Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69–0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment. It is characterized by unexplained and persistent post-exertional fatigue that is not relieved by rest. It is exacerbated by physical and mental exertion and other core symptoms such as cognitive, immunometabolic, autonomic, and neuroendocrine dysfunction2. It produces severe disability in patients, significantly interfering with their work activity and their daily life tasks3. In addition to fatigue, these patients have characteristic inflammatory and muscular symptoms, sleep dysfunction, and altered cognitive functions4. The symptomatic muscle blocks symptoms such as pain, generalized muscle weakness, fatigue after physical exertion, neurological symptoms , neurocognitive symptoms . The autonomic block , immunoinflammatory symptoms  and deficiency symptoms in the production of cellular metabolic energy. Sleep disturbances have been relevant since their description as their clinical entity. In all versions of the different ME/CFS diagnostic criteria, sleep disorders have played a key role, especially the presence of unrefreshing sleep and the importance of the Pittsburgh Sleep Quality Index (PSQI) questionnaire in the assessment of the severity of alterations in sleep quality and its association with fatigue, pain, psychopathology, and neurovegetative dysfunction5. ME/CFS, together with the symptomatic complexity that it presents, as a consequence of its multisystemic nature, is associated with different comorbid phenomena such as fibromyalgia, sicca syndrome, myofascial syndrome, psychopathology, ligament hyperlaxity, fasciitis plantar, degenerative vertebral disease or mechanical, shoulder tendinopathy, multiple chemical sensitivity, epicondylitis, carpal tunnel syndrome, osteoporosis, hypercholesterolemia, hypertriglyceridemia, vascular risk, endometriosis, thyroiditis, with a higher prevalence than that observed in patients not affected by ME/CFS6.Myalgic encephalomyelitis, commonly called chronic fatigue syndrome (ME/CFS), is a serious, complex, and chronic multisystem illness of unknown etiology, often triggered by a persistent viral infection . ME/CFS affects as many as 17 to 24 million people worldwide, and its prevalence is expected to double by 20307. Therefore, each subject to be diagnosed with ME/CFS must undergo a Fukuda criteria evaluation and procedure that each unit has established using batteries of validated self-administered questionnaires. As stated before, it is important to evaluate the disabling fatigue perception, sleep problems, and health-related quality of life using self-administered questionnaires such as the fatigue impact scale FIS408 and FIS89, PSQI10, and Short Form Health Survey (SF-36)11, Symptom Checklist-90-revised (SCL 90 R) psychological inventory12, hospital anxiety and depression scale (HAD)13. Ongoing placebo-controlled clinical trials to evaluate the clinical benefits of drugs on ME/CFS symptoms14 have changed some questionnaire scores from baseline to final study as a primary endpoint.In the study of ME/CFS, after the diagnosis and assessment of comorbid phenomena, it is essential to quantify and assess fatigue, quality of life, or anxiety/depression psychopathology using a battery of clinically self-administered questionnaires. Today there are few units specialized in ME/CFS in the world, with a relatively low number of duly documented cases and a lack of publicly available data compared with other disorders. Moreover, unfortunately, there are no commercially available diagnostic tests, no specific lab biomarkers, and no targeted FDA-approved drugs for ME/CFSThere is no consensus on the number and type of questionnaires that should be carried out, so not all units record the same number per subject. Consequently, it is complex for ME/CFS units to have many records of the questionnaires necessary to efficiently approach large longitudinal and multicenter studies of patients with this pathology using the latest advances in data analysis, such as Machine Learning techniques.15 to diagnose and treat diseases. Supervised learning involves training machine learning-based algorithms using labeled input datasets requiring however, to be efficient and get optimal results, a large number of records are needed. The learning occurs by comparing results with the expected outputs to identify errors and change the model's weights to infer knowledge. There are few publications, and all of them very recent, that refers to the application of different machine learning techniques in ME/CFS: seeking a new biomarker16, clustering17, or discovering the relationship between depression and ME/CFS18, using neural networks seeking omic biomarkers16 or neural networks classifiers19. While they all make important steps forward in understanding ME/CSF, the limited sample size makes generalization and translation of their findings to clinical practice or other datasets difficult. Also, as stated before, when there are no clear biomarkers to follow the evolution of the illness, like in ME/CSF, quality-of-life questionnaires are used to measure it14. There are several lines of investigation, such as clustering20 or finding relations between blood measurements with questionnaires data21.Machine Learning is a particular method of data analytics that automates model building as it relates to the development of models. Over the last years, it has been proven great performance of machine learning supervised algorithms in several clinical applications22 and clinical groups23. However, they are limited in size or content, might be incomplete, and their applications might be restricted. This problem can be overcome using synthetic datasets coming from simulations25. Synthetic datasets are generated to create data for improving the sample size of existing cohorts or filling in the missing values, preserving privacy while keeping the real data characteristics. Synthetic data generators preserve the statistical properties of the original. However, they do not reveal any information regarding real people and offer several benefits, such as overcoming real data usage restrictions of data sharing and patient consent. There is a need for developing synthetic datasets that would complement real-world data for various reasons26: ease of access, cost-efficiency, test-efficiency, patient privacy protection, completeness, and validation capabilities, handling missingness, complex interactions between variables, resulting sensitivity analysis statistics from latest classifiers and graphical modeling and resampling27. A common application of synthetic data generation in medicine is image generation simulating diseases. It helps to test and benchmark the performance and accuracy of different algorithms. Some recent applications are in the simulation of skin lesions28, brain atrophy in aging or Dementia29, generation of PET MRI scans for Alzheimer's disease30, tumor generation in the brain31, or breast cancer32.Therefore, there is an increasing demand to access large repositories of high-quality health datasets for better and more reliable predictions from supervised machine learning algorithms. Anonymized electronic health records are bought and sold by insuranceThis work aims to generate a robust and reliable synthetic data generator for ME/CFS questionnaires to produce high-fidelity and risk-free health care records, enhance existing public and private ME/CFS datasets for investigation and educational use, and are free of legal, privacy, security, and intellectual property restrictions.33 and Carruthers criteria34, and provided signed written informed consent and ethics committee approval. The data collected were anonymized in a database to which only those designated for the study had access, and in no case was any information known that could reveal or infer the participant’s identity.This prospective cross-sectional study includes 2,522 subjects diagnosed with ME/CFS from the Vall d’Hebron University Hospital, Barcelona, Spain, 90.5% females (mean age 48.11 ± 10.31 years) and 9.5% males (mean age 44.41 ± 11.35 years). Data for SF-36, HAD, FIS8, FIS40, SCL 90 R, and PSQI questionnaires has been obtained and recorded from 2008 to 2021. See Table 35. The graph structure allows us to capture the pattern of interactions between the nodes . Graph (or network) analysis is used to study relationships between individuals to discover knowledge about global and local structures. The study of structure networks helps to decide the optimal order36.Graph theory was used to analyze the relationships between the subscales of each questionnaire. A graph is a collection of nodes  joined together in pairs by edges (undirected) or arcs (directed)37 in case of direct correlation. An In this work, the graph nodes are defined as all subscales, and the edges are defined as moderate or strong correlations between nodes . The linear correlation between two subscales is represented by The relation of subscales between each test is related in Table The study of the relationships mentioned above should indicate the order to generate our machine learning models. The SF-36 is prevalent and will be used in our model as initial data. The rest of the order will be given by the relationships between the different tests so that those with a stronger relationship are consecutive in the model. The strength of the relationship is measured in terms of the percentage of connections between the test nodes.n is the number of validated responses and f the number of questions. That is the first training data. As predicted, it has to be a second questionnaire which the same n and f1 questions. The model must generate a predicted matrix with the same dimension. The next step has as input matrix the initial matrix concatenated with the last predicted matrix and the second questionnaire response matrix for prediction, as shown in Fig. Real data of all of six questionnaires are required to train and build the models. First, an input matrix represents the validated answers of a number of patients, where 39 shows that both models offer similar performance in structured data.Classification and regression models can be used. The goal is to provide 186 output dimensions that must be calculated step by step. The output is compared with the real data set to validate the model. The results are validated using the t-student test. The strategy is to validate one questionnaire. The next step is concatenating the questionnaire answers matrix as input with the final output with different models. It has tested machine learning and deep learning algorithms step by step. The validation system has measured whether real and synthetic data come from the same populations within t-student statistics. The models tested have been regressors and classifiers. The comparison between XGBoost and Deep Neural Networks (DNN)40. Accuracy is defined if The F1-score can be interpreted as a harmonic mean of precision and recall, where an F1-score reaches its best value at one and worst score at zero. The relative contribution of precision and recall to the F1-score are equal. The formula for the F1 score is:Mean error is defined as the ratio of overall value questionnaire predicted versus comprehensive value sample questionnaire. The series for t-student value is defined as the sum of all answers of each questionnaire variable, predicted, and sample data.The authors declare that the procedures followed were by the regulations of the responsible Clinical Research Ethics Committee and by those of the World Medical Association and the Helsinki Declaration. The research protocols were approved by the Ethics Committee of the Vall d'Hebron University Hospital, the first “Population-based Registry of Patients with Chronic Fatigue Syndrome” approved on 18/10/2006.In our proposed model, an entclass1pt{minimaIn supplementary material, the second network analyzes the subscales as a node and the same relationship as an edge. The SF-36 subscales (green) have strong relationships with HAD (magenta) and FIS8, and FIS40 . SCL 90 R (brown) has a strong relationship with HAD. Furthermore, PSQI (blue) has no relationship except the total psqi value. The strength of the relationship is measured in terms of the percentage of relationships between the test nodes. The initial test is SF-36, and its nodes have relationships with 100% of HAD’s nodes (3 of 3) and only 25% of SCL 90 R (4 of 12). HAD’s nodes have a 100% relationship with SCL 90 R’s nodes. SCL 90 R has a relationship with the unique FIS8 node, which has relationships with all four FIS40 nodes. The last test with few relations is PSQI. Consequently, the order decided according to aforementioned relationships is: HAD, SCL 90 R, FIS8, FIS40, and PSQI.41. The parameters tuned were max_depth, gamma, reg_alpha, reg_lambda, colsample_bytree, min_child_weight, subsample, n_estimators and eta. Hyperopt has been used for hyperparameter tuning41. Both must be trained for each question; therefore 14 models have to be trained. The order on a set predicted value is {0, 1, 2, 3}, and the trained value is {1, 2, 3}, where in both cases, greater values show worse health status. Regressor predicted rounded to compare between real data. The results of the model are analyzed with XGBoost and the regression and classification are compared. The mean regression error is much higher than the classification error (32.50% vs. 3.16%). Therefore, the regression model is discarded in the following analyses  limitations in physical activities because of health problems; (2) limitations in social activities because of physical or emotional problems; (3) limitations in usual role activities because of physical health problems; (4) bodily pain; (5) general mental health ; (6) limitations in usual role activities because of emotional problems; (7) vitality (energy and fatigue), and (8) general health perceptions and is one of the most used quality life questionnaires used and evaluatedThe questionnaires can be answered quickly and are regularly available in primary care and specialized medical consultations. Some applications offer automated analyzed results that inform essential information about patient health conditions.The graph theory has been used to decide the order of the modeling cascade. Although a deeper analysis of these relationships should be the subject of another, more specific work, in this case, it informs us of the order used in our model. These relationships will characterize our model, which will be more robust with more records analyzed. Our dataset is unusually great in SFC, which becomes robust to our models.Our synthetic dataset generator applications fill in missing data of real datasets from any other five questionnaires. For those, ME/SFC dataset clinical units with SF-36 questionnaire answers but missing others could build a complete dataset.(1) Single-center trial. (2) Unit of reference in diagnosing and treating CFS/ME, which may be biased towards more severe cases and a longer evolution time than studies in primary care. (3) No information is available on parameters such as the results of the two-day ergometric test for assessing exercise intolerance, a neuropsychological battery for assessing cognitive impairment, and neurovegetative dysfunction, e.g., heart rate variability. (4) That this is a prospective study with cross-sectional data collection. It is not a longitudinal study.Synthetic patients can be simulated with models of ME/CFS questionnaires data and corresponding standards of care to produce risk-free realistic synthetic healthcare records at scale. An open-source generator offers high-fidelity synthetic data for investigation and educational use, free of legal, privacy, security, and intellectual property restrictions.Supplementary Information.


Non-alcoholic fatty liver disease (NAFLD) is a condition characterized by the accumulation of fat in the liver without excessive alcohol consumption. Lifestyle modifications, such as adopting a healthy diet, represent the standard treatment for NAFLD. However, the impact of the Dietary Approaches to Stop Hypertension (DASH) diet on oxidative stress biomarkers in patients with NAFLD remains unclear. Therefore, this study aimed to determine the effect of the DASH diet on total antioxidant capacity (TAC), catalase (CAT), superoxide dismutase (SOD) levels, and body composition in overweight and obese patients with NAFLD.n = 35) or the control group  for 12 weeks, with both groups following a calorie-restricted diet.A total of 70 overweight and obese patients aged 1870 years were randomly assigned to either the intervention . After adjusting for potential confounding factors, such as age, sex, diabetes, smoking, physical activity, and baseline values, the DASH diet maintained its significant effects on TAC and SOD compared to the control diet (p = 0.03). However, there were no significant differences in CAT levels between the two groups. Moreover, a significant reduction in visceral fat (p = 0.01) and a marginally significant decrease in BMI (p = 0.06) were observed in the DASH group compared to the control group after adjusting for potential confounders.The mean age of participants was 43.1 ± 8.1 years in the DASH group and 45.1 ± 8.6 years in the control group. At the end of the study, a significant difference was observed in the mean TAC and SOD levels between the two groups (In conclusion, our study showed that following the DASH diet for 12 weeks in overweight and obese patients with NAFLD has beneficial effects on TAC, SOD, and visceral fat. These findings support the use of the DASH diet as a potential therapeutic intervention for the improvement of oxidative biomarkers in patients with NAFLD.www.irct.ir/, identifier IRCT20170117032026N3. Non-alcoholic fatty liver disease (NAFLD) is a condition characterized by the accumulation of lipids in the liver, without excessive alcohol consumption . NAFLD iThe prevalence of NAFLD is reported to be between 14% and 30% of the general population in different regions of the world . Given tRecent evidence suggests that food and nutrition play a noteworthy role in the pathogenesis and management of NAFLD , 8. HoweAs nutritional epidemiology continues to expand our understanding of the impact of food and nutrition on health, it is becoming increasingly evident that adherence to a healthy diet can help reduce the risk of NAFLD through antioxidant pathways . NAFLD i2. The diagnosis of non-alcoholic fatty liver disease was made by a gastroenterologist. Inclusion criteria comprised individuals who did not have any of the following conditions: pregnancy and breastfeeding, hereditary hemochromatosis, a history of jejunal bypass surgery or gastroplasty, the use of hepatotoxic drugs such as calcium channel blockers and synthetic estrogens, patients with hypothyroidism, Cushing's syndrome, kidney failure, kidney stones, cardiovascular disease, individuals with a history of hepatitis B and C, Wilson's disease, supplement use , and individuals who adhered to a special diet in the last 3 months. Finally, individuals who were unwilling to comply with the recommended dietary regimen were also excluded from the study.The present study enrolled individuals diagnosed with NAFLD using transient elastography (liver fibro-scan), aged between 18 and 70 years, and with a body mass index (BMI) ranging from 25 to 40 kg/mwww.irct.ir/, IRCT20170117032026N3).The present study adhered to the principles outlined in the Declaration of Helsinki  and obtaOur research was a randomized controlled trial involving 70 patients with NAFLD, recruited from gastroenterology clinics affiliated with Shahid Sadoughi University of Medical Sciences, Yazd, Iran. The selection of patients was based on predefined inclusion criteria, and they were assigned to either the intervention (DASH diet) or control (Control diet) group using a computer-generated random allocation system. The intervention group received the DASH diet, while the control group received the standard weight loss and healthy diet. Randomization was based on a random table sequence, according to a computer-generated allocation system.In accordance with a stratified randomization approach, the study participants underwent block randomization based on their body mass index (BMI) at a 1:1 ratio (block size of 2). BMI in the randomization process was classified into two groups (overweight and obesity). This randomization process was carried out by an independent statistician who had no involvement in the collection or input of participants' data. Subsequently, the randomization list was provided to the researcher responsible for assigning participants to their respective study groups. Both the researcher and the participants were not blinded to the study allocation. The random allocation was generated by F.M., Sh.R. facilitated enrollment, and Z.Z. and F.Sh. were responsible for assigning participants to the intervention groups. The basic characteristics of the participants, including age, sex, marital status, smoking status, consumption of food supplements and drugs, and history of diabetes and other diseases, were obtained through face-to-face interviews and questionnaire completion on the day of the patient's visit to the Laboratory at Shahid Sadoughi University of Medical Sciences in Yazd, Iran. TAC was the primary outcome and other variables  were the secondary outcomes of the study.2 and 700 kcal for those with a BMI greater than 31 kg/m2 were subtracted from their total energy requirements. Participants in both groups were asked not to change their physical activity levels throughout the study.The energy requirements of the study participants were estimated by considering their basal metabolic rate, physical activity levels, and the thermic effect of food. The Harris–Benedict equation was employed to determine the basal metabolic rate. All participants had a body mass index indicating overweight or obesity. Consequently, a calorie restriction of 500 to 700 kcal lower than each individual's calculated energy requirement was imposed. Specifically, 500 kcal for those with a BMI ranging from 25 to 31 kg/mThe DASH diet included 50–55% of energy as carbohydrates, 15–20% as protein, and 30% as total fat. It was rich in fruits, vegetables, whole grains, and low-fat dairy products and low in saturated fat, cholesterol, and refined grains. The food program was rich in potassium, calcium, magnesium, protein, and fiber and received a maximum of 2,400 mg of sodium per day. Patients were asked to remove salt from the table and add only one tablespoon of salt to their food to meet this goal.The control group received a diet that consisted of 15–20% of energy derived from protein, 50–55% from carbohydrates, and 30% from fat, to promote weight loss and overall health. To encourage adherence to the protocol, the participants received text messages (SMS) or phone calls twice a week.For participants with diabetes, carbohydrate intake was carefully managed based on their medication regimen, with specific distribution guidelines provided for three main meals and three snacks . AdditioThe physical activity levels of participants were evaluated using the International Physical Activity Questionnaire (IPAQ), a validated instrument widely utilized in research studies . MetabolA 3-day food record, consisting of 1 weekend day and two non-consecutive weekdays, was utilized to assess the dietary intake of participants at both the baseline and post-intervention stages. Moreover, phone interviews were employed to evaluate participants' compliance with the prescribed dietary regimen.2.The participants' height was measured using a stadiometer  while they were in a standing position with shoulders and hips against a wall and without shoes. Weight was measured using a digital scale  while wearing light clothes and without shoes and recorded to the nearest 0.1 kg. Body mass index (BMI) was calculated by dividing weight by height squared and expressed in kg/mThe waist circumference (WC) was measured according to a standard protocol using a tape measure placed between the lower rib and iliac crest. Waist circumference (cm) was then divided by hip circumference to determine the waist-to-hip ratio (WHR) (cm). Muscle mass, body fat mass, and visceral fat percentage were evaluated using bioelectric impedance analysis (Omron KaradaScan).Venous blood samples of 10 mL were obtained from all participants after a 7- to10-h fasting period. Subsequently, the samples underwent processing and serum separation before being stored at −70°C until further analysis. TAC, CAT, and SOD concentrations were determined via colorimetric assays conducted with NavandSalamat kits from Iran. The analysis was performed using an ELISA reader .t-test, while the independent samples student t-test was employed to compare the average changes in the intervention group with the control group. Furthermore, to compare variables after controlling for confounders such as age, sex, diabetes status, education, baseline values, physical activity, and smoking, analysis of covariance (ANCOVA) was conducted. A P < 0.05 was employed to determine statistical significance. The sample size calculation for detecting a difference of 2 units of TAC between groups at a two-tailed alpha level was based on the study by Razavi Zadeh et al.  version 24 software . The normality of the quantitative data was assessed via the Kolmogorov–Smirnov test. Differences in qualitative variables between the two groups were compared using the chi-Square test. Intragroup changes were evaluated using the paired h et al. . The calh et al. .p = 0.04).From January 2022 to April 2022, we enrolled 70 patients with NAFLD. The follow-up period was extended for 3 months beyond April. Out of the initial 70 participants, 34 individuals were assigned to the DASH diet group, while 33 were allocated to the control diet group. One participant from the DASH diet group was excluded from the study due to pregnancy, and two individuals from the control diet group were removed due to non-compliance with the dietary regimen. The baseline characteristics of the participants are shown in p = 0.01), and nuts, seeds, and legumes (P < 0.001), while consuming lower amounts of meat (P < 0.001) and fat (P < 0.001).p = 0.06).t-test analysis demonstrated a significant reduction in the average WC (p < 0.001), WHR (p < 0.001), hip circumference (p < 0.001), and FM (p < 0.001) after the intervention in the DASH diet group compared to baseline values. Moreover, the ANCOVA analysis showed that after adjusting for confounders, the effect of the DASH diet compared to the control diet on visceral fat was significant (p = 0.03).Paired t-test analysis, a significant difference was observed in the mean of change of TAC and SOD at the end of the study between the two groups (p = 0.02). Furthermore, after controlling for potential confounding variables, including age, sex, diabetes, baseline values, smoking, and physical activity as covariates, the DASH diet maintained its significant effects on TAC and SOD compared to the control diet.Based on the results of the independent t-test analysis, the average SOD levels in the DASH diet group increased significantly at the end of the study compared to the baseline values (p < 0.001). However, in the control diet group, the increase was slight and not statistically significant (p = 0.87). Interestingly, no significant differences were observed within or between the groups in terms of CAT levels in both the crude and adjusted models  levels in patients with gestational diabetes mellitus  and obesAdditionally, a recent meta-analysis of three randomized clinical trials found no significant effect of the DASH diet on TAC . HoweverIn contrast, Sureda et al. found a positive correlation between the Mediterranean diet (MD) and SOD and CAT levels in an observational study that explored associations between the MD and oxidative stress biomarkers. However, there was no effect on CAT enzyme levels in this study . The MD Previous studies have shown that high-sugar diets may result in mitochondrial dysfunction in adipose tissue, leading to metabolic disorders and elevated levels of serum triglycerides and VLDL cholesterol, and ultimately oxidative stress . The DASFurthermore, the DASH diet's high content of vitamin C, calcium, magnesium, and arginine  may reduce oxidative stress by decreasing the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, the main enzyme responsible for generating superoxide . The DAS2O2) used in our experiment was not high enough to induce an increase in catalase activity. Additionally, it is possible that other enzymes that consume H2O2, such as glutathione peroxidase, may have increased and offset any potential increase in catalase activity. Unfortunately, we did not measure the activity of these other enzymes in our experiment. In contrast, previous research has shown that a high-fat diet can significantly increase catalase activity. However, we can justify our findings by pointing out that the DASH diet, which was used in our study is not high in fat and may therefore not lead to a significant increase in catalase activity  in both DASH and control groups is likely related to energy restriction . The famIn the DASH group, there was a significant decrease in FM compared to the control group, but there were no significant changes in FFM. These findings are consistent with Mahdavi et al.'s study on body composition, which showed a significant decrease in FM but not FFM in hemophilic adolescents receiving the DASH diet . The mosThe strength of our investigation includes its randomized clinical trial design, which is recognized for its rigorous methodology in clinical research, enhancing the credibility of our findings. Moreover, to the best of our knowledge, this investigation represents the pioneering effort to examine the effect of the DASH diet on oxidative stress factors  among patients with NAFLD. Additionally, accurate assessment of non-alcoholic fatty liver was achieved using the fibro-scan method, ensuring precise measurement of participants' liver condition. Both male and female participants were included in the study, contributing to a more comprehensive understanding of the diet's effects across genders.Despite the strengths of our investigation, some limitations should be considered. Our sample consisted of overweight and obese individuals with NAFLD, and therefore, the generalizability of our findings to other populations may be limited. Additionally, although we utilized a validated questionnaire, physical activity and food intake were self-reported which may introduce bias . It is cIn conclusion, our study suggests that the DASH diet may be a promising dietary option for individuals with NAFLD as it has the potential to reduce BMI, visceral fat, and antioxidant indices. However, further studies are necessary to explore other potential benefits of the DASH diet for this population.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The present study adhered to the principles outlined in the Declaration of Helsinki  and obtaZZ, AN, and MH contributed to the conception and design of the study. ZZ, FS, FR, HH, SR, and JZ participated in data acquisition. ZZ, AN, and FM contributed to data analysis and data interpretation. ZZ and AN participated in manuscript drafting. AN finalized the manuscript. All authors contributed to the article and approved the submitted version.


This study focused on the research hotspots and development trends of the neuroimaging of attention deficit hyperactivity disorder (ADHD) in the past thirty years.The Web of Science database was searched for articles about ADHD neuroimaging from January 1992 to September 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals.2,621 articles were included. More and more articles have been published every year in the last years. These articles mainly come from 435 institutions and 65 countries/regions led by the United States. King's College London had the highest number of publications. The study identified 634 authors, among which Buitelaar, J. K. published the largest number of articles and Castellanos, F. X. was co-cited most often. The most productive and cited journal was Biological psychiatry. In recent years, burst keywords were resting-state fMRI, machine learning, functional connectivity, and networks. And a timeline chart of the cluster of keywords showed that “children” had the longest time span.Increased attention has been paid to ADHD neuroimaging. This work might assist researchers to identify new insight on potential collaborators and cooperative institutions, hot topics, and research directions. Attention-deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder among children. The worldwide prevalence has been estimated between 2 and 7%, with an average of approximately 5% , functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET), single-positron emission computed tomography (SPECT), and magnetic resonance spectroscopy (MRS), have revealed abnormalities in brain function in patients with ADHD. fMRI is a non-invasive, high spatial resolution technology detecting functional changes in the brain through changes in cerebral blood oxygen levels. The complex pathophysiology of ADHD has been further investigated by resting-state MRI (rs-fMRI) and task-state MRI (task-MRI) functional magnetic resonance imaging studies . The literature search was constrained to articles and review articles, eliminating other document types.This study was conducted by searching the Science Citation Index Expanded (SCI-Expanded) in the Web of Science (WoS). The relevant literature was published between 1 January 1992 and 20 September 2022, and the search subject terms were identified as (ADHD OR “Attention Deficit Disorder with Hyperactivity” OR “Hyperkinetic Syndrome” OR “hyperkinetic disorder” OR “Attention Deficit Hyperactivity Disorder”) AND , and a polynomial regression model was constructed, aiming to predict the number of articles in 2022. The data from the WoS were exported in “Full Record with Cited References” format and saved with the prefix “download.” “Node Types” select “Author,” “Institution,” “country,” and “Keyword” for visual analysis to generate a visual co-occurrence map. “Pruning” selects “Pathfinder” and “Pruning sliced networks” to prune each slice of the network to reduce the density of the network and improve its readability.k = 15), and pruning (pathfinder and pruning sliced network). Scimago Graphica 1.0.25 visualized the collaborative relationships of authors from different countries. In the visual diagram, each node was deemed as an element, and its size referred to the frequency of occurrence. Cooperation or connection among various elements was marked with a connection line. The node with high betweenness centrality (>0.1) is the key point in this field, which is also a concern of CiteSpace. The purple ring represents centrality. Highly cited elements are often considered to be the research hotspots in this field. The flow diagram of this study is shown in CiteSpace was employed for constructing a base map for hotspots and knowledge in nephrotoxicity research, and for centrality detection , England , and Germany  published the most articles. The United States contributed almost half of all publications, with the first article published in 1993 which was earlier than any other country, showing great contribution in volume and importance. Maps drawn after the national visual analysis include 65 countries/regions and 326 links. As shown in n = 168, 19.2%), Harvard University , Radboud univ Nijmegen , National Institute of Mental Health , and Yale University . It is worth noting that all these institutions are located in developed countries. The National Institute of Mental Health (NIMH) has a greater centrality (0.18). NIMH published the first article in 1993 about the child and adolescent psychopathology research , Rubia, Katya , Franke, Barbara , Castellanos, Francisco Xavier , and Hoekstra, Pieter J  had the highest citation counts, followed by Rubia K (n = 775), Barkley RA (n = 581), Shaw P (n = 532), and Biederman J , it is deemed as a knowledge basis of the research frontiers 's brain program, which was launched in the United States in 2013, has significantly increased funding and promoted ADHD neuroimaging research. The American periodical Biological Psychiatry (IF = 12.81) also has the most articles and citations. It is noteworthy that China, which ranks fourth in terms of the number of papers issued, is the only developing country in the top five. Research of the United Kingdom, Germany, and the Netherlands possess a central and significant position. Currently, most studies are conducted in developed countries such as Europe and the United States. Some scholars proposed that further research from less developed countries is needed to understand the interactions of neurobiological and socio-cultural elements in ADHD  at King's College London is the premier for neurosciences research in Europe. Its cited publications about mental health were the most (the first 1%) . However, an analysis of these institutions showed that articles on ADHD were mostly published by research universities, including a small number of hospitals. ADHD will evolve toward precise clinical phenotypes in the next few years. Thus, relevant research units should establish cooperation with hospitals with accurate diagnostic capabilities as increasing clinicians publish literature on ADHD.In this study, we learn more about the outstanding authors who contribute greatly to studies in this field. Author information can clearly identify the author's contribution to the field and establish potential partnerships. Buitelaar J, Rubia K, and Franke B contribute the greatest number of publications in the field of ADHD neuroimaging. Buitelaar J comes from the University of Bergen, and his research interests include ADHD, autism, and aggressiveness and impulsivity problems. Dr. Buitelaar takes an integrative strategy that includes clinical and phenotypic research, neuroimaging, cognitive research, genetics, pharmacology, and preclinical techniques. Rubia, K is a professor at King's College London, and his expertise is the pharmacological imaging of ADHD drugs and neurotransmitter manipulations, PET imaging in adults with ADHD, and so on. Franke B comes from Radboud University Nijmegen Medical Center. Dr. Franke B studies the influences of genetic factors on mental health problems and traits such as ADHD and focuses on understanding the biological mechanisms explaining the effects of such factors on the etiology of mental problems. In addition, we found that these three authors were members of the World Federation of ADHD and the ENIGMA-AHDH Working Group. Buitelaar J is the vice president of the World Federation of ADHD, while Franke B is a co-founder of ENIGMA. Through these organizations, researchers can strengthen communications and cooperation between different countries or institutions in this field. Scientific researchers including these three authors published the “The World Federation of ADHD International Consumption Statement” in 2021. Through an in-depth study of more than 2,000 participants, the authors came up with 208 specific descriptions of ADHD, which pointed out seven pieces of evidence-based evidence about the brain difference of ADHD  The rs-fMRI can be used to investigate spontaneous brain neural activity during the rest state without requiring the preformation of any task  Machine learning: at present, guardian interview and clinical observation are mainly used to determine whether a child suffers from ADHD, which may vary greatly because of regional divergence. If a single scale or indicator is used to diagnose or predict ADHD, the reliability will be insufficient. Therefore, we suggest that some data (such as sociodemographic information and neuroimaging) can be used to assist except the scale and relevant indicators. Increasing evidence shows that machine learning (ML) can be applied to neuroimaging data for the diagnosis and efficacy prediction of ADHD  Functional connectivity: ADHD is considered a neurodevelopmental disease with “brain disconnection,” including structural and functional connectivity disorder. Functional connectivity (FC) analyses are a common method of analyzing fMRI data  Network: In recent years, there has been increasing acceptance that ADHD is a distributed functional network disorder. Patients with ADHD suffer from different degrees of alternation in default mode network (DMN), frontoparietal network (FPN), ventral attention network (VAN), dorsal attention network (DAN), affective network (AN), somatosensory network (SSN), and visual network Rubia, . HoweverReferences with the strongest CB published recently may help us to investigate the research hotspots in this field. Listed in The latest valuable cited literature provides valuable information for future research directions. Hoogman et al.  used extThe current research is subjected to some limitations. First, only articles from the WoS and English articles were included, which might lead to bias in language and publication. Second, some studies unpublished or published newly were not included in this study. Third, the number of citations and centrality of the articles might vary depending on the particular time when the search was conducted. However, it is certainly believed that it has clarified the overall condition, emerging trends, and future directions of studies on ADHD, showing high reference values and providing some valuable suggestions for further study.In conclusion, this study revealed the status of relevant studies, hot topics, and trends concerning neuroimaging in ADHD from 1992 to 2022. During the past three decades, the number of relevant articles has increased significantly, which suggests that neuroimaging in ADHD has a positive development prospect. Most ADHD neuroimaging articles are from developed countries, and the authors are encouraged to develop close cooperation with others from various institutions and countries. Notably, “resting-state Fmri,” “machine learning,” “functional connectivity,” and “network” may be the hot topics in this field. With the rapid development and maturation of brain imaging technology, scholars could conduct more relevant research about the diagnosis and prognosis of ADHD.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.JW and NS contributed to conceiving, designing, and revising the manuscript. HD and ZH contributed to the analysis and manuscript writing. ZL, LC, and YH contributed to data collection. JW and YZ contributed to manuscript revision. All authors contributed to the article and approved the submitted version.


There are many studies on the impact of physical activity on health but few studies on the relationship between physical activity and medical expenditure among the elderly. Based on the China Health and Retirement Longitudinal Survey (CHARLS) database and selected 4456 valid samples, this paper used a two-part model to analyze the effects of high, moderate, and low physical activity intensity on medical expenditure. It is found that the intensity of physical activity was negatively correlated with medical expenditure, and the medical expenditure of the high physical activity intensity group was significantly lower than that of the low physical activity intensity group. For example, compared to people with no physical activity, the total medical expenditure decreased by 22.4%, 40.4%, and 62.5% per week in those with low, moderate, and high physical activity intensity. Thus, the government should provide more places for the elderly to exercise, planning special exercise areas for the elderly in community playgrounds, such as a dancing square, which will also help the elderly to increase their amount of exercise per week and develop a daily exercise habit. The aging of the population is a far-reaching global problem that poses a severe challenge not only to the health of the elderly but also to the government’s medical expenditure policy. The U.S. spends about USD 3.6 trillion on healthcare, accounting for 18% of the GDP (USD 4030 per capita), while China spends only 6% (USD 214 per capita), far less than the growing demand for healthcare . In China, the number of medical visits was 1.286 billion in 2000 and 3.884 billion in 2021. The overall growth rate from 2000 to 2021 is 202.02%, with an average annual growth rate of 118 million people. At the same time, China’s per capita medical expenditure is on the rise, from 2000 yuan in 2015 to more than 3500 yuan in 2019 . Medical expenditure needs are closely related to the physical health status of the elderly, and physical activity (PA) directly affects health status [Scholarly research on medical expenditure flourished in the 1990s , mainly p > 0.05), but the two-year lagged variable (p = 0.03) and the three-year lagged variable (p = 0.01) for physical activity prevalence were negatively associated with Medicare costs, indicating a time-lagged relationship. Secondly, when it comes to the medical expenditure for different diseases, Wu et al. (2019) [However, there is not much research on the impact of physical activity (PA) on medical expenditure, and it was not until the past five years that it began to attract attention. The relationship between physical activity (PA) and medical expenditure stems from the impact of physical activity (PA) on health ,17. Amon. (2019)  found th. (2019) . Araujo . (2019)  found a . (2019)  found th. (2019)  examinedIn recent years, with the increasingly serious aging of China’s population, more and more scholars have studied the impact of physical activity on medical expenditure by taking China as a case study. Wang et al. (2019) , Su et aIn summary, there are more and more studies on huge medical expenditure and its influencing factors, but the relevant research on the impact of physical activity on medical expenditure has only begun to increase in the past five years, especially after the COVID-19 outbreak. On the basis of previous literature, this paper further studies this problem. Under the theoretical framework of the Andersen model, this paper selected the newest data of the China Health and Retirement Longitudinal Survey (CHARLS) to confirm the relationship between physical activity intensity and medical expenditure in middle-aged and elderly people, which also provides some insights for government policies.The marginal contribution of this study is that it classified low, moderate, and high physical activity intensity and used the two-part model to confirm the relationships between the different physical activity intensities and medical expenditures in middle-aged and elderly people.The China Health and Retirement Longitudinal Survey (CHARLS) is a large-scale interdisciplinary survey project conducted by the National School of Development, Peking University, and jointly executed by the China Social Science Survey Center and the Youth League Committee of Peking University. It is a major project, funded by the national natural science foundation, that aims to collect a dataset of those over 45 years of age and the elderly families in China. The questionnaire design of CHARLS is based on international experience, including the U.S. Health and Retirement Survey (HRS), the U.K. Aging Tracking Survey (ELSA), and the European Health, Ageing and Retirement Survey (SHARE). Multi-stage sampling was adopted in the project. The PPS (probability proportion to size) sampling method was adopted in both county/district and village sampling stages. The CHARLS questionnaire included basic personal information, family structure and financial support, health status, physical measurements, medical service utilization and health insurance, work, retirement and pension, income, consumption, assets, and basic community information. CHARLS’ access response rate and data quality are among the top of similar projects in the world, and the data have been widely used and recognized in academia. These empirical data come from the latest public data of CHARLS . All the research operations were performed using Stata 16 software.Theoretical models of health cost or medical expenditure have developed rapidly in the past two decades . One of Some of the variables are described as follows:Explanatory variables. As for the utilization of medical services, most scholars tend to evaluate the utilization frequency of medical services and the consumption expenditure of medical services. In terms of medical service consumption expenditure, scholars generally select outpatient expenditure and inpatient expenditure within a certain period of time to refine the total medical expenditure. Outpatient expenditure refers to the medical treatment outpatient expenditure in the past year; inpatient expenditure is the total hospitalization expenditure for the most recent visit within the past year; total medical expenditure is the sum of the most recent outpatient expenditure and inpatient expenditure in the past year. It is important to note here that the reason we drew the distinction between outpatient expenditure and inpatient expenditure is because outpatient expenditure was basically paid by individuals; inpatient expenditure, on the other hand, is largely covered by medical insurance in China, so it makes sense to study them separately.Physical activity intensity. Does the individual do this type of activity consistently for at least 10 min per week? Inactive is “0,” low-intensity physical activity is “1,” moderate-intensity physical activity is “2,” and high-intensity physical activity is “3.” In the CHARLS database survey, the original question was “Do you usually take this type of activity for at least 10 min every week?”, with type of physical activity including: (1) Vigorous-intensity activity ; (2) Moderate activity  Mild activities such as walking . In this paper, the type of physical activity is renamed low-intensity physical activity, moderate-intensity physical activity, and high-intensity physical activity.Education level. Education level is divided into four categories according to the respondents’ highest level of education (excluding adult education): “0” is no formal education; “1” refers to those who have received primary education or below ; “2” is education above primary school: secondary school or below; “3” is a college education or above.Access to medical resources. The number of kilometers traveled from the respondent’s home to a medical facility receiving outpatient or inpatient medical services was reflected in the number of kilometers.Self-rated health status. Self-rated health status comes from the subjective evaluation of the health status of the respondents in the CHARLS database, and there are five measures for the respondents to evaluate themselves: very good, good, fair, poor, and very poor. In this data processing, the self-rated health status is assigned a 0 for very poor and poor, 1 for fair, and 2 for good and very good.According to the preliminary observation of variable data, we find that there are a lot of “0” values in medical expenditure data. In order to avoid the possible endogenous problems caused by this result, this paper adopts a two-part model as an empirical tool.Before the introduction of health economics analysis, the two-part model was widely used in meteorology and other research fields ,40,41). The two-part model is mainly used to deal with endogeneity problems caused by medical expenditure with a large number of “0” values. The two-part model includes the probability model of medical treatment and the medical expenditure level model, which are independent of each other; that is, the level of individual medical expenditure has nothing to do with whether they choose medical treatment or not. The first part of the two-part model usually uses the probit model to estimate the probability of individuals choosing to use medical and health services. The second part estimates the level of individual medical expenditure for individuals with medical expenditure.The specific process is as follows. First, the probit model is used to estimate the probability of individuals choosing to use medical and health services (Equation (1)). Second, the medical expenditure level model is used to estimate the medical expenditure of individuals with a positive probability of using medical and health services in the first step (Equation (2)). This formula can be used by ordinary least squares (OLS), generalized linear model (GLM), and other estimation methods.As Deb et al. (2017)  states, We found that the medical expenditure data itself showed the characteristics of “multi-zero value, large amount gap.” The specific performance is: first, for the vast majority of individuals in a short period of time, the medical expenditure is zero; second, there is a large gap in medical expenditures between individuals with medical expenditures in a short period of time; third, there are very few individuals with very high medical expenditures. For this, we perform logarithmic processing on corresponding variables to correct this skewed distribution, and the effects are as follows  used the linear OLS model to estimate the logarithm of non-zero medical expenses in the second part of the expenditure model when studying the relationship between population aging, time near death, and medical expenses based on the data from the follow-up survey of health factors affecting the elderly in China , etc. ThIn detail, according to the characteristics of the applied data, this paper adopts log formalized least squares (LOLS) to correct the skewness distribution of medical expenditure data. Then, the least squares method was used to estimate individual medical expenditure with a positive probability of using medical and health services. Finally, the estimated probability of seeing a doctor in the first step is multiplied by the estimated individual medical expenditure in the second step to obtain the final estimated individual medical expenditure (Equations (3) and (4)).In this empirical study, a total of 4456 valid samples were selected, with an average age of 62.955 years old. The majority of the samples were female, with education levels generally remaining at the primary school level. The logarithmic per capita annual income of the sample households was 18,917.838, with an average of 1.055 chronic diseases per sample and poor self-rated health status. The average outpatient expenditure of the sample is 748.613 yuan; the average hospitalization expenditure is 6564.428 yuan; and the average total medical expenditure is 7313.041 yuan.Detailed descriptive statistics of variables are shown in Before formal empirical analysis, we need to carry out the multicollinearity test of variables  of all explanatory variables ), and thIn addition, due to the biased distribution of medical expenditure data in our sample data, direct OLS regression often leads to endogenous problems such as selection bias  per week; that of the people with moderate physical activity intensity decreased by 26.4% (p = 0.039) per week; and that of the people with high physical activity intensity decreased by 50.7% (p = 0.000) per week.Outpatient expenditure: Compared to the people with no physical activity, the outpatient expenditure of the people with low physical activity intensity decreased by 24.8% (p = 0.035) per week, and that of those with high physical activity intensity decreased by 28.7% (p = 0.001) per week.Inpatient expenditure: People with low physical activity intensity had a 0.55% (per week) reduction in inpatient expenditure compared to the people with no physical activity, but the results were not significant. The inpatient expenditure of those with moderate physical activity intensity decreased by 17.4% (p = 0.040) per week in those with low physical activity intensity, 40.4% (p = 0.000) per week in those with moderate physical activity intensity, and 62.5% (p = 0.000) per week in those with high physical activity intensity.Total medical expenditure: Compared to people with no physical activity, the total medical expenditure decreased by 22.4% (p = 0.044), the inpatient expenditure decreased by 6.8% (p = 0.032), and the total medical expenditure decreased by 17.1% (p = 0.040). Urban residents have higher medical expenditures, and the results are at the significance level of 1%. As age increases, the probability of outpatient visits decreases; the probability of inpatient visits increases; and the total medical expenditure increases. Men spend more on medical expenditure than women. The distance between hospitals and medical expenditure are proportional, but the influence coefficient is small. For each additional chronic disease, outpatient expenditure increased by 11.9%; inpatient expenditure increased by 4.1%; and total medical expenditure increased by 14.2%, all of which were significant at the 1% significance level.In addition, the results of covariate regression showed that drink could reduce medical expenditure, in which the outpatient expenditure decreased by 11.5%  Outpatient expenditure a: the qu(2) Inpatient expenditure b: the qu(3) Total medical expenditure c: the qu(4) In general, in each legend of It can be seen from the robustness test that the quantile regression results also recognize the inverse change relationship between physical activity intensity and medical expenditure, so the empirical results are considered to be relatively robust.In the context of the recent COVID-19 shock and the current worsening economic situation, people are more sensitive to medical expenditure, especially since the high medical expenditure of middle-aged and elderly households increases their risk of falling back into poverty. This concern has also prompted people to pay more attention to physical activity. Therefore, study of the relationship between different physical activity intensities and medical expenditure is of practical significance to the behavioral science of middle-aged and elderly people. This paper selected data from the CHARLS database, classified low, moderate, and high physical activity intensity in middle-aged and elderly people, and discussed their respective effects on medical expenditure.First of all, the results showed that the medical expenditure of people with high physical activity intensity was significantly lower than that of people with low physical activity intensity in China. This conclusion is similar to that of Marashi et al. (2019)  and Watap = 0.035) for moderate-intensity weekly exercisers and 28.7% (p = 0.001) for high-intensity weekly exercisers. This conclusion is supported by previous literature such as Araujo et al. (2018) [Secondly, physical activity intensity has a reverse change relationship with the outpatient expenditure, inpatient expenditure, and total medical expenditure. Furthermore, compared to people with no physical activity, the effect of high, moderate, and low physical activity intensity on medical expenditures was increased. In terms of hospital expenditures, for example, the weekly low-intensity exercisers had a 0.55% reduction in hospital expenditures compared to the people with no physical activity, but the results were not significant. Inpatient expenditure was reduced by 17.4% (. (2018)  and Wang. (2018) .p = 0.044); inpatient expenditure decreased by 6.8% (p = 0.032); and total medical expenditure decreased by 17.1% (p = 0.040). This finding is consistent with the research conclusion of Song et al. (2020) [Thirdly, health behaviors such as drinking, smoking, and other living habits are also important factors affecting the medical expenditure of middle-aged and elderly people. Our results showed that drinking can reduce medical expenditures: the outpatient expenditure decreased by 11.5% (. (2020) . In addi. (2020) . In addiIn conclusion, both the two-part model and the robustness test of quantile regression confirm the inverse relationship between physical activity intensity and medical expenditure in middle-aged and elderly people, which also provides some insights for government policies, especially in the context of the recent end of the COVID-19 pandemic . GovernmAt the end of the paper, it must be mentioned that our study still has some limitations. For example, in the CHARLS database survey, the original question “Do you usually take this type of activity for at least 10 min every week?”, is the 10 min every week question reasonably designed? While this question is not a problem of our own making and is the responsibility of the CHARLS database research team, we also need to think about the future. In addition, our empirical results in


This mini review aims at summarizing the current state-of-the-art of empirical unlearning and intentional forgetting (U/IF) research at the individual, team, and organizational level. It adds to an earlier review and incorporates 31 recent studies from 2019 to 2022. The review reveals that predictors based on the organization’s adaptation context , organization level , individual task-related , and person-related level  variables relate to process variables, such as the type of U/IF, the U/IF content , and information processing variables . The outcome variables are at the organizational level , team level performance level, the individual task performance level , and person-related level . The analyzed studies at the team and organizational levels preferred cross-sectional study designs or in-depth qualitative methods, which severely limits the possibility of making causal statements. In contrast, at the individual-level studies use longitudinal designs as well to make temporal aspects of U/IF visible. But these individual level results are limited in terms of their generalizability to other levels. Even though all studies make valuable contribution to the understanding of antecedents and outcomes of U/IF, the temporal and process-related aspects of how U/IF unfolds at the different levels and subsequent options for its deliberate facilitation remain empirically little elaborated. It is proposed that in addition to studying the antecedents and consequences of U/IF in cross sectional designs, the topic needs more longitudinal designs to capture the nature of the U/IF processes in organizations. The previous reviews by Why is this review needed? Individual, team, and organizational memories shape present and future choices, behaviors, and strategies . This reThis review is needed because the gap between theoretical assumptions and the number of articles on U/IF models is growing much faster than the number of empirical papers that attempt to test at least some of the model assumptions . From a What happened so far in the unlearning and intentional forgetting literature? In recent decades, U/IF, as an organizational concept, has received attention in the discussion on the prerequisites for learning in organizations, innovation, management of change, and new product development . The conU/IF in organizations involves attitudes and processes that deliberately impede the recall of certain organizational memory items from organizational storage bins, such as individual or team memories or routines and practices, to adapt to the changing affordances in the (market) environment.As the theoretical distinction between unlearning and intentional forgetting is not well defined, both concepts are combined in the present review. Both concepts include the process of “letting go” of once-useful mindsets , p. 19 aAs mentioned above, the need for an updated review of empirical results is derived from the ongoing imbalance between the  number of empirical studies compared to the (high) number of conceptual papers ,b. AdditAs the research gap is inherently obvious due to the small number of empirical studies in comparison to the number of theoretical models and concepts, the present mini review aims at encouraging researchers to build on existing results, discuss the existing research methods and to compared empirical approaches with a variety of research strategies, to add findings from different, e.g., national or business contexts and thereby to jointly built a comprehensive understanding of the antecedents, processes and results of unlearning and forgetting in “real life.”Otherwise, it may be feared that the hitherto fragmented empirical landscape and the associated inconsistent field of findings will weaken the persuasiveness and thus the use of the concept, and that researchers and practitioners will turn away from U/IF in frustration.2.2.1.This review was conducted based on the guidelines of The selected conference proceedings were included as scholars in the field of U/IF from business information systems publish in conference proceedings . Only those studies with U/IF in their titles that referred to an intentional and deliberate process to actively adapt to a changed environment or task were included. Studies that included accidental forgetting were excluded.2.2.As the empirical state of the art is in the center of the present mini review, the studies included are presented with an emphasis on the central empirical research question, the research strategy , the sample, the level of analysis  and the main findings.The levels of analysis  were chosen as they represent shared assumptions in organizational psychology, knowledge management, and organizational behavior about on “where” organizational memory storage bins and processes are located and “where” unlearning processes unfold . The stu2.2.1.2.2.1.1.cognitive control strategies to support U/IF in air traffic control task adaptation in two experimental studies (N = 66 and N = 105). The participants first learned and performed an initial version of the task, received different instructions for control strategies , performed an altered version of the task with new execution rules, and finally worked on a memory test. The instruction to intentionally forget best supported the participants’ performance in applying the new rules.In an experimental longitudinal laboratory setting, critical reflection. Critical (premise) reflection is perceived as a higher-order, active, and purposeful process of investigating the justifications for one’s beliefs. In a cross-sectional field study, critical reflection using survey data from 271 employees of Japanese organizations. Like Comparably, In a combination of an experimental laboratory study and a longitudinal field study, 2.2.1.2.Practices or procedures that were outdated, rarely consulted, or used were easy to unlearn. Knowledge and skills that were no longer relevant to the current practice were not completely unlearned but remained as interesting memories or amusing anecdotes. Firefighters needed to trust the effectiveness of any new practices or the credibility of new knowledge to consent to unlearning the old practices and replacing them with new ones.routines. U/IF was negatively related to defensive routines.N = 90) found that the availability of DSS triggered the forgetting of decision-related background information, which in turn increased users’ mental resources for additional tasks, decision quality, and well-being. Moreover, trust in the system was found to be a relevant predictor of “letting go.” In an experimental laboratory study using a simulated sales planning task supported by a computer-based decision and U/IF-support system (DSS), N = 16 workers in a production line) to discover the differences in U/IF dependent on a continuous or episodic change of a production routine. In continuous change conditions in which changes in specific production steps occurred stepwise during multiple production processes, U/IF performance depended on the kind of routine changes: actions that were newly introduced and actions that needed to be omitted were more difficult to forget than changes in the way a specific step needed to be executed. Additionally, the participants made the same “forgetting errors” repeatedly and subsequently after an action changed and were maintained over time.Similarly, 2.2.2.2.2.2.1.risk aversion were antecedents of inability and resistance to U/IF. By contrast, raising awareness, providing temporal and spatial freedom, and facilitating an error-forgiving climate support U/IF through a more entrepreneurial, error-forgiving, or open-minded organizational culture that enables teams to break free from obsolete routines, patterns, mental models, or perceptions.In a holistic multiple-case study on new product development (NPD) teams, 2.2.2.2.teams’ information processing, which subsequently led to NPD success  at the team level. The results showed that managers’ exploration activities promoted individual unlearning through the mediating effects of learning goal orientation and reflection.Similarly, 2.2.3.2.2.3.1.of organizational culture based on the Organizational Culture Assessment Inventory, OCAI instrument  on U/IF, Concerning the effect organizational readiness (an antecedent of practice change) was supported by U/IF, with a stronger effect of U/IF on routines than on beliefs. By contrast, the presence of gossip and counterfactual knowledge decreased the likelihood of organizational readiness for using prefabricated building parts.In the construction sector, competitive intensity, which is the degree to which a firm faces competition in its market and a firm’s products can be quickly replaced by those of other competitors, a cross-sectional survey study by competitive intensity is positively associated with knowledge integration and U/IF. However, firm size strengthened the relationship between competitive intensity and knowledge integration and weakened the relationship between competitive intensity and organizational unlearning.In the context of 2.2.3.2.knowledge management and organizational outcomes was addressed in the study by The relationship between U/IF, affects inclusive innovation through supply chain green learning and the moderating role of green control ambidexterity using survey data from 217 firms. Inclusive innovation means providing support to low-income groups to participate in innovation activities equally and share innovation achievements  played a mediating role in the relationship between organizational forgetting and cross-boundary innovation. As the mediating effect of exploratory knowledge sharing was more robust than exploitative knowledge sharing, the authors concluded that “abandon the old” and “discipline the new” in U/IF could continuously promote positive organizational development.To study the relationship between U/IF and breakthrough and to cross the boundary from the original limits, 2.2.3.3.social media strategic capability, which is the ability to acquire and integrate information from social media into its knowledge base and the optimization of in- and outflow of knowledge in alignment with its strategic directions, With an emphasis on knowledge integration capability provides an important connection between U/IF and interorganizational knowledge transfer. Practically spoken, U/IF supports discarding useless routines and integrating useful ones.Addressing the setting of cross-border mergers and acquisitions (M&As), strategic flexibility.To investigate the relationship between U/IF (updating routines and knowledge) and relearning jointly facilitating strategic flexibility, knowledge creation capability, U/IF, and absorptive capacity on firms’ innovative performance. The data were collected from 194 small- and medium-sized enterprises . In their study, knowledge creation capability positively influenced the correlation between U/IF on firms’ innovative performance. Additionally, absorptive capacity mediated the relationship between knowledge creation capability, U/IF, and innovation performance.2.2.3.4.open-ended U/IF  and goal-directed U/IF (organizations implement specific knowledge structures incompatible with established ones). Open-ended U/IF requires breaking patterns, creating space for experimentation, ensuring (transparent) communication, providing time, and encouraging failure. Conversely, goal-directed U/IF requires splitting a change process into steps while providing actors with a clear idea and guidelines of what they should unlearn and forget while reducing the effect of outdated knowledge .success beliefs, which embody what an organization believes to be related to positive outcomes) and unlearning of what did not work (failure beliefs or choices with negative consequences). They found positive short-term effects of U/IF on success beliefs and long-term effects of U/IF on failure beliefs. Organizations gain more from unlearning failure beliefs because organizational failure experiences generally exceed the number of success experiences. Both types of unlearning are superior to not unlearning in a wide range of different extents of environmental changes.Based on a simulation study, 2.2.4.In the period of 2019 to 2022, there was no empirical paper published on the interorganizational level that met the inclusion criteria (see Review process section), but one paper addressed the industrial district level. At the industrial district (ID) level , 3.3.1.The results revealed predictors based on the following:Organizational level context related variables .Organizational level variables .Team level variables .Individual level person-related variables .Individual level task-related variables .The studies also addressed the following variables:Type of U/IF Forgetting content Processing variables The outcome variables were found at the following levels:Organizational level , with diverse temporal dynamics of unlearning success or failure beliefs.Team level .Individual level task related  and person-related variables .Several mediating effects were reported  or a single person (a manager responsible for strategic management or the Chief Innovation manager?). Second, we need a deeper understanding of how the discovery of U/IF requirements diffuse through the organizational structures if there are no U/IF processes implemented so far? How is U/IF in organizations operationalized in terms of process-steps of a dynamic capability? Third, we know about the barriers to organizational learning, will we also find barriers to U/IF? Can we proactively enhance U/IF processes in teams and organizations? Are there facilitation techniques that are more powerful compared to others? Do effect sizes of facilitation techniques depend on the nature of the organization or the market environment in which it operates?Even though the reviewed studies make an important contribution to the understanding of antecedents and outcomes of U/IF, the temporal aspects of how U/IF unfolds at the different levels and subsequently options for its deliberate facilitation appear empirically little elaborated. That means that from a research strategies perspective, the empirical investigation of underlying processes of U/IF (and not U/IF as a result) and the possible impacts of its deliberate facilitation are underinvestigated. Additionally, the models that are tested are not very well linked to previously established conceptual or theoretical models on U/IF. Further research is needed that systematically links (earlier) theoretical proposition, e.g., by In sum, it is a positive sign that the number of empirical studies of U/IF has increased, but for future theory building and empirical evidence developments, research at different levels needs to be more systematically related and integrated to support the progression of the U/IF field.The world is full of global challenges and crisis—contributing to the understanding and shaping of U/IF processes in organizations as an important coping strategy to face these future challenges might make a relevant difference if we will succeed.AK conducted the literature search, analyzed the papers, and wrote, edited, and revised the paper.This study was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft), with grant number KL2207/5-2 and KL2207/6-2.The author declares 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.


Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration  technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness ( Vitis vinifera L.) is a commonly cultivated fruit crop around the world, which is rich in sugar, organic acids, vitamins, minerals, and other nutrients [Grape , and Vis-NIR performed better in phenols, SSC, and TA prediction. As can be seen, all these studies indicated that Vis-NIR or NIR spectroscopy shows excellent potential in determining the ripeness of various fruits. However, according to our literature search, there are few reports on table grapes and the prediction of SSC, TA, and maturity index using Vis-NIR spectroscopy. Furthermore, it is challenging to achieve a handheld portable operation in determining fruit maturity among the majority of previous studies, which required laboratory instruments.At present, spectroscopy technology, especially for near-infrared (NIR) or visible–near-infrared (Vis-NIR), is extensively utilized in the area of agricultural product detection because of its rapid, nondestructive, low cost, and reliability characteristics ,11. A nuIn this study, we utilized a miniature spectrometer with fiber optic signal couplers to perform Vis-NIR spectroscopy for handheld operation. Fiber optics offers high transmission rates and flexibility for transient acquisition of spectra and piggybacking of spectral acquisition systems in large fields . To our The objectives of this study were to (1) analyze the changes in color, texture, SSC, and TA content of grapes at different maturity stages; (2) develop prediction models using Vis-NIR spectra for SSC and TA in grapes; and (3) evaluate the application efforts of the models in measuring grape ripeness.Vitis labruscana L. cv. Kyoho) were collected from the Cao Xinzhuang vineyard  in four development stages  during July and August of 2021 by experienced growers. The grapes without visible damage or disease were selected. The sampling process followed a predetermined principle, wherein two rows of grapes were randomly selected as fixed sampling points, and five berries were randomly picked from the top, middle, and bottom of each cluster using the five-point sampling method [In this study, table grapes . The wavelength region scanned was from 200 to 1100 nm with a sampling interval of 0.5 nm. A halogen lamp with 12 V Bulb/HL2000 was utilized as light source, and was installed at the internal part of the dark box, which also consisted of the handheld light unit . Each saL* (lightness), a* (redness/greenness), and b* (yellowness/blueness) were determined around the equatorial region [C* (Chroma) and h* (Hue angle) were calculated according to Equations (1) and (2) [The skin color of grape samples was tested using a colorimeter . Measurements were performed in the CIELab system. l region . C* (Chr and (2) . Five reThe texture characteristics of grape samples were measured using a texture analyzer . Grape samples were compressed twice in succession by 30% of their equatorial height at a rate of 0.5 mm/s . In thisThe real quality parameters of grape sample, soluble solids (SSC), and total acid (TA) were obtained from traditional destructive tests. This step was conducted immediately after collecting the spectral data of the samples. The grape juice, excluding skin and seeds, was used to measure SSC using a digital brix refractometer . TA content was determined using a Mettler automatic potentiometric titrator . The average of the SSC/TA values that were obtained from three repeated tests was calculated and recorded as the final reference value.In spectral analysis, it is an important step to preprocess the obtained spectral data with appropriate preprocessing methods to enhance the accuracy of prediction models . There aThe spectral data generally contain large amounts of redundant information and noise, which affect the robustness of models and increase the computation time ,25. To sPartial least squares regression (PLSR) is a commonly used method for statistical analysis, which has strong processing ability on multi-collinearity of linear regression and effectively reduces the negative influence of information loss and obtains a better modeling effect ,28. Thusn is the number of samples used in the calibration set; iy is the measured value of samples’ quality parameters; iŷ is the predicted value of samples’ quality parameters. In general, the spectral model’s prediction outcome is optimal when both Model performance was assessed using the statistical terms of coefficients of determination for the calibration set  and SPSS 26  on a laptop . The analysis of variance (ANOVA) was conducted to compare the data along ripening stages. Differences at L*, a*, and b* were within the ranges of 25.15–35.80, 0.77–8.02, and 2.07–7.31, respectively. The L* and b* values decreased significantly (p < 0.05) from ripening stage I to IV, while the a* values increased significantly (p < 0.05) from ripening stage II to IV. The aforementioned findings indicated that as the grape’s growth time increased, there was a gradual reduction in the green portion of the peel and an increase in the red portion aligning with the observations depicted in a* values was not observed between stage I and II, indicating a gradual color change in grapes during this period. This phenomenon may be attributed to water stress resulting from dry weather conditions [Fruit skin color is often considered a prominent and highly utilized quality indicator that significantly affects consumer’s acceptance . A previnditions . At the nditions .C*) is used to determine the different degree of a hue in comparison to the grey color, which is considered as the quantitative attribute of colorfulness. A previous study found a significant correlation between C* and the color intensity of fruits [C* value increased significantly (p < 0.05) with the development of ripeness. Therefore, it can be seen that the higher ripeness degree resulted in a more intense purple-red color on the grape skin. The hue angle (h*) is generally represented the color in degrees, i.e., 0° for red, 90° for yellow, 180° for green, and 270° for blue [h* values in grapes between the ripening stages from I to IV. It indicated that the grape color changed from yellow-green to purplish-red with the development of ripeness, and the ripening stage significantly influenced the color of grapes.Chroma (f fruits . The C* for blue . Accordip < 0.05) from stage I to III, while there was a slow hardness decrease in the ripening stages III to IV. The average hardness of grapes in stage I was 1435.87 g, then it gradually fell to 967.49 g when grapes proceeded to stage IV. The gradual softening of the fruit during ripening was evidenced by the study of Liu et al. [Texture properties of fruits are the key factors of ripeness of fruits and are influenced by changes in cell wall structure and composition during the ripening process . The texu et al. . Previouu et al. ,39.p < 0.05) from the stages III to IV, and the chewiness in stage I differed significantly (p < 0.05) from the stages II to IV. Additionally, the chewiness decreased sharply once stage I was completed, which was consistent with the trend in hardness, proving a positive correlation (p < 0.01) between hardness and chewiness [Furthermore, the elasticity of the grapes in the ripening stages of I to II presented significant difference (hewiness . The aboThe soluble solids (SSC), titratable acid (TA), and the ratio of SSC to TA (SSC/TA) are the critical quality indicators that affect the taste index and the consumer’s acceptance of fruits ,41. Aboup < 0.05) from stage I to II. It may be due to the sudden increase in temperature during the ripening stages from I to II, which resulted in the high-speed degradation of organic acids. However, there was no longer a significant variation in SSC from stage III to IV, indicating that the grapes had reached the peak level of maturity. As can be seen, the higher the maturity, the sweeter the grapes taste. Therefore, harvesting grapes in the optimal stage of ripeness becomes crucial to ensure the production of flavorful fruits [l fruits . In summThe raw spectral data contained 1507 individual wavelengths. In order to eliminate the useless information within the full spectral data, CARS was used to select the effective wavelengths. As can be seen from PLSR was utilized to explore the complicated spectral data and establish prediction models for evaluating SSC and TA. Additionally, PLSR models were established using both full spectra and effective wavelengths, and six common preprocessing methods were utilized. Results of PLS models based on full spectra and effective wavelengths are shown in As can be seen in progress . When co < RPre2 . When coFor TA prediction, except for 1st and 2nd derivative, other preprocessing methods did not significantly improve the model prediction accuracy  increased, while color in L*, b* and h*, hardness, chewiness, flexibility and TA content gradually decreased. Subsequently, the spectral prediction models of SSC and TA in grapes were established. The results indicated that the model prediction performance is significantly affected by the spectral preprocessing methods. The combination of 1st derivative preprocessing and full spectra yielded the most effective PLS spectral prediction models of SSC and TA, providing a reference and theoretical basis for rapid and nondestructive detection. However, further research is necessary to develop more accurate and robust prediction models. This can be achieved by increasing the sample size and incorporating more grape cultivars. Additionally, the utilization of additional statistical methods, such as linear regression analysis  and deep learning techniques , can help improve the prediction accuracy and model establishment. Furthermore, it is crucial to recalibrate the prediction models when applying them to grapes harvested in different years, which will enhance the reliability and feasibility of the developed models.In this study, the changes in physicochemical properties of grapes during ripening were explored, providing a scientific foundation for the maturity grading of grapes during this period. Results showed that as ripening advanced, the values of color in