horn-Schunck algorithm-applied color images were classified into three types: insertion, withdrawal, and stop. Therefore, frames from colonoscopy images of 112 patients were classified as insert, withdrawal, and stop as the standard for deep training set. To make a standard setting, a colonoscopy video was reproduced at a speed of 0.7 times, and a user observing it pressed the direction key of the keyboard in real time. The up, down, and space buttons were matched to insert, withdrawal, and stop, respectively, and the frames were classified based on the input keyboard values. Five gastroenterologist with more than 5 years experience participated in this work, and the values selected by more than three were used as the standard . An overCNN deep learning was applied based on the standard created above . Horn-ScThe CNN structure and training procedure are described in The learning was carried out with batch size 128  images. The model is optimized for hyper-parameters by a randomized grid search method . We initFinally, the trained network was validated using the 5-fold cross-validation method . Five grThe model was trained and tested on a Windows 10 Pro system with Intel i9-9900K CPU 3.6-GHz processor, 2TB SDD, 64 GB RAM, GeForce RTX 2070 Gaming OC D6 8GB.To determine the location of the cecum, we analyzed the direction of the scope’s movement. Based on the results of the analysis, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. Because the cecum is a large turning point that distinguishes the insertion phase from the withdrawal phase, the turning point of transition from insertion to withdrawal is the candidate for the cecum. However, some scopic insertion-withdrawal repeat movements often occur during colonoscopy, and turning points can be found very often. Therefore, in this study, the scope movement for a certain range was analyzed to find the real turning point. All points that transitioned from insertion to withdrawal were recorded. These turning points were referred to as a cecum candidate point.We set a certain range “t” and calculated the sum of the graph area he cecum . If the In this study, we proposed the system that can analyze the colonoscopy video and provide meaningful direction information of endoscope’s movement and time-location information of the cecum via visualized summary report. To ask for comments on the system and reflect gastroenterologists’ requirements, we had implemented a FGI. Seven gastroenterologists with more than 5 years experience participated in this FGI, and it was conducted in the face-to-face meeting. The comments on necessity referred in this research were selected by FGI, and ideas about visualization were also recruited and validated. This FGI simply asked positive and negative opinions, and the results are as follows. All seven doctors determined that this system would positively benefit physicians and patients. Six doctors agreed that a visualized summary report based on this system would be helpful for medical records management and data sharing. Five doctors agreed that the results could be used for proficiency testing.In this study, we trained and tested the CNN models using 328,927 frames from 112 colonoscopy videos. To facilitate physician’s diagnosis during colonoscopy, we recruited most patients receiving colonoscopy for the second time.Since the features of Horn–Schunk-algorithm applied images were difficult to specify, we applied the CNN deep learning method to the images for high accuracy. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. We regarded the turning point as a cecum candidate point when the total area sum in a certain section recorded the lowest. We checked the existence of the actual cecum within a certain interval The movement of the scope, shown in At the 1 s scale, the result was too microscopic and not suitable for intuitive observation. However, since the outlier belonging to a small number was removed from the 10 s scale, the movement of the scope and the position of the cecum could be intuitively grasped. In addition, as the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer.In this study, we developed and verified a system that can detect the direction of scope’s movement from colonoscopy video using Horn–Schunk algorithm by applying CNN deep learning methods. The motion change of the video is extracted through the Horn–Schunk algorithm for calculating optical flow. Through this algorithm, we can compute the pixel’s motion change between consecutive frames, and the direction of scope’s movement was trained and determined through the CNN. The extracted information about the direction of scope’s movement was visualized and added to the summary report of colonoscopy video (SRCV). In addition, the time-location of the cecum was calculated based on the results of direction of the scope’s movement and included in the SRCV. This proposed system can assist physician’s observation of the colonoscopy video and provide helpful information.The videos used in this research were favorable for image acquisition and image processing because skilled physician performed the colonoscopy. This may have resulted in lowering the false-negative results when the system was applied to the video. In a previous study, we extracted 1 frame of images in 0.3 s from a colonoscopy video. The higher the extraction frequency, the more frames can be extracted, so more images can be used in the system. However, in applying the Horn-Schunck algorithm in this study, we extracted one frame at 0.5 s. Note that the Horn-Schunck algorithm can only calculate motion vectors for small changes . TherefoIn previous studies, we have classified informative frames and non-informative frames from the colonoscopy video with high accuracy . The SVMWe did not consider real-time use in this study. This is because the movement of the scope is meaningful when analyzed with the whole video. When the insertion and withdrawal movements of the scope throughout the video are analyzed, a clear distinction between the insertion phase and the withdrawal phase can be obtained. As a result, the position of the cecum can be accurately calculated.In the methods section, we used the certain range value “t” to find the real cecum point accurately. When analyzing 112 patient images, we found numerous light insertion-withdrawal repeat movements. With the exception of polypectomy, the scopes frequently repeat insertion-withdrawal movements to find the polyps . In addiIn this study, we used the CNN deep learning method to classify the movement of the scope. The images used for deep learning input were the Horn-Schunck algorithm-applied images, and the motion vector was expressed in color. In the images, the layer did not have to be deep because the directions were classified according to the color distribution only. The CNN architecture used in this study was developed with reference to LeNET and VGG, and the layer structure was experimentally optimized. The trained network was validated using the 5-fold cross-validation method, and overfitting did not occur. As a limitation of this study, we did not test data from outside the hospital. In a future study, we plan to conduct research and testing with independent data from other hospitals.During colonoscopy, the CIT was recorded by a physician. However, as the technology is under development today, the trend is to automatically store the data generated during the medical procedure, and the information generated during colonoscopy also needs to be automatically analyzed and stored . In our In this study, only the insertion, withdrawal, and stop phases were analyzed and determined. Through FGI, gastroenterologists have expressed the need to identify and visualize the anatomical structure of the large intestine. In a future study, if left and right movements can be recognized, the direction of the colonoscope can be recorded in more detail. Based on this, it is expected that the anatomical structure of the large intestine can be deduced and the anatomical position with time can be indicated.Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency . When viWith the proposed system, we believe that if the current handwritten medical records can be automatically summarized together with more detailed, graphical information, it will be useful for physicians and patients and will improve medical services . The res10.7717/peerj.7256/supp-1Dataset S1Original frame image (raw data) was extracted from colonoscopy video.In this study, colonoscopy was performed using a high-resolution colonoscopy device . Colonoscopy videos were acquired using a video capture card , after signal branching from the CV260SL. The video was converted to an MP4 format to avoid alteration of the resolution, and the resolution was 1920*1080, 30fps. The colonoscopy video was decomposed into frames. A frame was extracted as a PNG file per 0.5 s using Virtualdub software.Click here for additional data file.10.7717/peerj.7256/supp-2Dataset S2Original frame image (raw data) was extracted from colonoscopy video.In this study, colonoscopy was performed using a high-resolution colonoscopy device . Colonoscopy videos were acquired using a video capture card , after signal branching from the CV260SL. The video was converted to an MP4 format to avoid alteration of the resolution, and the resolution was 1920*1080, 30fps. The colonoscopy video was decomposed into frames. A frame was extracted as a PNG file per 0.5 s using Virtualdub software.Click here for additional data file.10.7717/peerj.7256/supp-3Supplemental Information 1Click here for additional data file.


From 1991 onward, the European Union (EU) member states have had to comply with the Nitrates Directive. The aim of this directive is to sustainably protect ground and surface waters from pollution with nitrogen (nitrate) originating from agriculture. Agriculture is, on an EU level, the largest single source of nitrate  pollution, although households and industries also contribute to some extent. An important element in the directive is the reporting every 4 years on the monitoring of ground- and surface-water quality. Furthermore, all 15 member states are compelled to designate so-called Nitrate Vulnerable Zones (NVZs). These are regions where the nitrate concentrations in the groundwater amount to 50 mg/l or more. In addition to Codes of Good Agricultural Practice, valid on a countrywide basis and often consisting of voluntary-based measures, specific Action Programmes with mandatory measures have to be developed for the NVZs. The first reporting period ended in 1995. This paper describes the progress in member states’ compliance with the Nitrates Directive during the second period (1996–1999), with a focus on the agricultural practices and action pro- grammes. An evaluation of the member states’ reports shows that good progress is being made on the farmers’ awareness of the need to comply with EU regulations on the protection of the aquatic environment. Action programmes are valuable tools to enforce measures that lead to a reduction of the water pollution by agricultural activities. Regional projects show that significant improvements can be achieved  while maintaining crop yields and thus maintaining the economic potential of agriculture.


For every newborn who dies within the first month, as many as eight more suffer life-threatening complications but survive ). However, there is no universally agreed-upon definition or assessment tool for NNM. This study sought to describe the development of the Neonatal Near-Miss Assessment Tool (NNMAT) for low-resource settings, as well as findings when implemented in Ghana.This prospective, observational study was conducted at two tertiary care hospitals in southern Ghana from April – July 2015. Newborns with evidence of complications and those admitted to the NICUs were screened for inclusion using the NNMAT. Incidence of suspected NNM at enrollment and confirmed near-miss (surviving to 28 days) was determined and compared against institutional neonatal mortality rates. Suspected NNM cases were compared with newborns not classified as a suspected near-miss, and all were followed to 28 days to determine odds of survival. Confirmed near-misses were those identified as suspected near-misses at enrollment who survived to 28 days. The main outcome measures were incidence of NNM, NNM:mortality ratio, and factors associated with NNM classification.Out of 394 newborns with complications, 341 (86.5%) were initially classified as suspected near-misses at enrollment using the NNMAT, with 53 (13.4%) being classified as a non-near-miss. At 28-day follow-up, 68 (17%) had died, 52 (13%) were classified as a non-near-miss, and 274 were considered confirmed near-misses. Those newborns with complications who were classified as suspected near-misses using the NNMAT at enrollment had 12 times the odds of dying before 28 days than those classified as non-near-misses. While most confirmed near-misses qualified as NNM via intervention-based criteria, nearly two-thirds qualified based on two or more of the four NNMAT categories. When disaggregated, the most predictive elements of the NNMAT were gestational age < 33 weeks, neurologic dysfunction, respiratory dysfunction, and hemoglobin < 10 gd/dl. The ratio of near-misses to deaths was 0.55: 1, yet this varied across the study sites.This research suggests that the NNMAT is an effective tool for assessing neonatal near-misses in low-resource settings. We believe this approach has significant systems-level, continuous quality improvement, clinical and policy-level implications. In 2019, 2.5 million newborns globally died within a month of birth, with nearly 2 million dying in the first week after birth . Such evSeveral scoring tools have been used to assess severe morbidity among neonates, and to date, no standard criteria exist for neonatal near-misses , 7. Neondescribe the development of the Neonatal Near Miss Assessment Tool (NNMAT) for low-resource settings; 2) identify the incidence of neonatal near-misses at two tertiary care hospitals in southern Ghana; 3) compare the incidence rates of neonatal near-misses to institutional records of neonatal mortality ; and 4) to identify the strongest predictors of death when comparing newborns who experienced a neonatal near-miss with newborns known to have died within the first 28 days after birth.This study aimed to: 1) This was a prospective, observational, multi-site study conducted April–July 2015 at two tertiary referral hospitals in southern Ghana . GloballStudy sites included the maternal and neonatal units of Komfo Anokye Teaching hospital (KATH) in Kumasi and Cape Coast Teaching Hospital (CCTH) in Cape Coast. KATH is the teaching hospital affiliated with the Kwame Nkrumah University of Science and Technology School of Medical Sciences (KNUST-SMS) and serves as the referral center for most of central Ghana. Each year approximately 11,000 women give birth at KATH. CCTH is the teaching hospital of the University of Cape Coast, School of Medical Sciences (UCC-SMS) and serves as the main referral hospital for much of the rural central and parts of western regions of Ghana. The hospital oversees about 2800 births per year. At the time of this research, each hospital provided specialty care for sick newborns, with capacity to provide bag and mask ventilation, oxygen and incubator care with radiant warmers , and phototherapy. Routine laboratory tests, such as complete blood counts (CBC), serum bilirubin and electrolytes, were available, but delays in processing sometimes limited the utility and utilization of such tests.NNMAT , was iteratively reviewed and revised, resulting in a tool with four categories: evidence of severe complications (akin to the “pragmatic category” seen in the literature), interventions conducted (akin to “management markers of severity”), organ-based dysfunction, and investigations conducted. Definitions conformed to the International Statistical Classification of Diseases and Health Related Problems (ICD-10). , Apgar scores < 7 at 5 min, birth asphyxia, congenital malformations, or indications that they were being transferred to the NICU) were identified and screened with the Neonatal Near Miss Assessment Tool (NNMAT) to identify potential near-miss cases. In addition, all babies admitted to the neonatal units at KATH and CCTH – regardless of where they were born – were recruited for participation as soon as possible following admission to the neonatal units. Babies less than 28 weeks gestation  or less than 500 g birthweight were excluded, as were those who were stillborn.Trained research assistants reviewed the admission ledgers at each site to identify newborns with complications. Parents of newborns with complications who had been admitted were approached, asked about their willingness to participate in a study and were taken through a written consent process if they agreed. The admission ledger was used to complete the NNMAT, supplementing information from the medical record with information from the physician or nurse on duty as necessary. NNMAT forms were completed using Qualtrics data collection software  on a hand-held tablet as soon as possible after admission, typically within 24 h. Babies were followed at 7, 14, and 28 days to determine survival. For those discharged home, families were contacted via telephone and asked to provide an update on the health of the baby.In addition to generating the NNMAT, the two primary outcome variables of interest in this study were near-miss categorization and death. Table Data were exported from Qualtrics into Stata version 13.1  for cleaning and analysis. Frequencies were generated across all indicators assessed. Summary statistics reflecting total number of births, live births, and neonatal deaths for the study period were collected from each hospitals’ records, allowing for the calculation of neonatal near-miss incidence. Neonatal near-miss incidence was calculated by dividing the number of neonatal near-misses recorded by the number of live births recorded, in keeping with the predominant method of the existing literature . Confidep < .05 in these preliminary models were carried forward into a final multivariate model to illustrate the strongest predictors of mortality. For the final model, a conservative p value of 0.01 was taken as statistically significant to account for multiple comparisons. In addition, sensitivity, specificity, positive predictive values, and negative predictive values of the NNMAT were calculated.Pearson’s Chi Square analysis was conducted to compare those who died with those who survived the first 28 days after birth. Multivariate logistic regression was conducted (with death before 28 days as the outcome variable) to determine which categories of the neonatal near-miss tool were most predictive and then which elements within each category were most predictive. Variables significant at Ethical approval was obtained from the Institutional Review Boards of Kwame Nkrumah University of Science and Technology, University of Cape Coast, University of Ghana, and the University of Michigan.p <  0.001), yet there was no difference in terms of location of birth, vaginal birth vs. c-section, or study site between those lost to follow-up and those successfully followed. Each site had an 89% retention rate.A total of 394 newborns across the two sites were enrolled, screened with the NNMAT, and successfully followed to 28 days. A total of 441 newborns were initially enrolled, yet 47 babies were lost to follow-up. Those 47 babies were less likely to be suspected near-misses at enrollment than those successfully followed to 28 days (Table N = 260) exhibited some evidence of severe complications at enrollment, and 74.4% (N = 293) underwent interventions indicative of near-miss status within 24 h after birth. Slightly less than half (N = 186) qualified as a suspected near-miss based upon organ dysfunction criteria. Only 5.8% (N = 23) qualified based upon investigation-based criteria. A total of 341 were classified as a suspected neonatal near-miss, based upon having positive responses to any of the categories: evidence of severe complications, intervention-based criteria, organ system dysfunction, or investigation-based criteria. Thirteen percent of babies recruited did not meet any of the neonatal near-miss criteria , despite having been identified as experiencing some type of complication or being admitted to the NICU.Out of all newborns followed, 66% .Figure The sensitivity of the NNMAT – or the proportion of newborns who died before 28 days who screened positively on NNMAT – was 98.5%. The specificity of the NNMAT – or the proportion of newborns who did not die by 28 days who had a negative result on the NNMAT – was 15.9%. The positive predictive value (PPV) of the NNMAT – the likelihood that being labeled as a confirmed near-miss predicts death among this sample of newborns – was 19.6%. The negative predictive value (NPV) – the likelihood that not being labeled as a confirmed near-miss would be associated with not dying – was 98.1%.Table p = 0.001), as does neurologic dysfunction , respiratory dysfunction , and hemoglobin < 10 gd/dl . Performance of chest compressions was also included in the model, suggesting a seven times higher odds of death among babies who underwent chest compressions, yet the p-value of 0.019 exceeded our threshold of 0.01.Table This study showed that out of 394 newborns with complications, 341 (86.5%) were initially classified as suspected neonatal near-misses using the NNMAT, and 274 (70%) were ultimately classified as confirmed near-misses as they survived to 28 days. Those newborns with complications who were classified as suspected near-misses at enrollment using the NNMAT had 12 times the odds of dying before 28 days than those not classified as suspected near-misses. While most newborns qualified as suspected and confirmed near-misses via intervention-based criteria, nearly two-thirds of newborns qualified as suspected and confirmed near-misses based on two or more of the four NNMAT categories. When disaggregated, the most predictive elements of the NNMAT were gestational age < 33 weeks, neurologic dysfunction, respiratory dysfunction and hemoglobin < 10 gd/dl. The overall ratio of near-misses to deaths was 0.55:1, yet this varied across the study sites.This research suggests that the NNMAT is an effective tool for assessing neonatal near-misses in low-resource settings. Similar to the findings of Kale et al.,  the NNMAThe NNMAT’s sensitivity was 98.5%, indicating a high likelihood of identifying babies at risk of death before 28 days. However, the NNMAT had a positive predictive value of 19.6%, which could be considered relatively low in its ability to successfully predict death. However, the NNMAT is designed to identify those babies who almost died but ultimately survived, and in that case, a low PPV is less worrisome than it might be in other contexts. Interestingly, the NNMAT’s negative predictive value was extremely high (98.1%), indicating that those not classified as a near miss are extremely unlikely to die before 28 days. It is also possible that the NNMAT may be most effective and efficient if directed to a high risk population, such as those newborns admitted to the NICU rather than including all newborns with complications.This study is an important addition to the neonatal near-miss literature. We leveraged what was known about maternal near-miss assessment in LMICs and combined that with extant learnings from the nascent field of neonatal near-miss research to generate and test a tool across two tertiary care centers in Ghana. Our findings build on the results of Nakimuli et al.  in Ugand. In addition, these findings also suggest a role for programs that follow newborns beyond their discharge from the NICU. Current global movements in newborn care are emphasizing more than survival – including an effort to ensure that newborns don’t just survive, but that they have every opportunity to thrive [.This study has several important implications. First, the NNMAT illustrates that it is possible to use a simple tool to assess markers that are readily observed in low-resource settings to categorize newborns with complications as suspected neonatal near-misses or not, and then follow them to 28 days to confirm whether they were indeed a near-miss. While some might argue that the pragmatic markers of gestational age, birth weight, and Apgar score used in previous research are sufficient for that purpose, our results indicate that evidence of respiratory distress, neurologic dysfunction and low hemoglobin levels provide valuable additional information with which to predict outcomes. A newborn’s positive classification as a suspected near-miss on the NNMAT conferred a statistically significant, 12-fold increased risk of death before 28 days. This has enormous implications for clinical management, including the possibility of implementing a standard methodology for assessing newborns and alerting staff to especially high-risk babies that require additional attention. Given that most babies admitted to a NICU are seriously ill, it may be difficult to imagine further triage. Yet the NNMAT indicates that further triage may indeed be warranted, especially among more junior-level providers who may not immediately recognize the seriousness of newborn symptoms and are likely to be the ones in charge during overnight and weekend shifts. Such a precedent is illustrated by the British Association of Perinatal Medicine’s new guidelines for management of prematurity, including risk stratification of premature newborns based on some of the same factors identified in the NNMAT: birthweight, gender, multiple births, and congenital anomalies . In addio thrive .Our findings also suggest that the NNMAT may be useful in driving the development of quality improvement programs, as individual facilities identify the most common challenges they face through routine monitoring of NNMAT scores against mortality outcomes. Potential differences across individual facilities raises another important implication of the NNMAT. We found wide variability in near-miss to mortality ratios across tertiary care hospitals that we assumed would be similar, suggesting that there may be case-mix issues that preclude ‘fair’ comparisons of outcomes across settings. In other words, the NNMAT may provide a mechanism for individual facilities to assign scores to patients entering the NICU that can then be used to contextualize subsequent outcomes data.This study has several limitations worth noting. First, our initial sample size of 441 was reduced to 394 we were able to follow to 28 days post-birth. This loss-to-follow-up was more pronounced in the non-near-miss group, potentially affecting our findings. A second limitation is that we used aggregate institutional neonatal mortality rates as the comparison against observed rates of neonatal near-misses to create the ratio of near-misses to deaths. Institutional neonatal mortality rates typically include any baby who dies in the facility, including those born outside the facility (which may inflate the numbers), but they do not include those babies who die after discharge . These rates also use live births in the facility as their denominator, while potentially including babies born outside their facilities in the numerator. Nonetheless, this imperfectly calculated rate allows us to compare neonatal near-misses  with institutional neonatal mortality, providing an approximation of the ratio of near-misses to deaths. While these calculations were consistent across facilities, and are thus comparable, the ratios of neonatal near-misses to neonatal mortality presented here must not be confused with population-level neonatal mortality rates. Another limitation of our study relates to the use of yes/no/don’t know in the NNMAT. In settings where record keeping or communication between providers is poor, or where certain interventions or investigations are not available, or where laboratory tests take a long time to come back, it is possible that the “don’t know” responses could lead to an underestimation of the number of babies who might qualify as a potential near-miss. However, we believe the NNMAT is broad and inclusive enough to capture other risk markers even if some are not known. The timing of the near-miss assessments is also worthy of discussion. We recruited participants as soon as possible after admission, and as such our NNMAT assessments were typically conducted in the first day or two after birth. Assessments completed later in the first week might have yielded different exposures to treatments or interventions and thus our findings may look different. Given lack of clarity regarding the ideal time to assess a newborn for near-miss status, future research is warranted that explores ideal timing of the NNMAT administration. Finally, our study is limited in its duration of follow-up. Future research that follows newborns to 3, 6, 9 and 12 months is warranted to determine the long term impact of neonatal near-miss events.In summary, we have developed, evaluated, and demonstrated the successful use of a pragmatic heuristic in the form of the NNMAT to identify neonatal near misses. We believe this approach has significant systems-level, continuous quality improvement, clinical and policy-level implications.


The aim of this work was to assess magnesium’s function in the above process and to explore the associated signaling pathway.Magnesium . RNA-Sequencing was carried out for assessing transcriptome alterations in DPSCs during odontogenic differentiation associated with high extracellular Mg2+. KEGG pathway analysis was performed to determine pathways related to the retrieved differentially expressed genes (DEGs). Immunoblot was performed for assessing magnesium’s role and exploring ERK/BMP2/Smads signaling.DPSCs underwent culture in odontogenic medium with the addition of 0, 1, 5, or 10 mM MgCl2+-enriched microenvironment promoted odontogenic differentiation in DPSCs via intracellular Mg2+ increase. Consistently, the positive effect of high extracellular Mg2+ on odontogenic differentiation in DPSCs was blocked by 2-APB, which reduced Mg2+ entry. RNA-sequencing identified 734 DEGs related to odontogenic differentiation in DPSCs in the presence of high extracellular Mg2+. These DEGs participated in many cascades such as MAPK and TGF-β pathways. Consistently, ERK and BMP2/Smads pathways were activated in DPSCs treated with high extracellular Mg2+. In agreement, ERK signaling inhibition by U0126 blunted the effect of high extracellular Mg2+ on mineralization and odontogenic differentiation in DPSCs. Interestingly, BMP2, BMPR1, and phosphorylated Smad1/5/9 were significantly decreased by U0126, indicating that BMP2/Smads acted as downstream of ERK.Mg2+-enriched microenvironment promotes odontogenic differentiation in DPSCs by activating ERK/BMP2/Smads signaling via intracellular Mg2+ increase. This study revealed that Mg2+-enriched microenvironment could be used as a new strategy for dental pulp regeneration.Mg Regener in MSCs .2+ acts as an intracellular second messenger connecting cell-surface receptor induction and cytosolic effectors [2+ is the fourth most abundant cation in the human body and is critical for ATP-dependent phosphorylation of DNA, RNA, and enzymes, and the average Mg2+ concentration in dentin is about 1% (wt/wt) [2+ is involved in the bio-mineralization of bones and teeth and directly affects crystallization and pattern generation of the inorganic mineral phase [2+ transporters TRPM7 and CNNM4 result in deficient dentin mineralization, confirming Mg2+ involvement in tooth development [2+ diets show deficient dentin and enamel mineralization [2+ transporter Magt1 plays an important role in odontogenic differentiation of bone marrow MSCs by regulating intracellular Mg2+ [2+ and its transporter regulate odontogenic differentiation in human DPSCs, with involvement in dentin mineralization [As reported, Mgffectors . Mg2+ is (wt/wt) –8. Mg2+ al phase . Evidencelopment , 12. Anilization . Our prelar Mg2+ . Studieslization .2+ effects on odontogenic differentiation in DPSCs remain undefined. Till now, the altered transcriptome of DPSCs undergoing odontogenic differentiation induced by an Mg2+-enriched microenvironment has not been assessed; thus, we determine the alteration of transcriptome using RNA sequencing. Moreover, we aim to clarify the role of Mg2+-enriched microenvironment in odontogenic differentiation of DPSCs and determine the associated signaling pathways.However, the regulatory mechanisms of MgStudies involving patients had approval from the Ethics Committee of Sun Yat-Sen University. DPSCs were collected from non-diseased pulp tissues of caries-free third molars of patients. DPSCs were isolated according to a previous report  and main2 for 1, 2, and 3 days, respectively. Cell Counting Kit-8  was applied as directed by the manufacturer.DPSCs seeded in 96-well plates at 1000 cells/well underwent culture in growth medium after addition of 0, 1, 5, or 10 mM MgCl4 cells/well underwent culture in α-MEM containing 10% FBS at 37 °C. At 80% confluency, DPSC culture was performed in odontogenic medium with 100 nM dexamethasone, 10 mM β-glycerophosphate, and 0.2 mM ascorbic acid, with the addition of 0, 1, 5, or 10 mM MgCl2, respectively. After 7 days, the protein amounts of RUNX2, DSP, and DMP-1 were determined by automated immunoblot. After 14 days, mineralization nodules were assessed by Alizarin red S staining.DPSCs seeded in 12-well plates at 3 × 10Alizarin red S  staining was carried out as proposed by the manufacturer. Destaining was performed with 10% cetylpyridinium chloride monohydrate buffer for 30 min, and optical density was obtained at 575 nm. Relative extracellular matrix mineralization was calculated for each sample.2+ across the cell membrane, we inhibited Mg2+-entry by treatment with 100 μM of the TRPM7 inhibitor 2-APB  [Given that TRPM7 was reported to transport Mg, China) .Total protein from DPSCs was obtained with RIPA . Automated immunoblot was carried out with Simple Wes  as directed by the manufacturer. In brief, 2.5 μg total protein samples were mixed with the standard fluorescent master mix and added to prefilled Wes assay plates, alongside antibody diluent (Protein Simple); primary antibodies targeting BMP2 , BMPR1 (Affinity), p-Smad1/5/9 (Affinity), RUNX2 (Affinity), DSP , DMP-1 , ERK, p-ERK, JNK, p-JNK, p38, p-p38 , and β-Tublin  were probed; anti-rabbit and anti-mouse secondary antibodies conjugated to Streptavidin-HRP (Protein Simple) were used for detection. The compass software (Protein Simple) was utilized for analysis.Intracellular magnesium amounts in DPSCs were assessed with Mag-Fluo-4-AM  as directed by the manufacturer. Briefly, Mag-Fluo-4-AM was added at 2 μM and incubated for 30 min. This was followed by three cell washes. Fluorescence intensities were obtained by flow cytometry  in triplicate assays.p < 0.05 was obtained by the Benjamini-Hochberg method.RNA-seq was carried out according to our previous work . Brieflyhttp://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes pathway  analyses were carried out for determining biological functions and enriched metabolic or signal transduction pathways associated with the differentially expressed genes (DEGs), respectively.Gene Ontology  for 2 h every 2 days.To assess MAPK signaling involvement, DPSCs underwent culture in 12-well plates in odontogenic medium with the addition of 0, 1, 5, or 10 mM MgClp < 0.05 indicated statistical significance.Experiments were performed three times. Data were expressed as mean ± SD and were assessed by ANOVA test with SPSS 17.0 ; Passage 3 DPSCs were examined. As shown in Fig. 2+ on their proliferation, DPSCs underwent incubation in Mg2+-enriched media at different concentrations  for 1, 2, and 3 days, respectively. Cell Counting Kit-8 analysis showed that high extracellular Mg2+ did not affect DPSC proliferation .To evaluate the impact of high extracellular Mg2+ on their differentiation, DPSCs were cultured in Mg2+-enriched odontogenic medium at different concentrations . After 7 days, RUNX2, DMP-1, and DSP protein amounts were markedly elevated in DPSCs treated with 1 mM, 5 mM, and 10 mM Mg2+, respectively, compared with 0 mM Mg2+ . These results showed that the Mg2+-enriched microenvironment promoted the odontogenic differentiation of DPSCs.To analyze the impact of high extracellular Mgg2+ Fig. a. Consisg2+ Fig. b. Howeveups Fig. . These r2+-enriched odontogenic media at different concentrations  were assessed flow cytometrically using Mag-Fluo-4-AM. Figure 2+ was starkly increased in DPSCs treated with 1 mM, 5 mM, and 10 mM Mg2+, compared with the 0 mM Mg2+ group. However, there was no difference in intracellular magnesium of DPSCs treated with 5 mM and 10 mM Mg2+, which is consistent with their comparable effects on odontogenic differentiation.Intracellular magnesium levels in DPSCs treated with Mg2+ across the cell membrane [2+ entry was significantly blunted by 2-APB . The positive effect of high extracellular Mg2+ (5 mM Mg2+) on odontogenic differentiation of DPSCs was also blocked by 2-APB . These results indicated that high extracellular Mg2+ promoted odontogenic differentiation in DPSCs via intracellular Mg2+ increase.Given that TRPM7 was reported to transport Mgmembrane , we nextng TRPM7 ) , exposure to high extracellular Mg2+ (5 mM Mg2+) for 7 days resulted in 734 DEGs in DPSCs, including 250 upregulated and 484 downregulated .Compared with control cells . KEGG pathway analysis indicated that the DEGs retrieved participated in many pathways, e.g., MAPK and TGF-β pathways .In GO analysis, DEGS were mostly involved in developmental process, anatomical structure development, organism development, system development, channel activity, metal ion binding, integral component of plasma membrane, etc. Fig. c. KEGG pays Fig. d.2+during odontogenic differentiation of DPSCs. To verify this finding, DPSCs were incubated in odontogenic medium with the addition of 0, 1, 5, and 10 mM MgCl2 for 7 days, respectively. The results showed that ERK phosphorylation was markedly enhanced in DPSCs treated with 1 mM, 5 mM, and 10 mM Mg2+ compared with the 0 mM Mg2+ group, but p38 and JNK phosphorylation showed no change . In accordance, the positive effect of high extracellular Mg2+ on ERK phosphorylation was blocked by 2-APB  , which reduced intracellular Mg2+amounts .As shown above, KEGG pathway analysis revealed MAPK signaling activation by high extracellular Mgnge Fig. a. In accM7) Fig. b, which nts Fig. b.Fig. 62+. The results showed that BMP2, BMPR1, and phosphorylated Smad1/5/9 were significantly increased in DPSCs cultured in odontogenic medium containing high extracellular Mg2+. In accordance, the positive effects of high extracellular Mg2+ on BMP2, BMPR1, and phosphorylated Smad1/5/9 were blocked by 2-APB  .As reported previously, BMP2 is a TGF-β family member controlling odontogenic differentiation of stem cells , 19. To Mg2+Fig. c. In accM7) Fig. d.2+-enriched odontogenic medium (5 mM Mg2+) for 7 days . Interestingly, BMP2, BMPR1, and phosphorylated Smad1/5/9 amounts were also significantly decreased by U0126, indicating that BMP2/Smads acted as downstream of ERK .We inhibited ERK signaling with U0126, and mineralization nodules were markedly decreased, as well as RUNX2, DSP, and DMP-1 protein amounts in DPSCs treated with Mg2+-enriched microenvironment promoted odontogenic differentiation in DPSCs by activating ERK/BMP2/Smads signaling via intracellular Mg2+ increase .Taken together, these findings suggested that Mgase Fig. .Fig. 8S2+ to enhance dentin regeneration in DPSCs [2+ transporters TRPM7 and MagT1 regulate odontogenic differentiation in stem cells [2+ for ALPL activity. Luder and colleagues [2+ transporter, caused mineralization abnormalities in both enamel and dentin, in association with starkly reduced magnesium amounts. Consistent with previous studies, the present work determined that Mg2+-enriched microenvironment enhanced odontogenic differentiation in DPSCs by altering the formation of mineralization nodules as well as RUNX2, DMP-1, and DSP protein amounts, through intracellular Mg2+ concentration increase.Regenerative endodontic therapy involves biological procedures for replacing damaged dental structures such as dentin and root, and cells of the dentin/pulp complex . DPSCs ain DPSCs . Our preem cells , 15. Nakem cells  reportedlleagues  demonstr2+ affects odontogenic differentiation remains unknown. Therefore, RNA-Seq was carried out to assess transcriptome alterations in DPSCs exposed to Mg2+-enriched microenvironment. Interestingly, GO and KEGG pathway analyses revealed 734 DEGs contributing to odontogenic differentiation in DPSCs induced by high extracellular Mg2+. The obtained DEGs participated in many pathways, such as MAPK and TGF-β pathways.However, how high extracellular Mg2+, but p38 and JNK phosphorylation levels remained unchanged, indicating ERK/MAPK signaling is induced by high extracellular Mg2+. Consistently, blunting ERK/MAPK signaling with U0126 blocked the effects of high extracellular Mg2+ on mineralization and odontogenic differentiation in DPSCs.MAPK signaling regulates stem cell proliferation, migration, and differentiation –23. Yu a2+ plays a pivotal role in ERK/MAPK signaling induction [2+ deprivation downregulates p-ERK1/2 in MDCK cells, and Mg2+ re-supplementation reverses this effect [2+ amounts [2+, which results in inhibited odontogenic differentiation of bone marrow MSCs [2+ was assessed flow cytometrically with Mag-Fluo-4-AM. As shown above, intracellular Mg2+ amounts were starkly increased in DPSCs treated with high extracellular Mg2+. In accordance, the positive effect of high extracellular Mg2+ on ERK phosphorylation was blocked by 2-APB, which reduces intracellular Mg2+ amounts, indicating that high extracellular Mg2+ activates ERK/MAPK signaling by increasing intracellular Mg2+.According to previous studies, intracellular Mgnduction –30. For s effect , 30. Mea amounts , 28. Ourrow MSCs . In the 2+-enriched odontogenic media. As expected, the positive effects of high extracellular Mg2+ on BMP2, BMPR1, and phosphorylated Smad1/5/9 were blocked by the Mg2+transporter inhibitor 2-APB, indicating that BMP2/Smads signaling is activated during odontogenic differentiation induced by Mg2+-enriched microenvironment. This finding corroborated Cheng et al. [2+.BMP2, a member of the TGF-β family of proteins, contributes to odontogenic differentiation in DPSCs , 19. Cong et al.  showing 2+ promotes odontogenic differentiation in DPSCs by activating ERK/BMP2/Smads signaling via intracellular Mg2+increase.Interestingly, ERK signaling inhibition by U0126 resulted in significantly decreased BMP2, BMPR1, and phosphorylated Smad1/5/9 amounts, indicating that BMP2/Smads is a downstream signaling of ERK. Taken together, these findings demonstrated that high extracellular Mg2+. In addition, Mg2+-enriched microenvironment enhanced odontogenic differentiation in DPSCs by activating ERK/BMP2/Smads signaling via intracellular Mg2+ increase.Overall, significant transcriptome alterations were detected in DPSCs during odontogenic differentiation associated with high extracellular Mg