Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting.Methods: Two independent data sets of portable chest radiographs  were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians.Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories .Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians. Critically ill patients often have organ dysfunction and require frequent and intense monitoring. Portable chest radiography is key to assessing cardiopulmonary function in the intensive care unit (ICU), allowing clinicians to identify pathological findings such as pneumonia, pneumothorax, pleural effusion, and atelectasis –7. A revSubstantial investigations have documented computer-aided detection (CAD) systems for medical images –21. AdvaTherefore, we developed a new algorithm using supervised machine learning with two independent datasets. We hypothesized that the accuracy of our supervised machine learning technique would increase as the number of radiograph readings increased; we also hypothesized that the technique would accurately and quickly identify pathological findings from portable chest radiographs in a clinical setting.Data set 1 (a single Japanese center intensive care unit data): Consecutive portable chest radiographs  of a hospital based radiographic database for ICU patients who admitted between April 2017 and December 2018 were retrospectively extracted and used by the study team member from the ICU at Chiba University Hospital, Japan. This tertiary referral hospital ICU where approximately 1,800 patients admitted in a year had 22 beds and was utilized by patients following emergency room admission and after elective surgery, accounting for approximately 80% of ICU beds. Of 3,351 screened patients, we selected 380 chest radiographs, in which a single diagnosis could be made from one of the following categories: atelectasis, pneumonia, pleural effusion, and no emergency. The diagnosis was made on the basis of clinical signs, laboratory data, and other images, including computed tomography (CT) with a radiologist report and bedside ultrasound. Two board-certified ICU physician with the specialty of interventional radiology reviewed all radiographs and labeled each imaging with one of the diagnoses in a comprehensive and coherent manner. If there was any doubt in diagnosis, the radiograph was excluded from further analysis.Data set 2 : ChestX-ray8 dataset provided by the NIH clinical center, which contains 8,508 weak supervised multi-label methods to classify and locate the text-mined 14 common thorax diseases, mined from the text radiological reports via natural language processing techniques were used . BecauseAll clinical datasets were provided with DICOM format. The resolution of the original images in the single center ICU datasets sizes 2,430 pixels height and 1,994 pixels width and that of original images in the NIH datasets sizes 1,024 pixels height and 1,024 pixels width. To align them, all images in the single center ICU datasets are cropped and resized into 1,024 pixels height and 1,024 pixels width.This study was approved by the Institutional Review Board of Chiba University Graduate School of Medicine  and was performed in accordance with the committee's guidelines.We used two different deep convolutional neural network architectures. Dense convolutional network 121 (DenseNet 121), which connects each layer to every other layer in a feed-forward fashion named DenseBlock. DenseNet121 has several compelling advantages and details are found in the supplemental digital content [see The area under the curve (AUC) for atelectasis and pneumonia was particularly low at 0.574 and 0.499, respectively, in the four-class simultaneous classification of atelectasis, pneumonia, pleural effusion, and no abnormalities in DenseNets 121 conducted in a preliminary experiment. Therefore, we developed a two-stage classification method in which the combination of atelectasis and pleural effusion, and pneumonia and pleural effusion were once classified as the same class, and then separated them into each class in the second stage. The proposed network model of this study runs on Keras 2.1.5 with Python 3.4 on Ubuntu 16.04.4 LTS.In adaptive ensemble learning, an arbitrary number of models are selected from 10 models generated by iterative learning, and the average of the certainty output values of each model is calculated. For model selection, the optimum number and optimum combination were determined from 10 models. We found that it was optimal to select 4 out of 10 models in the proposed two-stage classification method. Using this adaptive ensemble learning, the average AUC improved to 0.672.To compare the accuracy and efficiency of the machine learning algorithm, five board-certified critical care physicians and three senior emergency residents voluntarily annotated images from the clinical samples which were dedicated data and part of the Chiba university hospital collected. To test physician's diagnostic accuracy, we developed a web-based software which was developed originally in our laboratory based on Java Script and can show a portable chest radiograph to a physician and the physician can input the diagnosis adjusting the dial of for categories  according to the physician's confidence from 0 to 100. We chose 53 chest radiographs which were randomly selected from Dataset 1. In a test, one physician successively reviewed 53 chest radiographs one by one using the developed software which automatically record the duration to diagnose a single portable chest radiograph . The diaP-values < 0.05 were considered to be statistically significant. All statistical analyses were performed using R®  and PRISM® version 7 .The sensitivity and specificity for the ability of the CNN to classify atelectasis, pneumonia, pleural effusion, and no emergency were calculated. The ROCs were plotted by varying the operating threshold. For the ROC curves, standard error, 95% confidence intervals, and comparisons between AUCs were made using a non-parametric approach. The adjusted Wald method was used to determine 95% confidence intervals for sensitivity and specificity. P < 0.05) (P < 0.01) and from 75 to 100% of the samples (P < 0.01) (P < 0.05) and 75% (P < 0.01) of the samples with an increase in the number of data points, respectively (P < 0.05) and 25 to 100% of the samples (P < 0.05) . In atelectasis, the AUC increased as the number of training data points increased, with a significant difference between 25 (280 images) and 100%  of the samples ( < 0.05) . In pleu < 0.01) . In pneuectively . For no  < 0.05) , with anP = 0.030; pleural effusion, AUC 0.856 (95%CI 0.754–0.958) vs. 0.706 (95%CI 0.657–0.752), P = 0.007; pneumonia, AUC 0.702 (95%CI 0.571–0.869) vs. 0.744 (95%CI 0.643–0.829), P = 0.881; no emergency, AUC 0.751 (95%CI 0.597–0.906) vs. 0.698 (95%CI 0.615–0.792), P = 0.476).The average time for ICU physicians to complete a review of 53 chest radiographs was 12 min . The algorithm finished the same dataset within 9.66 s. The diagnostic accuracy represented by AUC was higher by machine learning compared to those physicians in all categories: atelectasis, AUC 0.744 (95%CI 0.583–0.904) vs. 0.557 (95%CI 0.507–0.636), In the present study, we developed a novel automatic detection system to aid ICU physicians in identifying pathological findings from portable chest radiographs. In the case of atelectasis and pleural effusion, the system performed extremely well compared to board-certified ICU physicians. Another advantage that should be highlighted is that the supervised machine learning technology had an extremely fast diagnostic time. The system could interpret one image per 0.18 s. Since the proposed model can run on the general deep learning framework, it can be adapted to widely other environments.In this study, we validated that the diagnostic accuracy improved as the sample size increased with a large sample size (NIH repository data). The accuracy of this dataset is considered to be >90%, and so it would be sufficient to test our hypothesis. However, because of the uncertainty of the diagnosis randomly lurk under a cloak of the provided chest radiographs, we used our own in-hospital dataset to develop the machine learning algorithm, which is key to improving the accuracy of our system. We developed a novel two-stage classification method from this dataset. Incorporating machine learning into diagnostic imaging could lead to rapid therapeutic interventions by determining image results quickly with fewer errors. As for the cost, it is unknown since the business has not yet been developed.It is important to aim for higher sensitivity to rule out acute pathology. Sensitivity is referred to as recall in the machine learning field. The sensitivity of our system for acute pathology was 0.846 for atelectasis, 0.846 for pneumonia, and 1.00 for pleural effusion. On the basis of our findings, the clinical application of this system is wide; the technology allows for reducing the risk of misdiagnosis and saving physician's efforts. The system requires true diagnosis; correct labels of radiographs need to be provided by clinicians. However, our data indicate that, after a period of training, once the system has taken a sufficient sample size, an end-user will experience high satisfaction in the diagnoses; the diagnoses would be consistent with their own at high accuracy (AUC >0.9) and at an extremely fast processing time.While detecting pneumonia, machine learning had a poorer performance compared to the other abnormal findings. Poor performance in machine learning is either due to overfitting or underfitting of the data. Overfitting occurs when the trained model does not generalize well to unseen cases but fits the training data well. Assessment of the training curves can be used to evaluate the possibility of overfitting. In our study setting, it was apparent that the data loss on the validation data was much greater than that on the training datasets, which suggested the possibility of overfitting. The trend becomes more prominent when the training sample size is small. This is one of the main limitations of this study.Since the present study developed algorithms with high predictive value, the future direction of research would be clinical application. The COVID-19 pandemic would require the speedy X-ray diagnosis system with high processing capability which detect the severe pneumonia in the shortage of the specialist physician –26. ThisThe study had other limitations that should be addressed. First, the number of learning images needed to be reduced by removing duplicate labels from the dataset. For example, for pneumonia, the ChestX-ray14 database had 1,107 pneumonia images; however, those included other labels such as pneumonia/atelectasis, pneumonia/pleural effusion/, and pneumonia/mass. We chose 300 images that were solely labeled as pneumonia to avoid the double count of diagnosis. It is plausible that the system performance decreased because the number of learning images was limited in pneumonia. Second, the resolution of image samples was lower than that of normal X-ray images. In our hospital, physicians usually use images with a resolution higher than 2,000 pixels; however, the images needed to be resized to 1,024 pixels in the training and validation datasets. Further studies are warranted to test the effect of image resolutions on diagnostic accuracy by comparing physicians and machine learning techniques. Third, this study lacked the topographic diagnoses within the chest X-rays , and other pathologies or information that can be read in the radiographs, such as positioning of devices. However, this would not be a problem except for the increase in the amount of information that would have to be contributed to the learning system or for the addition of other layers of neurons to the model. Furthermore, this study did not have the data of the baseline demographic and clinical characteristics of participants including patients age, sex, BMI, diagnosis, and SOFA score. However, the machine learning without patient's characteristics would be versatile and increase the ability of accuracy form the images with limited information. Finally, this study has several limitations including sources of potential bias, statistical uncertainty, the limited number of cases, the number of investigators from the same geographical area, and generalizability in another environment . Statistical uncertainty is important since the sample size calculation was not performed in this study. Since we developed new prediction algorithms using machine learning approaches and could not speculate a proper sample size, we did perform sample size estimation and analyzed available data. However, the strength of this study is comparison of the software performance with human chest x-ray readers. Expansive study, which is statistically built using this study results and add new images, in particular from different geographical regions to improve performances and generalizability of the model with multicenter even international collaborations, would be desirable.We developed an automatic detection system for portable chest radiographs of ICU patients using two-stage classification method which performed superior to board-certified ICU physicians in the case of atelectasis and pleural effusion. The diagnostic accuracy improved as the sample size increased. The diagnostic time of the machine learning system was significantly shorter than that of physicians.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board of Chiba University Graduate School of Medicine. The Ethics Committee waived the requirement of written informed consent for participation.KT and TNakad designed the conception of the study. KT, TD, and TNakag performed acquisition of data. KT, NT, TD, and TNakag analyzed statistics. KT and KS drafted the manuscript. All authors made interpretations of data, added intellectual content of revisions to the paper, and gave final approval of the version to be published.Research reported in this publication was supported by an institutional internal funding.KS has a patent right of metabolic measurements in critically ill patients and grant/research supports from Nihon Kohden Corp. This does not alter the authors' adherence to all the journal's policies on sharing data and materials. TNakad, KT, TD, YY, and TNakag are the inventor of the patent. TNakad is the CEO of Smart119 Inc. and owns the stock. The remaining 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.


Different disaster activities should be performed smoothly. In relation to this, human resources for disaster activities must be secured. To achieve a stable supply of human resources, it is essential to improve the intentions of individuals responding to each type of disaster. However, the current intention of Disaster Medical Assistance Team (DMAT) members has not yet been assessed.To facilitate a smooth disaster response, this survey aimed to assess the intention to engage in each type of disaster activity among DMAT members.An anonymous web questionnaire survey was conducted. Japanese DMAT members in the nuclear disaster-affected area  and the non-affected area  were included in the analysis. The outcome was the answer to the following question: “Will you actively engage in activities during natural, human-made, and chemical (C), biological (B), radiological/nuclear (R/N), and explosive (E) (CBRNE) disasters?” Then, questionnaire responses were compared according to disaster type.The intention to engage in C (50), B (47), R/N (58), and E (52) disasters was significantly lower than that in natural (82) and human-made (82) disasters (P <.001). The intention to engage in CBRNE disasters among younger participants (age ≤39 years) was significantly higher in Group A than in Group N. By contrast, the intention to engage in R/N disasters alone among older participants (age ≥40 years) was higher in Group A than in Group N. However, there was no difference between the two groups in terms of intention to engage in C, B, and E disasters. Moreover, the intention to engage in all disasters between younger and older participants in Group A did not differ. In Group N, older participants had a significantly higher intention to engage in B and R/N disasters.Experience with a specific type of calamity at a young age may improve intention to engage in not only disasters encountered, but also other types. In addition, the intention to engage in CBRNE disasters improved with age in the non-experienced population. To respond smoothly to specific disasters in the future, measures must be taken to improve the intention to engage in CBRNE disasters among DMAT members. Hence, the need to manage these disasters is increasing.4 Among them, chemical (C), biological (B), radiological (R), nuclear (N), and explosive (E) (CBRNE) disasters are considered specific. In such disasters, a rapid and smooth response is required to save the lives of patients. However, in the Fukushima Daiichi Nuclear Power Plant  accident (2011), one of the most well-known radiological/nuclear (R/N) disasters, it was challenging to smoothly run disaster response activities at all times.5 Therefore, when providing medical treatment in areas with various hazards, measures should be taken in advance to facilitate disaster activities.In patients with critical conditions, the initial response of the rapid response team or medical emergency team is the most important factor correlated with prognosis.8 Hence, this is a major cause for the lack of human resources and is associated with difficulties in facilitating CBRNE disaster activities. A previous study revealed that factors such as self-confidence, incentives, and family understanding affect the intention of firefighters to engage in nuclear disaster activities.9 However, the current intention of all medical responders to participate in CBRNE disaster activities has not been fully elucidated.With consideration of factors that can prevent a smooth response to CBRNE disasters, the lack of human resources is a major concern. Disaster responders have a low intention to engage in specific disaster activities. Some surveys have shown that even individuals who are willing to respond to natural hazards avoid involvement in nuclear disasters or those involving communicable diseases due to anxiety and lack of knowledge.11 Japanese DMAT members can decide whether or not to respond when they receive dispatch requests. On the other hand, to date, the team plays a major role in large-scale disasters in Japan, and its members are the most important disaster medical responders in Japan. Therefore, a survey about the intention of DMAT members to engage in short-term CBRNE disaster activities must be conducted to facilitate a smooth response.In Japan, the Great Hanshin earthquake of 1995 has led to the development of a disaster medical system. Moreover, the Disaster Medical Assistance Team (DMAT), which responds to various disasters, has been established. The DMAT comprises physicians, nurses, and logisticians, as defined in the Basic Disaster Management Plan based on the Japan’s Disaster Countermeasures Basic Act.This study aimed to conduct a web questionnaire survey among DMAT members from two different areas (one with nuclear disaster experience and the other without). To smoothen each specific disaster response, the current intentions of DMAT members to engage in CBRNE disaster activities were examined. Moreover, future measures that can improve such intentions were evaluated.This was a cross-sectional study. An anonymous web questionnaire survey was conducted from October 2020 through November 2020. The website URL of the questionnaire was distributed by research members via e-mail to DMAT members in the two different areas. That is, one was a nuclear disaster-affected area (Group A) and the other was a non-affected area (Group N). In total, 204 participants from both areas responded. However, only 178 provided complete responses . These data were then included in the analysis . The EIS between each disaster was compared with the analysis of variance and the Tukey-Kramer test for multiple comparisons. Sub-analysis was performed for male participants only. Further analyses were conducted according to age and area. The EIS was compared between the younger and older and nuclear disaster-affected and non-affected groups using the student’s t-test. All statistical analyses were performed using JMP 14 , and a P value of .05 was considered statistically significant.The participants were divided into four groups according to age and area: younger Group A , older Group A , younger Group N , and older Group N .1; available online only).The characteristics of younger participants in Group A n = 28), younger participants in Group N (n = 56), older participants in Group A n = 51), and older participants in Group N (n = 43) are depicted in Table , and old8, youngeBased on the intention to engage in various types of disasters, the EIS for all CBRNE disasters among younger participants was significantly higher in younger Group A than in younger Group N , FDNPP accident (2011), and atomic bomb explosion during World War II (1945).16 By contrast, within the previous decades, there was no significant B disaster, except the COVID-19 pandemic. Moreover, lack of knowledge to disaster-related matters may result in an increase in the DMAT members’ perception of risks, which may consequently lead to avoidance in engaging to disaster activities.17 Based on these aspects, the fact of unknown might lead to a lack of disaster response image for this study population, and this might affect the result of low EIS in B disasters.Biological disasters had the lowest EIS. This result may be attributed to the recent COVID-19 pandemic. That is, it is similar to a previous survey, which reported that there is a trend toward lower intention to engage in situations involving communicable diseases compared with R/N hazards.18 The latter is strongly influenced by not only experience, but also culture or nationality, and Japanese are known to have a higher-risk perception than other nationalities.20 Moreover, age affects risk perception.19 Individuals may perceive CBRNE disasters as similar based on experiences with a certain type of CBRNE disaster at a young age. Moreover, they can lower their risk perception for other CBRNE disasters. By contrast, the EIS for R/N disasters alone in older participants was significantly higher in Group A than in Group N, and there was no difference in the EIS for other CBRNE disasters between the two older groups. In the sub-analysis comparing the EIS of younger participants in Group A and older participants in Group N, there was no significant difference according to all types of disasters. Group A experienced R/N disasters at a young age, indicating that they acquired knowledge and experience at an early stage, which could have developed over a long period of time. As shown in Table In the younger group (age: ≤39 years), the EIS was significantly higher in Group A than in Group N for all CBRNE disasters. It is easy to imagine that those who have experienced R/N disasters (Group A) have a higher EIS for R/N disasters. However, the result showed that they also had a higher EIS for C, B, and E disasters. In other words, the results indicated that the experience with R/N disasters had a positive impact on the intention to engage in other CBRNE disasters. Thus, an experience with one specific disaster may help develop adaptability or gain confidence to face other specific disasters in general, thereby improving one’s intention to engage to such disasters. In this study, the reason for the abovementioned phenomenon has not been validated. However, authors have considered the high-risk perception of young-aged Japanese population. A previous study has revealed that there is a relationship between behavior and risk perception.Younger participants in Group N had a low EIS for CBRNE disasters. Thus, EIS increases with age and experience. Assessing what they have gained through aging or experience will help to identify the important points for interventions to elevate the EIS of individuals who engage in disaster activities in the future. There are several possible factors with consideration of age and experience. These include acquisition of knowledge and skills or interest. However, further research on specific measures should be conducted. By efficiently and accurately addressing these factors, the number of people who will engage in disaster activities will increase. Then, this will lead to a stable supply of human resources in the future.The website URL of the questionnaire was sent to the registered e-mail addresses in the mailing list. However, whether all DMAT members have received the e-mail was not validated. Moreover, some people might have registered more than one e-mail address. Meanwhile, others might not have received the e-mail due to changes in address. Therefore, the actual collection rate was not verified. This survey was conducted on DMAT members from two areas only. Thus, DMAT members from other areas or individuals with other occupations that might involve engagement in various disasters were not included. Hence, further surveys must be conducted.Japanese DMAT members had a low intention to engage in CBRNE disaster activities compared with natural and human-made disaster activities. To respond smoothly to specific disasters, measures to efficiently improve the intention to engage in CBRNE disaster activities are required.


Objective: To investigate the profiles of brain volumetric measurements in children with attention deficit hyperactivity disorder (ADHD), and the consistency of these brain volumetric measurements derived from the synthetic and conventional T1 weighted MRI (SyMRI and cT1w MRI).Methods: Brain SyMRI and cT1w images were prospectively collected for 38 pediatric patients with ADHD and 38 healthy children (HC) with an age range of 6–14 years. The gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), myelin, myelin fraction (MYF), brain parenchyma volume (BPV), and intracranial volume (ICV) were automatically estimated from SyMRI data, and the four matching measurements  were extracted from cT1w images. The group differences of brain volumetric measurements were performed, respectively, using analysis of covariance. Pearson correlation analysis and interclass correlation coefficient (ICC) were applied to evaluate the association between synthetic and cT1w MRI-derived measurements.Results: As for the brain volumetric measurements extracted from SyMRI, significantly decreased GMV, WMV, BPV, and increased NON volume (p < 0.05) were found in the ADHD group compared with HC; No group differences were found in ICV, CSF, myelin volume and MYF (p > 0.05). With regard to GMV, WMV, BPV, and ICV estimated from cT1w images, the group differences between ADHD and HC were consistent with the results estimated from SyMRI. And these four measurements showed noticeable correlation between the two approaches .Conclusion: Our study demonstrated a global brain development disability, but normal whole-brain myelination in children with ADHD. Moreover, our results demonstrated the high consistency of brain volumetric indices between synthetic and cT1w MRI in children, which indicates the high reliability of SyMRI in the child-brain volumetric analysis. Based oIn the brain volumetric analysis in children with ADHD, much of the previous studies are based on conventional T1 weighted (cT1W) images using the automatic software, such as FreeSurfer , FSL , etc. However, the brain segmentation based on cT1W images has some limitations in clinical research. First, the computational process usually takes minutes to hours, which limits its application for clinical use. Second, it requires high contrast between gray and white matter in order to separate the different structures, thus the segmentation results may be influenced by the image quality, the MRI field strength and sequence settings, and even the developmental degree of the brain . Hence, Based on the above literature, in the current study our goal is to investigate the profiles of brain volumetric measurements in children with ADHD using SyMRI. In addition, to test the robustness of SyMRI in the brains of children, the consistency of brain volumetric indices between synthetic and cT1w MRI have been verified.This study was approved by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University (No.[2019]328) and informed consent was obtained from all participants’ guardians. The inclusion criteria are: (1) clinically diagnosed ADHD patients or healthy children without ADHD. (2) Age ranges from 6 to 14 years . The exclusion criteria are: (1) having a history of any other nervous system disease or have any brain lesion found by MRI. (2) Have ever received any therapy that may have an impact on nervous system development. (3) Cannot tolerate the MR scan.A total of 38 pediatric ADHD patients and 38 healthy pediatric volunteers were enrolled in this study during April 2019 to March 2020, with an average age of 9 years. Both groups were comparable in age, sex, and handedness. The male-to-female ratios of both groups are all around 5:1. The 38 ADHD patients included 18 cases of the inattention subtype and 20 cases of the combined subtype, but no case of the hyperactivity-impulsivity subtype. All of the patients were treatment-naïve, i.e., without any drug therapy or behavioral therapy.The patients were diagnosed with ADHD based on the criteria of DSM-IV, as well as the parent and teacher reports on Conners Symptom Questionnaire . Both the parent and teacher ADHD indices ≥ 75th percentile were considered to be ADHD positive.The healthy volunteers were excluded from the diagnosis of ADHD based on the same reports, with both parent and teacher ADHD indices < 75th percentile. All of the participants received parent face-to-face interviews to confirm diagnostic status.Whole-brain MRIs were collected on a GE 3.0 Tesla scanner . A 3-dimensional localizer scan was performed for the placement of the scanning area. Then a coronal T2-weighted sequence was collected to rule out any cranial lesion, followed by a sagittal three-dimensional T1-weighted fast -spoiled gradient echo-based sequence (T1w FSPGR) . For SyMRI, a two-dimensional multiple-dynamic multiple-echo (MDME) pulse sequence, comprising four automatically calculated saturation delay times and two echo times, was applied to acquire the axial sections. The parameters are as follows: TR = 10205.0 ms, TE = 11.3 ms, flip angle = 20°, thickness = 2 mm/no gap, pixel size = 2.0 mm × 2.0 mm, NEX = 1.00, ETL = 16, scanning time = 5.5 min.1 Harvard University, Boston, MA, United States)  . All scaallosum” . BPV wasFor SyMRI, segmentation and volume estimation of myelin and brain tissue, including white matter, gray matter, CSF, and NoN were performed based on the acquired quantitative values, using SyMRI software . The map of white matter, gray matter, CSF, NoN, as well as the volume of these compositions were automatically calculated according to the process offered by the operation manual. Since SyMRI divided the brain parenchymal into white matter, gray matter, and NoN, BPV was calculated as the sum of the volume of them. ICV was calculated as the sum of BPV and CSF. Brain parenchymal fraction (BPF) was calculated as the ratio of BPV to ICV. Myelin fraction (MYF) was calculated as the ratio of myelin volume to ICV .t-test for dimensional data. Pearson correlation analysis and interclass correlation coefficient (ICC) were used to evaluate the association between SyMRI and T1 derived measurements . Analysis of covariance was used to compare the segmented global structural volumes and related fractions measured by both methods separately, controlled for age and gender. A value of P < 0.05 was considered significant.The statistical analysis was performed using the SPSS v21.0 . The Shapiro-Wilk test was used to assess the normality of the data. We compared the demographic data using the chi-square test for all categorical variables and a As r-values are 0.692, 0.643, 0.898, 0.789, respectively, p < 0.001, ICC values are 0.809, 0.782, 0.946, 0.873, respectively)  .p < 0.05), WMV , BPV  were significantly smaller in the ADHD group than the control group. While the ICV was comparable in the two groups   .p < 0.01), smaller WM V , smaller BPV , and similar ICV .When comparing the specific brain tissue volume acquired by SyMRI within each group, the ADHD group was also shown to have smaller GMV . While the CSF volume, myelin volume, and myelin fraction of the two groups did not show any statistical difference   .In this study, we present the data of the specific brain structure and myelin volumetry in children with ADHD. The reduction of GMV, WMV, BPV, and BPF, and an increase of NON, were found in our study. While the myelin volume, MYF, and ICV were not found to have any statistical significance between the two groups. What is more, we also explore the consistency of brain segmentation with cT1w imaging and SyMRI in pediatric patients with ADHD and healthy children.As a kind of developmental disorder, ADHD was thought to correlate with brain development disability, as well as abnormal neural network interplay . The GMVSeveral imaging techniques have been used in previous studies to assess the integrity of myelin in white matter, such as diffusion tensor imaging, magnetization transfer (MT) imaging, and myelin water imaging. However, the indices estimated from these methods could not specifically reveal the myelin content. Unlike previous studies, our study directly calculated the myelin volume in ADHD by SyMRI methods, which offers a new insight into the myelin maturation of white matter in the developing brain. The results of our study also indicated that though the reduction of WM volume has been demonstrated in children with ADHD, the myelin volume did not show any difference between groups. Considering that most of the myelin is located in white matter, these results may indicate that the maldevelopment of glial cells, other than myelination, contributes to the alteration of white matter microstructure in the drug naïve children with ADHD. Moreover, the altered myelination may only exist in a specific region but not in the whole cerebral white matter, which has been indicated by another study using MT imaging . More stAmong all the parameters that can be measured by both methods, e.g., GMV, WMV, BPV, and ICV, the measurements were all shown to have a good correlation between the T1-derived and SyMRI methods, especially the BPV and ICV. What is more, both of the methods get similar results when comparing ADHD patients and the healthy control. Since the T1-derived brain tissue segmentation and volume estimation by FreeSurfer is widely accepted in neuroscience studies, the SyMRI seems to have comparative reliability both in the measurement and the clinic.However, it was also found in this study that the volumetric output of gray matter and white matter with the two methods did not show a correlation as high as that in BPV and ICV. It may be caused by the different definitions of the brain component in these two softwares. First, the tissue segmentations from SyMRI and cT1w images were computed with different principles. The tissue segmentations from T1 images were based on the voxel intensity and the universal brain segmentation template using FreeSurfer . While Sin vitro histological experiments (The SyMRI overcomes the segmentation of the cT1w images in: (1) the ease-of-use and time-saving of the postprocessing program; (2) more quantitative values can be acquired, e.g., myelin volume. The validity of the brain tissue and myelin volume measurement by SyMRI has been verified by several eriments , 2017. Aeriments . (3) Theeriments . Howevereriments .There are also some limitations of our study. First, the sample size is relatively small. Second, all of the data were collected in the same model of MRI scanner, thus all scanners used were from the same manufacturer. Studies done with multiple vendors are needed in future studies to investigate the intra-scanner variability . Third, In summary, our study indicated the global brain development disability but normal whole-brain myelination of children with ADHD. This study also demonstrated the high consistency of brain segmentation with cT1w image and SyMRI in children with ADHD. In a way, the SyMRI can replace the cT1W images for brain segmentation and volume estimation in children over 5 years.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Institutional Review Board of The First Affiliated Hospital of Sun Yat-sen University (No.[2019]328). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.YC, SS, and ZY contributed to the conception of the study. YD and ZW performed the experiment. LQ, HZ, and ML contributed significantly to analysis and manuscript preparation. YC, SS and LQ performed the data analyses and wrote the manuscript. MF and JC helped perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted version.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.


Physiotherapy in oncology is a process closely related to cancer treatment methods. Rehabilitation is based on physical activity in various forms involving the musculoskeletal system but also affects the emotional state. Physical activity influences physical and psychological well-being of people undergoing oncological treatment, in the course of which the most common psychiatric disorders are depression, substance use disorder, sleep disorders, fatigue syndrome, resulting in worsening of the quality of life. Difficulties in implementing physical exercise in cancer patients pose a challenge to treatment teams. Physiotherapy in oncology is a process closely related to cancer treatment methods. The main tool for the improvement of people with cancer is physical activity in all possible forms, both in passive and active involvement of the motor system. The term “motor activity,” is used in the world information system as “physical activity.” It is a component of a healthy lifestyle. Without it, any health strategy, its maintenance and multiplication is impossible, and in case of children, moreover, a proper development. Physical activity is a basic determinant of fitness and physical efficiency, which are also measures of health.Both in healthy and sick people, physical activity significantly influences the level of physical performance and is an integral component of a healthy lifestyle, therefore it must be included in the strategy of maintaining health, understood not only as the absence of disease, but full physical, mental and social well-being. This fits in with the WHO  definitiWe know that physical activity improves not only the somatic state but also the mental state of patients. Epidemiological studies  show thaFarmer et al. in an 8-year follow-up, found a 2-fold increased risk of clinical depression in women with low physical activity. Furthermore, physical activity not only reduces the risk of illness/relapse, but can also be considered a form of therapy for depression . This isThe use of various supportive forms of therapy is particularly important in oncology. Cancer treatment methods—conservative, surgical and radiotherapy—are the cause of complications such as scars that limit tissue mobility, contractures, joint mobility disorders, statics disorders, swelling and a number of other complaints related to the course of therapy. These limitations affect functional impairment, reduce quality of life, and consequently the psychological state. Patients experience depression, grief, anger, rage, feelings of inferiority, guilt, and anxiety. At the root of these reactions is severe stress created by the diagnosis of the disease, uncertainty about the prognosis, inadequate social support, poor relationships with medical staff, and the impact of traumatizing life events. Young age, loss of independence, coherence disorders, sense of life threat, physical pain, medical interventions, lack of control over the course of the disease, hospitalizations, changes in physical appearance additionally increase the risk of developing depression in oncology patients and influence its course , 9.The mere suspicion of cancer is a difficult fact for patients to accept. Also the highly stressful process of diagnosis, implementation of treatment and the course of therapy may be the reason for mental disorders. The most frequent mental disorders occurring in the course of cancer treatment are depressive and anxiety disorders, sleep disorders, fatigue syndrome and reduced quality of life. Therefore, it seems significant to diagnose them early in oncological patients and to provide psychological support while taking into account the necessity of comprehensive oncological rehabilitation. Psychophysical aspects of patients' improvement at every stage of oncological treatment should be carried out in cooperation between a psychologist and a physiotherapist, and implementation of broadly defined rehabilitation in patients during chemotherapy, radiotherapy and surgical treatment at all stages of cancer is a standard of treatment management. As already mentioned, a significant role can be played by physical activity already at the stage of cancer prevention and, above all, supporting the process of cancer treatment. The use of movement during rehabilitation supports cured patients in returning to normal functioning.With numerous counter-indications to the use of physical methods in cancer, physical activity is the recommended form of physiotherapy in various types of cancer, including advanced stages of the diseases .p < 0.05).However, it is often difficult to implement and accept systematic physical activity in treated patients. This problem was described by Frawley et al.  in patieIn general, poor physical health, depressive symptoms, and lower health-related quality of life (HRQL) are associated with decreased physical activity. It is sometimes difficult to determine whether poor general health causes aversion to physical activity or whether lack of physical activity is the cause of poor general health .Among the reasons for difficulty in engaging in physical activity, we can mention poor well-being associated with side effects of cancer treatment and cancer-related fatigue (CRF) syndrome. CRF, defined as the inability to exert both physical and intellectual effort, has a multifactorial etiology and considerable individual variability. It differs from typical fatigue in its degree of severity. It is described as weakness and lack of energy, impaired attention, causes difficulty in performing even a small physical effort. It is usually accompanied by apathy, which is characterized by an inability to engage in mental activity and a rapidly occurring feeling of fatigue and discouragement. Unlike typical fatigue, it does not subside with rest and sleep. Despite some similarities to depression, it is experienced as a separate condition, causing a drop in mood. It persists during treatment, but in some cases also after treatment has ended. Although no gold standard treatment is currently available for this syndrome, randomized controlled trials have shown beneficial effects of physical activity compared with control in reducing fatigue, with a mean effect size of −0.27 , 14.Although physiotherapy has a documented positive effect on both the somatic and psychological recovery of people treated for cancer , 16 diffMikkelsen et al.  describep < 0.001).A similar study by Frikkel et al.  noted thI2 = 100%, p < 0.00001] and ranged from 0.14 (95% CI = [0.04–0.24]) to 0.93 (95% CI = [0.91–0.95]). Pain, hot flashes, non-Caucasian race and menopausal period, especially depressive symptoms and fatigue, were statistically significantly associated with the occurrence of sleep disorders  showed a pooled estimate of 0.40 [95% confidence interval (CI) = [0.29–0.52], isorders .Motivating to be active during and after cancer treatment is an interdisciplinary task for both medical staff and patients' families. Non-medical predictors such as the location of the rehabilitation center, experiences with previous physical activity, and the level of difficulty of the applied physical tasks are also mobilizing factors. These factors should be considered when developing and implementing exercise interventions , 22.Planned and deliberate physical activity affects psychological well-being because it is inherently oriented toward health and high quality of life .In particular, physical activity, understood as any kind of body movement induced by working muscles and causing an energy expenditure exceeding the resting energy level, plays a significant role in primary prevention and is a complement to therapy based on established and traditional methods of treating depressive disorders. In cancer, prevention and effective treatment of depression can significantly affect the prognosis of the underlying disease .The most common side effects of oncological treatment are disorders of the locomotor system , reduced efficiency of the circulatory system, lymphedema, reduced efficiency of the respiratory system, e.g., due to impaired lung ventilation. The range of physiotherapeutic actions in such cases can be preventive, curative or palliative. These activities include preventing complications and functional consequences of treatment, alleviating symptoms of terminal illness and improving quality of life. Physical activity can potentially counteract a number of side effects of chemotherapy or hormonal therapy, such as fatigue syndrome, weight gain, muscle atrophy, hot flashes, nausea, or increased susceptibility to infection.We know that physical activity can be therapeutic for individuals with severe mental illness who generally have sedentary lifestyles and experience numerous lifestyle-related medical complications. On a physiological level, regular exercise can potentially increase levels of serotonin, brain-derived neurotrophic factor (BDNF), and endothelial growth factor (VEGF) .Appropriately selected regular physical activity can be a form of prevention of depressive disorders. It should be treated as a technique supplementing pharmacological and psychotherapeutic treatment of depressive disorders, including those occurring in the course of cancer. This is the opinion of the European Psychiatric Association supported by the International Organization of Physical Therapists in Mental Health (IOPTMH) concerning physical activity as a method of treating serious mental illnesses .p < 0.00001, I2 = 84%] and a large effect size in favor of neuromuscular exercise   in oncology patients are one of the most controversial topics in rehabilitation planning. Cancer is cited as a counter-indication to their use. The development of imaging techniques puts this issue in a new light. Based on the evidence, most articles  report bThe relationship between physical activity, psychological well-being and better cancer treatment is very clear. For example, among the recommended non-pharmacological treatments for depression in breast cancer patients, yoga with meditation is mentioned alongside psychotherapy as reducing depressive symptoms in these patients. However, more research is needed to determine the magnitude of this effect, depending on the severity of depression and the presence of co-existing diseases .p = 0.000), depression (p = 0.000), and fatigue (p = 0.002) was observed in the hypnosis group and a decrease in anxiety (p = 0.024) in the yoga group, while no change in the studied parameters was found in the CBT group and the control group without any of these interventions. The combination of different physiotherapeutic approaches seems to be an interesting psychological approach to improve the well-being of patients with breast cancer. However, further research is needed to better understand the mechanisms of such interventions and their long-term impact on quality of life.Grégoire et al.  comparedExercises based on yoga, can be used as a form of physical activity. The work of the musculoskeletal, circulatory and respiratory organs during practice is evident. Studies –39 confiAlthough the mechanisms of the observed improvements in mental health are not yet fully understood, yoga techniques can be used as an addition to pharmacotherapy. Given the growing awareness of the impact of lifestyle, stress reduction, and the importance of moderate exercise for health, various relaxation and movement techniques may complement therapies used for most psychiatric disorders. No negative side effects of supplementing standard therapies with exercises based on various forms of yoga have been demonstrated. If yoga dampens or limits both fatigue and inflammation, then regular practice could have substantial health benefits .Another study compared five areas of rehabilitation after breast cancer treatment, including exercise and physical activity, complementary and alternative medicine, yoga, lymphedema management, and psychosocial interventions. Clear evidence of effectiveness was found for exercise and yoga. Exercise interventions improved shoulder mobility, reduced lymphedema, pain, fatigue, and improved quality of life. In contrast, yoga improved quality of life, reduced anxiety, depression, sleep disturbances, fatigue, and gastrointestinal symptoms. The effects of complementary and alternative medicine on nausea, pain, fatigue, anger, and anxiety were demonstrated, but these results should be interpreted with caution due to the poor methodological quality of the studies included in the evaluation . PhysicaAccording to a meta-analysis by Zeng et al.   seems to be an interesting rehabilitation method for women with breast cancer. Sánchez-Lastra et al.  conducteP < 0.05). In group 2, there was no significant improvement in the parameters compared with the pre-exercise period. When the two exercise groups were compared, there were significant differences in 6MWT in the pilates exercise group (p < 0.05).Another form of exercise worth recommending is Pilates. It is a system of harmonious, slow exercises involving all muscle groups, combined with breathing exercises. Eyigor et al.  conductePhysical activity is not only important in post-treatment rehabilitation, but is also part of the process of cancer therapy and prevention of cancer reoccurrence. In fact, physical activity after treatment for breast cancer, colorectal cancer and prostate cancer has been shown to reduce the risk of recurrence of the disease .p < 0.001), depression (p < 0.001), anxiety (p < 0.001), and stress (p = 0.038). Statistically significant results were found for the effects of exercise on fatigue (p = 0.004), depression (p = 0.016), body image (p = 0.007), and quality of life (p = 0.001).Movement is a manifestation of human physical and mental health. Therefore, activating patients only in illness is a bad strategy. The concept of lifelong activity should also include the period after recovery from cancer. Successful cancer treatment and longer life of cured patients require maintenance of physical fitness and quality of life. Physical activity is a good and inexpensive way to address physical and psychological problems. A meta-analysis by Saskija et al.  examinedn = 444). The purpose of the study was to investigate whether regular exercise can reduce the long-term side effects of complementary treatment for breast cancer and improve quality of life. Women aged 35–68 years who had completed complementary chemotherapy or started hormonal therapy for breast cancer within the previous 4 months participated in the study. Physical activity levels were assessed by diary, physical fitness by a 2-km walk test, quality of life by the EORTC QLQC30 and BR-23 questionnaires, fatigue by the FACIT-fatigue scale, and depression by the Beck Depression Inventory (BDI) 13-item scale. Participants who improved their level of physical activity over the 5-year follow-up were more likely to improve their global health score (p = 0.016), physical (p = 0.009), social (p = 0.013), role functioning (p = 0.005), and fatigue (p = 0.002). Better performance on the 2-km walk test was associated with improvements in global health, physical and role functioning, body image, future outlook, and fatigue .This is supported by the results of the multicenter, randomized phase III BREX  study onThe results obtained allow us to claim that improving the level of physical activity or physical fitness causes a positive change in the quality of life of patients with breast cancer.p = 0.003), improved self-esteem (p = 0.029) and multiple HRQoL domains (p ≤ 0.05) compared to subjects without such training. Effects on CRF (p = 0.031) and vitality (p = 0.015) persisted after 3 months of follow-up. Changes in cardiorespiratory fitness mediated improvements in CRF and HRQoL. Improvements in CRF were greater for TCS with inactive lifestyles, lower fitness, higher testosterone, and clinical fatigue at the beginning  dedicated to cancer prevention and treatment have reportedThe American Cancer Society has published guidelines  on diet Physical activity levels for adults should be 150–300 min of moderate-intensity physical activity per week or 75–150 min of high-intensity physical activity or an equivalent combination; reaching or exceeding the upper limit of 300 min is optimal. Children and adolescents should do at least 1 h each day of moderate to vigorous activity per week.According to the ACS, it is important for cancer prevention to reduce sedentary lifestyle, lying down and watching TV and other forms of screen entertainment.Recommended activities include exercise and recreational activities such as walking, dancing, recreational cycling, skating and roller skating, horseback riding, kayaking, yoga, jogging and running, fast cycling, strength training, swimming, skipping, aerobics, and martial arts. Recommended sports include downhill skiing, golf, volleyball, basketball, badminton, tennis, soccer, field hockey, lacrosse, and singles tennis . Daily aThe presented information proves that physical activity is an effective method of supporting cancer treatment and plays an important role in its prevention. Individualization of physiotherapy programmes has a positive effect on patient co-operation. In order to overcome barriers, patients in advanced stages of cancer should be offered programmes that include information, motivational counseling and individualized exercise training. Collaboration with mental health professionals can also have a significant impact on patients' motivation to engage in physical activity.It is clear that more than one intervention can have a positive effect on a particular symptom and that the effects depend not only on the type of intervention but also on how and when the intervention is delivered. Further research is needed to develop specific guidelines for the use of physical activity according to the somatic and psychological status of patients with cancer.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This study was financed by university funds, no external funds were used.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.


Purpose: Stress and migraine are often comorbid. However, no studies have examined stress severity in a sample of migraine patients. That is why this study investigated the determinants of stress level in a sample of migraine patients with a family history of migraine (MWFH) in Saudi Arabia. Material and Methods: A quantitative observational study with a cross-sectional data collection and convenient sampling of (MWFH) was performed in Madinah, Saudi Arabia. Participants completed the Perceived Stress Scale-4 (PSS-4) and a list of items to register clinical history and demographics information. Results: Out of eight independent variables that were explored, only two variables —both sleep related, i.e., insufficient sleep  and non-refreshing sleep , F = 5.13, p < 0.001, R2 = 0.244—were associated with stress severity. The majority of (MWFH) were female (73.7%), recorded a lack of habitual physical activity (56.2%), received treatment for co-morbidities (56.9%), reported sleep insufficiency (54%), non-refreshing sleep (52.6%) and traumatic incidents (50.4%). Discussion: Stress severity increased with sleep complaints, indicating a comorbidity of stress–sleep problems among migraine patients with a family history of migraine. Psychological or perceived stress is a situation in which individuals perceive their environment requires more resources than the person is capable of adjusting to . Such a The relationship between stress and migraine has a multidimensional aspect that may appear in adverse physical and psychological situations in affected individuals . Co-morbStudies on migraine patients showed that the most common sleep disturbances are insomnia, daytime sleepiness, sleep apnea, and parasomnia which also has an impact on quality of life and is associated with increased disability ,6.Studies on personality traits in migraine showed that migraineurs display increased neuroticism and anxiety and are anti-aggressive. Also, some of these personality traits could be an effect of serious and insufficiently processed life events, as it is shown that migraine patients may have a history of maltreatment, especially during childhood. Studies reported increased neuroticism in both sexes in a longitudinal study on migraine subjects between 19 and 29 years of age. Farther more, an increase amount of neuroticism was associated with a higher incidence of migraine, which was more marked for female patients ,5,6,7.Therefore, research focused on understanding migraine epidemiology, correlates, and pathophysiology in this group may help in targeted management of their migraine-related problems. This study investigated the applicability of a model to predict changes in the stress severity level among clinically diagnosed (MWFH).This was a quantitative observational study to investigate psychological stress level and determine its correlates in (MWFH). A cross-sectional approach to data collection was implemented on a convenient sample of (MWFH) who presented at the outpatient division of Ministry of Health hospitals in Madinah, Saudi Arabia. The study was carried out during a four month period from October 2019 to January 2020. Inclusion criteria were adults with migraine headache complaints for at least six months, based on the diagnosis criteria for migraine based on the International Classification of Headache Disorders, and also a family history of migraine. Exclusion criteria were having headache–migraine but not meeting any of these: (i) headache–migraine complaints for at least six months, (ii) no account/record of family members having migraine–headache, and (iii) under 18 years of age. Headache–migraine features in patients were based on the reported history, clinical evaluation, and medical records. Family history of migraine–headache was based on the subjective account of patients.The researcher presented the questionnaire-based survey forms and clinical evaluation during the patients’ regular visits to the outpatient department. Patients were screened and those fulfilling inclusion criteria were asked to participate. All those deemed suitable to participate and who provided written consent to participate were further given a brief introduction about the aim and objective of the study in simple language.Altogether, 137 migraine–headache patients with a family history of migraine (age: 27.3 ± 7.0 years) participated. Participants were required to respond to the study questionnaire at interviewer-administered sessions. The questionnaire package contained a shortened version of the Perceived Stress Scale known as PSS-4, and collected a clinical history and demographic information.PSS is a widely used subjective tool to assess severity of psychological stress in clinical and research settings ,13,14. PA set of questions with both open- and closed-ended items was used to collect information related to social, demographic, and clinical history. The response to the question regarding clinical diagnosis of headache was based on participants’ medical records. In turn, this information was based on expert clinical diagnosis by neurologists at Madinah hospitals using the International Classification of Headache Disorders. Participants were required to record their age, gender, sports activity, treatment for other (apart from migraine-headache) medical conditions, recent traumatic accidents/incidents among family/friends, and sleep-related complaints, etc.Analysis was performed by the SPSS version 26.0. Percentage, mean, and standard deviation were estimated for presenting descriptive statistics. The multiple linear regression model was used to assess association between dependent variables, PSS-4 total score indicator of stress severity level, and independent variables. Demographic variables and clinical parameters were taken as independent variables.The average values of age and PSS-4 total scores were 27.3 ± 7.0 years and 8.1 ± 2.5, respectively in the study sample . Most paF = 5.13, p < 0.001, R2 = 0.244 (p = 0.042) and complaints of not feeling refreshed after sleep (p = 0.037) significantly predicted stress levels , gender, age, sports activity (Yes/No), currently under treatment for medical conditions (Yes/No), complaint of insufficient sleep (Yes/No), not feeling refreshed after sleep (Yes/No), and recent traumatic incident in the family, e.g., death of a relative or close friend (Yes/No). The study model significantly predicted changes in stress levels in the study sample,  = 0.244 . Complais levels . Those cs levels . Similars levels .To the best of this researcher’s knowledge, this is the first study to investigate correlates of stress severity level in a sample of clinically diagnosed (MWFH). The findings of this study reveal increasing stress severity was predicted by sleep disturbances such as complaints of insufficient sleep and of not feeling refreshed after sleep. The majority of participants (MWFH) had migraine without aura, were females, participated in no regular sport or exercise activities, were undergoing treatment for co-morbid medical conditions, complained of insufficient sleep, and had a recent traumatic incident in the family.A robust approach of multivariate analysis involving multiple linear regression found the model with eight predictor variables was statistically significant and explained about 24% of the changes in the variance level of stress severity. This finding reaffirms and adds to the accumulated evidence about the positive association between stress and migraine . RafiqueHowever, if a collective appraisal is made using evidence from epidemiological studies, the outcome of this study, animal model investigations, and mechanistic studies, then most seem to support the stress-migraine relationship. Malone et al. reasoned the stress–migraine relationship may have implications for additional health conditions and diseases . Indeed,In this study, the sample of migraine patients was comprised of three distinct diagnoses groups, of which migraine without aura, formed the biggest group. This is similar to previous reports about the overwhelming presence of migraine without aura group as the most common type of migraine Schramm et al., using the prevalence and predictor analysis of migraine that was used for cardiovascular events in the Heinz Nixdorf Recall study data, found that of all active migraine cases, migraine without aura formed one of the biggest groups .In this study, the majority of participating (MWFH) were female. The fact more women get migraines is perhaps one of the most common findings from previous studies ,18. Pavln = 1,468,404 adults), comorbidity was a predominant feature of migraine patients, with as many as 25 out of 31 health conditions being significantly higher among migraine sufferers [In this study, the majority of (MWFH) had no regular exercise or sports activity. This is consistent with previous reports . In a reufferers ,21.Studies on gender differences were found in personality traits and in the number of stressful life events, in particular those rated as strongly negative. Furthermore, anxiety, depression, and tension-type headache were twice as frequent in women. It was advice that stress susceptibility, life events, and parallel disorders, especially of psychiatric nature, ought to be considered when investigating and treating individuals with migraine ,23,24.Studies on psychological/behavioural techniques for migraine and tension-type headache showed them to be effective for reducing headache frequency and intensity and improving sleep parameters such as sleep quality and sleep time ,25.In this study, the majority of participants (MWFH) had a recent traumatic incident in the family. Chowdhary and Enam discovered that migraine forms the biggest group of headache complaints among patients who have experienced traumatic incidents .One noteworthy limitation of this study was the relatively small sample size of the study group, 137. This was done for feasibility, in light of the patient flow rate and confirmed diagnosis based of the International Classification of Headache Disorders in the participating facility. Though, the model explained about 1/4th of the variance in the level of the stress severity. This is quite substantial, however, there exists scope to further build on this finding. Future studies with a more extensive list of predictors may yield better models to account for even more variance levels in the changes of stress severity among migraine patients with respect to independent variables. This study relied on the word of migraine patients as to their family history of migraines. This approach may lead to an underestimation of migraine prevalence and incidence .In brief, the outcome of this study showed that stress severity increases with sleep insufficiency and non-refreshing sleep in (MWFH). Additionally, the findings showed that among (MWFH), headaches occur mainly in females, those who lack habitual physical activity, have comorbid health conditions, suffer from sleep insufficiency, and have experienced traumatic incidents among family or friends.


Background: This study examines the interaction between a history of binge drinking (BD), alone or with cannabis consumption, and the effects of acute alcohol exposure on immediate visual memory (IVM)  in adolescents of both sexes.Method: Two hundred and ninety adolescents, grouped into refrainers, binge drinkers and subjects with a history of simultaneous BD/Cannabis co-use, received a risk dose of alcohol or a control drink.Results: Consumption Pattern (refrainers vs. binge drinkers vs. BD/Cannabis consumers) was not significant, while Treatment  was significant in both sexes. Also, male binge drinkers' performance in the faces memory task was poorer than that of refrainers and BD/Cannabis consumers who consumed the control drink. BD/Cannabis consumers performed this task as capably as refrainers. In women, binge drinkers performed better than refrainers in scene memory and IVM-IQ tests when given alcohol, and binge drinkers performed worse than refrainers after consuming the control drink.Conclusions: Acute alcohol consumption worsens IVM. Cannabis exerts a buffering effect in men. A cognitive tolerance effect is observed in women. Exposure during adolescence to alcohol, alone or with cannabis, can trigger different cognitive effects in men and women that could endure into adulthood. We have previously observed differential effects of alcohol on memory performance in adolescent binge drinkers, with immediate visual memory (IVM) proving to be particularly sensitive to impairment by alcohol , 2. The Researchers have investigated the effects of BD on different kinds of memory. Among them, word fragment completion, free recall, and IVM appear to be the most sensitive in adolescents and young adults, as they are affected by moderate doses of alcohol (BAC = 0.3 −0.38 g/L) e.g., , 7]. Hig. Hig7)].On the other hand, according to a report from the Spanish Observatory of Drugs and Addictions , 13.4% oThe effects of alcohol and cannabis co-use on cognitive functions have been the subject of less research, and are more ambiguous than those of these substances when consumed alone. Some authors –15 consiExperimental results of research performed with subjects of a single sex are sometimes extrapolated to both sexes. Sex should be considered as an important biological variable in basic and preclinical research, and results should not be automatically applied to both men and women . Sex difTaking into account the scarcity of studies evaluating the cognitive effects of alcohol and cannabis co-use in healthy adolescents, and considering the potential vulnerability of females to the neurotoxic effects of alcohol, the main objective of this research was to study experimentally the interaction between a history of BD, alone or simultaneously with cannabis use, and the effects of acute alcohol exposure on IVM  in adolescents of both sexes. IVM was assessed by the Wechsler Memory Scale 3rd Edition , as it iTwo hundred and ninety 18–19 years old adolescent students  from the University of Valencia, Spain, filled in a self-report questionnaire about physical and psychological health, frequency and level of consumption of alcohol, cannabis, alcohol with cannabis, or other drugs, and hours and quality of sleep. The participants were recruited for the study based on strict exclusion and inclusion criteria. The exclusion criteria were as follows: taking medication; a history of mental disorders ; an irregular sleep pattern (non-restorative sleep and/or irregular schedule); having consumed, even sporadically, any drug or having a history of substance use (including those studied herein); and having first-degree relatives with drug misuse problems. The following inclusion criteria were used: age 18–19 years old; a healthy body mass index ; good health ; and being refrainers, occasional consumers of alcohol, binge drinkers or BD and cannabis co-users. The participants were classified as refrainers if they had never consumed alcoholic drinks or had drunk very sporadically. They were classified as alcohol consumers with a BD pattern in accordance with the NIAAA criteria for Spain [see ], i.e., The participants were told to follow their normal sleep pattern and meal routine, and to eat 1 h before the experimental session.A telephone interview of approximately 15 min was conducted with each subject in order to confirm the information previously provided in the self-report and to fix a date and time for the test session. The participant's data were verified again on the test day. The data concerning the menstrual cycles of the female groups were registered in the self-report and during the telephone interview, and cycle phase was considered in the test in order to counterbalance this variable in each group.The Alcohol Use Disorders Identification Test (AUDIT)  was emplThe Cannabis Abuse Screening Test (CAST)  is a sixA digital automatic blood pressure monitor  was employed to register systolic and diastolic blood pressure and heart rate in all the subjects.An alcoholmeter  was employed to evaluate BAC before and after (20 and 50 min) drink intake.A drug test  was employed to analyze the presence of drugs in a saliva sample collected from each participant before drink intake.IVM was assessed using the Wechsler Memory Scale 3rd Edition  . The IVMThe experimental procedure was approved by the Research Ethics Committee of the University of Valencia , and was in accordance with the Helsinki Agreement. All the subjects provided written informed consent to participate in the study. According to their Consumption Pattern  and the Treatment received (Control drink and Alcohol), the participants were assigned to one of six experimental conditions for each sex: Refrainers-Control drink (R-Co); Refrainers-Alcohol (R-A); Binge Drinkers-Control drink (BD-Co); Binge Drinkers-Alcohol (BD-A); BD/Cannabis-Control drink (BD/C-Co) and BD/Cannabis-Alcohol (BD/C-A).The participants were instructed to abstain from any intake of alcohol, caffeinated beverages, drugs or medication and strenuous exercise for 24 h before the experimental session, and to refrain from eating and smoking at least 1 h prior to the session. At the beginning of the experimental session BAC was measured in BD and co-user subjects using the alcoholmeter to ensure that they had not consumed alcohol. A saliva sample of the co-users was also analyzed by the drug test to make sure that they had not used other drugs.In addition, a problematic use of alcohol or cannabis among the BD subjects and co-users was assessed using the AUDIT and CAST test, respectively. None of the subjects was found to be alcohol or cannabis-dependent . Next, each subject received a flavored refreshment  contained in cans of 330 ml, alone or mixed  with distilled drinks of alcohol content = 40% vol. (vodka or gin) at a risk dose of 120 ml . The subjects were instructed to consume their drink within a period of 20 min. The participants ate a light snack  and the beverages were always consumed in the presence of a research assistant. After finishing the drink, all the subjects rinsed their mouths with water, and BAC was repeatedly measured every 5 min throughout the waiting period, until it peaked (~20 min after consuming the drink). The subjects performed the IVM tests, faces memory test and scenes memory test, in a counterbalanced manner, while BAC was descending. The BAC of each experimental subject was measured again at end of the experiment.BAC was 0.00 g/L for men and women before the alcoholic drink, and was 0.36 ± 0.008 g/L for men and 0.53 ± 0.01 g/L for women afterwards. It is important to point out that, although all the subjects consumed the same amount of alcohol, the statistical differences in BAC between men and women did not allow a direct comparison between the sexes. On the other hand, it was possible to study the effects of alcohol on each sex.Data from men and women were analyzed separately, as BAC was found to be statistically different between the two sexes. The data were submitted to parametric analysis after checking that they met the criteria for normality and homogeneity of variances. Each analysis  contained the between-subject factors Consumption Pattern  and Treatment (Control drink and Alcohol). When their interaction was statistically significant, pairwise comparisons were carried out. All analyses were performed using the “SPSS” Statistics software package, version 26.0 for Windows .F = 1.705, ns]. The factor Treatment was statistically significant , with subjects given alcohol showing lower scores than those given the control drink  = 3.523, p < 0.05]. Post hoc comparisons showed that the R-A and BD/C-A groups performed worse than the R-Co and BD/C-Co groups, respectively (ps < 0.05); and that the BD-Co group performed worse than the R-Co and BD/C-Co groups (ps < 0.05), while no differences were detected between the last two groups  = 0.567, ns] and Treatment  were not statistically significant, and neither was their interaction .The factors Consumption Pattern . The factor Treatment was statistically significant , with subjects given alcohol showing lower IVM-IQ than those given the control drink  = 0.105, ns].There was no significant main effect of Consumption Pattern on performance . The factor Treatment was statistically significant , with subjects given alcohol scoring lower than those given the control drink  = 0.341, ns].There was no significant main effect of Consumption Pattern on performance . The factor Treatment was statistically significant , with subjects given alcohol obtaining lower scores than those given the control drink  = 8.167, p < 0.001]; the R-A group performed worse than the R-Co group, and BD-Co and BD/C-Co participants performed worse than the R-Co subjects, while the BD-A group performed better than the R-A group (ps < 0.05) (see There was no significant main effect of Consumption Pattern on performance . The factor Treatment was statistically significant , with subjects receiving alcohol displaying lower IVM-IQ than those given the control drink  = 5.051, p < 0.01], with the R-A group performing worse than the R-Co group, the BD-Co group performing worse than R-Co subjects, and the BD-A group outperforming the R-A group (ps < 0.05) (see There was no significant main effect of Consumption Pattern on performance [rink see . The int.05) see .A distinctive contribution of this study is to evaluate experimentally, together, the impact of an acute alcohol consumption episode and a BD history of consumption on IVM  in adolescent men and women. We could not directly examine sex differences in IVM functioning among our adolescent population, but our study provides interesting data for each sex.We have observed that a moderate acute dose of alcohol (BAC = 0.36 g/L in men and 0.53 g/L in women) is enough to impair faces memory and IVM-IQ in men, and faces and scenes memory and IVM-IQ in women, and corroborate that IVM is sensitive to the neurotoxic effects of acute alcohol consumption , 7. HoweThe literature suggests that this BD pattern and its maintenance over time have differential effects on memory . In the The pattern of alcohol and cannabis co-use did not affect the IVM in our study. Simultaneous alcohol and cannabis use in young drinkers (18–25 years old) has been associated with an increase of negative consequences . The exiIn line with the aforementioned evidence, we have observed, after a previous year of simultaneous consumption, a dampening effect of cannabis on the deteriorating effects of alcohol in the faces memory test when performed by men. After receiving the control drink, co-users performed this task as well as refrainers, while participants with a BD history performed worse than refrainers and co-users. It is plausible that the neuro-inflammatory effects of alcohol, responsible for cognitive decline, were counteracted by the anti-inflammatory efficacy of cannabis. Nevertheless, as other authors  have poiIn women, the interaction Consumption Pattern X Treatment was statistically significant with respect to scenes memory and IVM-IQ, and alcohol generated an effect of cognitive tolerance. This suggests that our binge drinkers developed tolerance in such a way that the deteriorating effect of alcohol on their scenes memory and IVM-IQ after drinking alcohol was weaker than that seen in refrainers. Thus, binge drinkers performed better than refrainers when given alcohol  and binge drinkers performed worse than refrainers after consuming a control drink (as their memory would have been damaged). The existence of this cognitive tolerance is endorsed by the fact that the scenes memory and IVM-IQ of female binge drinkers receiving the alcoholic drink did not differ from those of binge drinkers given the control drink. This phenomenon of cognitive tolerance was not so obvious in co-users, since there were differences in scenes memory between refrainers and co-users who received the control drink, but there were no differences in scenes memory or in IVM-IQ between refrainers and co-users who received alcohol. On the other hand, the phenomenon of women beginning to drink earlier and progressing more rapidly than men from the first exposure to the addiction phase, known as the “telescoping effect” –35, coulvs. a history of BD pattern leads to differential effects on cognition that depend on the type of memory in question. In conclusion, exposure during adolescence to alcohol, alone or with cannabis, can trigger different cognitive effects in men and women, which contribute to enduring cognitive deficits in adulthood. Moreover, our findings are consistent with the greater vulnerability of adolescent women to the neurotoxic effects of alcohol. Further research is needed–particularly, longitudinal studies including women and exploring the timeframe of consumption of these substances–in order to confirm the aforementioned findings and consolidate our conclusions. This will allow us to better understand the mechanisms underlying the effects on memory of alcohol, consumed alone or simultaneously with cannabis, and to develop optimal treatment methods for cannabis /alcohol dependence in men and women.The alcohol acutely consumed by the participants in our study  was close to the so called “risky alcohol consumption” for men and women  . Some ofThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Research Ethics Committee of the University of Valencia . The patients/participants provided their written informed consent to participate in this study.Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was supported by the Universitat de València (Grant UV-INV_AE18-779336) and Generalitat Valenciana (Grant PROMETEO-II/2015/020), Spain.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.


Sleep is essential for mental health at all ages, but few studies have investigated the importance of sleep for mental health in early childhood. Therefore, this study examined the association between mental health and sleep habits/problems in children aged 34 years.n=415; 211/204 boys/girls) were recruited for this study. Their mental health was assessed using the Strengths and Difficulties Questionnaire (SDQ), and their sleep habits/problems were evaluated using the Child and Adolescent Sleep Checklist.Children aged 3 to 4 years who were living in the community (n=76) and a control group (n=339). In terms of sleep habits, which included total sleep time, bedtime, wake time, and nap conditions, there were no differences between the two groups. Regarding sleep-related problems, however, anxiety before going to sleep (p=0.026), circadian rhythm abnormalities (p=0.014), and sleepiness during classes outside of naptimes (p=0.031) were significantly higher in the poor mental health group than in the control group. Multiple regression analysis showed that poor mental health status was significantly associated with sleepiness and snoring .Based on the total difficulties score of the SDQ, the children were divided into two groups: a poor mental health group (The mental health status of 34-year-old children was associated with sleep-related problems, namely sleepiness and snoring. Healthcare providers should pay attention to childrens irregular sleep-wake patterns; moreover, interventions for appropriate sleep hygiene will reduce the psychological burden on both children and their families. Sleep is associated with cognitive , physicaSleep duration recommendations can help to determine the normative distribution of sleep duration and are important for surveillance and for informing public policies, interventions, and the general publics healthy sleep behaviors , 9. Howe10Mental health problems are strongly related to lower educational achievement, maladaptive personality traits, community disorganization, and a higher number of risky behaviors, such as substance abuse or violence . The worSleep is considered important during early childhood; however, there have been few studies of sleep in children aged 3 to 4 years. Therefore, limited information is available about the association between their mental health and sleep habits/problems. Thus, the aim of this study was to investigate the association between mental health problems and sleep habits/problems in community-dwelling children aged 3 to 4 years.The present study was conducted in Toon City, Ehime Prefecture, Japan, which had a population of 34,600 and 234 births in 2016. We included all children who attended 42-month health checkups at a local health center between October 2016 and March 2019.Participants completed a demographic sheet, the Strengths and Difficulties Questionnaire (SDQ), and the Child and Adolescent Sleep Checklist (CASC). The demographic sheet included questions about basic information, such as the childs age, gender, and whether the child attended nursery school, kindergarten, or stayed at home.The parents provided information about their childs mental health using the parent version of the SDQ, which describes the childs functioning during the preceding 6 months and is used for the detection of emotional and behavioral problems , 21. It n=415), Cronbachs was 0.594.The cut-off values for the total and subscale scores of the SDQ for age 24 years were established so that approximately 80% of children, whose total difficulties score ranged from 0 to 12, were in a close to average group, 12% of children, whose score ranged from 13 to 15, were in a slightly raised group, 4% of children, whose score ranged from 16 to 18, were in a high group, and 4% of children, whose score ranged from 19 to 40, were in a very high group, as defined by the provisional banding of the SDQ scores for 24-year-old children . In the n=415), Cronbachs was 0.658.Sleep habits and problems were assessed by asking the parents to complete the CASC . This chThe participants were born between February 2014 and July 2017 and attended health check-ups at age 42 months between October 2016 and March 2019. The consultation rate of health checkups in Toon City is almost 100%. We sent a written explanation of our study and its purpose to the childrens parents along with the non-anonymized questionnaires, namely the demographic sheet, SDQ, and CASC. Those who agreed to participate in our study were asked to bring the completed questi onnaires to the health checkup, and they handed them to the public health nurses, who had received an explanation about the study and understood its purpose.SD), and categorical variables were expressed as numbers and percentages. Among the four groups, the very high, high, and slightly raised groups were defined as the poor mental health group. The close-to-average group was defined as the control group. The Mann-Whitney U test was used to compare sleep habits between the poor mental health and control groups. The scores on the three sleep domains  were also compared between the two groups using the Mann-Whitney U test. Exact tests were used to compare the percentages of children with each sleep-related problem between the two groups. To elucidate the main factors that affected the childrens mental health, the 24 sleep problems listed in the CASC were entered into a multiple regression analysis, using the backward stepwise method. The close-to-average group was scored 0, the slightly raised group was scored 1, the high group was scored 2, and the very high group was scored 3 as the dependent variable. All tests were two-sided and assumed a 5% significance level. All data were analyzed using SPSS Statistics for Windows, version 22.0 .Continuous variables were expressed as meanstandard deviation . In addition, it was performed with the cooperation of Toon City. Consent to participate was assumed with the submission of the completed questionnaires.The participants demographic characteristics and sleep habits are shown in Tablen=5; 4 boys, 1 girl) were classified as being in the very high group, 3.4%  were in the high group, 13.7%  were in the slightly raised group, and 81.7%  were in the close-to-average group. There were no differences in age, gender, or attending kindergarten or nursery school or staying at home among the four groups . After the health check-ups at age 42 months, public health nurses picked up 4 out of 5 children in the very high group, 12 out of 14 in the high group, and 32 out of 57 in the slightly raised group as the subjects of follow-up about developmental disabilities.TableWe divided the four groups into two groupsa poor mental health group and the control groupbased on the cutoff points of the SDQ scores. In terms of sleep habits Table, the Manp=0.214). Conversely, the scores on the nighttime and daytime domains of the poor mental health group were significantly higher than those of the control group . Tablep=0.026, 0.014, and 0.031, respectively).TableMultiple regression analysis of mental health status and sleep-related problems showed that mental health status was significantly associated with sleepiness during classes (I get sleepy during class outside of naptime), snoring (I snore), and anxiety before going to sleep  compared with when they had healthy sleep (10h in bed per night for five nights) . AnotherIn terms of the daytime domain, circadian rhythm abnormality and sleepiness negatively affected mental health in children aged 3 to 4 years. Sleep/wake patterns are influenced by both environmental and genetic factors that vary across different populations and cultures 37. Adole44The main finding of our study was that sleepiness and snoring were strongly associated with mental health status in children aged 3 to 4 years. Sleepiness is a very common complaint among schoolchildren, and data on its actual prevalence differ from study to study. The varying prevalence of daytime sleepiness depends on the questions used, sample sizes, study area, year the survey was conducted, age, and ethnicity. Moreover, each study can capture only limited aspects of sleepiness, and this must be considered when interpreting the results . SleepinSnoring is the major clinical symptom of obstructive sleep apnea (OSA), which is a condition that is characterized by recurrent episodes of gas exchange abnormalities and repeated arousals that affect between 1 and 3% of 2- to 8-year-old children 59. ChildOur findings have several clinical implications. It is difficult for parents, especially first-time parents, to notice abnormalities in their childs circadian rhythm. Parents tend to think that their childrens circadian rhythms are natural and take them for granted; therefore, parents do not consult anyone, and the children and their parents gradually become exhausted. Moreover, an abnormality in the circadian rhythm affects daytime functioning and leads to poor mental health. Once parents can understand that abnormality in circadian rhythm is not because of the way they have raised their children, they will easily accept the existence of sleep disturbances or poor sleep hygiene. Interventions for better sleep hygiene could then be accepted by children and their parents, which would consequently constitute an early intervention for the childrens development. We hope that our findings promote not only good sleep hygiene but also early intervention for mental health and development. However, more research is required to address longitudinal changes in developmental trajectories and sleep habits/problems.This study had several limitations. First, the CASC was originally developed in Japanese and has served as a valid and effective tool for the assessment of parents perspectives of their children; however, it has not been fully validated. Further research must be conducted to assess the criterion validity of the questionnaire. Second, the cutoff points of the SDQ scores for 24-year-old children were defined in the UK, and Japanese children might have different cutoff points. Indeed, the percentages of the very high and high groups were 1.2 and 3.4%, respectively, which were smaller than 4 and 4% in the UK, respectively . Third, This study revealed a strong association between poor mental health and daytime sleepiness and snoring among Japanese children during early childhood. Parents need to pay attention to their childrens sleep-wake patterns, including daytime sleepiness and snoring during sleep. Public health nurses and healthcare providers should intervene to address inadequate sleep hygiene at an earlier stage to promote childrens mental and physical development.