To explore the microstructural changes of white matter in children with pediatric autism by using diffusion kurtosis imaging (DKI), and evaluate whether the combination of tract-based spatial statistics (TBSS) and back-propagation neural network (BPNN)/support vector machine (SVM)/logistic regression (LR) was feasible for the classification of pediatric autism.DKI data were retrospectively collected from 32 children with autism and 27 healthy controls (HCs). Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), radial kurtosis (RK), fractional anisotropy (FA), axial diffusivity (DA), mean diffusivity (MD) and Radial diffusivity (DR) were generated by iQuant workstation. TBSS was used to detect the regions of parameters values abnormalities and for the comparison between these two groups. In addition, we also introduced the lateralization indices (LI) to study brain lateralization in children with pediatric autism, using TBSS for additional analysis. The parameters values of the differentiated regions from TBSS were then calculated for each participant and used as the features in SVM/BPNN/LR. All models were trained and tested with leave-one-out cross validation (LOOCV).p < 0.05, TFCE corrected). And we also found DA lateralization abnormality in Superior longitudinal fasciculus (SLF) (the LI in HCs group was higher than that in pediatric autism group). However, there were no significant differences in FA, MD, MK, DR, and KR values between HCs and pediatric autism group . After performing LOOCV to train and test three model (SVM/BPNN/LR), we found the accuracy of BPNN (accuracy = 86.44%) was higher than that of LR (accuracy = 76.27%), but no different from SVM .Compared to the HCs group, the FAK, DA, and KA values of multi-fibers  were lower in pediatric autism group (Our proposed method combining TBSS findings with machine learning (LR/SVM/BPNN), was applicable in the classification of pediatric autism with high accuracy. Furthermore, the FAK, DA, and KA values and Lateralization index (LI) value could be used as neuroimaging biomarkers to discriminate the children with pediatric autism or not. Autism spectrum disorder (ASD) is a specific type of pervasive developmental disorder characterized by deficits in social and verbal communication skills, and restricted repetitive behaviors . Some evNowadays, the early diagnosis of ASD is mainly based on the clinical symptoms, but most children with ASD have no typical clinical manifestations, which makes early diagnosis very difficult. However, some researches have reported appropriate systematic rehabilitation interventions in the early stages of autism could improve the symptoms of most patients , 8. It iDiffusion kurtosis imaging (DKI) is one of the state-of-the-art sequences that could be utilized to describe and sensitively detect microstructural changes of the complex brain tissues based on non-Gaussian water molecule theory . Tract-bMachine learning methods, like support vector machine (SVM), have been applied to distinguish the children with mesial temporal lobe epilepsy with hippocampal sclerosis and healthy children . RecentlWe hypothesize that there is one DKI parameter-based machine learning model can efficiently make a distinction whether a child has ASD or not. Firstly, we employed TBSS to explore the abnormalities of brain regions, and secondly, we combined the abnormal parameters of DKI and BPNN to make classification. Unlike the previous analysis using predefined ROI , 19, thiAll participants in our study were recruited in the Third Affiliated Hospital of Zhengzhou University between May 2020 and May 2021. Fifty-nine participants were retrospectively included into the study, including 32 children who were first diagnosed as ASD in the hospital and 27 healthy children. All patients were included based on the follow criteria: (1) 36–60 month old, right-handedness, (2) gestational age ≥37 weeks, (3) all patients met the ASD criteria of DSM-V , (4) the score of CARS  ≥30 points, (5) no history of head trauma and convulsion, no history of other mental diseases, no family history of neurological diseases, no history of psychotropic drug treatment, (6) no focal or diffuse lesions on imaging. All healthy controls  did not have any seizure and other developmental or neurological disorders.2; scanning time = 7 min 23 s. All subjects had no apparent structural damage, who were examined by two radiologists based on conventional MRI images.MRI acquisition was performed on 3T MR scanner (GE Signa Pioneer 3.0T). Thirty minutes before examination, all participants were given chloral hydrate (0.5 ml/kg) enemas. For stable image quality, earplugs with cotton balls were used to keep them from noise from the machine. After routine sequences scan, axial DKI was performed on all participants. The scanning parameters were: TR/TE = 8,200 ms/2.3 ms, FOV = 200 mm×200 mm, acquisition matrix = 256 × 256, NEX = 1, slices thickness = 4 mm, number of slices = 27; gradient values b: 0, 1,000 and 2,000 s/mmhttps://horosproject.org/). The latest AW4.7 version no longer supported the previous DKI processing platform, so GE's development team transferred the algorithms that included DKI image processing algorithm from the AW4.6 FuncTool platform to Horos and called it iQuant. After importing the images in the workstation, FA/MD/Da/DR and FAK/MK/KA/KR maps could be obtained automatically. We then exported the parameter images to a workstation on a VMware Linux virtual machine with FMRIB Software Library V5.0  installed. FMRIB's Diffusion Toolbox (FDT), one tool of FSL, was employed to correct image distortions. Then, we used another tool of FSL named the Brain Extraction Tool (BET) to removed non-brain structure from the images.All raw DKI image were processed on the iQuant workstation . This workstation is an upgraded version of the commercial version of the software Horos  and the symmetric skeleton that flipped the left side of the original skeleton to the right side. Their 4D prealigned data  was projected onto the symmetrized skeleton to generate the 4D file . Next, we used the command (fslswapdim) to complete left-right switching of the 4D file to generate the flipped 4D file. The lateralization indices (LI) were calculated based on a formula as follows :LI = (symmetrised_skeletonised– flipped) ÷ (symmetrised_ skeletonised + flipped).p < 0.05, after accounting for multiple comparisons by using family-wise error (FWE). Finally, we mapped the FWE-corrected statistical maps of p < 0.05 onto the JHU-WM Tractography Atlas in MNI space to localize and anatomically label the skeletal regions that showed significant differences in TBSS. Then we extracted the clusters detected in TBSS analysis and calculated their DKI parameter values as the raw features. Thereafter, principal component analysis (PCA) was used to select valid features from 59 participants, where 90% of the original information of the original features is retained.We used the FSL randomize tool for statistical analysis, using 5,000 random permutations per test. Two contrasts were estimated: ASD greater than healthy controls and healthy controls greater than ASD. Considering that FA or other parameter maps might also be influenced by age or sex, we included age and sex as covariates for statistical analysis, trying to make the observed differences in parameters of DKI between groups independent. Threshold-free cluster enhancement (TFCE)  was usedp < 0.05 indicated statistical significance, and all statistical tests were two sided.The statistical analysis on baseline data was performed on IBM SPSS Statistics 25.0 . We used independent t-test to compare the differences in continuous values (age). The Chi-square test was employed to compare the differences in counting values. The Kruskal-Wallis test was used for the comparison of Ordinal rank variables. The Back-propagation network were conducted in python (version 3) using TensorFlow (version 1.2.1). In our case, the network was four-layer back-propagation (BP) neural network. The numbers of neurons in input layer and two hidden layers were 10, 16, and 8, while the numbers of neurons in output layer was 1. We adopted the hyperbolic tangent function (tanh) function as the activation function in hidden layer, and the Sigmoid function was adopted as activation function in output layer. For the loss function, binary cross entropy loss was adopted, which wasi of y referred to the ordinal number of subjects . Therefore, the loss value would be large if the subject belonged to ASD group (y = 1) and its prediction values [p(y)] was approaching 0, otherwise the loss value would be small. N was the total number of samples, and in our study, N = 59. Before the network was trained, the weights of the network are initialized from a Gaussian distribution with μ = 0, σ = 0.01  and biases were initialized to 0. The batch size was 3, and then we trained the model for minimizing the values of loss function by Adam optimizer, of which the learning rate was setting at 0.001. To avoid the overfitting of the network, the L2 regularization was adopted, of which the parameter was setting at 0.01. L2 regularization enabled smaller values in the model and reduced model complexity; and we reduced the probability of overfitting by adding a dropout layer after each hidden layer; the dropout layer increased network diversity by randomly decreasing the number of neurons during training to avoid model overfitting, and in our study, the dropout layer parameter was set to 0.4 (40% of neurons were randomly deactivated in the hidden layers). The network structure is shown in where p(y) was the predicted value from network, whose range was 0 to 1. y was a label of the input subjects (y = 0 and y = 1) indicated the subjects belong to the Healthy control group (HC) and ASD group respectively, and the subscript To make full use of the subjects, leave-one-out cross validation (LOOCV) was adopted to estimate the classifier performance. In training steps, 58 subjects were used to training the network. And in testing steps, 1 subject would be used to evaluate the trained model. Finally, a total of 59 repetitions of the above loop were required to predict the participants' labels. The accuracy, specificity, sensitivity and F1-score of models were used to quantify the performance of the model, which were calculated as:where TP, TN, FP, and FN meant true positive, true negative, false positive, and false negative respectively.For comparing the BPNN against traditional classifiers, the support vector machine (SVM) with radial basis function (RBF) and linear kernel and Logistic Regression (LR) model were performed in python.Support vector machines are model-based learning algorithms that perform classification mainly by employing a hyperplane. The hyperplane can be constructed using kernel functions such as linear, radial basis functions (RBF), etc. In our research, we mainly used linear as well as RBF. In SVM, there is an important parameter C ; the higher the C, the more unacceptable appears the error, meaning easy to overfit; and the smaller the C, easy to underfit. For the RBF, there is another important parameter g (gamma), which determines the distribution of the data after mapping to the new feature space.Logistic regression is a widely used model for data classification, and it is often used in binary classification. It builds a probabilistic model that calculates the probability that the output variable y will be 0 or 1 given the input variable x. In this model, there is an important parameter C′ (regularization coefficient). It is inversely proportional to the penalty coefficient, which means that the smaller the value of C′, the stronger the regularization effect and the greater the penalty effect on the parameters. And in our experiments, we also experimented with the four solvers  to find the best combination of parameters using grid search.In order to optimize the classifier parameters, we performed a grid search for all the hyperparameters of the all models (BPNN/LR/SVM). For SVM and LR, there are two parameters to optimize . And for BPNN, there are six parameters to be optimized . The hyperparameters and their values are shown in n = 59) to evaluate the performance of the models. We also calculated the receiver operating characteristic (ROC) curve for all models and compared the area under the ROC curve (AUC) of all models by using Delong's test  (p > 0.05). We also performed Delong' test to do comparison on different models' AUC values, which shown no statistical differences in AUC between different models   . In addi > 0.05) . The ROCIn clinical practice, most routine MRI findings are negative, which is detrimental to the early diagnosis of autism , 26. HowIn our study, TBSS, an automated analysis based on tract, was adopted to study micro-structure changes of white matter fiber tracts in children with ASD. Decreased FAK, KA, and DA in extensive fiber bundles were observed in ASD group compared to healthy control group, indicating widespread white matter damage in children with ASD. We also found that the children with ASD show increased LI in DA in SLF compared to healthy control group, indicating developmental abnormalities of brain lateralization in children with ASD.FAK is like FA to some extent measuring anisotropy but adopts the kurtosis tensor for calculation, which leads to less errors from complex white matter fiber arrangements . When thThe KA and DA parameters of DKI are also commonly used kurtosis and diffusion metrics. KA is a directional kurtosis parameter parallel to the long axis of the diffusion tensor, which reflects the integrity of the axon. DA is a metric of diffusion along the axis, which was proved to be a biomarker of axon damage . In our Previous studies have shown that children with ASD have thalamic-frontal connectivity disorders . In addiCurrently, our study is the first to use DKI to study brain lateralization in children with ASD. Because of its ability to assess human growth and development, the lateralization index is widely used in different fields, such as behavioral assessment . In our Our results of TBSS on DKI image might provide efficient diffusion and kurtosis features as a possible biomarker for classification models based on machine learning methods. Most previous brain science research on classification has focused on the predefined ROI changes , 19. HowWe tried to use BPNN for classification, which was better than LR in terms of accuracy, although not much different from SVM accuracy. We also compared the specificity and sensitivity as well as the AUC values of the four models, and although the differences were not statistically significant , 7, the There are some potential limitations in our study. First, this study had a small sample size and lacked validation with independent datasets. More large multi-center datasets need to be assessed in the future to confirm the results. Second, the imaging data is single, and the mechanism behind it can be more effectively elucidated if multiple imaging modalities are combined, including fMRI such as resting-state functional MRI. However, for children, the acquisition of imaging data is still a challenge. Although our method achieves an acceptable level of accuracy, it is still not a substitute for traditional diagnostic methods, and the future combination of clinical indicators as well as the combination of multidisciplinary indicators will facilitate further confirmation of our study.Our study shows that the combination of TBSS of DKI and machine learning methods might be effective in discriminating the children with ASD or not. Furthermore, FAK, KA, and DA parameters and LI values have the potential to be used as biomarkers to differentiate children with early ASD from those with normal development. Future studies with large and multi-center samples may be helpful to further elucidate the DKI parameters and the underlying neurobiological mechanisms, and our proposed method may be useful for future multidisciplinary diagnosis of early ASD.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the Third Affiliated Hospital of Zhengzhou University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.XH: experimental design, paper writing, and statistics. YS and XH: data acquisition. XZhang and XZhao: experiment design and funding provision. PG: language modifications. All authors contributed to the article and approved the submitted version.


To investigate the association between amino acids related to the urea cycle and diabetic nephropathy (DN) in two independent cross-sectional studies.We obtained the medical records of 145 individuals with DN and 596 individuals without DN who attended an annual health examination at Liaoning Medical University First Affiliated Hospital (LMUFAH), China, from May 2015 to August 2016. From April 2018 to April 2019, we collected medical records of another 741 individuals: 338 individuals with DN and 403 individuals without DN from the Second Affiliated Hospital of Dalian Medical University (DALIAN), China. Binary logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI).In two independent cross-sectional studies, we observed that citrulline was consistently associated with DN risk . However, ornithine, arginine, and the ratio of arginine to ornithine were consistently unrelated to DN risk, and the ratios of other amino acids in the urea cycle were inconsistently associated with DN risk. Citrulline was consistently associated with DN risk in two independent cross-sectional studies in Chinese adults. When the pancreas does not produce enough insulin or the body is unable to use insulin effectively, symptoms such as excessive urine excretion (polyuria), thirst (polydipsia), frequent hunger pangs, weight loss, loss of vision, and fatigue may occur; we call this disease diabetes –3. DiabeDiabetic nephropathy (DN) is a result of a complex interaction between metabolic processes, is a result of long-term tubular , 6 and gEarly studies have found that 5%–40% of T2D patients eventually develop DN , which iIt is well known that disease leads to changes in the pathophysiological processes of the body, which ultimately cause corresponding changes in metabolites. Furthermore, metabolites reflect the environment in which the cells live, which in turn is closely related to the nutritional status of the cells, the effects of drugs and environmental pollutants, and the influence of other external factors. Therefore, analyzing certain metabolites and comparing them with those of non-diseased individuals to find biomarkers of the disease will provide a better approach to disease diagnosis.Considering the seriousness of the dangers of DN, we urgently need to find biomarkers that can identify populations at high risk of DN and predict this disease at an early stage. In recent decades many novel biomarkers related to diabetes and its complications were found through metabolomics . ExperimIn this study, we aimed to explore whether plasma levels of amino acids related to the urea cycle were associated with DN risk using two independent cross-sectional surveys.Liaoning Medical University First Affiliated Hospital (LMUFAH) is a national comprehensive tertiary class A hospital in China. By retrieving electronic medical records, data from 741 T2D patients (596 non-DN and 145 DN patients) were collected from May 2015 to August 2016. The Second Affiliated Hospital of Dalian Medical University (DALIAN) is the largest comprehensive grade 3A hospital in the Southern Liaoning province of China, with nearly 2 million emergency department visits per year. In accordance with the inclusion and exclusion criteria below, we collected data from 741 T2D patients (403 non-DN and 338 DN patients) to address our research questions. Diabetes patients were diagnosed in accordance with the World Health Organization’s criteria from 1998  for patients in the non-DN group, a diagnosis of T2D without DN, and (2) for patients in the DN group, a diagnosis of T2D with DN . The excWe obtained the data from the hospitals’ computer systems, including demographic characteristics, anthropometric measurements, relevant clinical measurements, and metabolism measurements. Demographic characteristics data included age, gender, current smoking status, and current drinking status. Anthropometric measurements included weight, height, and blood pressure. Clinical measurements on lipid profiles were collected. Metabolism measurements included amino acids related to the urea cycle . Anti-diabetic measures included oral anti-diabetic agents and insulin. The statuses “current smoker” and “current drinker” indicate patients who currently smoke and currently drink alcohol, respectively, regardless of whether they have reduced their consumption. There were no major changes in treatment and mainstream medications for people with diabetes, with negligible differences over time.The protocol of the study was approved by the Ethics Committee for Clinical Research of the LMUFAH and the Ethics Committee for Clinical Research of the DALIAN. Informed consent was waived because of the nature of the retrospective study, which was in accordance with the Helsinki Declaration of 1964 and its later amendments. Height, weight, and blood pressure were measured by specially trained doctors and nurses using standardized methods. Patients had to remove heavy clothing and shoes while weight and height measurements were obtained. Weight and height measurements were accurate to one decimal point. Blood pressure was not measured until patients had been resting in a sitting position for at least 10 minutes. Body mass index (BMI) was calculated by dividing weight in kilograms by squared height in meters.After a continuous fasting period of at least 8 hours, patients’ blood samples were collected. Lipid profiles were assayed by an automatic biochemical analyzer, including TG, CHOL, HDL-C, and LDL-C. Metabolomic profiles of amino acids including ornithine, arginine, and citrulline were assayed.Methods for the measurement of plasma amino acids have already been discussed in detail in previous publications . BrieflyU-tests or Student’s t-tests were used to compare differences in continuous data. Chi-squared tests were used to compare the differences in qualitative data. Binary logistic regression was used to obtain odds ratios (ORs) and their 95% confidence intervals (CIs). A structured adjustment scheme was used to control for confounders: model 1, unadjusted; model 2, adjusted for gender and age; model 3, adjusted for covariates in model 2 plus BMI and systolic blood pressure (SBP); model 4, adjusted for covariates in model 3 plus TG, CHOL, HDL-C, and LDL-C; and model 5, adjusted for covariates in model 4 plus anti-diabetic measures. Metabolites’ associations with DN risk were pooled with an inverse variance-weighted fixed-effect meta-analysis. Before being introduced into regression models, amino acids related to the urea cycle and their ratios were scaled to SD concentrations. All statistical analyses were performed using the Statistical Analysis System 9.4 . A two-tailed p-value <.05 was considered statistically significant.Continuous data of normal distribution were expressed as mean ± standard deviation (SD); if normal distributions were not accepted, the data were expressed as the median (interquartile range). Qualitative data were presented as a number (percentile). Mann–Whitney The clinical characteristics and the baseline demographics of the study individuals from the two populations are presented in The DALIAN patients had a median age of 61.00 years (interquartile range: 53.00 to 68.00 years) and a median BMI of 26.22 kg/m² (interquartile range: 24.10 to 29.06 kg/m²). HDL-C levels for those in the DN group were significantly lower than for those in the non-DN group. Age, BMI, SBP, and anti-diabetic measures for those in the DN group were significantly higher than for those in the non-DN group. Moreover, gender; current smoking status; current drinking status; levels of DBP, TG, CHOL, LDL-C, ornithine, arginine, and citrulline; and the ratios of arginine to ornithine, citrulline to ornithine, and arginine to citrulline in both the DN and non-DN groups were not significantly different.In the LMUFAH population and DALIAN population, SBP and anti-diabetic measures for those in the DN groups were significantly higher than for those in the non-DN groups, and HDL-C levels in both the DN and non-DN groups were significantly different.In the LMUFAH population, citrulline was positively associated with DN risk in the univariate analyses. The association was still significant after further adjustment for traditional risk factors [OR (95% CI) of per SD increase for citrulline: 1.200 ]. However, the associations of ornithine and arginine with DN risk were not significant.In the DALIAN population, citrulline was positively associated with DN risk in the univariate analyses. After further adjustment for traditional risk factors, the association was still significant [OR (95% CI) of per SD increase for citrulline: 1.189 ]. However, the associations of ornithine and arginine with DN risk were not significant.In the fixed-effect pooled analysis, the associations of amino acids related to the urea cycle with DN risk were in accordance with findings in the LMUFAH and DALIAN populations. Citrulline was still positively associated with DN risk. The OR (95% CI) of per SD increase was 1.252  for citrulline in the univariate analyses. After further adjustment for traditional risk factors, the pooled effect size was 1.194  for citrulline. The associations of ornithine and arginine with DN risk were not significant Table 2.The relationship between ratios of amino acids in the urea cycle and DN is presented in In the DALIAN population, the association between the ratio of citrulline to ornithine and DN risk was significantly different in the univariate analyses and in the multivariate analyses [the OR (95% CI) of per SD increase for citrulline–ornithine: 1.192 ]. However, the ratios of arginine to ornithine and arginine to citrulline were not associated with DN risk.In the fixed-effect pooled analysis, the ratio of citrulline to ornithine was associated with DN risk in the univariate analyses and in the multivariate analyses [OR (95% CI) of per SD increase: 1.170 ]. The associations between the ratios of other amino acids and DN risk were not significantly different.One of the serious complications of T2D is DN; patients with DN have an increased risk of developing cardiovascular and cerebrovascular diseases , 24. Howvia arginine  have not been evaluated in the present study; therefore, direct evidence about the association between amino acids related to the urea cycle and DN is lacking. Second, the data we collected mainly focused on DN patients in Jinzhou and Dalian, which cannot necessarily be generalized to all DN patients in China. Third, our patients with T2D and DN were in-patients. Their condition might be more serious than that of general patients with T2D and DN. Finally, this study is cross-sectional design: experimental values of blood urea nitrogen, serum ammonia, serum creatinine, proteinuria, albuminuria, and dietary factors were not included in this study, so we cannot derive a causal relationship between amino acids related to the urea cycle and DN. In summary, the findings of the study need to be evaluated and validated in a broader population.The present study has important implications for public health. DN is one of the major public health problems worldwide, and it has caused a huge burden on the global health system. Moreover, the incidence of progression of DN to ESRD is increasing . TherefoIn conclusion, citrulline was consistently associated with DN risk in two independent cross-sectional studies of amino acids related to the urea cycle and DN risk. As this was a cross-sectional study, it is necessary to confirm the findings through studies in other populations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee for Clinical Research of the LMUFAH and the Ethics Committee for Clinical Research of the DALIAN. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.SL and YC conceived the project and designed the experiments. PC wrote the manuscript. BH analyzed data. MH, YJ, RC, and CC collected the information and contributed to the writing of this manuscript. All authors edited the final version of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Key Research and Development Program of China , the Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control, the Development and application of clinical mass spectrometry detection technology for important small molecule metabolites (2020JH2/10300116), the Key R&D Program of Liaoning Province and the Dalian Laboratory Medicine Mass Spectrometry Technology Innovation Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The authors thank all doctors, nurses, and research staff at the Liaoning Medical University in Jinzhou and the Second Affiliated Hospital of Dalian Medical University for their participation in this study.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 global burden of stroke is still high, particularly in developing countries, with hypertension serving as the main risk factor. The awareness of stroke among hypertensive patients is crucial for stroke prevention. This study was aimed at identifying the awareness of being at risk of stroke and its determinant factors among hypertensive patients in Banyumas, Indonesia.  This was a cross-sectional study conducted in Primary Health Centers (PHCs) in Banyumas Regency, Indonesia, from April to August 2019. A simple random sampling technique was used to select the representatives' PHCs. Hypertensive patients who came regularly to the PHCs were consecutively recruited. Data were collected through a self-reported questionnaire and medical records. The main outcome was the level of participants' awareness of being at risk of stroke. Multivariate logistic regression analysis was performed to measure determinant factors associated with the level of awareness. p 0.007; OR 2.321, 95% CI 1.326-4.064, p 0.003; and OR 6.033, 95% CI 2.450-14.858, p < 0.001, respectively).  Out of 457 patients investigated, 77.46% had a low level of awareness. Low knowledge of hypertension, low income, and no history of previous stroke were associated with a low-level of awareness (odds ratio (OR) 1.942, 95% CI 1.195-3.158,  Majority of hypertensive patients were unaware of being at risk of stroke. Knowledge of hypertension, income, and history of previous stroke are factors that may influence the awareness of being at risk of stroke among them. This emphasizes the need to provide better campaigns and education programs to raise the awareness of stroke in a community setting, particularly for the population at risk. Stroke is the leading cause of morbidity and disability worldwide . The gloIndonesia has the highest rates of age-sex standardized stroke mortality and disability adjusted life-years related to stroke among South-East Asian  . The aveHypertension is the most common risk factors for stroke and the main modifiable factor attributed to disability after stroke , 11. TheRaising public of stroke awareness would be beneficial for better stroke prevention strategies, particularly for the population at risk. Approximately 80% of new stroke incidence could be avoided by improving knowledge related to risk factors of stroke . The knoThis study used a cross-sectional design. The population of this study was hypertensive patients who came regularly to Primary Health Centers (PHC) in Banyumas Regency to do a routine check-up every month. There are 39 PHCs in Banyumas Regency, in which their locations are dispersed over a large area. Due to the difficulties in reaching the faraway locations and limited budget, we decided to recruit the participants from 5 PHCs as representatives. Simple random sampling using a computer-generated random number  was performed to select 5 out of 39 PHCs. These five PHCs are located in rural areas (3 PHCs) and urban areas (2 PHCs).Subsequently, participants were recruited in those 5 PHCs consecutively during April-August 2019 until a minimum sample was achieved. The inclusion criteria were hypertensive patients aged >35 years old who came to the PHCs for a routine checkup. Hypertensive patients with comorbidities such as aphasia, dementia, or other neuropsychiatric diseases were excluded from the study.N is the sample size, Zα is the statistic corresponding to the level of confidence, P is the expected prevalence (that can be obtained from the same studies or a pilot study conducted by the researchers), Q = 1–P, and d is the precision (corresponding to effect size).We estimated the minimum sample size and found that it might be acceptable to represent the entire population in Banyumas Regency. The sample size was calculated using the following formula for cross-sectional studies :(1)N=ZαZα = 1.96 and d = 0.05, the minimum sample size is as follows:Based on the previous study , the proBy adding a 15% nonresponse rate, the final sample size was 441. A total of 457 participants responded completely in this study, with an average of 91 patients recruited from each PHC. All patients signed a written informed consent prior to the investigation.We conducted a cross-sectional survey by interviewing the participants based upon a questionnaire. The questionnaire for the independent variables consists of three components, including demographic characteristics, knowledge on hypertension, and stroke risk factors.Demographic characteristics included sex, age, education level, marriage status (married/widow), living with , and income. Income was categorized as <minimum regional payment (MRP) or <134 USD/month, and ≥MRP (≥134 USD/month) based on the minimum standard income in Indonesia  and histWe measured patients' knowledge on hypertension as a risk factor for stroke by using the Hypertension Knowledge-Level Scale (HKLS). HKLS has high validity and reliability for measuring the level of knowledge on hypertension in adults . It has Stroke risk factors included medication adherence, history of hypertension (controlled/uncontrolled), diabetes mellitus, history of dyslipidemia, history of previous stroke, history of atrial fibrillation, history of heart diseases, history of anxiety, and history of sleep apnea. The medical history was investigated by a questionnaire with “yes” and “no” answers. However, we also inquired about their medical records to validate the answers.Medication adherence was measured by a self-reported questionnaire related to the blood pressure medicine, consisting of five questions , 23. TheFor the dependent variable, we measured the awareness of being at risk of stroke among the participants by a self-reported question modified from previous study : “How sup value of <0.05 and odds ratio (OR). SPSS version 16 was used for all the analyses.We performed the chi-square test to analyze the differences between categorical variables in the baseline characteristics. Factors associated with the awareness of stroke were measured using univariate logistic regression analysis. Subsequently, these associated factors were further analyzed using multivariate logistic regression to measure their contributions to the level of stroke awareness after controlling the covariates. Statistical significance was indicated by a p 0.004) and income level .We involved 457 patients with hypertension. Most of them were female (80.7%), were aged >55 years old (75.7%), had low education level (79.6%), had low HKLS (56%), were married (71.8%), were living with family (51%), and had low income (67.1%). Three hundred and fifty-four (77.46%) of the 457 patients had low awareness of stroke, whereas 103 (22.54%) had high awareness of stroke . Factorsp 0.029; OR 1.970, 95% CI: 1.264-3.071, p 0.003; and OR 4.504, 95% CI: 1.953-10.390, p < 0.001, respectively).p < 0.001).The study shows that knowledge of hypertension, income, and a history of previous stroke were associated with the awareness of stroke. The majority of the participants had poor awareness of stroke. To our knowledge, this study is the first study in Indonesia which demonstrated that hypertensive patients with low knowledge of hypertension, low income, and no history of previous stroke were more likely to be unaware that they were being at risk of stroke.This finding is in accordance with prior studies which showed that most participants were not fully aware of stroke and its risk factors , 25, 26.Corroborating with our findings, a previous study reported that 78.3% of patients had inadequate knowledge, 21.7% had moderate knowledge, and none of them had adequate knowledge on hypertension and stroke prevention . Two preIn the study, we also found that low income was positively associated with stroke risk awareness. This finding is supported by a prior study from the 2017 National Health Interview Survey in the United States which showed that individuals with low income were more likely not to be aware of all stroke symptoms and risk factors . AnotherThe awareness of being at risk of stroke in this study was also influenced by having a history of stroke. In accordance with our finding, a cross-sectional study among stroke patients in Spain reported that history of prior stroke was associated with higher knowledge on stroke warning signs and risk factors . AnotherHypertension and diabetes mellitus (DM) are established risk factors of stroke. In the present study, we found no association between prior history of hypertension and DM with the awareness of stroke. Our findings corroborate the previous study which demonstrated that a history of hypertension and DM was not associated with knowledge on stroke . ContradResults in the present study contribute to providing more evidence that stroke awareness is still insufficient in the community setting. This indicates an urgent need to disseminate stroke education and awareness campaigns in Indonesia, particularly targeting people who have a higher risk of stroke. The strength of this study is that this was the first study that identifies the association between HKLS  and the awareness of stroke. Other studies did not use HKLS to explore the knowledge level of hypertension; they rather explore more about the knowledge level of stroke warning signs/symptoms or stroke risk factors. In addition, we also found that low HKLS (low knowledge of hypertension) contributed to low awareness of stroke. To our knowledge, this was also the first to report.Nevertheless, this study has some limitations. First, we used one-item self-reported questionnaire to measure the awareness of being at risk of stroke. This may not be enough to validate the level of awareness. Therefore, future studies should elaborate the questionnaire with other more valid parameters to measure the level of stroke awareness. Second, the investigation related to their knowledge of stroke warning signs/symptoms was not further explored. Third, the participants in this study were recruited only from PHCs in Banyumas Regency in Indonesia. Therefore, the findings need careful interpretation for generalization. Finally, we only used a cross-sectional design. Thus, we could not provide the causal relationship between the awareness of stroke and the determinant factors although the direction of the association was consistent with prior studies.Majority of hypertensive patients were unaware of being at risk of stroke. Knowledge of hypertension, income, and history of previous stroke are factors that may influence the awareness of being at risk of stroke among them. This emphasizes the need to provide better campaigns and education programs to raise the awareness of stroke in a community setting, particularly for the population at risk.


Bovine viral diarrhea is one of the diseases that cause huge economic losses in animal husbandry. Many countries or regions have successively introduced eradication plans, but BVDV still has a high prevalence in the world. This meta-analysis aims to investigate the prevalence and risk factors of BVDV in the world in recent 10 years, and is expected to provide some reference and theoretical basis for BVDV control plans in different regions.Relevant articles published from 2010 to 2021 were mainly retrieved from NCBI, ScienceDirect, Chongqing VIP, Chinese web of knowledge (CNKI), web of science and Wanfang databases.128 data were used to analyze the prevalence of BVDV from 2010 to 2021. BVDV antigen prevalence rate is 15.74% (95% CI: 11.35–20.68), antibody prevalence rate is 42.77% (95% CI: 37.01–48.63). In the two databases of antigen and antibody, regions, sampling time, samples, detection methods, species, health status, age, sex, breeding mode, and seasonal subgroups were discussed and analyzed, respectively. In the antigen database, the prevalence of dairy cows in the breed subgroup, ELISA in the detection method subgroup, ear tissue in the sample subgroup, and extensive breeding in the breeding mode were the lowest, with significant differences. In the antibody database, the prevalence rate of dairy cows in the breed subgroup and intensive farming was the highest, with a significant difference. The subgroups in the remaining two databases were not significantly different.This meta-analysis determined the prevalence of BVDV in global cattle herds from 2010 to 2021. The prevalence of BVDV varies from region to region, and the situation is still not optimistic. In daily feeding, we should pay attention to the rigorous and comprehensive management to minimize the spread of virus. The government should enforce BVDV prevention and control, implement control or eradication policies according to local conditions, and adjust the policies in time. Bovine viral diarrhea virus (BVDV) is the main pathogen of bovine viral diarrhea BVD , 2, and BVDV is widespread in the world and can cause gastrointestinal, respiratory and reproductive diseases. The induced immunosuppression can increase the probability of infection of other diseases . BVDV reAccording to the positive rate of BVDV in different species, many articles have been meta-analyzed. Knowing the prevalence of BVDV in time can not only provide data support for the formulation of BVDV prevention and control policies, but also provide technical guidance for practical production.This paper makes a meta-analysis on the prevalence of BVDV infection among cattle in the world in recent 10 years. Through the summary of the latest data and the thinking caused by eradication plans in different regions, the following questions are addressed: “What should we do to control BVDV? How should the control plan be carried out under different circumstances?”. We hope to observe the effectiveness of current prevention and control measures and provide reference for further prevention and control of BVDV in the future.We searched six databases of PubMed, ScienceDirect, Web of Science, CNKI, VIP, and Wanfang, and find articles published in Chinese and English from 2010 to May 20, 2021. Designed to filter prevalence data for all BVDV, the specific search process is as follows:PubMed search strategy is as follows: According to MeSH terminology, the following keywords were used to search: “Diarrhea Viruses, Bovine Viral,” “Cattle,” and the Boolean operators “OR,” “AND” in the “Keyword/Title/Summary” field alone or in combination.A: We search for “Cattle” based on MeSH terminology: ((((((((((((((((((((“Cattle”[Mesh]) OR (Cow)) OR (Cows)) OR (Bos indicus)) OR (Zebu)) OR (Zebus)) OR (Holstein Cow)) OR ) OR (Dairy Cow)) OR ) OR (Dairy Cows)) OR (Beef Cow)) OR (Beef Cows)) OR ) OR (Bos grunniens)) OR (Yak)) OR (Yaks)) OR (Bos taurus)) OR ) OR (Domestic Cow)) OR (Domestic Cows).B: We search for “Diarrhea Viruses, Bovine Viral” based on MeSH terminology:  OR ) OR (Bovine Pestivirus)) OR (Bovine Pestiviruses)) OR ) OR (Bovine Diarrhea Virus)) OR (Bovine Diarrhea Viruses)) OR ) OR ) OR ) OR ) OR .C: We used the Boolean operators “OR” for the entry terms and “AND” for the MeSH terms. (((((((((((((((((((((“Cattle”[Mesh]) OR (Cow)) OR (Cows)) OR (Bos indicus)) OR (Zebu)) OR (Zebus)) OR (Holstein Cow)) OR ) OR (Dairy Cow)) OR ) OR (Dairy Cows)) OR (Beef Cow)) OR (Beef Cows)) OR ) OR (Bos grunniens)) OR (Yak)) OR (Yaks)) OR (Bos taurus)) OR ) OR (Domestic Cow)) OR (Domestic Cows)) AND  OR ) OR (Bovine Pestivirus)) OR (Bovine Pestiviruses)) OR ) OR (Bovine Diarrhea Virus)) OR (Bovine Diarrhea Viruses)) OR ) OR ) OR ) OR ) OR ).Use advanced search in ScienceDirect and Web of Science databases to improve the accuracy of your results, enter subject terms “cattle,” “Diarrhea Viruses, Bovine Viral,” “prevalence” and select research articles to search. The VIP database was searched for articles by selecting the subject headings “bovine” and “bovine viral diarrheal mucosal disease” or “bovine” and “bovine viral diarrhea virus.” Wanfang and CNKI search strategies are: The theme words “bovine” and “bovine viral diarrhea mucosal disease” or: “bovine” and “bovine viral diarrhea virus” or “bovine” and “BVDV” were selected.In order to collect comprehensive data as much as possible, Google Academic will further search the related articles of the collected articles.Eligible articles are screened according to the inclusion exclusion criteria below.Inclusion criteria:(1) Study on the prevalence of BVDV infection;(2) Literature between 2010 and 2021.5.20;(3) The species is cattle and the source is clear;(4) The type of article is experimental research article;(5) Literature published in Chinese or English.Exclusion criteria:(1) Repetitive articles;(2) Articles that cannot be downloaded;(3) Study animals were vaccinated or model animals;(4) Research data is not clear;(5) Sample size <30.® Excel ® 2019 MSO (16.0.14228.20216) 32 is used to sort and compile the data mentioned above.Import the search database results into the EndNote (EndNote X 9.3.3) reference manager software  for screening, delete duplicate articles, and then two reviewers further screen according to the article title and abstract. Obtain key data information from all relevant studies, including the first author, sampling year, country, mainland, sample type, detection method, variety, season, health status, age, gender and breeding mode. Microsoft The level of proposal evaluation, formulation and evaluation methods determines the quality of selected literature. The scoring standard includes the following four aspects, whether it is random sampling, sampling time, whether the sampling method is detailed, whether the detection method is detailed, and whether there are more than four factors. “Yes” is 1 point, and the maximum is 5 points. Based on the above standards, the article is divided into three grades 0–1, 2–3, 4–5, respectively.Under the guidance of PRISMA 2020, the article strictly follows its requirements and completes the systematic evaluation and meta-analysis .p < 0.05. Q- test (X2 and p representation) was used to evaluate the heterogeneity among the studies, and the forest map was used for visual analysis. The degree of heterogeneity was further evaluated by I2  were used (p = 6.975e−08 (In antigen detection, a total of 46,211 cattle were tested, and 3,488 BVDV-positive cattle were tested, with a positive infection rate of 15.74% (95% CI: 11.35–20.68, ere used . The egg.975e−08 . The fun.975e−08 . The tri.975e−08 .2 = 0.0664, I2 = 100%, P = 0.00, p = 0.4935 (In antibody detection, a total of 55,349 samples were tested, of which 24,585 were positive, and the positive rate was 42.77% (95% CI: 37.01–48.63, = 0.4935 . The fun= 0.4935 . The dat= 0.4935 .BVDV is one of the most important bovine infectious diseases with global animal health and economic impacts. BVDV infection will not only cause huge economic losses to the breeding industry, but also in animal research and medical industry related serum, vaccines and other biological not infected with BVDV but contaminated with BVDV, which has a huge economic impact . BVDV caThe article searched all articles on the epidemiology of bovine BVDV in 2010–2021. The meta-analysis included 128 articles. Through the analysis, it is expected to investigate the latest data on the global prevalence of BVDV and provide data support for the prevention and control of BVDV. The detection of BVDV is usually divided into the detection of antigens and the detection of antibodies. A positive antigen represents the current prevalence of animals carrying BVDV pathogens, making it clear that the virus is spreading and harming the population. Positive antibody indicates infection, vaccine immunization or transient infection. As individuals immunized with vaccine are excluded, positive antibody in the article can be considered as being infected by virus. Both have important guiding significance for the description of the BVDV epidemic.In the regional subgroup, there were fewer test samples in Europe, possibly due to large-scale vaccinations in Europe and not included in the study; On the other hand, it may be due to the fact that many European countries have eradicated BVDV or the prevalence rate has dropped to 1.5% . ExampleIn the regional subgroups, the low prevalence rate in North America and the high prevalence rate in South America and South America may result in a small number of articles and be unrepresentative due to the limitations of search. There is no data on antigen testing in Africa, while the prevalence of antibody testing remains high. This also reminds us that although many African countries have carried out surveillance and culling of BVDV, it will take a long time to eliminate BVDV. Asia has the largest literature and the infection rate remains high, with no significant differences in regional subgroups. The high infection rate in Asia may be due to the lack of a sound control plan and a surge in herd numbers due to the rapid development of the cattle industry. From the measures in different regions, we can find that the control plan in Poland suggests that it is very important to control the possible risk of virus transmission if the eradication plan is to be successful. From the German plan, it can be found that voluntary policies are not enough to achieve freedom from disease, and the initial implementation of voluntary policies eventually leads to mandatory plans . The conDue to different control measures in different regions, a subgroup analysis of time for global BVDV antigen testing and antibody testing found that the prevalence after 2017 did not decrease significantly compared with the prevalence before 2017. Previous articles have analyzed that in the global region, the prevalence of PI showed a downward trend from 1982 to 2016, while the level of antibody prevalence was relatively stable . Our datIn the breed subgroup, the antibody test results were the highest among dairy cattle, with significant difference. On the one hand, it may be due to the long service period of dairy cows, which have more opportunities to contact with pathogens. Studies have also confirmed that the positive rates of tuberculosis, brucellosis and bovine leukemia virus in dairy cows are higher than those in beef cattle , 166. OnAge has long been considered the most common influencing factor associated with infection rates . The datThe survey data of the article shows that the prevalence rate is relatively high in winter and spring, no matter for antigen detection or antibody detection. This may be due to the breeding season in spring and winter. It has been reported that in winter and spring, both male and female animals have strong reproductive performance, which is more conducive to cattle breeding , 176. ThMany articles point out that gender does not have a large influence on infection rates, and bulls are just as susceptible as cows . The resIn the introduction of BVDV 2018, the diagnosis of BVDV includes nucleic acid detection of QPCR, antigen antibody detection of ELISA, IHC, VN and virus isolation. PCR method can almost meet all purposes of detection, including making group animals free from infection, individual animals free from infection before moving, promoting the implementation of eradication policy, confirmation of clinical cases and detection of infection rate. PCR detection method has the advantages of convenience, rapidity and large sample size. The ELISA method is not applicable to animals with acute infection. IHC is mainly for diagnostic investigation. VN and virus isolation are usually used in laboratory research. It can be seen in the antigen detection data that the positive rate of PCR test is higher than the positive rate of ELISA test. There have also been reports of low sensitivity and accuracy of ELISA testing compared to PCR . Young aThe results of the survey show that most of the antibody test samples are derived from serum, and the test is relatively mature. The feces positivity rate was highest among the antigen-tested samples, followed by blood samples and ear tissue sample. The ear tissue sample is significantly different from other samples, and its low prevalence rate may be due to the fact that the sample was collected from the area where BVDV purification was carried out. Feces samples and blood samples can reflect the prevalence of infection. Feces sample collection is more convenient, the harm to cattle is small, but there is a possibility of cross-infect; Stress may occur on cattle during blood sample collection; Ear tissue samples are often used for the removal of PI cattle under the purification policy of various regions. For the detection of BVDV in cattle, the most appropriate samples shall be taken according to local conditions to make the detection results comprehensive and correct .Breeding mode has always been a key factor affecting BVDV infection. An article survey shows that the low prevalence of grazing and breeding is due to the low density of grazing and breeding . HoweverWhen BVDV infection occurs, the clinical symptoms of acute infected animals usually include temperature rise to 40°C, diarrhea, oral erosion, etc. There are few or no clinical symptoms observed in other infections . MucosalThrough our meta-analysis, we found that the prevalence of BVDV in the world is still very high. In the areas where the eradication plan is implemented, attention should still be paid to controlling the possible transmission risk of the virus. In terms of time span, the control and elimination of BVDV requires the joint efforts of all countries and regions to develop reasonable and effective prevention and control programs to eliminate PI animals. At the same time, the elimination of BVDV requires a certain degree of patience, timely grasp the epidemic situation, and improve the prevention and control policy. Different control measures should be taken for different breeds of cattle, and strict management policies are required to reduce BVDV infection. After calves are born, they should be tested for antigen in time to reduce the birth of PI cattle. BVDV detection and elimination should be done well in winter and spring breeding seasons. For cows, it is necessary to timely detect whether there is antigen infection before pregnancy to prevent the production of PI cattle. The ear tissue samples selected for antigen monitoring are more accurate, VN detection method has a higher accuracy, while PCR detection method has a wide detection range and a large sample detection volume. Generally, ELISA is used to detect serum samples. In raising cattle, attention should be paid to the cleanliness and hygiene of the breeding environment.To sum up, based on the epidemiological situation of BVDV in different areas, the eradication and prevention policies should be formulated and revised in time. Meanwhile, it is necessary to strengthen the awareness of herders to diseases and increase the awareness of veterinary and other related professionals to prevent and control BVDV. Our meta-analysis still has some limitations. The main reasons are as follows: 1. Due to the choice of language and database, it was not included in all studies. 2. The data cannot be downloaded or excluded from the inclusion and exclusion criteria. 3. Many countries do not have perfect testing procedures and do not test all cattle.The original contributions presented in the study are included in the article/RD and KS: idea and concept. NS: writing and editing of the manuscript. RD: funding. H-YL, L-ML, QW, and KS: revision of the manuscript. Q-XM, T-TW, WZ, and T-LY: collection and extraction of data. TT and J-YY: database establishment. TL, N-CD, QW, and J-ML: data analysis. All the authors contributed to the editing of the manuscript and approved the final manuscript.


The purpose of this study was to identify the types and contents of non-pharmacological delirium prevention interventions applied to inpatients in general wards, and to verified the effectiveness of the interventions on the incidence of delirium. We performed an extensive search of bibliographic databases and registries  using terms to identify delirium, prevention, and non-pharmacological. We searched all databases from their inception to January 2021 and imposed restriction on language of publication in English and Korean. We included studies if they were conducted as all types of randomized controlled trials (RCT), involving adult patients aged 19 years or more who were admitted to a general ward. We included trials comparing non-pharmacological intervention versus usual care. The entire process of data selection and extraction, assessment of risk of bias with ROB2.O was independently performed by three researchers. The estimated effect size was an odds ratio (OR) and 95% confidence interval. The fixed effects model and general inverse variance estimation method were adopted. The type of non-pharmacological delirium prevention interventions for inpatients in general ward was mainly multi-component intervention to correct delirium risk factors. The content and intensity of non-pharmacological interventions varied greatly depending on the characteristics of the patient and the clinical situation. As a result of the meta-analysis, non-pharmacological multi-component intervention was effective in reducing the incidence of delirium, and it was confirmed that it was effective in reducing the incidence of delirium in both the internal and surgical wards. It was confirmed by quantitative evidence that non-pharmacological interventions, especially multi-component interventions, were effective in preventing delirium in general ward inpatients. Delirium is an acute clinical syndrome showing decreases of consciousness and concentration with cognitive and perceptual disturbances . It is tDelirium is caused by cognitive impairment, sleep deprivation, immobility, vision impairment, hearing impairment, dehydration, and other risk factors . It has Pharmacologic intervention has been widely applied to prevent delirium. However, previous systematic reviews have shown that a pharmacologic intervention is not effective in reducing the incidence of delirium , 9. On tNon-pharmacological interventions can be divided into single component interventions and multicomponent interventions aimed at resolving modifiable risk factors among risk factors for delirium. Music therapy and staff education are single interventions for general ward inpatients . AlthougThere are various types of non-pharmacological interventions for delirium prevention. Most of previous systematic reviews and meta-analysis studies on non-pharmacological interventions for inpatient delirium prevention have focused on multicomponent intervention , 18, 19.The purpose of this study was to identify the types and contents of non-pharmacological delirium prevention interventions applied to inpatients in general wards, and to verified the effectiveness of non-pharmacological delirium prevention interventions on the incidence of delirium. That would provide a scientific evidence for non-pharmacological delirium prevention interventions for general ward inpatients.In this study, core questions  were selected according to the Cochrane Handbook for Systematic Reviews of Interventions . SpecifiAmong key questions in the data selection criteria, population, intervention, and comparison were included. To systematically review studies on the effectiveness of delirium prevention intervention, outcome variables for delirium prevention intervention were not included. Non-experimental studies such as studies on critically ill patients, studies on cancer patients admitted to hospital wards, studies on patients admitted to nursing hospitals, studies that applied drug intervention to prevent delirium, observational studies, and review studies were also excluded from data selection criteria. This study did not apply for IRB approval because only literature was used as a research subjects.One researcher with extensive experience in systematic literature review and meta-analysis research conducted a systematic data search on January 11 to 15, 2021. The database was selected based on the COSI  model presented by the National Library of Medicine in the United States. As a search source, core databases such as Ovid-MEDLINE, EMBASE, and CENTRAL  were used. The standard database CINAHL was also used. An ideal database was added by manual search using the snowball method. PubMed and Google Scholar were also used. Search engines in Korea, including Research Information Sharing Service (RISS), Database Periodical Information Academic (DBpia), Korean Studies Information Service System (KISS), National Assembly Library , the National Digital Science Library (NDSL), and the Korea Citation Index (KCI) were used. Based on ‘Medical Subject Headings, MeSH’ and ‘EMTREE’, search terms were selected and search strategies were established. Search functions such as Boolean operators and truncated search were used. ‘Delirium’ [MeSH Terms], ‘’ [Textword], Interventionk ‘Primary Prevention’ [MeSH Terms], ‘(prevent* OR reduc* OR stop* OR taper* OR cut* down" OR avoid*)’[Textword] were used to select participants. The study design used the HSSS search filter suggested by Cochrane. To supplement the search, a handwritten search based on references was added, and there was no restriction on the year of publication.Searched data were managed using the bibliographic management program (EndNote v20.0). First, duplicate literature was removed. The primary selection was based on the title and abstract of the paper. According to data selection and exclusion criteria, the original text of the paper was reviewed. The literature to be included in the systematic review was finally selected through secondary selection.Data were extracted using the data collection format designed by this research team. Contents included in literature characteristics included author, year, country name, number of participants and dropout rate, age, gender, type and contents of intervention, dose, delivery method, and so on. Outcome variable included the incidence of delirium.The first screening of data selection was performed on January 18–22, 2021. The second screening was performed on January 25-February 12, 2021. Quality assessment was performed on February 15–19, 2021. Collection of data was performed on March 1–30, 2021. The entire process of data selection, quality evaluation, and data collection was independently performed by three researchers. In case of disagreement, results were reviewed and discussed based on the selection and exclusion criteria to derive a consistent result.For assessment of risk of bias, the Cochrane Coalition’s RCT quality evaluation tool   was used2 is 50% or less [To calculate the effect size on delirium incidence rate, the Cochrane Coalition Review Manager version 5.4 was used for analysis. Since the delirium incidence rate was dichotomous, meta-analysis was performed by calculating the number of delirium occurrence frequencies in the experimental group and the control group. The effect estimate was described with an odds ratio (OR) and 95% confidence interval. As for the statistical model, fixed effects model is recommended when I or less . In this2 statistics after confirming whether the direction of the effect size and the confidence interval overlapped with each other through a forest plot, a visual method. I2 was interpreted as small size heterogeneity if I2 ≤ 25%, moderate heterogeneity if 25% < I2 ≤ 75%, and large heterogeneity if I2 > 75% or more [The heterogeneity between the studies was evaluated using the Chi-square test and Higgin’s I or more .Publication bias of the literature was visually confirmed through funnel plots. If funnel plots were asymmetrical, data could be biased. Thus, Egger’s test was performed. For the degree of influence of publication bias, the effect size was estimated by filling in the estimated effect size of studies. The trim and fill method was used to compare and analyze values before and after correction to confirm the degree of influence of publication bias .During quantitative synthesis, results were not presented in Part 1 A11 in  of the mA total of 1,738 studies were collected through a systematic literature search process. After removing 159 duplicate studies, 1,384 were additionally removed based on the title and abstract. After reviewing original texts of 195 selected studies, a total of 178 were excluded: 44 that did not meet the subject criteria, 36 that did not meet the non-pharmacological preventive intervention, five that did not meet the outcome variable criteria, 90 that did not design RCTs, two that lacked information on outcome variables and interventions in the abstract, and one that was not English or Korean. Finally, 17 papers were included in the systematic literature review and meta-analysis .Looking at the risk of bias assessment of the literature to be analyzed, the risk of bias occurring during the randomization process was low in 8 cases, the risk of bias due to deviation from the intended intervention was low in 14 cases, the risk of bias due to missing intervention outcome data was low in 15 trials, the risk of bias in the intervention outcome measurement was low in 15 trials, and the reported risk of selection bias was low in 11 trials. As a result of the final determination according to ROB 2.0 evaluation algorithm, four studies with low risk of bias, seven studies with some concern, and six studies with a high risk of bias were found .All 17 papers to be analyzed were published between 2001 and 2020, of which 16 were published in academic journals and one was an abstract. Regarding the place where the study was conducted, eight were surgical wards, six were internal medicine wards, and five were geriatric wards. There were total of 4428 subjects , of which 1011 in the experimental group and 902 in the control group were males. The dropout rate was 4.21% in the experimental group and 2.12% in the control group. The age distribution of subjects was 70–87.6 years in the experimental group and 69–87.0 years in the control group. Regarding the type of non-pharmacological delirium prevention intervention, there were three single-component interventions: exercise programs, music listening, and cognitive training. Fourteen multicomponent interventions were identified, modified HELP was applied in three studies and cognitive training was implemented in two studies. Ten multicomponent interventions were effective in reducing the incidence of delirium, and three of the ten multicomponent interventions used mHELP. In seven studies that reported the number of delirium prevention interventions, it varied from 1 to 5 times/day, while 11 studies did not report it. The total duration of delirium prevention intervention was from discharge to discharge in six studies, 5–7 days in one study, and no report in 10 studies. In ten studies, nurses participated as mediators. Geriatrician, geriatric psychiatrist, patient family, patient, exercise specialist, trained volunteer, physical therapist, occupational therapist, nutritionist, interdisciplinary team, psychology student, and certified allied health assistant participated as an arbitrator. Comparative intervention was routine care in 14 studies. As a tool for diagnosing delirium, CAM was used in 14 studies. OBS, numerical score and patient chart also were used in one study, respectively .In this study, based on non-pharmacological delirium prevention interventions recommended by the NICE guideline  and the p < 0.001). However, the heterogeneity between studies was intermediate, with I2 of 43.0%    .p < 0.001). The heterogeneity between studies was moderate, with I2 of 37.0% . As a result of a meta-analysis of the remaining five studies targeting patients with surgical diseases out of a total of 14 studies, there was a statistically significant difference with an OR of 0.63 (95% CI: 0.49~0.82) . The heterogeneity between studies was intermediate with I2 of 47.0%    .p < 0.001), was confirmed to have a publication bias. Therefore, as a result of confirming the effect of publication bias on research results using the trim-and-fill method to correct for publication bias, six missing values due to publication bias were estimated. When six papers were used as input and publication bias was corrected, the OR was 0.64, confirming that publication bias affected meta-analysis results because the value changed by more than 10% from an OR value of 0.54 before correcting for publication bias.Asymmetry was confirmed as a result of confirming the degree of visual symmetry using a funnel plot for the publication bias test. As a result of Egger’s regression test to determine whether the degree of asymmetry was statistically significant, the outcome variable, the delirium incidence rate Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


Participants had a mean body mass index of 18.3 ± 2.7 kg/m2 and its variation in the intervention group (−2.7 ± 0.5 kg/m2) was significantly different from that in the control group (3.41 ± 0.8 kg/m2). In the experimental group, there were significant differences between the proportion of children who were overweight, underweight, normal weight, or obese before and after intervention (p < 0.05). The best results were seen in the female sex, and after the intervention, there were no more girls with obesity. Furthermore, there were significant waist circumference decrement effects in the intervention group compared to the control group (p < 0.05). Finally, many of the participating children acquired healthy eating habits. Therefore, the quantitative results obtained suggest that a school intervention program represents an effective strategy to prevent and improve the problem of childhood overweight and obesity.Childhood obesity represents a serious public health problem and this study evaluates the effectiveness of a 6-month educational intervention on lifestyle, nutrient adequacy, and diet quality in the school setting in improving the knowledge and behavior of primary school children regarding correct eating habits. The strategy was implemented over a 6-month period and participants were randomly assigned to either the intervention group ( Childhood overweight and obesity represent a major global health problem , and theTherefore, the monitoring of weight status in children is important for health disease prevention . The devSeveral lifestyle interventions were able to lower anthropometric and cardiometabolic risk factors , 23–26 aObviously, as children spend a lot of their time in school, it is important to implement school programs aimed at defining and maintaining a healthy lifestyle –35, thusTo understand the effects of promoting nutrition adequacy, a sample of 398 children was selected from different schools. These schools are located in two cities with similar socioeconomic status and have not previously participated in health promotion programs. Schools were secondary-level public schools, with attendance from 8:00 to 13:00.n = 200 and n = 198, respectively). All children were healthy and free of any disability or musculoskeletal, cardiological, neurological, or respiratory diseases or dysfunctions. Before the inclusion of the children in our program, a parent or legal representative of each child signed informed consent. The study was conducted in accordance with the Helsinki Declaration and the European Union recommendations for Good Clinical Practice . The University's Research Ethics Committee approved the study.The sample was composed of 194 boys and 204 girls, who were allocated to a control group and a group participating in an intervention  were discussed with the active involvement of everyone.The educational intervention covered 12 lessons for the subject's biology and alimentation implemented by classroom teachers. The first part (six lessons) aimed at increasing awareness and information regarding energy balance-related behaviors, with supporting materials, such as a pocket-sized diary, to monitor own behavior. The second part (six lessons) aimed at facilitation of choice to improve one of the behaviors, setting personal goals, identifying barriers, improving self-efficacy, and evaluating the change process. In this way, the children and families understand how to organize their weekly meal planning without detailed prescriptions. The control schools followed their regular curriculum.All measures were recorded at baseline (T0) and 6 months from baseline (T1) at the completion of the intervention.Body weight and height were ascertained, in duplicate, with standard techniques. Waist circumference was measured, in duplicate, at the iliac crest, and at mid-respiration using a non-elastic measuring tape. The ratio between waist circumference and height (WHR) was calculated. The cut-off used to represent CV risk for WHR was 0.500 .For children and adolescents, the Center for Disease Control and Prevention defines overweight as a body mass index (BMI: weight in kilograms divided by height in meters squared) between the 85th and 95th percentiles and obesity as a BMI at or above the 95th percentile for sex and age .The parents completed structured no validated questionnaires, which included the eating habits of their children (food frequency questionnaire) and their familiarity with certain food items. This questionnaire was administered twice to the parents of the children, the first time at the beginning of the study and the second time at the end of the intervention; in both cases, 48 h were given for completion. The data collected with the questionnaire were analyzed by extrapolating the frequency of each item considered in the proposed survey. Descriptive data was represented by the percentage.t-test has been performed. For the association of different items of the questionnaire, a chi-square test was performed. The significance level was set to p < 0.05.Means and standard deviations were described for the total sample and for both the participants and control groups. All data have been tested for normality using the Kolmogorov–Smirnov test. To identify differences between groups, the The sample consisted of 397 children who were randomly divided into two groups, the intervention group, and the control group. p > 0.05; Coherent with the random assignment of children to the intervention and control groups, no significant differences were observed between these groups in terms of average weight, height, BMI, and proportion of overweight or obese children .As regards the detection of percentiles in the different age groups, we have highlighted that overweight and obesity in children increased with age . In factThe most striking results were obtained in females, since girls with obesity were no longer detectable after intervention .p < 0.001)  . In cont< 0.001) .p < 0.001)  in the participating group, while it remained unchanged in the control group. Furthermore, the number of children who had a ratio >0.50 in T0 also decreased significantly .WHR also includes height and is, in normal-weight children as well, an important indicator of excess upper body fat . Hence, n = 398) ate breakfast regularly. A more equal distribution of the mid-morning meal was observed, with 32.6% “yes,” 35.6% “sometimes,” and 31.8% “no” . The children in the control group maintained the same percentages .In T0, while as mentioned earlier there was an equal distribution of the mid-morning snack, the afternoon snack was consumed by 80.8% of children . In T1, 2 = 10.805, p < 0.0001, χ2 = 16.133, p < 0.0001, respectively).Among fruit, chips, and cold cuts, the first choice for a midmorning (48%) or afternoon snack (52%) was sweets . In T1, 2 = 9.311, p = 0.0002). Although experts advise against them, 12.5% of children drink sugar-added and non-nutritious drinks every day (p = 0.0001).An important aspect of the food education program was to consume healthy and complete meals consisting of a first course, second course, vegetables, and fruits. From the data analysis, it emerged that 80% of the population consumes vegetables almost every day; and even 20% never eat vegetables . After overy day . In T1 tAs the number of overweight and obese children has increased in recent decades, the need to support healthy nutrition and physical activity in children has never been more urgent.Thus, the present study aims to investigate the effectiveness of an education program, regarding correct eating habits on children's weight status.In Europe, higher levels of children overweight and obesity have been reported in southern countries , particuIn our study, we find that the number of overweight students was about 20% , a percentage which is in line with what was previously reported for our region . More prSince this study was conducted from 2017 to 2018, these higher rates of obesity are not caused by the COVID-19 pandemic-dependent lockdown but were due to pre-existing socio-economic conditions and other health indicators.In childhood obesity, the inadequate intake of fruit and vegetables and the consumption of too many high-calorie snacks play a very important role, with 41% of total calories coming from chips, chocolate bars, soft drinks, fruit drinks, sugars, syrups, preserves, fats, and oils .As known, family, friends, school, marketing, and the media influence children's food choices. To prevent childhood obesity, it is useful to combine family and school programs and nutrition education with adequate physical activity . Habits An important source of primary nutrients in the diet of both adults and young people is breakfast –53, whicWe suggested that the children eat five meals a day, introducing the consumption of mid-morning and afternoon snacks into their diet. Some healthy eating habits were acquired in most cases: eating fruit as a midmorning and afternoon snacks and avoiding the consumption of cold cuts and sweet foods. It is worth noting that 63% and 73% of children chose fruits for morning and afternoon snacks respectively, and 78.5 ate fruits more than three times a day. These results showed better percentages than the international data of “NCD Risk Factor Collaboration (NCD-RisC) ,” in whiTherefore, our intervention gave an encouraging result within a scenario in which foods rich in fibers have been replaced by products rich in fat and sugars, with a high level of processing . Most ofThe present study has some limitations, primarily the different ages of the participants. However, the weight loss was substantial as well some lifestyle indicators have undergone significant improvements. Another limitation of the present study is the lack of data on the socio-economic, cultural, and educational levels of parents. Larger studies with long-term follow-up are therefore needed to corroborate our findings.We evaluated the effect of a 6-month-long intervention on lifestyle and diet quality in schools in Southern Italy. The children and adolescents participating in the study achieved a reduction in BMI, waist circumference, and better adherence to nutritional recommendations assessed by the diet quality indices. The school intervention program represents an effective strategy for preventing the problem of childhood overweight and obesity.antonella.muscella@unisalento.it.The datasets presented in this article are not readily available because of participant privacy. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by IRB of Department of Biological and Environmental Science and Technologies (DiSTeBA), University of Salento, Via Prov. le Lecce-Monteroni, 73100 Lecce, Italy. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.AM, VC, and SM: conceptualization. ADM, VC, AM, and SM: data curation. VC, GM, and ADM: investigation. AM and SM: project administration and original draft. AM: supervision and writing. SM: writing—review and editing. 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.


M = 16.76, SD = 1.58), from junior and senior secondary schools was used in this study. Confirmatory factor analysis and exploratory structural equation modeling were conducted, and conventional fit indices were used to assess model fit. Results on the original LSSS model indicated the need for model re-specification in the current sample. A re-specified LSSS, consisting of the original eight factors, but only 34 of the original 43 items, demonstrated improved fit and adequate internal consistency. Scores derived from the re-specified LSSS proved to be a valid estimate of life skills development in the current sample of youth sport participants. This has important implications for the utility of the LSSS in different cultures.With the growing interest in sport-based positive youth development (PYD) programs across the African continent, there is a need to establish suitable measures to evaluate the success of these programs in fostering PYD. The Life Skills Scale for Sport (LSSS) was recently developed as a sport-specific measure of life skills development. Despite its good psychometric properties among British youth sport participants, cross-cultural evidence indicates differences in the conceptualization of the eight factors measured by the LSSS. To determine the suitability of the LSSS for use in the African youth sport context, this study examined the latent structure and reproducibility of scores produced by the scale in a sample of youth sport participants from Botswana and Ghana. Cross-sectional data from a sample of 495 youth athletes , aged 12–21 years ( Converging with positive psychology, the positive youth development (PYD) framework represents a strength-based, rather than a problem-focused approach to youth development . The advIn light of recommendations to create more sport-specific measures of life skill development , the LifSince the initial validation of the LSSS , furtherInterest in the role of sport in promoting PYD, as well as the use of sport-based youth development interventions, has grown tremendously over the past decade across the African continent . The two countries were deemed ideal for this research because they are located in separate regions of Africa that are considered to be culturally diverse. Botswana is located in southern Africa while Ghana is located in West Africa. Ascertaining whether the LSSS has good psychometric properties in these two regions of Africa is an important step in determining the tool’s appropriateness for use when conducting sport-based PYD research across contexts. Current evidence on the psychometric properties of the LSSS shows that this tool has promise when used in other cultures. Testing existing tools for their appropriateness and relevance to different contexts is key to monitoring and evaluation of youth sport programs and their effectiveness in fostering PYD.M = 16.76, SD = 1.58), from public, middle, and high (junior and senior secondary) schools in Botswana and Ghana taking part in a sport-based PYD research project. A total of 495 participants were retained for examining the latent structure and reproducibility of the LSSS. A total of 223 of the original 718 participants were excluded from this study  because of incomplete demographic information and unverifiable data about their athlete status. Approximately 52% of the participants in the retained data were male and all participants were of African descent. Botswana had 342 participants , aged 12–20 years , from seven public schools in Gaborone, the capital. Ghana had 153 participants  aged 12–21 years  from four public schools in Accra, the capital. Participants played a wide range of sports, with the top three sports being soccer (∼30%), volleyball (∼17%), and track field (∼11%). Other sports were handball, basketball, cricket, karate, boxing, softball, badminton, tennis, field hockey, and netball.This cross-sectional study targeted a purposive sample of 718 youth athletes, aged 12–21 years  of partner universities and the government ministries (departments) responsible for youth, sport, and education in each country. Additional approvals were obtained from the schools. Consent and assent were received from all parents/guardians and participants prior to enrollment in the study. Parental consent and student assent forms were distributed to participants who identified as school athletes at their schools. Participants took these forms home along with a letter of introduction that stated the objectives of the study. The signed consent and assent forms were returned to the project team prior to survey administration. All selected participants agreed to participate and also received parental consent to complete the surveys. Surveys were completed in school halls or classrooms with the help of trained undergraduate research assistants. Participants completed questionnaires themselves under the guidance of research assistants and returned questionnaires upon completion. Test administration lasted approximately 30 min.A demographic information questionnaire was used to collect data on age, gender, and type of sport played.Life skills development through sport was assessed using the Life Skills Scale for Sport LSSS; . The 43 Confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) were conducted to test the hypothesized factor structure of the LSSS in Botswana and Ghana. While CFA provides a useful tool to test theoretically established constructs, there are concerns regarding its restrictiveness. CFA permits items to load only on their intended factor, with potential cross-loadings with other factors being restricted to zero . This reThe robust maximum-likelihood (MLR) estimator was applied to all analyses to deal with possible non-normality in the measures. The missing data were handled using the full-information maximum-likelihood (FIML) estimator .The models were evaluated using the following model fit statistics: chi-square goodness-of-fit tests, the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root-mean-square error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR). CFI and TLI are considered to be acceptable when values are greater than 0.90 ; RMSEA vThe internal consistency of the eight LSSS subscales was assessed with Cronbach’s alpha. A Cronbach’s alpha greater than 0.70 is considered to be good and an alpha between 0.60 and 0.70 is considered acceptable .2(832) = 1344.76 (p < 0.001); CFI = 0.865; TLI = 0.853; RMSEA = 0.045; and SRMR = 0.050. Although the RMSEA and the SRMR met the cut-off criteria for the goodness of fit, the CFI and TLI values were below the threshold of 0.90. The ESEM model presented a better fit: χ2(587) = 960.11 (p < 0.001), CFI = 0.924, TLI = 0.883, RMSEA = 0.040, and SRMR = 0.029. Although all the goodness-of-fit indices considered had improved, the TLI still remained slightly below the acceptable threshold of 0.90.The summary of the model fit indices for the eight-factor LSSS model with 43 items is presented in the upper section of The correlations among factors also remarkably decreased in the ESEM model see . Inter-fAlthough the initial eight-factor model’s overall fit statistics were generally considered adequate, We therefore re-specified the model based on the magnitude of factor loadings and theoretical considerations. The items that had standardized factor loadings on their target factor less than 0.30 and/or had cross-loadings less than 0.15 difference from the primary factor loading were selected . Nine it2(499) = 712.42 (p < 0.001); CFI = 0.917; TLI = 0.907; RMSEA = 0.040; and SRMR = 0.045]. Also, AIC and BIC indices indicated that the re-specified model was preferable compared to the original model. The factor loadings of the revised CFA model presented in 2(317) = 435.67 (p < 0.001); CFI = 0.962; TLI = 0.933; RMSEA = 0.034; and SRMR = 0.023]. The standardized factor loadings for the revised ESEM model show that the items have salient loadings on their target factors without noticeable cross-loadings, despite three items  having relatively low loadings below 0.4  to 0.83 (Time management), demonstrating acceptable internal consistency for all subscales see . After rThe LSSS was developed with the aim of creating a sport-specific measure of like skills development. While scores produced by the LSSS were found to be reliable and valid among samples of British youth sport participants , only foConsistent with previous findings , some reSimilar to previous cross-cultural examinations of the LSSS , the finMeasurement issues have been particularly relevant to PYD research, and more specifically within the context of sport . On one To provide an example, when the original 5-factor structure  of the 37-item YES-S was examined among a sample of youth sport athletes, only 22 resulting items were retained that supported the scale’s factor structure . This leConsistent with previous validations of the LSSS , adequatDespite these contributions, this study is not without limitations. The small sample size should be noted as one of the limitations of this study, particularly given that the original LSSS model we tested consists of 43 items. This small sample prevented us from testing possible invariance in the meaning and structure of the scale by group membership. For example, this study recruited youth sport participants from a wide age range (12–21 years), which may have resulted in a heterogenous sample with regard to levels of cognitive, social, emotional, and other domains of development. As such, differing levels of maturation may have influenced the perception of the development of the eight life skills measured by the LSSS. While insufficient power due to a small sample limited the ability to assess measurement invariance in the current study, future research may account for these differences by examining measurement invariance of the LSSS across varying age groups.Undoubtedly, the validation of psychological measures is an ongoing process . While tWithin the broader context, this study adds evidence for the suitability of the LSSS in evaluating the eight life skills reported to develop through sport . SpecifiImportantly, this study provides validity evidence for the scores produced by the LSSS within two African countries that are culturally different and geographically distant from one another. This instrument can therefore aid researchers and sport practitioners in the monitoring and evaluation of youth sport programs and their effectiveness in fostering PYD.https://doi.org/10.6084/m9.figshare.17049860.The data presented in this study can be found in online repositories. The link to the repository and accession number is The studies involving human participants were reviewed and approved by the Institutional Review Boards of the University of Botswana and the University of Ghana. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.LM led the conception, design, writing, and revisions to the manuscript. CR contributed to the writing and revisions of the manuscript. SK conducted the statistical analyses and wrote the results section. TT, TM, CA, and RO contributed to the conception, design, data collection, curation, and revisions. 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.