The COVID-19 pandemic poses great challenges to higher education. Universities had to change their infrastructure to full remote teaching and learning environments in a very short time. Lecturers and students were forced to adjust their established routines and concepts of teaching and learning. During the first nationwide lockdown in Germany, we explored students' anticipations regarding the risks and chances of this challenging situation. They were asked about the negative and positive effects of this sudden switch to online university courses and the relevance personally ascribed to each of these expected effects. A sample of 584 students provided 3,839 statements, which were examined by means of qualitative content analysis. While 57.7% of the statements concerned negative effects, 42.3% dealt with positive ones. The range of expected negative and positive effects was wide, but key themes emerged particularly frequently. While the mentioned effects were generally considered to be of high personal relevance, negative effects were rated as significantly more relevant, but with only a small effect size. The relevance of negative effects was considered higher by master students than by bachelor students. Relevance ratings were significantly higher for the first effect mentioned compared with all subsequent effects, indicating an ease-of-retrieval effect, which is relevant from both a methodological and content perspective. The results provide important insights into students' perspectives on remote learning that will be significant beyond the current pandemic, as they can guide sustainable measures by exploiting opportunities and mitigating risks. We discuss practical implications and methodological limitations of the study. Since early 2020, the COVID-19 pandemic has had a considerable influence on society ; 403 students were in a bachelor's degree program and 181 were in a master's degree program. The sample was heterogeneous regarding the study program, but most of the participants (404) were enrolled in a teacher training program covering a range of scientific disciplines, 71 were studying psychology, and 38 were enrolled in a media-related program.This study is the first part of a larger survey conducted in April and May 2020. The median duration to complete this survey part was 393.50 s. Incentives to participate were not provided. Conditions of participation were a minimum age of 18 years and enrollment at a German university. Students enrolled at distance-learning universities were excluded. The final sample contained 584 students from different universities . Subsequently, they were asked to state up to five negative and positive effects they expect from the transition to remote teaching and learning (maximum 150 characters per statement). The instructions were as follows: “The coronavirus pandemic necessitates an  complete conversion of university courses to a remote mode. Please indicate up to five negative/positive effects that you currently expect for your own studies due to this changeover.” Given that the sudden outbreak of the pandemic, with all the resulting constraints and repercussions on everyday life and study activities, primed a rather negative mindset, we first asked all participants to name the expected negative effects. Afterwards, we explicitly asked them to reflect on potential positive effects. The students additionally rated the effects mentioned in terms of relevance, using a scale ranging from 1 (hardly relevant) to 5 (highly relevant).We performed a qualitative content analysis based on the standard approach by Mayring , followiAmong the negative effects associated with the transition to remote teaching and learning, the most frequently mentioned effects concerned social interaction and communication: The students provided 300 statements indicating a general decrease or lack of social interaction and communication. A fear of less contact, interaction with, and support from other students was expressed in 208 further statements, whereas 117 additional statements predicted less contact and interaction with lecturers and university staff as well as reduced feedback and support. It is to be noted that many statements addressed psychosocial variables, namely problems with self-regulation in terms of self-studying, self-organization, self-discipline, and problems with structuring everyday life (168), perceived uncertainty due to a lack of information (141), increased effort required for studying in terms of time and workload (129), decreased motivation in study activities and learning (69), and a lack of clear separation between study activities and private life, including childcare (21). A further substantial category are negative expectations regarding the quality of remote teaching and learning, including performance assessment (304), followed by negative effects on the course of studies, including study time extension (192), and the closure of university facilities and services and hence impeded access to university resources (180). Moreover, the students mentioned general problems and concerns regarding technology (48), technological problems on the student side, including hardware and software (57), but only rare problems regarding the stability of digital services (9). Several statements addressed the lack of an appropriate working environment (62) and negative effects on health (26). A general negative affect was stated 10 times. Finally, some statements (19) referred to financial problems.Regarding positive effects of remote teaching and learning, almost one third of all statements referred to increased flexibility, including flexibility in time and work management (306), in the (asynchronous) reception and processing of course materials (107), in learning location (53), and an increased flexibility in general (61). The largest single category was the saving of time (325), but many statements also expressed a gain in media- and study-related competences and skills (217), hope for progress in digitalization in universities and society (142), hope for a better work-life balance due to remote learning (103), and slowing down as well as getting more sleep (18). Only some statements reflected an expected increase in the quality of digital teaching and learning (63), financial gains and savings during the challenging pandemic situation (20), increased motivation in study activities and learning (5), and protection of the environment (7). A few statements indicated benefits for communication and interaction (22). Importantly, only 18 statements addressed the underlying reason for the transition to remote teaching and learning, namely staying healthy.SD = 1.40) negative effects and 2.78 (SD = 1.56) positive effects. A t-test for paired samples showed that this difference was significant, t(583) = 14.08, p < 0.001, d = 0.58. Hence, the ratio of negative to positive effects was unequal in favor of the negative effects. There were no significant differences between bachelor and master students regarding the number of reported negative effects, t(582) = −1.19, p = 0.236, d = −0.11, and positive effects, t(582) = 1.23, p = 0.220, d = 0.11. More details on the categories most frequently mentioned by the two student groups are depicted in Overall, 2,215 negative effects were mentioned, which corresponds to 57.7% of all statements, in contrast to 1,624 positive effects (42.3%). The students reported between zero and five negative and positive effects, respectively. On average, each student stated 3.79  and positive effects , t(526) = 3.95, p < 0.001, d = 0.17. Supporting H2, negative effects were perceived as more personally relevant, but with a small effect size. Relevance of negative effects was considered higher by master than by bachelor students, t(402.89) = 2.78, p = 0.006, d = 0.24. No significant difference between student groups was found regarding positive effects, t(537) = −0.32, p = 0.751, d = −0.03.We analyzed the relevance that students personally ascribed to the effects mentioned. Based on 527 students who provided at least one negative and one positive effect accompanied by relevance ratings, we compared the average relevance of negative effects (p < 0.001), but it was based on a greatly reduced sample size as only a few students reported ten effects in total. When inserting five levels to the ANOVAs (for five effects rated), we found a significant result for negative effects, F = 15.42, p < 0.001, ps < 0.001 (Bonferroni-adjusted). No further significant differences were found. Similarly, relevance ratings of positive effects also decreased, F = 6.81, p < 0.001, ps ≤ 0.005. When only the first three effects were included, the number of students in the ANOVAs substantially increased  were separately computed for positive and negative effects. A prior MANOVA, including relevance ratings of five negative and five positive effects, indicated a significant result , followed by negative effects on the course of study (77), and a decrease in the quality of teaching and learning (60). Regarding positive effects, students most often referred to time-saving aspects (172), followed by more flexibility in time and work management (132), and a gain in media- and study-related competences (64). A complete overview is presented in the In spring 2020, the COVID-19 pandemic forced teachers and learners to abandon familiar teaching and learning routines, and universities were suddenly required to create remote study environments. The perspective of students on this new role of remote learning has also been examined quantitatively by Adnan and Anwar  reception and processing of course materials, choice of learning locations, and in general. These advantages of remote learning had already been highlighted previously  could be seen as a limiting factor. We do not know whether the ratio of anticipated effects would change in favor of positive effects if the participants had been asked about them first or if the sequence had been randomized. The fact that the onset of the pandemic and the unprepared switch to remote learning were accompanied by an overall negative mindset, as documented by many studies from this period , the present survey study did not require the approval of an ethics committee, because the research did not pose any threats or risks to the respondents, it was not associated with high physical or emotional stress, and the respondents were informed about the objectives of the survey in advance. At the beginning of the study, participants were informed that the data of this study will be used for research purposes only and that all data are collected anonymously. Thus, no identifying information was collected. Participants who prematurely abandoned the survey were not included in the analyses and all of their data were deleted from the dataset. Informed consent to participate in this study was provided by clicking a corresponding box, and participation was voluntary in all cases.In accordance with local legislation and institutional requirements, an ethics board approval was not required for this study on human participants. In Germany, as stated by the German Research Association (DFG, TH, AA, and KK designed the study, performed the analyses, interpreted the results, and wrote the manuscript. TH and AA collected the data. KK organized and supervised data collection and acquired funding. 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.


Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a “second eye” for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology. Magnetic Resonance Imaging (MRI) has become indispensable in the diagnosis of neurological diseases and is considered the diagnostic tool of choice for spinal cord pathologies of various etiologies. Not only is MRI considered the gold standard for intervertebral disc disease , which iDeep Learning, a certain type of machine learning, is based on the fact that so-called Artificial Neural Networks (ANNs) use large amounts of data to create an algorithm that can ultimately correctly assign unknown data based on its “experience”. In doing so, the network always recognizes the same patterns and thus learns to assign them to certain categories, similar to radiologists. Compared to previous machine learning techniques, Deep Learning has the ability to process huge amounts of data and learn image features directly from the raw data. However, Deep Learning systems are often referred to as black boxes because it is not possible to identify the criteria used by the network to learn and make predictions  are the foundation of machine learning with image data. Inspired by the human brain, they process information in a similar way via many intermediate elements, also known as neurons. The neurons are part of different layers. The first layer, through which data enters the network, is called input layer. This is followed by one or more layers called hidden layers. In these layers the data is transformed. Within the layers, all neurons are interconnected. How strongly each neuron is networked with another depends on its weight. Weights are real numbers and represent adjustable parameters. One could call them control knobs that define the input-output function of the machine. These weights are adjusted until the network can make good predictions regarding its training data. Last is the output layer, which creates the CNN's prediction. Once the network is successfully trained, it can be applied to unknown data , sensitivity (68.42%), and specificity (87.09%) in detecting left atrial enlargement on thoracic radiographs of dogs compared to board certified radiologists.Banzato et al. developed a CNN to detect degenerative liver disease on ultrasound images of dogs. In addition, the accuracy in diagnosis was to be compared with cytologic liver findings and blood serum biomarkers. The CNN predicted diagnoses with 91% accuracy, 100% sensitivity, and 82.8% specificity, outperforming all other diagnostic tests . FurtherDeep Learning based CNN via supervised learning, which should be able to detect and discriminate different spinal cord pathologies on thoracolumbar MR images of dogs. It was hypothesized that the CNN would have the ability to detect and discriminate various spinal cord diseases on canine thoracolumbar MR images. This study could provide first insights on the approach, limitations, and sensitivity and specificity of CNNs in MRI interpretation.The aim of this study was to train a Thoracolumbar MR images of 500 dogs served as the basis for the study in order to generate a sufficiently large data set for the neural network. MR images were obtained from the archives of the Department of Small Animal Medicine and Surgery, University of Veterinary Medicine, Hannover, Germany, between January 2016 and August 2020, as well as from the archives of the Royal Veterinary College, Small Animal Referral Hospital, London, England, between January 2002 and March 2014. Both T1- and T2-weighted images in transverse and sagittal planes, totaling 2,693 images, were used as the basis for network training.Since the network should be able to distinguish between different spinal cord pathologies, MR images with the following diagnoses were used for network training: unremarkable, intervertebral disc extrusion (IVDE or Hansen Type I), intervertebral disc protrusion (IVDP or Hansen Type II), presumptive fibrocartilaginous embolism (FCE), or acute non-compressive nucleus pulposus extrusion (ANNPE) , syringomyelia and neoplasia. MR images of dogs with disc herniation or protrusion were only included if surgery was performed in order to confirm the suspected pathology and localisation. In the case of neoplasia, a pathologic report (either biopsy or postmortem report) was considered as appropriate evidence. Clinical signs and well-defined MRI variables , 20, 21 MR images were obtained using either a 3.0 T high-field MRI scanner  or a 1.5 T high-field MRI scanner . All dogs underwent general anesthesia and were positioned in dorsal recumbency during the imaging process.Digital Imaging and Communications in Medicine (DICOM) format and anonymized. In the next step, data from the 500 dogs were divided into a training set (75% or 375 dogs) and a test set (25% or 125 dogs). Randomization of both training and test set was performed viawww.random.org.All MR images were exported in n = 2), hemangiosarcoma (n = 2), hemangioma (n = 2), multiple myeloma (n = 1), nephroblastoma (n = 1), osteosarcoma (n = 2), lymphoma (n = 3), fibrosarcoma (n = 1), round cell tumor (n = 1), metastases from mammary carcinoma (n = 2), and metastases from prostate carcinoma (n = 1). Furthermore, 39 dogs with unremarkable MR images  were included. The distribution of the assessed diagnoses within the 500 dogs was as follows: 284 dogs with IVDE , 38 dogs with IVDP , 108 dogs with FCE/ANNPE , 13 dogs with syringomyelia , and 18 dogs with neoplasia , including the following pathologies: meningioma .The label edema was assigned 59 times, the label IVDE 833 times, the label IVDP 149 times, the label FCE/ANNPE 379 times, the label neoplasia 167 times and the label syringomyelia 35 times. 1,118 images had no label, thus were unremarkable.After the training phase was completed, the data from the remaining 125 dogs were used to test how accurate, specific, and sensitive the CNN was in terms of detecting and distinguishing between different spinal cord pathologies.t-test were used, where appropriate.Sensitivities, specificities, positive predictive values, negative predictive values, and accuracies were calculated based on contingency tables with true positive, true negative, false positive and false negative decisions per image for each diagnosis, plane, and sequence. 95% confidence intervals (95% CI) for sensitivity, specificity, and accuracy were calculated using the Clopper-Pearson method. 95% CIs for the positive and negative predictive values are the standard logit CIs given by Mercaldo et al. . Certainwww.medcalc.org) and Prism 9 software from GraphPad  were used. Two-sided tests were used and a p ≤ 0.05 was considered significant.For statistical and graphical analysis, MedCalc , the label IVDP 27 times (2.44%), the label FCE/ANNPE 397 times (35.86%), the label syringomyelia 5 times (0.45%), the label neoplasia 200 times (18.07%), and the label edema 22 times (1.99%). 276 times (24.93%) an image was incorrectly labeled as inconspicuous.Deep Learning based CNN was trained using thoracolumbar transverse or sagittal T1- and T2-weighted MR images from dogs with various myelopathies, including IVDE, IVDP, FCE/ANNPE, syringomyelia and spinal cord neoplasms. Included cases of IVDE, IVDP and neoplasms were either histopathologically or surgically confirmed. After training, the CNN was able to identify IVDE, IVDP and FCE/ANNPE lesions with a high sensitivity and specificity but lacked to reliably detect syringomyelia or neoplastic lesions. Currently, Deep Learning with CNNs is considered the state-of-the-art method regarding pattern recognition and classification of image data and has the ability to process huge amounts of data with ease . In addition, diagnostic accuracy was significantly dependent on the experience of the observer . In thisIn addition, the success of the network is largely dependent on the supervisor, who labels the data and thus provides the basis for the algorithm . NeverthThis study shows that training success can be achieved even with a small amount of data, as long as the individual labels are not too similar. If certain patterns within the data are too similar, training success cannot be achieved with a small amount of data, as can be seen with the diagnoses “neoplasia” and “syringomyelia.” In addition to the small amount of training data for neoplasia, their expression can be very different among patients concerning their morphology and their tissue of origin . This maIn this study, adding spinal cord tumors did not add value to the overall success of the CNN. Among all mislabeled images, the label “neoplasia” was the second most common mislabeled image, accounting for 18.07%. In order to train the CNN with images of spinal cord neoplasms, post-contrast images were intentionally omitted to evaluate whether the algorithm could detect altered structures without contrast agent. However, as a result, the CNN incorrectly assigned the label “neoplasia” more frequently. Overall, the network showed little success in detecting spinal cord tumors on any sequences.Regarding the diagnosis of syringomyelia, the CNN obtained the worst results. Although syringomyelia is fundamentally very different from the other pathologies due to its relatively wide, fluid filled cavities in the spinal cord, the CNN incorrectly defined them most often as “inconspicuous.” This can likely be explained by the fact that syringomyelia resemble the epidural fat in parasagittal cross-sectional T2-weighted images, which also appears to be hyperintense . In addiWith regard to the detection of FCEs and ANNPEs, which were combined in this study, the CNN performed very well, especially on transverse T2-weighted images. FCEs and ANNPEs accounted for the second largest proportion of training data, which is reflected in the training success. At the same time, the FCE/ANNPE label was the most common misattributed label, accounting for 35.86% of all misattributed labels. Other causes of hyperintensity in the spinal cord than FCE/ANNPE would include syringomyelia or edema due to disc herniation . HoweverIn this study, FCEs and ANNPEs were combined and not differentiated. However, Fenn et al. showed that inter-observer agreement to differentiate FCEs and ANNPEs on MR images based on certain criteria was moderate (kappa = 0.56) and intra-observer agreement was moderate to good  . This deIn future, CNNs could relate image data to signalment and clinical history simultaneously and better results in differentiating various pathologies could be obtained. For example, ANNPEs are often associated with traumatic events and FCEs with a peracute onset of non-progressive and non-painful clinical signs , 28, wheSeveral studies on spinal cord diseases have shown that there are not only discrepancies in the interpretation of MR images between radiologists with different levels of experience, but also that the same imager can make different diagnoses at different time points on the same MR images , 28. WelAnother advantage of the CNN, however, is that it assigns a certainty to its predictions. Similarly, a radiologist would make certain diagnoses with a high degree of certainty or may be uncertain about a diagnosis. If the CNN is used as a second opinion tool, the CNN's diagnosis can be better assessed on the basis of the certainty. Regarding this study, the network made diagnoses on transverse MR images with higher certainties than on sagittal MR images.Overall, the training success of the CNN regarding the results of sensitivities, specificities, certainties and an error rate of 14.39% can be considered successful and suggests that this CNN is capable of detecting and differentiating various spinal cord pathologies. Since this CNN was trained with a total of 1,622 labels, extended training with more labels could lead to an even better performance of the network.Limitations of this study are the comparatively small data set with an uneven distribution of each pathological entity, resulting in different sensitivities and specificities for detecting each pathology. Future studies could evaluate whether CNNs would achieve better results in detecting and differentiating various spinal cord pathologies with larger data sets.Deep Learning-based CNNs have the potential to successfully learn and make predictions based on MR images in terms of detecting and distinguishing between different spinal cord pathologies in dogs. In this study, despite a limited data set, the diagnoses IVDE, IVDP, and FCE/ANNPE could be detected and distinguished from each other. In contrast, the success of the predictions regarding the diagnoses “syringomyelia” and “neoplasia” was insufficient. Since the success of the network is largely dependent on the number of training data, larger data sets should be used in future studies. Future studies also need to explore the influence of the field strength and the brand of the MRI machine on the performance of CNNs.CNNs should not be viewed as the sole diagnostic tool, but rather as a “second eye.” In this context, CNNs can be trained with almost infinite data and their learning potential can therefore be increased immeasurably over time.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was conducted in accordance with the German Animal Welfare Act within the law of animal welfare, following the ethical guidelines of the University of Veterinary Medicine, Hannover . Written informed consent was obtained from the owners for the participation of their animals in this study.AB compiled the clinical data, labeled the imaging data set, and was actively involved in drafting the manuscript. HV compiled the manuscript and trained AB to correctly label the images. AB, SM, and HV designed the study, interpreted the statistical analysis, and were involved in data presentation. JW, NC, and JS-M were involved in providing access to and training of CNN. SD provided and interpreted clinical data from the Royal Veterinary College. All authors contributed to revisions of manuscript and approved the submitted version.This manuscript was financially supported by the University of Veterinary Medicine, Hannover, through Open Access Publishing funding.JW, NC, and JS-M own vetvise GbR and were involved in training the CNN. The commercial partner has not been involved in case recruitment and statistical analysis of the results. Furthermore, the commercial partner could not prevent the manuscript from being submitted for publication. 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.


Multimorbidity has an effect on life expectancy, while its effect on healthy life years is unclear. This study aims to investigate the associations between healthy life years lost due to multimorbidity and living risk.The participants of The China Health and Retirement Longitudinal Study (CHARLS) were assessed at four visits between 2011 (baseline) and 2018. At baseline, 13,949 individuals were administered surveys. A combined score based on seven health-related factors was calculated, and the participants were classified into 3 groups based on living risk. We used the adjusted Cox regression methods to examine the associations between living risk groups and multimorbidity. We estimated the healthy life years lost due to multimorbidity using the Sullivan method.P = 0.058) to 1.431  in sex, and ranging from HR 1.340  to 2.002  in area. At 45 years, the healthy life years lost in men was <0.27 years compared to women in the low-risk group. Hypertension increased the risk of multimorbidity with an HR of 1.5  in men. In urban areas, participants with diabetes had 3.2 times  higher risk of multimorbidity than participants without diabetes.A total of 9,091 adults aged 45 years or older  were analyzed in the CHARLS. The probability of no multimorbidity over 7 years decreased from 0.9947 to 0.9697 in the low-risk group, whereas the probability of multimorbidity in low living risk was lower than that of high living risk, ranging from HR 1.253 (95% CI.992–1.581; These findings indicate that a low-risk lifestyle could decrease the loss of healthy life years under multimorbidity. The probability of multimorbidity in women and in urban areas was high. Hypertension was correlated with the hazard risk of multimorbidity. Population aging has become a serious issue of global concern. Epidemiological studies have shown that the number and proportion of people aged 60 years and older in the population is increasing, and it is expected to increase to 2.1 billion in 2050 . MultimoHowever, the cause of the loss of healthy life years due to multimorbidity in the extended life years is still unclear, and there is no evidence for the influence of different living risks on the loss of healthy life years due to multimorbidity. In this study, we observed the incidence of multimorbidity during the follow-up period, adjusting for sex and area, and explored the association between the living risk and the healthy life years lost due to multimorbidity.n = 268); missing health-related data (n = 676); missing covariate data (n = 31); baseline comorbidity ; and death in 2011 (n = 46). A total of 9,091 observations were ultimately included , a national cohort consisting of baseline measurements taken in 2011 with biennial follow-ups, including assessments of demographic variables, health status and function, health care and insurance, work, and retirement and pension. The CHARLS's baseline survey included 13,949 individuals who received all survey questions. A stratified  multistage (county/district-village/community-household) PPS random sampling strategy was adopted. A detailed description of the CHARLS was published previously , 19. Forincluded .http://ncncd.chinacdc.cn/jcysj/siyinjcx/syfxbg/202101/t20210118_223798.htm free of charge.Age-sex-area-specific mortality data were obtained from the National Cause of Death Monitoring Dataset in 2018, a database generated by the nationally representative death cause monitoring system with 605 monitoring points across China. It can be obtained from the website We identified 11 self-reported diseases and the time of multimorbidity by asking “When was the disease first diagnosed or known by yourself?”: cancer or malignant tumor; chronic lung disease; liver disease; heart problems; stroke; kidney disease; stomach or other digestive disease; emotional, nervous or psychiatric problems; memory-related disease; arthritis or rheumatism; and hip fracture. Participants with 2 or more of these conditions were classified as having multimorbidity, and we combined the self-reported diseases collected at the three previous time points to determine the final time with multimorbidity. Because hypertension, diabetes or high blood sugar, and dyslipidemia may cause metabolic disorders and lead to the occurrence of multimorbidity, these diseases previously considered in the definitions of multimorbidity were not included in this analysis.2). We defined overweight or obesity as a BMI over 24.0 (kg/m2); otherwise, participants were categorized as not overweight or obese. Hypertension was defined as the participant being diagnosed with hypertension by a doctor. Having dyslipidemia was defined as the participant having been diagnosed with dyslipidemia , and total cholesterol, or a low high density lipoprotein level) by a doctor. Diabetes was defined as the participant being diagnosed with diabetes or high blood sugar by a doctor.All models were adjusted for sex, area , and age. Education was categorized into two levels: “having 9 years and less of education”  and “having over 9 years of education” . Smoking was categorized as having smoking experience  at the time of assessment or not having smoking experience. The participants were grouped as reported having drank alcoholic beverages  or not having drank in the past year. The body mass index (BMI) was calculated by the ratio of self-reported weight (kg) and the square of self-reported height ], and continuous variables were summarized by the mean and SD (M ± SD). The chi-square test and Fisher's exact test were used to evaluate the statistical significance of the differences in baseline characteristics. Then, we used the Cox proportional hazards regression, with time since baseline assessment as the start of follow-up, to assess the association between living risk and multimorbidity. If no disease was present at baseline, the time to multimorbidity was derived as the difference between the time when the second disease occurred and the time when the first disease occurred. If one disease was present at baseline, the time to multimorbidity was derived as the difference between the time of second diagnosis and baseline. Hazard ratios (HRs) and corresponding 95% CIs (95% CI) were calculated, and the Schoenfeld's residuals were used to verify the proportional hazard assumption. Three incremental models were fitted: Model 1—unadjusted; Model 2—adjusted for age; and Model 3—adjusted for age, sex, and area . We estiWe conducted two sensitivity analyses to assess the robustness of our results. First, we used a parametric Weibull model to assess the impact of different living risks on the prevalence of multimorbidity. In a second sensitivity analysis, the accelerated failure-time model (AFT) was used to explain the impact of different living risks on the duration of multimorbidity . For the combined health-related factor score, participants in the low-risk, medium-risk, and high-risk groups had disease probabilities of 53.06, 28.82, and 18.13%, respectively. The participants in the high-risk group were more likely to have one disease  did not have multimorbidity and had complete data available at baseline. The mean age of participants without disease was 58.49 ± 9.73 years, and it was 59.55 ± 9.50 years for participants with one disease. Most participants with one disease were women (52.6%). Participants with lower education (98.71%), not drinking (67.61%), having hypertension (48.84%), having dyslipidemia (24.57%), and having diabetes (8.81%) were more likely to have one disease  to 1.199  by sex and from 0.975  to 1.676  by area. HRs in the high-risk group ranged from 1.253  to 1.431  by sex and from 1.340  to 2.002  by area  and also lower in women [HR = 1.24 ]. In urban areas, people with diabetes had 3.2 times  higher risk of multimorbidity than people without diabetes.The associations between health-related factors and the hazard of multimorbidity are presented in In this cohort study, we found that in participants with the lowest health-related factor score (low-risk group), the probability of no multimorbidity was lower than that of the medium-risk group. We found that for people with multimorbidity in the low-risk group at age 45, the average healthy life years was greater regardless of sex and area as compared to those in the high-risk group. The results are nearly consistent in multiple sensitivity analyses. These findings are relevant for public health and also have implications for individuals, as the data suggest that membership in the low-risk group is associated with fewer lost healthy life years due to multimorbidity.To our knowledge, this is the first study to quantify the healthy life loss of the additional years of life caused by multiple diseases under different groups defined by living risks. In terms of the relationship between life years and multimorbidity, numerous studies have investigated whether the coexistence of diseases increases the loss of life expectancy. The results of a life table analysis found that living with multiple chronic conditions has a negative impact on life expectancy that is reduced by an average of 1.8 years with each additional chronic condition . A largeSeven health-related factors were included in this analysis, including smoking, drinking, overweight or obesity, hypertension, dyslipidemia, and diabetes, as these factors are related to multimorbidity. Education plays an important role in improving healthy lifespan . A previThe findings from this study confirmed that an association exists between living risks and multimorbidity, adding to the observed association with healthy life years lost due to multimorbidity. However, this study has several limitations. First, we did not include physical activity as a health-related factor to better assess the robustness of the results because of the lack of availability of metabolic equivalent (MET)-min/week data in baseline measurements. However, a number of large cohort studies have verified the contribution of physical activity in reducing the probability of multimorbidity and improving health expectations –36. Secohttps://charls.charlsdata.com/pages/data/111/zh-cn.html; http://ncncd.chinacdc.cn/jcysj/siyinjcx/syfxbg/202101/t20210118_223798.htm.Publicly available datasets were analyzed in this study. This data can be found here: The studies involving human participants were reviewed and approved by the National School of Development of Peking University. The patients/participants provided their written informed consent to participate in this study.XM, QW, JC, WM, LR, YC, and ZW analyzed, interpreted the data regarding the disease, and the health-related factors. XG, XZ, and QM coordinated the study. XM was a major contributor in writing the manuscript. XM, JC, and WM revised the manuscript. QM and XZ edited the manuscript. All authors read and approved the final manuscript.This project was funded by the Project of Standardized test point of telemedicine information system. The funding body had no role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.All 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.


Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility.Results: There were nine particular features in septic patients that were significantly associated with SAE. Predictors of individualized prediction nomograms included age, rapid sequential evaluation of organ failure (qSOFA), and drugs including carbapenem antibiotics, quinolone antibiotics, steroids, midazolam, H2-antagonist, diphenhydramine hydrochloride, and heparin sodium injection. The area under the curve (AUC) was 0.743, indicating good discrimination. The prediction model showed calibration curves with minor deviations from the ideal predictions. Decision curve analysis (DCA) suggested that the nomogram was clinically useful.Conclusion: We propose a nomogram for the individualized prediction of SAE with satisfactory performance and clinical utility, which could aid the clinician in the early detection and management of SAE. Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction caused by a dysregulated host response without central nervous system (CNS) infection . SymptomStaphylococcus aureus infection were associated with the development of SAE. Recently, Sepsis-associated encephalopathy is a diagnosis of exclusion. It is diagnosed in the absence of direct infection of the central nervous system, multi-organ failure, traumatic brain injury, fat embolism, and ingestion of illicit drugs . Due to The study aims to identify early and potential risk factors for SAE by a retrospective analysis of a large clinical database, and establish a comprehensive prediction model for SAE patients. The proposed nomogram can assist in clinical decision-making and identify sepsis patients at high risk for SAE, who should undergo further investigative tests, thus promoting early diagnosis and management of SAE.Medical Information Mart for Intensive Care (MIMIC III) between June 2001 and October 2012 was employed for this study. MIMIC III was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and Massachusetts Institute of Technology. There was no requirement for individual patient consent because anonymized health information was used. This is a publicly accessible single-center critical care database containing longitudinal data on 46,520 patients admitted to the ICU. The raw data were extracted using a structure query language (SQL) with Navicat, and further processed with R software.2 (PaCO2) ≥ 80 mmHg; (9) patients whose tracheas have been intubated at the time of admission, given analgesic, and sedated; (10) without an evaluation of GCS.Sepsis patients were defined as an infected on discharge according to ICD-9 codes, who were diagnosed as “sepsis”, “severe sepsis”, and “septic shock” and patient’s blood culture were positive. Based on the definition of sepsis 3.0, we included patients with SOFA score ≥ 2. Inclusion criteria were as follows: (1) more than18 years old; (2) admission time > 24 h in the ICU. Exclusion criteria: (1) with primary brain injury  –5; (2) mWe defined SAE in the study as sepsis with a GCS < 15 on the first day of ICU admission, delirium, cognitive impairment, altered mental status according to the ICD-9 code, and medicating with haloperidol. Altered consciousness caused by other reasons was excluded. GCS has been established as a clinically effective tool for characterizing SAE and distinguishing it from sepsis . For sedR statistical software  was used to retrieve patient information from the MIMIC III database. The following basic patient data were collected from each patient, including age, sex, admission type, marital status, and mean value of vital signs during the first 24 h of ICU stay, including heart rate, systolic blood pressure, diastolic blood pressure, respiratory rate, and temperature. Since ICU admission, the first laboratory data include alanine aminotransferase, aspartate aminotransferase, partial thromboplastin time, white blood cell count, lymphocyte, neutrophil, monocytes, eosinophils, hemoglobin, platelet, blood urea nitrogen, creatinine, and glucose. SAPS II, qSOFA score, SOFA score assessment of the severity of illness and organ failure on the first day of ICU admission, and GCS were also recorded. Comorbidity as coded and defined in the ICD-9 code ; site ofU test or Kruskal–Wallis test) was used for data with non-normal distribution or heterogeneity of variances. Categorical data were compared using the Pearson Chi-squared test.Data distribution was analyzed using the Shapiro–Wilk test. Continuous variables were expressed as the mean ± standard deviation (SD) or the median ; categorical variables were expressed as frequency and percentage. A non-parametric test (Mann–Whitney p < 0.05.Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection (Training set). A nomogram was constructed according to the multivariate logistic regression analysis results (Training set), and it was internally validated using a 1,000 bootstrap resampling procedure . The performance of the nomogram was assessed using discrimination and calibration . The proposed nomogram’s discrimination ability was quantified with a receiver operating characteristic (ROC) curve analysis and the AUC. The calibration was carried out by plotting the calibration curve to analyze the association between the observed incidence and the predicted probability. Decision curve analysis was performed to assess the clinical utility of the nomogram . Statistical analysis was conducted with R software (version 3.4.3). Statistical significance was defined as A total of 2,535 patients met the inclusion criteria for the study. About 80% of the patients were randomly assigned to the primary cohort, and 20% of the patients were randomly assigned to the validation cohort. About 41.6% of patients with sepsis-associated encephalopathy were detected. A matrix diagram of missing data is shown in the Data Profiling Report . We replP = 0.763), where the SAE patients were 41.5 and 42.2% in the primary and validation cohorts, respectively, and there were no significant differences in the clinical characteristics between the cohorts, which justified their use as training and validation cohorts.Patient characteristics in the primary and validation cohorts are given in 2-antagonist, proton pump inhibitor, steroids, non-steroidal anti-inflammatory drugs (NSAIDs), aspirin, clopidogrel, and sodium bicarbonate compared to the non-SAE group. There were no significant differences in gender, admission type, marital status, and comorbidity between the SAE and non-SAE patients.Patients in the SAE group were older than those in the non-SAE group in the primary cohort and validation cohort. More SAE patients used statins, beta-blockers, Hp < 0.001; qSOFA 2 (2–3) vs. 2 (2–2), p < 0.001; SAPS II 45 (35–57) vs. 40 (31–51), p < 0.001]. The hospital mortality rate of SAE patients was 64.7%. Patients in the non-SAE group had a higher incidence of mechanical ventilation (53.8 vs. 42.4%) and had longer ICU stay time than those in the SAE group.As shown in In the primary cohort , 89 feat2-antagonist as independent predictors  of the nomogram was 0.743 (95% CI: 0.720–0.766). The predictive SAE of the model’s sensitivity was 0.585 and specificity was 0.879. A cut-off was 0.435 calculated by Youden’s index .The decision curve analysis (DCA) for the SAE nomogram is in 2-antagonist, diphenhydramine hydrochloride, and heparin sodium injection, had a significant impact on the occurrence of SAE. The study has established a comprehensive visual prediction model which can provide a probabilistic estimate of SAE at the earlier stages in individual sepsis patients. Furthermore, the nomogram showed satisfactory validity, discrimination, and clinical utility.The study observed that 41.6% of sepsis patients with SAE were identified during admission to the ICU. A hospital mortality rate of up to 64.7% was observed in patients with SAE. We identified that clinically relevant risk factors for SAE, including age, qSOFA score, and medications such as carbapenem antibiotics, quinolones antibiotics, steroids, midazolam, HIn this study, 41.6% of sepsis patients suffered from SAE. Previous studies have published the rates of SAE in patients with sepsis ranging from 8 to 70% . This coOur cohort study showed that SAE patients had higher SOFA, qSOFA, and APACHE II when compared to non-SAE patients and also a high hospital mortality rate of 64.7%. It shows that SAE patients with more severe organ dysfunction are associated with an increased risk of mortality and the related adverse clinical outcomes. The result is consistent with Yang et al.’s study. SAE patients presented significantly high APACHE II, SOFA scores, and 30-day mortality in a recent retrospective analysis involving more than 2,400 SAE patients . Feng etWe identified clinical and potential risk factors for SAE, which confirms that SAE patients were older and had urinary system infection when compared to non-SAE patients. The results of our cohort study show that medications were a critical risk factor for SAE. The most significant impact is from the use of antibiotics, followed by analgesics, sedative drugs, and other drugs. The study demonstrated that more SAE patients used antivirals, cephalosporins, penicillin, antifungal, macrolides, aminoglycosides, quinolones, carbapenem in antibiotic, quinolones, and carbapenem are associated with SAE by filtervariables with the LASSO method. This method surpasses the method of choosing predictors based on the strength of their univariable association with the outcome. In the study, the use of quinolones and carbapenem tended to be associated with SAE, in line with the study by 2-antagonist, steroids, and heparin sodium injection were risk factors for SAE. It has been reported by many studies that H2-antagonist and steroids cause delirium  by the LASSO method. The nine potential predictors established a comprehensive visual nomogram for predicting SAE patients. The nomogram demonstrated adequate discrimination in the primary cohort , which surprisingly improved in the validation cohort . We developed and validated a nomogram in the study, which could assess clinical variables. Both physicians and patients could perform an individualized prediction of the risk of SAE with this easy-to-use scoring system, which is in line with the current trend toward personalized medicine. The most important reason for using the nomogram is based on the need to interpret the individualized needs for additional treatment and improve patient outcomes. DCA was applied in this study. This novel method offers insight into the clinical consequences based on the threshold probability, from which the net benefit could be derived. The decision curve showed that if the threshold probability of a patient or doctor was > 10%, then using the SAE nomogram to predict SAE added more benefit compared to not using the SAE nomogram.The lack of validated predictive tools for early-stage SAE in sepsis patients and the equivocal efficacy of SAE interventions prompted us to develop a novel predictive modeling system using the nomogram methodology. For the construction of the clinical features and risk factors, 89 candidate features were reduced to nine potential predictors (carbapenem antibiotics, quinolones antibiotics, steroids, midazolam, HThere were two limitations in the study. Firstly, the study was based on electronic MIMIC-III, whose data were generated during routine clinical practice. Thus, it is possible that the cohort selection is not exactly consistent with the definition of sepsis based on the guidelines, and neuroimaging data were also not included in the database. Besides, the study only conducted internal validation, and thus external validations are needed. Thus, the current nomogram can only provide a certain reference for SAE forecasts, and further modifications may be required once diagnostic methods are developed.A nomogram was established for individualized prediction of SAE in sepsis patients and it showed satisfactory performance. It can be conveniently used in the clinical setting and may help physicians to identify SAE patients on time. It can also help physicians to take timely intervention measures to reduce the incidence of SAE and improve patient prognosis.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants or patients/participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.LZ and YW developed the central ideas for this work and wrote the first draft of the manuscript. HZ and ZG collected the data regarding the manuscript. YL revised the manuscript, worked on the English, and made the final version of the manuscript. All authors read and approved the final manuscript.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.


RAGs recognize specific sequences (heptamer-nonamer) to generate a diversity of immunoglobulins. RAG expression is limited to early B and T cell developmental stages. Aberrant expression of RAG can lead to double strand breaks and translocations as observed in leukemia and lymphoma. The expression of RAG is tightly regulated at the transcriptional and posttranscriptional levels. MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the post-transcriptional regulation of gene expression. This study aimed to identify and catalog RAG regulation by miRNA during normal development and cancer. NGS data from normal B-cell and T-cell developmental stages and blood cancer samples have been analyzed for the expression of miRNAs against RAG1 . The analyzed data has been organized to retrieve the miRNA and mRNA expression of various RAG regulators (10 transcription factors and interacting partners) in normal and diseased states. The database allows users to navigate through the human and mouse RAG regulators, visualize and plot expression. miRAGDB is freely available and can be accessed at  Recombination activating gene 1 (RAG1), along with its partner RAG2, constitutes a lymphoid-specific protein complex involved in initiating V(D)J recombination, a DNA rearrangement-based process that assembles diverse immunoglobulins and T cell receptor genes . These avia cleavage at cryptic RSSs and non-B DNA structures , t , t ) J recombination when RAGs act in a non-physiological state ructures , 9–12. Vructures . The nicructures . Aberran14,18)) , 16. RAG) , 18.) , 16. RThe stage-specific function of RAG1 and RAG2 in lymphoid progenitors and their well-studied role in genomic instability warrants a regulatory mechanism for their expression. At the transcript level, RAG1 and RAG2 expression is regulated by several transcription factors, such as FOXO1, PAX5, IKAROS, and E2A . DeletioAnother central regulatory mechanism is the involvement of microRNAs. MicroRNAs (miRNAs) are small RNA molecules which are 18-24 nucleotide long. They regulate gene expression by binding to 3’UTR regions of mRNA molecules and suppress their expression by degrading them or blocking their translation , 23. SevHaving established the fact that RAGs are expressed only in the early stages of B and T cell development and pose a genome-wide threat in the context of pathological translocations, it is essential to study and collate information on its regulation at the transcriptional and translational level, that is, expression levels of miRNA and transcription factors associated with RAG1 and RAG2. In order to do so, we have profiled RAG1, RAG2 and their regulators in normal B and T cells and leukemia into a unique, comprehensive RAG specific database, miRAGDB. Apart from the visual representation of existing expression data, the database also offers a utility to upload data and plot a heatmap. Additionally, there is informative support in the form of links to MGI,UCSC, browser, UniProtKb, Entrez, Ensembl, OMIM, HGNC, databases and Atlas of Genetics and Cytogenetics in Oncology and Haematology which contain information on various RAG1 phenotypes.Publicly available Total RNA and small RNA Transcriptome Sequencing data from Human and mouse B and T cell developmental stages and T-ALL and CLL Leukemia data (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Based on phred score quality cut-off, low quality reads were trimmed from both the ends keeping a minimum of 18 nucleotides from an initial read length of 26-36 nucleotides. Post trimming the reads were mapped to the mouse reference (mm10) and human reference (hg38) genome, respectively using the Bowtie2 (6). The rpm values were extracted and analyzed for difference in expression. The miRNA known to regulate RAG1 and RAG2 were obtained from miRDB  and RPKM = read count/(transcript length/1000) ∗. The normalized values for RAG1, its interacting partners and the miRNA known to regulate RAG1 were plotted in a heatmap using the pheatmap package in R.For the T-ALL and CLL patients’ datasets, Normalized Read count files for miRNA transcripts and mRNA transcripts from 15 CLL patients were downloaded from the GEO datasets mentioned in © (https://shiny.rstudio.com/). We are providing an offline and online version of the database; the online version is deployed on the Rstudio server (https://www.shinyapps.io/) which works on static TSV/CSV files resulting from the analysis which contains expression data. At the same time, the offline version works on SQLite database. The data is fetched and rendered in a table or graphical format on the UI based on the user query.The database has been structured and constructed using the Shiny package from RStudioThe application is written in 3 separate R files, namely ui.R, server.R and global.R. The ui.R renders user interface, whereas server.R does processing of data as per user input and global.R reads all CSV files required for the database  and 48 T-ALL samples. This table includes a search dialogue box where the user can search for one of the 17 miRNAs and obtain expression values specific to that miRNA. Further down is a heatmap of those miRNAs  and RAG2 . We identified the expression profiles of these genes in human and mouse B cell development data and T cell development data. The RAG regulators page displays these expression values in a tabular format followed by a heatmap showing the same  or in implementing options that are not yet available are encouraged to contact BC (at vibha@ibab.ac.in). This article should be cited in research projects assisted by the use of miRAGDB. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The content of the database and the software for generation of custom expression visualization are freely available at the URL BC, SD, and SR conceived and coordinated the study. BC and SD obtained and analyzed the sequencing data. SW wrote the code to implement the database. BC, SD, and SW interpreted the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.We thank the Department of Information Technology, Biotechnology and Science and Technology, Govt. of Karnataka, India, and Department of Science and Technology (SR/FST/LSI-536/2012), India for infrastructure grant and the Bio-IT grant, which helped in the design of the study, collection, analysis and interpretation of data and in writing the manuscript. SD is supported by Department of Biotechnology (Ref. no BT/PR13458/COE/34/33/2015 and BT/PR13616/GET/119/9/2015), Govt. of India, India.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.


Results showed good model fit and internal consistency for the original four-factor structure. Three of the factors were strongly correlated, suggesting that SRB among children with ADHD is caused by several factors.Children with developmental disorders, such as attention-deficit/hyperactivity disorder (ADHD), are at high risk of school-refusal behavior (SRB) compared with their peers. One of the most used scales to assess SRB is the school refusal behavior scale – revised (SRAS-R). The SRAS-R has demonstrated good psychometric properties when used with the general population of children, but, recently, its validity has been questioned when used with children with developmental disorders. We tested the psychometric properties of the SRAS-R parental reports in 96 children with ADHD (M Attention-deficit/hyperactivity disorder (ADHD) is one of the most common developmental disorders, affecting around 5.9% of children and youth . ChildreA seminal paper by The four factors described in the SRAS-R describe two different functions of reinforcement – namely positive and negative reinforcements . PositivThe SRAS-R may be particularly useful for research and work with high risk SRB populations, such as children with developmental disorders like ADHD. However, scales validated for use with neurotypical children cannot automatically be assumed to be equally valid when applied with children with developmental disorders. A recent study has found that the SRAS-R parent report had unsatisfactory psychometric properties in a sample of children (mean age = 12) with autism spectrum disorder ; the oriGiven the findings of n = 92) as opposed to fathers (n = 4) and had university or college education of 1 year or more (73%).Parents of 96 children with ADHD were recruited through social media groups for parents of children with ADHD, and the authors’ professional and social networks. The parents were asked to complete an anonymous, online questionnaire, including demographic information and the SRAS-R. The study was prospectively reviewed and approved by the Norwegian Center for Research Data. The children with ADHD were between 10 and 15 years of age , and the majority were boys (61.5%). The majority of the responding parents were mothers (r = 0.61–0.78) in previous studies  estimation since the scale is ordinal , root mean square error approximation , and standardized root mean square residual  (r), and scale reliability was assessed with McDonald’s omega (ω) and Cronbach’s alpha (α) as measures of internal consistency . Pearson’s r of 0.10, 0.30, and.50, respectively, were interpreted as small, medium, and large correlations.Analyses were performed in JASP . Confirm ordinal . The fol ≤ 0.09) . Based o ≤ 0.09) , 2006 sup = 0.023, χ2 (251) = 367.433, p < 0.001, SRMR = 0.114, see p = 0.502); chi-square test  and SRMR (= 0.104) indicated poor fit. In the CFA of the four-factor solution without Items 20 and 24, all fit indices indicated a good to excellent model fit .CFA testing the two-factor model showed poor model fit  with the second factor , meaning that children scoring high on one of the two functions tend to score high on the other function. The same applied to the first and the third factors (seeking attention from significant others), which also correlated strong and significantly positively , and the second and the third factors . The fourth factor (seeking reinforcement outside of school) did not correlate significantly with any of the other factors . Analyses of internal consistency showed good to excellent reliability for the first, second, and third factors, and acceptable reliability for the fourth factor (see The first factor (avoidance of aversive situations at school) correlated strong and significantly positively [ctor see .This study enhances our knowledge about the psychometric properties of the SRAS-R and SRB among children with ADHD in at least three ways: (1) by demonstrating the suitability of the SRAS-R for children with ADHD in terms of both confirming the original factor structure and showing adequate reliability, (2) providing the first data on the psychometric properties of the Norwegian translation of the SRAS-R, suggesting the psychometric properties of the original version can be extended to the Norwegian context (at least in the case of children with ADHD), and (3) by comparing the four-factor structure to a simpler and theoretically justified two-factor structure, suggesting that the more complex four-factor structure is, in fact, a better representation of SRB in children with ADHD.Comparing our results with previous studies, it seems like the psychometric properties of the SRAS-R, when used with children with ADHD, are more similar to a neurotypical sample  than an Our study provides the first evidence of the practical utility of the SRAS-R when assessing SRB among children with ADHD and the psychometric properties of the Norwegian translation. This is important because teachers, educational psychological services, and child and adolescent mental health centers need psychometrical sound measures to assess SRB among these children. This is because children with ADHD are at higher risk of problematic school absence, school suspension, and SRB, but, at the same time, little is known about the reasons, and the reasons may differ considerably between individuals . The SRAChildren with ADHD generally present with elevated levels of mental health problems compared with neurotypical children e.g., . As suchAlthough the SRAS-R, in general, seems like a practical scale to get insights into the reasons for SRB and plan support and interventions accordingly, more knowledge is needed about which inter- and intrapersonal variables affect SRB in children with ADHD and how these variables relate to the different factors of SRB outlined in the SRAS-R. In addition to mental health problems, studies examining the impact of executive functioning and social skills would be particularly interesting. This is because deficits in both executive functioning and social skills are hallmarks of ADHD , and areN = 115 and 121). Furthermore, when interpreting the results, emphasis was placed on fit indices relatively robust to small sample sizes  instead of the chi-square statistic, which may fail to reject an unfitting model with a small sample. We also used DWLS estimation rather than the more commonly used maximum likelihood (ML) due to its suitability for ordinal scales but also its robustness when used with small samples (The most notable limitation of the current study is the relatively modest sample size for CFA, making replications in larger samples warranted. However, our sample size was close to that of previous CFA studies of the SRAS-R (e.g.,  samples . Still,  samples , but morThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.MM, CO, SO, and AN-H contributed to conception and design of the study. MM, CO, and AN-H were in charge of data collection. MM, CO, SO, AD, and AN-H planned the statistical analyses. MM, CO, and SO performed the statistical analysis. SO wrote the first draft of the manuscript. MM, CO, AD, and AN-H wrote sections of the manuscript. All authors contributed to manuscript revision, read, 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.


Autism Spectrum Disorder is a neurodevelopmental disorder, with a rapid increase in recognition over the past decade. Interest in alternative therapies is growing annually, such as dietary therapies including gluten-free and/or casein-free diet, and the ketogenic diet. However, there is no consensus on the efficacy and safety of dietary therapy in children with ASD up to now. This study aimed to assess the efficacy and safety of these diet interventions for children with ASD based on a meta-analysis of global data.Seven databases  were searched according to the established inclusion criteria, from the inception of the databases to August 18, 2021. The Cochrane Bias risk assessment tool was intended to assess the quality of the included studies. Review Manager 5.4 software was used as an efficacy analysis tool of the included studies, taking the core autistic symptoms and scales of ASD as therapeutic efficacy evaluations.P < 0.05). Two studies were performed in KD diet suggested a significant effect in core symptoms . No statistically significant changes were observed in the GFCF diet, GFD diet, cognition, communication, and stereotypical behaviors subgroups .In total, 7 RCTs with 338 participants were finally obtained. All studies assessed the association between core autistic symptoms and therapeutic diet, showing a statistically significant effect (standard mean difference (SMD) of −0.51, 95% confidence interval (Cl): −0.81 to −0.21), in which two studies which followed the GFD diet reported significant reductions in social behaviors , showing no correlation with the length of the interventions (The results of a meta-analysis suggest that diet therapies can significantly ameliorate core symptoms of ASD, and GFD diets are conducive to improving social behaviors. Although the results suggest the effectiveness of dietary therapy for ASD, limited by the small sample size of RCTs, more well-designed, and high-quality clinical trials are needed to validate the above conclusions.https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021277565. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, which affects several spheres of normal mental development with an onset in the first few years of life, usually prior to age 3 –3. ChildAs for ASD treatment for children and adolescents, the education and behavioral services therapies are primarily treated, and medication is the important adjunct . BehavioThe GFD is the dietary exclusion of gluten or gluten content of <20 mg/kg, including natural gluten-free foods  and alternatives to gluten-based cereals. Casein-free diets involve the dietary exclusion of casein, a protein found in dairy and other foods containing dairy or lactose products. GFCF is a dietary protocol combining GFD and casein-free diets. KD is a formula diet rich in fat, moderate in protein, and low in carbohydrates, with fat as the main source of calories. In recent years, these diets as a non-traditional treatment approach have been used for patients with ASD. It is currently believed that opioid peptides produced by the gastrointestinal tract upon digestion of gluten and casein can pass through the mucosa and cross the blood-brain barrier to reach the central nervous system, and then affect brain function as well as contribute to the development of ASD , 11. So This systematic review and meta-analysis was carried out corresponding to the Cochrane Collaboration recommendations  and perfTwo independent coworkers searched four English databases  and three Chinese databases  published from the inception of the databases to August 18, 2021. The search strategies were  AND  AND . No restriction was taken on the language, but studies carried out on adults and animals were excluded. Reference tables of all meta-analyses, involved reviews, and obtained papers were manually searched to check for studies that had not appeared previously. Inconsistency was resolved by consensus.Inclusion criteria in the meta-analysis were the following: (1) the diagnosis of ASD according to the International Classification of Diseases (ICD) or the diagnostic criteria of the Diagnostic Statistical Manual (DSM); (2) Age range was 2 18 years ; (3) the children in the intervention group received therapeutic diets ; (4) the patients in the control group were treated with regular diet or balanced nutrition diet; (5) the clinical follow-up was required to be at least 6 weeks; (6) the therapeutic efficacy was assessed by Core autism symptoms reported by clinician or observer; (7) RCTs.Exclusion criteria in our research were as follows: (1) Not RCTs, including abstracts only, reviews, conference proceedings, case reports, animal studies, and non-clinical studies; (2) No available date; (3) Duplicated studies or repeated analysis.According to the inclusion and exclusion criteria, two authors screened titles and abstracts independently to identify potential articles. Another two authors selected and decided the final studies included in the analysis by screening the full texts of the potential articles.Two researchers evaluated study details from all included studies and extracted data using standardized forms. The data included the following items: first author, year of publication, country, sample size, age, gender, interventions, study design, diagnostic criteria utilized, exclusion criteria of patients, follow-up, outcomes, mean and standard deviation, and side effects. In crossover trials the latter period after crossover was excluded, to avoid the potential presence of carryover effects of treatment from the first period .Disagreements arising during this process were resolved and reached consensus by collective discussion.The quality and risk of bias critical appraisals of included studies were assessed independently with conflicts resolved through discussion by three researchers on the basis of the Cochrane risk of bias tool for RCTs . The CocIn our research, we focused on the overall core autism symptoms of ASD and the four main core symptom subgroups including communication difficulties, social disorders, stereotypical behaviors, and cognition. In addition, we conducted subgroup analyses according to different intervention durations (ranging from 6 weeks to 12 months) and interventions .P < 0.05 suggested a significant difference. The heterogeneity test was undertaken by the Q test and I2 test. If a P-value > 0.1 (Q test) as well as I2 <50% (I2 test), heterogeneity was considered to be meaningless. Random effects model was used in this study. Sensitivity analysis was used to evaluate the stability and reliability of the combined results of each meta-analyses in this study. Sensitivity analysis was performed by removing one study at a time. Funnel plots were plotted to visually detect publication bias.Review Manager 5.4 software  was usedA total of 387 articles were obtained by searching seven databases, including 228 in English and 159 in Chinese. Fifty seven duplicates identified by Endnote software were excluded, 304 studies were excluded by screening the titles and abstracts, and the remaining potential 25 studies were searched at a full-text level. Finally, six English articles and one Chinese article with a total of 7 RCTs were included in the meta-analysis –23, 32. Our analysis included seven studies, all of them were RCTs, in which two were crossover RCTs [Elder et al.  and GonzThe quality assessment of the studies included in the meta-analysis was shown in A sensitivity analysis was performed to assess the results of our research for clinician-reported core symptoms, duration, different interventions, stereotypical behaviors, social behaviors, cognition, and communication using Review Manager 5.4 software. The effect of each study on the pooled results was evaluated by excluding a single study sequentially. Most results of sensitivity analysis showed stability effect, while the study performed by Elder et al.  may haveP = 0.0008) (P = 0.13 and I2 =39%) was considered to be meaningless.All included studies assessed the association between therapeutic diet and clinician-reported core symptoms of ASD and showed a statistically significant effect  . The hetP = 0.0003) indicated statistically significant improvement in clinician-reported core symptoms. No statistically significant changes were observed in GFCF (P = 0.06) and GFD (P = 0.09). Between-study heterogeneity was null in GFD and KD subgroups (both I2 = 0%), while the GFCF subgroup had moderate heterogeneity .A fixed-effects meta-analysis of different dietary interventions  was performed . The resP = 0.03); <6 months   .P = 0.02) indicated statistically significant improvement (P > 0.05).Four trials –19, 21 drovement . The othP > 0.05)  –S4. questionnaire, a valuable tool for assessment of GI symptoms) for GI system evaluation, no meta-analysis was conducted. Ghalichi et al. . The results of subgroup analysis indicated that: Firstly, we observed the benefits of KD dietary interventions in core symptoms of ASD (PKD = 0.0003), and no significant improvement of GFD and GFCF treatment (PGFD = 0.09 and PGFCF = 0.06); Secondly, it suggested that the effectiveness compared between the different duration of interventions showed no significant difference ; Thirdly, it indicated that GFD intervention improved the symptoms of social behaviors (P = 0.02), however, the cognition, communication, and stereotypical behaviors symptoms showed no improvement ; Finally, all studies reported that there were no extra safety concerns compared with the regular diet, and even had improvement in GI discomfort. These data thus lend support to dietary interventions as alternative therapies that can improve the management of ASD.Our results showed that, compared with regular diet, diet interventions can significantly promote clinician-reported core symptoms of children with ASD , the Public Health and Technology project of Tianjin (grant number TJWJ2021ZD007), and the Public Health and Technology project of Tianjin (grant number ZC20120). The sources of support had no influence on the content of the manuscript.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.


The optimal blood pressure (BP) target in older people according to frailty status remains uncertain. This article investigates how frailty affects the association between BP and cardiovascular diseases or mortality, specifically in young-old adults. A retrospective cohort was created for 708,964 older adults with a uniform age of 66 years. The association between BP and myocardial infarction (MI), stroke, or mortality was analyzed using Cox proportional hazards models. The Timed Up and Go test (TUG) was used as a measure of physical frailty. Mean follow-up was 6.8 years, detecting 38,963 (5.5%) events. There was a linear association between increasing systolic BP (SBP) or diastolic BP (DBP) and increased risk of incident MI and stroke, compared to the reference BP . The risk patterns with high BP remained similar in each TUG group . A similar pattern of increased risks was found in those who took antihypertensive drugs and who did not, however they were more pronounced in those who did not. The findings support the need to achieve the same BP target in young-old adults with or without frailty to lower the risk of MI, stroke, and mortality. Hypertension is the most common chronic disease in older adults and is a major risk factor for cardio-cerebrovascular disease, peripheral vascular diseases, cognitive disorders, and mortality. Despite having the highest prevalence and the greatest risk for cardiovascular (CV) morbidity and mortality, the prognosis for high blood pressure (BP) remains uncertain in the older population due to the heterogeneity of this group. Furthermore, management of high BP is hindered by other common geriatric problems, including frailty.Frailty is an aging-related syndrome of physiological decline, multisystem dysfunction, and susceptibility to adverse consequences. Substantial evidence from observational studies has demonstrated that frailty can attenuate or even inverse the association between higher BP and mortality in older ages  1,2,3,44,5,6,7,8Despite the evidence from observational studies, randomized trials have demonstrated a beneficial effect of antihypertensive therapy and tight BP control in very old adults; for example, the Hypertension in the Very Elderly Trial (HYVET) showed the beneficial effect of antihypertensive treatment versus placebo on cardiac mortality and several other CV outcomes in adults in their 80s ,10. FurtDespite the accumulating studies, the optimal target BP in older individuals remains controversial. Furthermore, most studies assessing frailty are conducted using adults older than 70 years, and evidence is lacking for relatively younger older (young-old) adults (65–74 years old) who can be active and functionally capable and for whom functional decline might have a greater clinical impact. In this study, we aimed to investigate the relationships between varying BP and CV outcomes and mortality in relation to frailty in young-old adults 66 years of age.Korea has a mandatory health insurance scheme where the Korean National Health Insurance Service (NHIS) provides healthcare coverage to all citizens and a mandatory biennial general health screening program to people aged 40 years and older. For people who are 66 years of age, assessment of geriatric physical and cognitive function is added to the basic health screening items as a program called the National Health Screening Program for Transitional Ages (NSPTA). The Korean NHIS maintains a database of demographic factors of age, sex, income, death date, utilization of medical facilities, and health screening results.n = 909,489). Any subjects who were unable to undergo the geriatric physical function test were excluded . Subjects who were diagnosed with MI, stroke, end-stage renal disease, and cancer before the health screening day  and those with missing records on covariates were excluded . The final population comprised 708,964 subjects.This study included a large-scale retrospective cohort including all individuals who were 66 years of age and participated in the NSPTA program from 2009 to 2012 . Since the data were provided anonymously by the Korean National Health Insurance (KNHI), the study was exempt from the need to gather informed consent from the subjects.Blood pressure was measured using a standard protocol provided by the NSPTA manual. The measurement protocol was to measure brachial BP after five minutes of rest in a sitting position. The study population was divided into eight groups according to observed SBP  and seven groups according to observed DBP .The Timed Up and Go test (TUG) was used as a measure of physical performance to determine physical frailty. This test has been used as a basic functional assessment for vulnerability in older adults ,16 and cThe endpoints of the study were newly diagnosed MI, stroke, or total mortality. Myocardial infarction was defined as International Classification of Diseases, 10th revision (ICD-10) code I21 or I22 during hospitalization or at least two records of each code. Stroke was defined as ICD-10 code I63 or I64 during hospitalization with claims for brain magnetic resonance imaging or brain computerized tomography. Information on date of death was obtained from the National Statistical Office in Korea. Subjects were followed from NSPTA screening date (baseline) and censored on the day of occurrence of MI, stroke, or death or until the last follow-up date (31 December 2017), whichever occurred first.2). Information on smoking status, alcohol consumption, and regular exercise was obtained through questionnaires. Smoking status was classified as none, past, or current smoker, and alcohol consumption was classified as none, mild, or heavy (≥30 g of ethanol/day). Regular exercise was defined as performing strenuous physical activity for at least 20 min more than once a week. Income level was classified into medical aid and quartiles for the rest.Body mass index (BMI) was calculated as body weight (kg) divided by height squared (mHypertension was defined using the following criteria: (1) ICD-10 code for hypertension (I10–I11) with at least one prescription of an antihypertensive agent or (2) SBP/DBP of at least 140/90 mmHg at health screening. Diabetes was defined using the following criteria: (1) ICD-10 code for diabetes (E10–E14) with at least one claim of anti-diabetic medication or (2) fasting glucose level ≥ 126 mg/dL. Glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease (MDRD) equation, and chronic pulmonary obstructive disease was noted based on diagnostic codes.Differences in characteristics between subjects with and without identified outcomes were evaluated using the t-test and Chi-square test.p-values were two-tailed, and a p-value < 0.05 was considered statistically significant. All statistical analyses were performed using the SAS version 9.4 software program .Cox proportional hazards analyses were performed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for MI, stroke, and total mortality according to BP levels and TUG scores. All models were adjusted for the following variables: sex, BMI, smoking, alcohol drinking, regular exercise, income, antihypertensive medication use, diabetes, chronic pulmonary obstructive disease, GFR, and hemoglobin level. The analysis was carried out in two steps. First, subjects with the reference BP and TUG < 10 s were set as the reference group and used to investigate the risk of the endpoints based on BP levels and physical frailty. Then, to study the independent effects of BP on the endpoints among subjects with similar frailty levels, another analysis was carried out using the reference BP group as the reference within each TUG group. The analyses were stratified by the use of antihypertensive drugs. All n = 38,963) experienced MI , stroke , or mortality from any cause . p < 0.001), more subjects with diagnosis of diabetes, and higher proportion of subjects taking antihypertensive drugs (p < 0.001) in the event group. Subjects in the event group also had slightly higher BMI, SBP (130.7 vs. 128.4 mmHg), and DBP (79.0 vs. 77.9 mmHg) levels and higher fasting blood sugar level compared to those without endpoint events. The low-density lipoprotein (LDL) level was not significantly different at baseline between the two groups.During a mean follow-up period of 6.75 years, 5.5% of the study subjects (The incidence rates (per 1000 person-years) of MI, stroke, and mortality generally increased across levels of SBP and DBP . CompareGeneral patterns were not different between those who took antihypertensive drugs and those who did not . IncreasIn this population-based study consisting of young-old adults of the same age (66 years), we found that increasing BP was associated with increased risks of incident MI, stroke, and mortality, and this relationship was observed regardless of frailty status. The excessive risks seen with elevated BP were found both in subjects who were and were not being treated with antihypertensive drugs, but the risks increased at a lower threshold among those who were not being treated.Findings from our study support the current evidence that frailty is associated with higher risk of CVD and mortality ,18,19. CIn our study, we did not find evidence to support higher BP goals for adults with frailty. Several epidemiologic studies have reported that frail, older people might not benefit from tight BP control or observed inverse association between BP and mortality . HoweverOur study also showed that the increased risk triggered by elevated BP was more prominent for stroke than for MI. This is consistent with previous studies suggesting a higher association between BP and stroke than between BP and MI. Although MI and stroke are characterized by some common aspects and similar known risk factors, different pathogenetic mechanisms lead to MI and stroke. Several population-based studies have shown that hypertension increases the relative risk of stroke to a greater degree than it does to MI ,23,24,25Stratification of subjects using and not using antihypertensive drugs further reinforces our conclusion. The HRs for association of BP with endpoints were more pronounced for subjects not on antihypertensive drugs than for those already on drugs. This might be partly due to the lower absolute risk in the reference group . However, it also denotes the risk of untreated hypertension and signifies the importance of BP control.Among subjects who were being treated with medication, while the association was not as pronounced as in those without medication, elevated BP was associated with MI and stroke risk, and the tight BP control group  did not demonstrate any higher risk (and probably showed a slightly lower risk) than the loose BP control group . This pattern was observed similarly in the abnormal TUG groups as well. While some guidelines acknowledged frailty as a consideration for caution when deciding on therapy for treatment of hypertension, our findings indicate that lenient BP targets should not be pursued in relatively young-old adults being treated for hypertension, even for those with frailty.There is concern about an adverse effect of tight BP control in that it can lead to excess CV risk or mortality and be a possible source of the J-shaped association ,27. A reWhile many older adults, especially young-old adults, function actively in the community and maintain physical fitness, they are often grouped simply as “older adults.” The uniqueness of this study is that it included adults 66 years of age only, allowing us to explore the association between BP and CVD and mortality in this distinctive population. The uniform age of the study subjects allowed us to control the age factor, which is one of the most important factors in research. The study is population-based and included more than 0.7 million older adults and might be a more representative sample than that typically recruited for clinical trials.Despite its strengths, there also are several limitations to the analysis. First, only baseline BP levels were analyzed, and their changes over time were not considered. There are potential residual confounders, such as other drugs prescribed for the comorbidities of hypertension or variable compliance with antihypertensive drug usage. Moreover, although the TUG is a reliable, valid tool for assessing physical function, it cannot replace a full physical function test, and there could have been over- or underestimation of the number of frail subjects when we tried to assess frailty with TUG only. Lastly, although having a uniform age for study participants was a uniqueness of study, we were not able to explore young-old adults of other age ranges, and this can limit the generalizability of our findings.In conclusion, there was a linear association between increasing SBP or DBP and increased risk of incident MI, stroke, and mortality in young-old adults, and the association was persistent regardless of frailty status. The elevated risk seen with higher BP was more pronounced in older adults who were frail than in those who were not. Data from this population-based study constitute robust evidence that supports the need for a common BP target in young-old adults with or without frailty to lower risk of MI, stroke, and mortality.